EPA/600/R-03/051
June 2003
The Feasibility of Performing
Cumulative Risk Assessments for
Mixtures of Disinfection By-Products in
Drinking Water
by
Linda K. Teuschler (Project Lead),
Glenn E. Rice, John C. Lipscomb
National Center for Environmental Assessment
Cincinnati, OH 45268
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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NOTICE
The U.S. Environmental Protection Agency through its Office of Research and
Development funded and managed the research described here. It has been subjected
to the Agency's peer and administrative review and has been approved for publication
as an EPA document. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
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FOREWORD
This report was developed by the U.S. Environmental Protection Agency's (EPA)
Office of Research and Development (ORD), National Center for Environmental
Assessment - Cincinnati Office (NCEA-Cin). It contains information concerning the
technical feasibility of conducting cumulative risk assessments for mixtures of
disinfection by-products (DBPs) in drinking water. Cumulative risk assessment is
defined here as an evaluation involving multiple chemicals via multiple routes of
exposure over time. This project was conducted in response to 42 USC § 300 of the
Safe Drinking Water Act Amendments of 1996, where it is stated that the Agency will
"develop new approaches to the study of complex mixtures, such as mixtures found in
drinking water..." In addition, the EPA's Office of Water and Office of Research and
Development jointly drafted a Research Plan for Microbial Pathogens and DBPs in
Drinking Water that calls for the characterization of DBP mixtures risk (U.S. EPA,
1997a). This report reflects current results regarding the characterization of DBP
mixtures via multiple route exposures.
Part of this effort is based on a report prepared by Wilkes Technologies, Inc. and
Anteon Corporation under GSA Contract Number GS-10F-0154K, administered by the
EPA's National Exposure Research Laboratory in Las Vegas. An external review of this
document was conducted in July 2002 through peer review contract No. 68-C-99-237
with Eastern Research Group, Inc. External reviewers were Drs. Gunther F. Craun,
Hisham EI-Masri, and John Little.
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EPA RESEARCHERS
This research was sponsored by the U.S. Environmental Protection Agency's
(EPA), National Center for Environmental Assessment - Cincinnati Division (NCEA-Cin)
Cumulative Risk Team. NCEA-Cin scientists conducted portions of this research and
are the authors of this report. A number of other EPA scientists also contributed their
ideas, provided discussions and review, and wrote text toward completion of this effort.
These individuals are listed below.
Primary Authors:
National Center for Environmental Assessment - Cincinnati, OH
Linda K. Teuschler (Project Lead)
Glenn E. Rice
John C. Lipscomb
Contributors:
National Center for Environmental Assessment - Cincinnati, OH
Richard C. Hertzberg
National Health and Environmental Effects Research Laboratory - RTF, NC
Jane Ellen Simmons
National Exposure Research Laboratory - Las Vegas
Jerry N. Blancato
Stephen C. Hern
Fred W. Power
Reviewers:
National Center for Environmental Assessment - Cincinnati, OH
Eletha Brady-Roberts
Glenn Suter
National Center for Environmental Assessment - RTF, NC
Gary Foureman
National Center for Environmental Assessment - Washington, DC
Femi Adeshina
John Schaum
National Risk Management Research Laboratory - Cincinnati, OH
Mark Magnuson
Office of Water
Michael Messner
IV
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TABLE OF CONTENTS
Page
FOREWORD iii
EPA RESEARCHERS iv
TABLE OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES viii
LIST OF ABBREVIATIONS ix
KEY DEFINITIONS x
EXECUTIVE SUMMARY xiv
1. INTRODUCTION 1
1.1. BACKGROUND 1
1.2. EXPOSURE MODELS 6
1.3. GUIDANCE ON CUMULATIVE RISK 8
2. CUMULATIVE RELATIVE POTENCY FACTORS 13
2.1. RELATIVE POTENCY FACTORS 13
2.2. THE CRPF APPROACH 16
2.2.1. Theory of the RPF Approach 18
2.2.2. RPF Calculations Using Exposures from Exposure
Assessment Models 19
2.2.3. RPF Calculations Using Internal Doses from PBPK Models .... 21
2.3. CRPF CALCULATIONS 22
3. DEVELOPMENT OF DBP MULTIPLE ROUTE EXPOSURE ESTIMATES ... 24
3.1. BACKGROUND ON DBP EXPOSURE ESTIMATION 24
3.2. RESEARCH RESULTS REGARDING MULTIPLE ROUTE DBP
ESTIMATES 27
3.2.1. Model Inputs for TEM 30
3.2.2. Model Inputs for ERDEM 35
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TABLE OF CONTENTS (cont.)
Page
3.2.3. Modeling Results 41
3.2.4. Sensitivity Analysis 59
4. COMBINING THE CRPF METHOD WITH EXPOSURE ESTIMATES TO
CONDUCT A DBP CUMULATIVE RISK ASSESSMENT 68
4.1. STRATEGY FOR CONDUCTING THE CRPF-BASED
ASSESSMENT 68
4.2. GROUP DBPS INTO SUBCLASSES BY COMMON MOA 70
4.2.1. Developmental and Reproductive Effects From
Exposure to DBPs 72
4.2.2. Carcinogenicity from Exposure to DBPs 73
4.3. CONDUCT DOSE RESPONSE MODELING OF TOXICITY
DATA 74
4.4. DEVELOP RPF ESTIMATES FOR EACH SUBCLASS AND
COMBINE USING THE CRPF APPROACH 83
5. FEASIBILITY OF CUMULATIVE RISK ASSESSMENT FOR
COMPLEX DBP MIXTURES 89
6. REFERENCES 93
APPENDIX 1: Developing Individual Human Exposure Estimates for
Individual DBPs
APPENDIX A: Figures Presenting Results of Pharmacokinetic Modeling
APPENDIX 2: A Conceptual Model for a Cumulative Risk Approach
VI
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LIST OF TABLES
No. Title Page
3-1 List of Chemicals for Exposure and Internal Dose Assessment 28
3-2 TEM Output for BDCM: Absorbed Dose Estimates (mg) for a
24-Hour Exposure 42
3-3 50th Percentile 24-Hour Absorbed Dose Estimates (mg) Output by
TEM 44
3-4 Summary of 24-Hour Absorbed Dose by Route for 50th
Percentile of the Population 47
3-5 Summary of 24-Hour Absorbed Dose by Route for 95th
Percentile of the Population 49
3-6 48-Hour PBPK Modeled Absorbed Doses for BDCM for the
Adult Male, Adult Female and Male Child 54
3-7 48-Hour PBPK Modeled Absorbed Doses for CHCL3 for the
Adult Male, Adult Female and Male Child 55
3-8 48-Hour PBPK Modeled Absorbed Doses for DCA for the
Adult Male, Adult Female and Male Child 56
3-9 48-Hour PBPK Modeled Absorbed Doses for TCA for the
Adult Male, Adult Female and Male Child 57
3-10 Average Relative Sensitivity Analysis of Absorbed Total
Dose for Water Use, Environmental and Chemical Parameters
for CHCI3 and DCA, Ranked by Absolute Value 61
3-11 Summary of the Most Sensitive Model Parameters for
Each Dose Metric 65
4-1 Example: DBPs Grouped into Subclasses by Common MOA 71
4-2 Incremental Cancer Risk per mg/kg-day 80
4-3 Illustration of CRPF Approach for Average Cancer Risk
Calculations 85
VII
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LIST OF FIGURES
No. Title Page
1-1 Dose Metrics for Environmental Contaminants 5
1 -2 Mapping of Risk Assessment Approaches to Drinking Water
Studies 10
2-1 CRPF Approach : Integration of Dose Addition and Response
Addition to Estimate Mixture Risk 17
3-1 Linking TEM Exposure Assessment Modeling with ERDEM PBPK
Modeling 29
3-2 TEM Modeling of Indoor Air Concentrations, Exposure and
Absorbed Dose Estimates 32
3-3 Compartmental Design of ERDEM PBPK Model 36
3-4 ERDEM Modeling of Tissue and Organ Level Absorbed
Dose Estimates 39
4-1 Dose-Response Development, Human Risk Estimates and RPF
Calculations for Each Single DBP 75
4-2 Schematic of CRPF Approach for Illustration of DBP
Mixture Cancer Risk 79
VIM
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LIST OF ABBREVIATIONS
AUC Area under the concentration-time curve
BCA Bromochloroacetic acid
BCAN Bromochloroacetonitrile
BDCM Bromodichloromethane
CAA Chloroacetic acid
CHBr3 Bromoform
CHCI3 Chloroform
CRPF Cumulative relative potency factor
DBA Dibromoacetic acid
DBAN Dibromoacetonitrile
DBCM Dibromochloromethane
DBP Disinfection by-product
DCA Dichloroacetic acid
DCAN Dichloroacetonitrile
DEEM Dose estimating exposure model
ERDEM Exposure related dose estimating model
HAA Haloacetic acid
HAN Haloacetonitrile
HED Human equivalent dose
ICED Index chemical equivalent dose
ILSI International Life Sciences Institute
MBA Bromoacetic acid
MLE Maximum likelihood estimate
MOA Mode of action
NCEA National Center for Environmental Assessment
NHAPS National Human Activity Patterns Survey
OPP Office of Pesticides Programs
PBPK Physiologically-based pharmacokinetic
QSAR Quantitative structure activity relationship
REGS Residential Energy Consumption Survey
REUWS Residential End Use Water Survey
RfD Reference dose
RPF Relative potency factor
SF Slope factor
TCA Trichloroacetic acid
TCAN Trichloroacetonitrile
TCE Trichloroethylene
TEM Total exposure model
THM Trihalomethane
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KEY DEFINITIONS
Absorbed Dose - the amount of a substance crossing a specific barrier through uptake
processes.1
Additivity - When the "effect" of the combination is estimated by the sum of the
exposure levels or the effects of the individual chemicals. The terms "effect" and "sum"
must be explicitly defined. Effect may refer to the measured response or the incidence
of adversely affected animals. The sum may be a weighted sum (see "dose addition")
or a conditional sum (see "response addition").2
Bioavailability - The state of being capable of being absorbed and available to interact
with the metabolic processes of an organism. Bioavailability is typically a function of
chemical properties, physical state of the material to which an organism is exposed, and
the ability of the individual organism to physiologically take up the chemical.1
Chemical Classes - Groups of components that exhibit similar biologic activities, and
that frequently occur together in environmental samples, usually because they are
generated by the same commercial process. The composition of these mixtures is often
well controlled, so that the mixture can be treated as a single chemical. Dibenzo-dioxins
are an example.2 (Note: this is slightly modified from the original version).
Chemical Mixture - Any set of multiple chemical substances that may or may not be
identifiable, regardless of their sources, that may jointly contribute to toxicity in the
target population. May also be referred to as a "whole mixture" or as the "mixture of
concern."2
Complex Mixture - mixture containing so many components that any estimation of its
toxicity based on its components' toxicities contains too much uncertainty and error to
be useful. The chemical composition may vary over time or with different conditions
under which the mixture is produced. Complex mixture components may be generated
simultaneously as by-products from a single source or process, intentionally produced
as a commercial product, or may coexist because of disposal practices. Risk
assessments of complex mixtures are preferably based on toxicity and exposure data
on the complete mixture. Gasoline is an example.2
Components - Single chemicals that make up a chemical mixture that may be further
classified as systemic toxicants, carcinogens, or both.2
Dose Additivity - When the effect of the combination is the effect expected from the
equivalent dose of an index chemical. The equivalent dose is the sum of component
doses scaled by their potency relative to the index chemical.2
Dose - The amount of a substance available for interaction with metabolic processes or
biologically significant receptors after crossing the outer boundary of an organism.1
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Dose-Response Assessment - A determination of the relationship between the
magnitude of an administered, applied, or internal dose and a specific biological
response. Response can be expressed as measured or observed incidence, percent
response in groups of subjects (or populations), or as the probability of occurrence
within a population.3
Dose-Response Relationship - The relationship between a quantified exposure
(dose), and the proportion of subjects demonstrating specific, biological changes
(response).3 U.S. EPA's draft 1996 Cancer Guidelines further state: "Whether animal
experiments or epidemiologic studies are the sources of data, questions need to be
addressed in arriving at an appropriate measure of dose for the anticipated
environmental exposure. Among these are:
whether the dose is expressed as an environmental concentration, applied
dose, or delivered dose to the target organ,
whether the dose is expressed in terms of a parent compound, one or
more metabolites, or both,
the impact of dose patterns and timing where significant,
conversion from animal to human doses, where animal data are used, and
the conversion metric between routes of exposure where necessary and
appropriate."
Effective Dose (ED10) - The dose corresponding to a 10% increase in an adverse
effect, relative to the control response.3
Exposure - Contact made between a chemical, physical, or biological agent and the
outer boundary of an organism. Exposure is quantified as the amount of an agent
available at the exchange boundaries of the organism (e.g., skin, lungs, gut).3 The MAS
presents a similar definition for exposure defining it as "An event that occurs when there
is contact at a boundary between a human and the environment with a contaminant of a
specific concentration for an interval of time; the units of exposure are concentration
multiplied by time."4 These definitions are also closely related to the term "Potential
Dose" which is used in this document and defined by MAS to imply "An exposure value
multiplied by a contact rate (e.g., rates of inhalation, ingestion, or absorption through the
skin) and assumes total absorption of the contaminant."
Exposure Assessment - An identification and evaluation of the human population
exposed to a toxic agent, describing its composition and size, as well as the type,
magnitude, frequency, route and duration of exposure.3
Extrapolation, Low Dose - An estimate of the response at a point below the range of
the experimental data, generally through the use of a mathematical model.3
Human Equivalent Concentration (HEC) or Dose (HED) - The human concentration
(for inhalation exposure) or dose (for other routes of exposure) of an agent that is
believed to induce the same magnitude of toxic effect as the experimental animal
species concentration or dose. This adjustment may incorporate toxicokinetic
information on the particular agent, if available, or use a default procedure, such as
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assuming that daily oral doses experienced for a lifetime are proportional to body weight
raised to the 0.75 power.3
Index Chemical - The chemical selected as the basis for standardization of toxicity of
components in a mixture. The index chemical must have a clearly defined
dose-response relationship.2
Internal Dose - A more general term denoting the amount absorbed without regard to
absorption process.1
Independence of Action - Mixture components that cause different kinds of toxicity, or
effects in different target organs; the risk assessor may then combine the probabilities of
toxic effects for the individual components.2
Model - A mathematical function with parameters that can be adjusted so the function
closely describes a set of empirical data. A mechanistic model usually reflects observed
or hypothesized biological or physical mechanisms, and has model parameters with real
world interpretation. In contrast, statistical or empirical models selected for particular
numerical properties are fitted to data; model parameters may or may not have real
world interpretation. When data quality is otherwise equivalent, extrapolation from
mechanistic models (e.g., biologically based dose-response models) often carries
higher confidence than extrapolation using empirical models (e.g., logistic model).3
Physiologically Based Pharmacokinetic (PBPK) Model - Physiologically based
compartmental model used to characterize pharmacokinetic behavior of a chemical.
Available data on blood flow rates, and metabolic and other processes which the
chemical undergoes within each compartment are used to construct a mass-balance
framework for the PBPK model.3
Point of Departure - The dose-response point that marks the beginning of a low-dose
extrapolation. This point is most often the upper bound on an observed incidence or on
an estimated incidence from a dose-response model.3
Potential Dose - An exposure value multiplied by a contact rate (e.g., rates of
inhalation, ingestion, or absorption through the skin) and assumes total absorption of
the contaminant.4
Response Additivity - When the response (rate, incidence, risk, or probability) of
effects from the combination is equal to the conditional sum of component responses as
defined by the formula for the sum of independent event probabilities.2
Similar Components - Single chemicals that cause the same biologic activity or are
expected to cause a type of biologic activity based on chemical structure. Evidence of
similarity may include parallel log-probit dose-response curves and same mechanism of
action or toxic endpoint. These components are expected to have comparable
characteristics for fate, transport, physiologic processes, and toxicity.2
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Similar Mixtures - Mixtures that are slightly different, but are expected to have
comparable characteristics for fate, transport, physiologic processes, and toxicity.
These mixtures may have the same components but in slightly different proportions, or
have most components in nearly the same proportions with only a few different (more or
fewer) components. Similar mixtures cause the same biologic activity or are expected
to cause the same type of biologic activity due to chemical composition. Similar
mixtures act by the same mechanism of action or affect the same toxic endpoint. Diesel
exhausts from different engines are an example.2
Simple Mixture - A mixture containing two or more identifiable components, but few
enough that the mixture toxicity can be adequately characterized by a combination of
the components' toxicities and the components' interactions.2
Target Organ - The biological organ(s) most adversely effected by exposure to a
chemical substance.3
Uptake - The process by which a substance crosses an absorption barrier and is
absorbed into the body.1
Sources
1U.S. EPA. 1992. Guidelines for Exposure Assessment; Notice. Federal Register.
57(104):22888-22938.
2U.S. EPA. 2000. Supplementary Guidance for Conducting Health Risk Assessment of
Chemical Mixtures. Risk Assessment Forum, U.S. Environmental Protection Agency,
Washington, DC. EPA/630/R-00/002.
2U.S. EPA. 2002. Integrated Risk Information System. Office of Research and
Development, National Center for Environmental Assessment, Washington, DC.
Online, http://www.epa.gov/iris
4NRC (National Research Council). 1991. Human Exposure Assessment for Airborne
Pollutants: Advances and Opportunities. National Academy of Sciences, Washington,
DC.
XIII
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EXECUTIVE SUMMARY
Assessment of potential human health risk(s) from disinfection by-products
(DBFs) in drinking water is needed because of widespread oral, dermal and inhalation
exposures to this complex mixture and because positive data from both epidemiologic
and toxicologic studies of DBFs raise concern for human health (U.S. EPA, 2000a).
Although these data suggest human health effects are possible, human exposures are
complex, making the interpretation of positive results difficult. Occurrence information
shows that the mix of DBFs may vary considerably with geographic location and water
treatment process. Furthermore, for the more volatile DBFs, inhalation exposures may
be greater than ingestion; for highly lipophilic DBFs, dermal exposures may also be
important. Information from toxicologic studies has focused primarily on single DBFs
administered orally at doses far above finished drinking water concentrations.
Information from positive epidemiologic studies suggests that exposures to different
mixtures of DBFs in various geographic locations may pose quite different health risks.
Thus, to develop a regulatory and risk reduction strategy, there is a need to consider the
health risks associated with DBF mixtures and the various exposures from contact with
finished drinking water.
Several risk assessment issues are of concern to managers responsible for
ensuring safe drinking water for the public. The first issue is to evaluate the association
between DBF mixture exposures and human health outcomes and thereby establish
whether or not human health risks are a significant concern. Because the evaluations
of this association are inconclusive and human health effects from DBF exposures are
possible, some drinking water regulations have been promulgated and others posed
with the goal of controlling levels of DBFs in the drinking water (e.g., U.S. EPA, 1979,
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1994a, 1998b). As rules go into effect, alternative drinking water treatment technologies
are developed to meet these new standards. Thus, a second important issue is to
choose among treatment options by evaluating whether changes in exposure impact
health risk(s) across various drinking water treatment systems and source waters. A
third issue for evaluation of DBP mixtures is that approximately 50% of the DBP mass
consists of unidentified total organic halide material, the toxicity of which is largely
unknown (Weinberg, 1999). By comparing whole mixture toxicity data with data on the
mixture components, the toxicity of the unknown fraction of the DBP complex mixture
can be evaluated.
U.S. EPA's National Center for Environmental Assessment - Cincinnati has
conducted research for assessing DBP health risks using a cumulative risk assessment
approach, defined here as multiple chemical exposures via multiple exposure routes
over time (U.S. EPA, 2000a). The evaluation of human health risks as a cumulative risk
assessment problem requires consideration of the following factors:
Exposure to multiple chemicals at low environmental concentrations,
Knowledge of toxic mode of action (MOA) and judgment regarding
similarity of MOA among DBPs, and extrapolation of animal bioassay
results from high to low doses
Dermal, oral and inhalation routes of exposure,
Measures of internal absorbed dose,
Human activity patterns that affect the types of water use and the amount
of contact time with the drinking water,
Physicochemical properties of the DBPs,
Physical properties of the indoor environment, and
Sensitive subpopulations.
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Incorporating many of these factors, research has been conducted to develop human
exposure estimates for individual DBFs from multiple exposure routes; whole body and
organ-specific internal doses are estimated for all three exposure routes for each
individual DBP. This report describes how these data can be used to assess DBP risks
using a newly developed risk assessment method, the Cumulative Relative Potency
Factors (CRPF) approach.
In this document two different mathematical models are employed to evaluate
human exposures. An Exposure Assessment Model generates estimates of exposures
at the body boundaries through human contact with the media, influenced by human
activity patterns. A Physiologically-Based Pharmacokinetic (PBPK) Model predicts
doses of DBPs experienced by relevant organs or target tissues. Three different
measures of dose are presented with respect to possible application of the CRPF
approach (see Figure E-1):
1) Exposures. The amount of a chemical available at the exchange boundaries
(e.g., skin, lungs, intestinal tract).
2) Total Absorbed Doses (e.g., blood concentrations). The amount of a contaminant
that is absorbed from all exposure routes without regard to specific absorption
processes.
3) Organ or Tissue Doses. The amount of a contaminant in an organ or tissue,
estimated from all exposure routes based on pharmacokinetic information.
The actual choice of dose metric, as well as the temporal element of each dose
measure, is influenced by available dose-response data. Oral dose-response animal
data exist for most of the major DBPs identified in the drinking water for cancer,
XVI
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Environmental Concentrations
^•"•i^
-^^ — i — ^^ —
C*~ ^" ' ^x
_^^Human Activity Rattecns^..-^
Dermal Exposure^
^
^'
Skin
^
,--'
"' i
i
Inhalation; Exposure
Barrier
i
Lung
i
\
Dermal Absorbed TJose
X
*s.
V.
^,
^*
•*,
^
X
Oral Exposure
"-.
""-^
Intestinal Tract
\
\
Inhalation Absorbed Dose
i
i
X 1
1
s
s
s
s
/
s
/
s
/
s
^sOral Absorbed Dose
s
Total Absorbed Dose
(Internal Dose)
Pharmaciokinetics
Tissue/Organ Dose
FIGURE E-1
Dose Metrics for Environmental Contaminants
XVII
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developmental and reproductive effects, and a number of systemic effects. Dermal and
inhalation dose-response animal data are relatively sparse. Thus, the development of
the CRPF approach that can be based primarily on the use of oral dose-response
information is a plausible research direction.
The goal of this document is to examine the feasibility of conducting a cumulative
risk assessment for drinking water DBP mixtures by combining exposure modeling
results with the CRPF risk assessment approach. Discussions within the document
include: presentation of the CRPF approach; exposure modeling results that provide
multiple route human exposure estimates for 13 DBPs; explanation of how these newly
developed exposure estimates may be used in the CRPF approach; and details
regarding the uncertainties and data gaps that define future research needs and
feasibility of completing a cumulative risk assessment for DBP mixtures.
THEORY OF THE CRPF APPROACH
The CRPF approach is a new method that combines the principles of dose
addition and response addition into one method to assess mixtures risk for multiple
route exposures (U.S. EPA, 2000a). (Using two subclasses, Figure E-2 illustrates how
the CRPF approach estimates risk from exposure to the mixture.) The CRPF approach
groups DBPs with a common MOA into subclasses. The MOA differ across the
subclasses, but the toxicological endpoint (or outcome) is the same. For each subclass,
an index chemical is selected to be representative of that subclass, and Index Chemical
Equivalent Doses (ICED) are calculated using the Relative Potency Factor (RPF)
approach (U.S. EPA, 2000b). The ICED is an important concept for the CRPF method
that is employed at two levels:
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Component
ICED for
Chemical A3 =
DoseA3*RPFA3
Dose Addition for
Chemicals in Set A
Component ICED
for Chemical C 1
= DoseCl*RPFCl
Component ICED
for Chemical C2
= DoseC2*RPFC2
Component ICED
for Index Chemical C3
= DoseC3*l
Dose Addition for
Chemicals in Set C
Subclass ICED for set A
Response
i
D-R Curve of
Chemical Al
Subclass ICED
o
D-R Curve of
Chemical C3
Subclass ICED
Subclass ICED for set C
Response Addition for
Total Mixture Risk
Risk for Set A
Evaluated at
Subclass ICED
Risk for Set C
Evaluated at
Subclass ICED /
Total Mixture
Risk as Sum of
Risks for Set A
and Set C
FIGURE E-2
CRPF Approach : Integration of Dose Addition and Response Addition to Estimate Mixture Risk
XIX
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1) Component ICED - refers to the ICED for an individual chemical within a
subclass.
2) Subclass ICED - refers to the ICED for all chemicals within the subclass,
computed by summing their Component ICEDs.
The RPF approach has been proposed for characterizing health risks associated
with mixtures of chemical compounds that are toxicologically similar (U.S. EPA, 2000b).
To develop an RPF-based risk estimate for a class of chemicals, good toxicological data
are needed for at least for one component of the mixture (referred to as the index
chemical). Scientific judgment and analysis of available data are used to assess the
relative toxicity of the other individual components in the mixture. The exposure levels of
the components in the mixture are scaled by their toxicities relative to that of the index
chemical resulting in Component ICEDs which are then summed to generate a
Subclass ICED. The risk posed by the subclass can be estimated using the dose-
response curve of the index chemical. For each subclass, the RPF approach uses
dose-addition to estimate risk for the toxicologic outcome common across the
subclasses. However, these subclass risks are independent of each other (i.e., the
toxicity caused by one subclass does not influence the toxicity caused by the other
subclass because their respective MOA are different), thus meeting the criteria required
to apply response addition; the subclass risk estimates are added to yield a risk
estimate for the total DBP mixture.
EXPOSURE MODELING
A comprehensive exposure modeling effort was implemented to estimate
population-based exposures and absorbed doses for 15 DBPs, incorporating
parameters for chemical volatilization, human activity patterns, water use behaviors,
ingestion characteristics, building characteristics, physiological measurements, and
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chemical concentrations in the water supply. The DBFs targeted for evaluation are
listed in Table E-1. In the final modeling exercise, data were insufficient to estimate
chemical properties for BCAN and Bromate; thus, exposure estimates were not
modeled for these two DBFs. Estimates were made for a three person family based on
data from women and men of reproductive age (ages 15-45) and children (age 6).
The exposure assessment model for this effort was the Total Exposure Model
(TEM) developed by Wilkes Technologies (Wilkes, 1998). The PBPK Model used was
the Exposure Related Dose Estimating Model (ERDEM). This model, formerly known
as DEEM (Dose Estimating Exposure Model), was developed by Anteon Corporation in
collaboration with the Human Exposure Research Branch of EPA's National
Environmental Research Laboratory in Las Vegas. Combining these two models into
one analysis provided the ability to evaluate target tissue dose (estimated using
ERDEM) as a function of a variety of behaviors, environmental factors, and other
exposure related parameters (estimated by TEM). Figures E-3, E-4 and E-5 illustrate
the flow of information in and out of the two models. Of particular note is that TEM is
used to develop 24-hour exposure time histories for the demographic groups of interest;
this output data set becomes input data to the PBPK model. Also, both models are
capable of producing estimates of total absorbed dose, although the ERDEM model
does so using more specific physiological functions than TEM. Only ERDEM produces
organ and tissue doses. The research report showing all details of the DBP analysis
(Appendix 1) includes the following information:
Detailed Information on the model parameter inputs for both TEM and
ERDEM
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TABLE E-1
List of Chemicals for Exposure and Internal Dose Assessment
DBP Subclass
Trihalomethanes
(THMs)
Haloacetic Acids
(HAAs)
Haloacetonitriles
(HANs)
Miscellaneous
Chemical Name
Chloroform (CHCI3)
Bromodichloromethane
(BDCM)
Dibromochloromethane
(DBCM)
Bromoform (CHBr3)
Chloroacetic acid (CAA)
Dichloroacetic acid (DCA)
Trichloroacetic acid (TCA)
Bromoacetic acid (MBA)
Dibromoacetic acid (DBA)
Bromochloroacetic acid
(BCA)
Dichloroacetonitrile
(DCAN)
Trichloroacetonitrile
(TCAN)
Bromochloroacetonitrile
(BCAN)
Dibromoacetonitrile
(DBAN)
Bromate
CAS Number
67-66-3
75-27-4
124-48-1
75-25-2
79-11-8
79-43-6
76-03-9
79-08-3
631-64-1
5589-96-8
3018-12-0
545-06-2
83463-62-1
3252-43-5
15541-45-4
XXII
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TEM Modeling of Input Data on Chemical Properties, Human
Activity Patterns, Human Intake Parameters, Building Characteristics
24 Hour Exposure Time Histories
Simulated by TEM for a human of a specified age and sex
ERDEM Modeling of Input
Data on Physiological Parameters
Input DC
Measure
Concent
Drinking
At the F
i
jja
dDBP
rations In
I Water
aucet
r
Estimated DBF
Concentrations
In Household Air
DBF Oral
External
Exposure
Estimates
DBF Dermal
External
Exposure
Estimates
DBF
Inhalation
External
Exposure
Estimates
DBF Oral
Absorbed
Dose
Estimates
DBF Dermal
Absorbed
Dose
Estimates
DBF
Inhalation
Absorbed
Dose
Estimates
i
DBF
Multiple
Route
Total
Absorbed
Dose
Estimates*
*
DBF Multiple Route
Tissue and Organ
Dose Estimates
- AUC Kidney
- AUCTestes
- AUC Liver
- AUC Venous Blood
L
*Note: Both models can
produce Total Absorbed
Dose Estimates
FIGURE E-3
Linking TEM Exposure Assessment Modeling with ERDEM PBPK Modeling
XXIII
-------
Physico-Chemical Properties, e.g.,
- Mass transfer coefficients
- Henry's Law constants
- Octanol/water partition coefficients
- Chemical concentrations in water
- Volatilization rates
Human Behavioral Characteristics, e,g,
- Frequencies/duration of water uses
for showering, bathing, faucets,
dishwashers, toilets, and clothes washer
- Appliance factors, such as flow rates,
water temperatures, cycles, etc.
Human Intake Characteristics, e.g.,
- Tap water consumption by age & sex
- Skin permeability coefficients
- Breathing rates
Building Characteristics, e.g.,
- Household air volume
- Air volumes for water-use zones
- Whole house air exchange rates
- Air flow between water-use zones
Distributions of Absorbed Dose Estimates
- Dermal
Uptake calculations represent steady-
state and non steady-state periods.
- Inhalation
Uptake calculations employ an
equilibrium calculation between inhaled
air and the bloodstream
- Ingestion, direct and indirect
Uptake calculations assume all of the
DBF in consumed water is absorbed
into the bloodstream.
- Total Absorbed Dose
24 Hour Exposure Time Histories
to be Used by ERDEM
- Breathing zone concentrations
- Respiratory Rates
- Dermal exposures
- Skin contact area
- Ingestion exposures
Outputs from TEM
FIGURE E-4
TEM Modeling of Indoor Air Concentrations, Exposure and Absorbed Dose Estimates
XXIV
-------
24 Hour Exposure Time Histories from
TEM for Dermal. Inhalation. Ingestion
- Breathing zone concentrations
- Respiratory Rates
- Dermal exposures
- Skin contact area
- Ingestion exposures
Physiological Parameters, e.g.,
- Compartment volumes (kidney, liver,
blood, fat, etc.) by demographic group
- Breathing rates by activity and
demographic group
- Compartment blood flows by activity
and demographic group
- Inputs for each chemical:
- Skin permeability coefficients
- Rate constants for gastrointestinal tract
- Partition coefficients, e.g., skin:blood,
air:blood, liver:blood, kidney:blood.
- Metabolism pathways and rate constants
e.g., V-Max, Km.
Inputs to ERDEM
ERDEM
Distributions of Tissue and
Organ Dose Estimates
- AUC Kidney
- AUCTestes
- AUC Liver
- AUC Venous Blood
- Total Absorbed Dose
Outputs from ERDEM
FIGURE E-5
ERDEM Modeling of Tissue and Organ Level Absorbed Dose Estimates
xxv
-------
Estimates of absorbed dose for oral, dermal, and inhalation routes of
exposure and total absorbed dose for 13 (of 15) DBFs using TEM
Estimates of total absorbed dose and tissue doses for the kidney, liver,
venous blood and testes/ovaries for 4 (of 15) DBFs using the PBPK
model, ERDEM.
A sensitivity analysis of the combined models for a selected set of
parameters.
Simulation results of the TEM modeling include distributions of absorbed dose
estimates for the dermal, ingestion (direct and indirect), and inhalation exposure routes
and total absorbed dose. In Appendix 1, a table is presented for each of the 13 DBFs,
containing the absorbed doses for a 24-hour period as a function of route, population
group, and percentile of the population. Table E-2 shows an example of the absorbed
dose estimates for BDCM. Table E-3 shows the 50th percentile absorbed dose
estimates for all 13 DBFs. In addition to these tables for the 13 DBFs, Appendix 1
provides plots of their respective cumulative distribution functions and histograms for
the dose estimates (see Section 4.2.2., Appendix 1).
The results of the uptake modeling provide information for comparing and
contrasting uptake as a function of the chemical, the population group and behavior,
and the route of exposure. General conclusions about the importance of each route for
a given chemical can be made by comparing the chemical uptake across each route.
However, specific conclusions can be problematic due to large uncertainties in some of
the model parameters, most notably the dermal permeability coefficient. A large range
of uncertainty exists in the dermal estimates that make it difficult to compare the dermal
route to the inhalation and ingestion routes. This is because the skin permeability rates
(Section 3.6.5. of Appendix 1) are generally poorly quantified. As a result, the
uncertainty in this parameter is quite large. The impact of this uncertainty is examined
XXVI
-------
TABLE E-2
TEM Output for BDCM: Absorbed Dose Estimates (mg) for a 24-Hour Exposure
Percentile
Total3
Dermal
Ingestion
Direct
Indirect
Total3
Inhalation
Female, Age 15-45
1
5
10
25
50
75
90
95
99
7.20E-03
1.35E-02
1.92E-02
3.96E-02
8.00E-02
1.66E-01
2.79E-01
4.13E-01
2.41E+00
Ob
Ob
1.54E-04
3.71E-04
2.70E-03
5.21 E-03
8.67E-03
1.21E-02
1.87E-02
1.03E-03
1.83E-03
2.46E-03
4.19E-03
7.73E-03
1.51E-02
2.76E-02
3.50E-02
8.49E-02
5.64E-04
7.64E-04
8.86E-04
1.23E-03
1.71 E-03
2.37E-03
3.18E-03
3.61 E-03
5.05E-03
2.49E-03
3.51 E-03
4.14E-03
6.05E-03
9.72E-03
1 .69E-02
2.95E-02
3.70E-02
8.60E-02
1.12E-04
2.66E-03
8.78E-03
2.35E-02
6.12E-02
1.42E-01
2.64E-01
3.88E-01
2.38E+00
Male, Age 15-45
1
5
10
25
50
75
90
95
99
6.25E-03
1.27E-02
1.97E-02
3.88E-02
8.43E-02
1.64E-01
2.95E-01
4.36E-01
1.93E+00
Ob
Ob
Ob
3.09E-04
2.90E-03
5.57E-03
8.73E-03
1.13E-02
1.84E-02
7.64E-04
1.55E-03
2.14E-03
4.05E-03
7.98E-03
1.55E-02
2.91 E-02
4.31 E-02
7.14E-02
2.79E-04
4.95E-04
6.49E-04
1.05E-03
1.85E-03
3.37E-03
5.67E-03
7.93E-03
1.31 E-02
2.18E-03
3.42E-03
4.35E-03
6.52E-03
1.11 E-02
1.86E-02
3.19E-02
4.68E-02
7.28E-02
1.01E-04
2.64E-03
6.07E-03
1.89E-02
6.05E-02
1.46E-01
2.74E-01
4.23E-01
1.91E+00
XXVII
-------
TABLE E-2 cont.
Percentile
Total3
Dermal
Ingestion
Direct
Indirect
Total3
Inhalation
Child, Age 6
1
5
10
25
50
75
90
95
99
3.51 E-03
6.98E-03
1.00E-02
1.95E-02
4.38E-02
9.48E-02
1.81E-01
2.29E-01
3.58E-01
Ob
Ob
Ob
9.26E-05
2.66E-04
2.67E-03
4.48E-03
5.63E-03
8.03E-03
4.66E-04
8.66E-04
1.17E-03
2.07E-03
4.02E-03
7.68E-03
1.32E-02
1.75E-02
3.25E-02
1.13E-04
2.26E-04
3.28E-04
6.03E-04
1.07E-03
2.17E-03
3.80E-03
5.37E-03
8.16E-03
1.10E-03
1.73E-03
2.27E-03
3.50E-03
6.03E-03
9.89E-03
1.53E-02
1.88E-02
3.54E-02
5.71 E-05
1.13E-03
2.98E-03
1.07E-02
3.36E-02
8.56E-02
1.73E-01
2.19E-01
3.51 E-01
3Note that total absorbed dose (by ingestion or by all three routes) is not equal to the
sum of the doses in each row. This occurs because each simulation provides a new
data point to each of the dose estimates represented in the columns; the percentiles are
then produced for each dose estimate (column) independently of each other.
Furthermore, because the total absorbed dose is the sum of independent random
variables, its variance is less than what is obtained when specific percentiles are
summed.
bThe zeroes entered in the dermal category represent the portion of the population that
has no dermal contact with the water supply during the simulated day. For the female
(age 15-45) population group, 6.9% had no dermal contact. For the male (age 15-45)
population group, 6.9% had no dermal contact. For the child (age 6) population group,
11.2% had no dermal contact.
XXVIII
-------
TABLE E-3
50th Percentile 24-Hour Absorbed Dose Estimates (mg) Output by TEM
Chemical
Total*
Dermal
Ingestion
Direct
Indirect
Total*
Inhalation
Female, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
3.00E-01
8.00E-02
5.12E-02
2.65E-02
4.45E-01
2.73E-02
2.90E-02
8.73E-03
3.76E-03
7.95E-03
1.83E-03
1.26E-04
7.09E-04
2.51 E-02
2.70E-03
2.47E-03
1.60E-03
1.16E-04
1.05E-05
1.71E-05
2.32E-04
1.06E-04
2.18E-04
4.08E-05
4.18E-06
1.79E-05
2.09E-02
7.73E-03
5.33E-03
2.88E-03
1.91E-03
1.20E-02
1.27E-02
3.74E-03
1.61E-03
3.40E-03
7.48E-04
5.23E-05
3.03E-04
3.76E-03
1.71E-03
1.40E-03
3.00E-03
1.99E-03
1.25E-02
1.32E-02
3.89E-03
1.67E-03
3.54E-03
7.79E-04
5.45E-05
3.15E-04
2.52E-02
9.72E-03
7.03E-03
6.55E-03
4.34E-03
2.72E-02
2.89E-02
8.51 E-03
3.66E-03
7.74E-03
1.70E-03
1.19E-04
6.89E-04
2.19E-01
6.12E-02
3.73E-02
1 .63E-02
1.15E-06
5.46E-06
9.27E-06
1.79E-06
4.33E-07
2.09E-06
4.39E-05
9.73E-07
1.88E-06
Male, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
3.02E-01
8.43E-02
5.49E-02
3.00E-02
5.09E-03
3.14E-02
3.34E-02
9.97E-03
4.29E-03
2.62E-02
2.90E-03
2.64E-03
1.70E-03
1.25E-04
1.16E-05
1.88E-05
2.50E-04
1.14E-04
2.16E-02
7.98E-03
5.50E-03
2.97E-03
1.97E-03
1.23E-02
1.31 E-02
3.86E-03
1.66E-03
4.00E-03
1.85E-03
1.52E-03
3.24E-03
2.14E-03
1.35E-02
1 .43E-02
4.20E-03
1.81E-03
2.84E-02
1.11E-02
8.10E-03
7.55E-03
5.00E-03
3.14E-02
3.33E-02
9.81 E-03
4.22E-03
2.13E-01
6.05E-02
3.79E-02
1 .68E-02
1.33E-06
6.20E-06
1.09E-05
1.99E-06
5.04E-07
XXIX
-------
TABLE E-3 cont.
Chemical
Total*
Dermal
Ingestion
Direct
Indirect
Total*
Inhalation
Male, Age 15-45
BCA
DCAN
TCAN
DBAN
9.08E-03
2.09E-03
1.45E-04
8.13E-04
2.35E-04
4.46E-05
4.47E-06
1.94E-05
3.51 E-03
7.72E-04
5.40E-05
3.12E-04
3.82E-03
8.41 E-04
5.88E-05
3.40E-04
8.93E-03
1.96E-03
1.37E-04
7.94E-04
2.35E-06
4.26E-05
1 .OOE-06
1 .99E-06
Child, Age 6
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
1.56E-01
4.38E-02
2.91 E-02
1.34E-02
1.84E-03
1.12E-02
1.19E-02
3.61 E-03
1.56E-03
3.29E-03
7.72E-04
5.20E-05
2.94E-04
1.87E-03
2.66E-04
2.59E-04
1.73E-04
1.35E-05
1 .26E-06
2.06E-06
2.70E-05
1.22E-05
2.53E-05
4.84E-06
4.76E-07
2.10E-06
1.09E-02
4. 02 E-03
2.77E-03
1.50E-03
9.92E-04
6.22E-03
6.61 E-03
1.95E-03
8.36E-04
1.77E-03
3.89E-04
2.72E-05
1.58E-04
9.19E-04
1.07E-03
7.72E-04
7.42E-03
4.92E-04
3.08E-03
3.28E-03
9.64E-04
4.14E-04
8.77E-04
1.93E-04
1.35E-05
7.81 E-05
1.26E-02
6.03E-03
4.18E-03
2.70E-03
1.79E-03
1.12E-02
1.19E-02
3.50E-03
1.51 E-03
3.19E-03
7.01 E-04
4.91 E-05
2.84E-04
1.19E-01
3.36E-02
2.21 E-02
8.77E-03
6.29E-07
3.01 E-06
5.22E-06
1.01 E-06
2.37E-07
1 .26E-06
2.57E-05
5.57E-07
1.07E-06
aNote that total absorbed dose (by ingestion or by all three routes) is not equal to the sum of the
doses in each row. This occurs because each simulation provides a new data point to each of
the dose estimates represented in the columns; the percentiles are then produced for each
dose estimate (column) independently of each other. Furthermore, because the total absorbed
dose is the sum of independent random variables, its variance is less than what is obtained
when specific percentiles are summed.
xxx
-------
by calculating the dermal uptake at the minimum and maximum values of the identified
range (Section 4.2.3. of Appendix 1).
Exposure patterns simulated by TEM were used as input values upon which
ERDEM based the exposure scenarios for simulations of tissue doses. The estimation
of tissue doses was accomplished by programming and operating a previously validated
PBPK model for each chemical, BDCM, CHCI3, DCA and TCA. These models were
standardized, so that flows and tissue volumes were consistent across the different
chemicals. ERDEM was constructed to simulate tissue doses of parent chemical in
several different tissues, identified as potential target organs of toxicity. ERDEM
estimated exposure metrics as area under the concentration-time curve (AUC) for liver,
kidney, venous blood, ovaries and testes averaged over 2 days. This differs from the
TEM modeling, in which results are presented as AUC averaged over a single 24-hour
exposure period. Table E-4 shows the ERDEM results for BDCM for three different
age-dependent models: the adult male, the adult female and the 6-year-old male child.
APPLICATION OF THE CRPF APPROACH
Because animal dose-response data are typically available for only a single
exposure route (usually oral), practical implementation of the CRPF approach for
multiple exposure routes requires route extrapolations. Few inhalation or dermal toxicity
data are available for the DBPs. Thus, although the CRPF analysis may be conducted
using separate exposures for each route, it is more logical to develop the approach so it
can be implemented using dose-response information on the oral route only. (PBPK
models may also be useful in constructing physiologically-based extrapolations across
different exposure routes.) The text that follows in this section focuses on the use of
XXXI
-------
TABLE E-4
48-Hour PBPK Modeled Absorbed Doses for BDCM for the Adult Male, Adult Female and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
10th
50th
90th
95th
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
0.00230
0.00450
0.455
0.00043
0.00176
0.00681
0.0134
1.31
0.00119
0.00517
9.98
9.98
10.0
9.95
9.96
0.0919
0.180
17.7
0.0161
0.0698
8.56E-06
1 .68E-05
0.00730
1.11E-05
9.04E-06
6.72E-05
0.000132
0.0201
2.73E-05
5.52E-05
9.58E-05
0.000188
0.0340
4.26E-05
8. 11 E-05
0.000884
0.00173
0.184
0.000188
0.000682
0.00386
0.00757
0.732
0.000714
0.00294
0.00643
0.0126
1.25
0.00114
0.00490
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
Demographic Group
0.00269
0.00372
0.457
0.000525
0.00203
Average
0.00721
0.00995
1.20
0.00133
0.00540
Standard
6.23
6.22
6.24
6.23
6.22
Skewness
0.0640
0.0883
10.6
0.0118
0.0479
Max
1 .02E-05
1 .4E-05
0.00793
1 .51 E-05
1.11E-05
Min
5.36E-05
7.39E-05
0.0206
3.33E-05
4.85E-05
5th
0.00013
0.00018
0.0328
4.41 E-05
0.000107
10th
0.00103
0.00142
0.177
0.000217
0.000778
50th
0.00424
0.00584
0.703
0.000794
0.00319
90th
0.00723
0.00994
1.22
0.00135
0.00539
95th
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
0.00132
0.00258
0.175
0.000377
0.00104
0.00149
0.00291
0.190
0.000392
0.00117
2.18
2.18
2.19
2.20
2.19
0.00899
0.0176
1.16
0.00244
0.00710
3.86E-06
7.57E-06
0.00174
6.51 E-06
4.38E-06
4. 85 E-05
9.52E-05
0.0126
4.3E-05
4.54E-05
0.000142
0.000279
0.0232
5.94E-05
0.000119
0.000815
0.00160
0.113
0.000251
0.000653
0.00342
0.00670
0.437
0.000921
0.00268
0.00440
0.00864
0.567
0.00118
0.00345
XXXII
-------
internal doses based on human exposures to all three routes. Working with the 13
DBFs for which example exposure and dose estimates have been developed (Appendix
1), it is envisioned that the following steps may be followed to conduct the CRPF-based
assessment.
Group DBFs into Subclasses by Common MOA
Collect, evaluate and select the highest quality MOA and dose-response
toxicology data; determine the best measure of a biologically effective dose (i.e.,
exposures, total absorbed doses, organ/tissue doses); identify subclasses of the
13 DBFs, grouping them by similar toxic MOA; determine the appropriate dose
metric (e.g., area under the curve for absorbed and tissue doses or the maximum
concentration).
Conduct Dose Response Modeling of Toxicology Data
Adjust administered animal doses to internal animal doses using bioavailability
factors; adjust the internal animal doses to internal human equivalent doses
using allometric scaling or PBPK modeling; develop dose-response curves for
individual DBFs; re-evaluate subclass groupings based on similarly shaped dose-
response curves within the exposure region of interest.
Develop RPF Estimates for Each Subclass and Combine Using the CRPF Approach
For each subclass, choose an index chemical and estimate RPFs; multiply each
component dose by its RPF to obtain the Component ICED; sum the Component
ICEDs to generate a Subclass ICEDs; Use the dose-response curve for the index
chemical to estimate risk for its subclass; sum the subclass risks to estimate the
total mixtures risk; develop a full risk characterization for the analysis, including
an analysis of uncertainty.
XXXIII
-------
CRPF ILLUSTRATION FOR DBFS
This procedure for applying the CRPF approach is illustrated for the cancer
endpoint only, utilizing two DBP subclasses, carcinogens that are that are thought to be
genotoxic and non-genotoxic. The basic schematic for this illustration is shown in Figure
E-6; the calculations for the illustration are shown in Table E-5.
For each subclass, an index chemical is chosen. (Figure E-6 indicates that
BDCM and DCA are the index chemicals for the genotoxic subclass and non-genotoxic
subclasses, respectively.) RPFs are then calculated for each member of the subclass
relative to the index chemical using the dose-response functions generated for the
individual DBPs. (Table E-5 shows the RPFs for each DBP, where the calculation was
conducted using a ratio of slope factors.) Then, within each subclass, the absorbed
dose for each DBP is multiplied by its RPF to calculate a Component ICED for each
member of the subclass; these estimates are summed to yield a total Subclass ICED.
The dose-response curve for the index chemical is used to estimate risk for that
subclass at the Subclass ICED.
Table E-5 provides an illustration of the cancer risk calculations that could be
made for a 70 kg adult male by combining the dose-response information with the TEM
total absorbed dose estimates shown in Table E-3. The 50th percentile doses (mg/day)
from Table E-3 are converted to mg/kg/day doses (dividing by 70 kg) and then
multiplied by the RPF for each DBP to obtain Component ICEDs. The sum of the
Component ICEDs form the Subclass ICEDs. The product of the Subclass ICEDs and
the MLE slope factor for the subclass index chemical provides an estimate of the
average cancer risk for that subclass. The subclass risks are then added to obtain the
final total average cancer risk for the whole mixture.
xxxiv
-------
Genotoxic
DBFs:
BDCM (Index
Chemical),
DBCM, CHBrS
SFoBCM
^FBDCM
SFcHBrS
SFBDCM
X
X
DBCM
Total
Absorbed
Dose
CHBrS
Total
Absorbed
Dose
Non-Genotoxic
DBFs:
DCA (Index
Chemical), TCA
DBCM ICED +
CHBrS ICED +
BDCM Dose =
Equivalent Dose
of BDCM
(mg/Kg day)
0>
{§.
o
a
w
0>
a:
'-jy^
^^j
BDCM Dose
TCAICED +
DCA Dose =
Equivalent Dose
of DCA
(mg/Kg day)
o:
Tumor Risk for
Genotoxic DBFs
at Subclass ICED
Tumor Risk for
Non-Genotoxic
DBFs at
Subclass ICED
DCA Dose
Mixture
Risk Estimate
Determine Subclasses;
Select Index Chemical
Develop Component ICEDs Develop
Develop RPFs Component Doses Subclass ICEDs
Estimate Subclass
Risks Using Index
Chemical Dose-Response
Sum Subclass Risks
To Estimate Total
Mixture Risk
FIGURE E-6
Schematic of CRPF Approach for Illustration of DBP Mixture Cancer Risk
xxxv
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TABLE E-5
Illustration of CRPF Approach for Average Cancer Risk Calculations
(Includes assumption of 100% bioavailability)
DBP
95% Upper
Bound Slope
Factor (SF)a
RPF
(SF/SF/
Total Absorbed Dose
for 70 kg Male
50%
mg/day
50%
mg/kg/day
Component
ICED
mg/kg/day
Subclass
ICED
mg/kg/day
Subclass
Risk
MLE Slope
Factor times
Subclass
ICED
Genotoxic Subclass
BDCMC
DBCM
CHBr3
6.20E-02
8.40E-02
7.90E-03
1.00
1.35
0.13
8.43E-02
5.49E-02
3.00E-02
1.20E-03
7.84E-04
4.29E-04
1.20E-03
1.06E-03
5.46E-05
2.32E-03
1.32E-05
Non-Genotoxic Subclass
DCAd
TCA
CHCI3
1.00E-01
8.40E-02
RfD=0.01
1.00
0.84
~
3.14E-02
3.34E-02
3.02E-01
4.49E-04
4.77E-04
4.31 E-03
4.49E-04
4.01E-04
~
8.49E-04
Total Mixture Average Cancer Risk
1.19E-06
1 .44E-05
aSlope factors for BDCM, DBCM, CHBr3 are from IRIS, (U.S. EPA, 2002c). MLE slope factors are from the same dose-response model as the 95%
upper bound slope factors. Slope factors for DCA and TCA, derived from data presented in Bull and Kopfler (1991) are included here to illustrate
the CRPF approach only and are not representative of EPA peer-reviewed, endorsed values. This illustration assumes exposures below the
CHCI3 Reference Dose (RfD) of 0.01 mg/kg/day do not contribute to carcinogenicity.
bSF.| is slope factor for index chemical; SFj is slope factor for ith chemical in the subclass.
cGenotoxic Subclass Index Chemical, Maximum Likelihood Estimate (MLE) of Cancer Slope Factor (SF) = 5.7E-3
dNon-Genotoxic Subclass Index Chemical, MLE SF = 1.4E-3
xxxvi
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It is noteworthy that a strength of the CRPF approach is that it can be applied
more broadly and expanded beyond this simple illustration using only six well-studied
DBFs. In this hypothetical example, the toxicity of each chemical was well
characterized. However, this approach can accommodate other DBFs for which fewer
toxicity data exist. For example, other genotoxic carcinogens exhibiting similar MOA to
BDCM may be present in the mixture. Although in vivo data may not be available,
RPFs can be derived using other measures of potency (e.g., in vitro genotoxicity data),
providing these data are relevant to the endpoint of interest and also exist for the index
chemical. Clearly, exposure estimates would also need to be developed for the CRPF
approach to be implemented.
The final step of such an effort is to fully characterize the uncertainties that exist
as a product of the analysis. This risk characterization should include uncertainties in
the CRPF process, including discussions regarding subclass development, choice of
index chemical, and the strength of the exposure assessment.
CONCLUSIONS
Exposure modeling techniques and risk assessment methods are available to
formulate CRA estimates for specified groups of DBFs. This analysis illustrates that
multiple route exposure estimates can be developed that account for human activity
patterns affecting contact time with identified DBFs in tap water by developing internal
dose estimates for selected DBFs. Although important data gaps still exist (e.g.,
chemical properties of some DBFs such as bromate, MOA data for appropriately
assigning DBFs into subclasses), additional data on these chemicals continue to be
developed by many researchers. Application of this approach may provide a more
scientific basis for evaluating risks posed by different mixtures of DBFs than
XXXVII
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comparisons developed based on concentrations of individual DBFs and single route
risk analyses. With sufficient data, applications of this approach should provide a more
useful comparison to epidemiologic studies than analyses based on concentrations of
individual DBFs and single routes of exposure. Cumulative risk estimates developed
using these approaches can be compared across different types of treatments of the
same source water or across geographic areas. These estimates of risk should be
compared on a relative basis, rather than an absolute basis. For example, a Hazard
Index or other component based mixtures risk assessment approach may be applied
(see U.S. EPA, 2000b) using cumulative dose estimates. For more difficult problems,
such as predicting actual risks from exposure to chlorinated drinking water (e.g.,
number of cases of cancer for a population served by a particular system), additional
research will be required before credible CRAs can be implemented. To improve upon
the current effort, the following information still needs to be developed:
1) A careful treatment is needed to determine MOA for the major DBFs of concern
for health risk assessment. At a minimum, MOA should be determined for
cancer, developmental effects and reproductive effects.
2) Dose response models need to be developed for the major DBFs of concern for
all relevant endpoints. Although some initial work has been done in the 1990's
(U.S. EPA, 2000a), this research should be updated to include the current
literature base. In addition, issues to be carefully considered in the development
of new dose response models include consideration of vehicle effects, non-linear
responses at low doses, different MOA at low and high doses, background
response rates, and litter effects.
XXXVIII
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3) The exposure and PBPK model predictions used in this analysis need to be
further evaluated against independent data sets.
4) Improved quantitative skin permeability rates need to be developed. A large
range of uncertainty exists in the dermal estimates that make it difficult to
compare the dermal route to the inhalation and ingestion routes. Similarly, much
uncertainty associated with inhalation exposures could be reduced through better
estimation of volatilization.
5) A factor that limited the exposure modeling results to 13 of the 15 chemicals was
lack of data on chemical properties, e.g., Henry's law constant, Kow, boiling
point, vapor pressure, liquid and gas phase diffusivities (see section 3. for a
chemical-specific detailed list). This is a important data gap, particularly because
bromate was not included in the exposure modeling estimates. (Bromate, a
suspected carcinogen, is of concern for high bromide source waters where
ozonation is the primary disinfectant for the treatment system.)
6) Some physiological parameters are still needed for improved PBPK modeling.
The sensitivity analysis (based on CHCI3 and DCA) indicated that certain
parameters could produce relatively large changes in the exposure estimates.
These included: alveolar ventilation rates, blood flow in the kidney, volume in the
liver, liver metabolism Vmax, volume in the body, the partition coefficient for
testes/blood, and stomach to portal blood rate.
7) Future exposure modeling efforts should ensure that a complete uncertainty
analysis be conducted and that the sensitivity analyses include all modeled
chemicals and demographic groups in the study.
xxxix
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8) Research needs to be conducted to determine whether populations sensitive to
particular DBFs or DBP classes exist. Sensitivity may arise through different
activity patterns among people (e.g., long vs. short shower durations),
toxicokinetic differences among individuals, and toxicodynamic differences
between individuals.
9) Approximately 50% of DBFs in the finished drinking water consists of unidentified
material. EPA has conducted research to identify these DBFs (Richardson,
1998), to estimate the potential toxicity of these chemicals (Moudgal et al., 2000;
Woo et al., 2002), and to estimate the additional health risk from exposure to this
unknown fraction of DBFs (Teuschler et al., 2001; U.S. EPA, 2000a). Research
needs to be conducted to enhance the CRPF approach to account for the
potential toxicity of the unknown fraction.
While comprehensive lists of needed research are useful, they generally provide
little insight as to which of the research needs are of the highest priority. The current
understanding of the risks that DBPs pose through multiple exposure routes would be
improved ultimately through the successful conduct of any research listed here. To
determine which areas of research would be most useful in refining risk estimates,
quantitative human health risk estimates for DBPs need to be developed, including
detailed analyses of uncertainly and variability. The research needs could be evaluated
based on the expected improvement in the confidence in estimated DBP risks. This
evaluation could serve as a ranking approach for DBP research needs.
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1. INTRODUCTION
1.1. BACKGROUND
Assessment of potential human health risk(s) from disinfection by-products
(DBFs) in drinking water is needed because of widespread oral, dermal and inhalation
exposures to this complex mixture and because positive data from both epidemiologic
and toxicologic studies of DBFs raise concern for human health (U.S. EPA, 2000a).
Although these data suggest human health effects are possible, human exposures are
complex, making the interpretation of positive results difficult. Occurrence information
shows that the mix of DBFs may vary considerably with geographic location and water
treatment process. Furthermore, for the more volatile DBFs, inhalation exposures may
be greater than ingestion; for highly lipophilic DBFs, dermal exposures may also be
important. Information from toxicologic studies has focused primarily on single DBFs at
doses far above finished drinking water concentrations. Information from positive
epidemiologic studies suggests that exposures to different mixtures of DBFs in various
geographic locations may pose quite different health risks. Thus, to develop a
regulatory and risk reduction strategy, there is a need to consider the health risks
associated with DBF mixtures and the various exposures from contact with finished
drinking water. Given this need, the U.S. Environmental Protection Agency (EPA) has
conducted research for assessing DBF health risks using a cumulative risk assessment
approach, defined here as multiple chemical exposures via multiple exposure routes
overtime (U.S. EPA, 2000a).
The need to conduct a risk assessment for DBF mixtures arose both as a legal
mandate and also as a logical scientific direction. Under 42 USC § 300 of the Safe
Drinking Water Act Amendments of 1996, it is stated that EPA will "develop new
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approaches to the study of complex mixtures, such as mixtures found in drinking
water..." In addition, the EPA's Office of Water drafted a Research Plan for Microbial
Pathogens and DBPs in Drinking Water that calls for the characterization of DBP
mixtures risk (U.S. EPA, 1997a). In response to these mandates, U.S. EPA's National
Center for Environmental Assessment - Cincinnati produced a report, identifying the
major issues for consideration to conduct scientifically credible and comprehensive DBP
mixtures risk assessments (U.S. EPA, 2000a). The report concludes that the evaluation
of human health risks from exposure to DBPs is a cumulative risk assessment problem
and recommends consideration of the following factors:
Exposure to multiple chemicals at low environmental concentrations,
Knowledge of toxic mode of action (MOA)1 and judgment regarding
similarity of MOA among DBPs,
Dermal, oral and inhalation routes of exposure,
Measures of internal absorbed dose,
Human activity patterns that affect the types of water use and the amount
of contact time with the drinking water,
Physicochemical properties of the DBPs,
Physical properties of the indoor environment, and
Sensitive subpopulations.2
of Action (MOA) is defined as the set of biological events at the target
tissue or target organ leading to a toxicologic outcome. A toxicologic outcome is
considered to be damage to the organism at any level of biological organization (i.e.,
molecular, cellular, tissue,...).
Sensitive subpopulations are groups of individuals in a population with increased
likelihood over the average population to express an adverse health effect resulting
from exposure to a contaminant. The reasons for this sensitivity may be unknown, but
could include factors such as age, sex, genetic predisposition, nutritional status,
immune system deficiencies, etc.
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Incorporating many of these factors, research has been conducted to develop
human exposure estimates for individual DBFs from multiple exposure routes; whole
body and organ-specific internal doses are estimated for all three exposure routes for
each individual DBP. This report describes how these data can be used to assess DBP
risks using a newly developed risk assessment method, the Cumulative Relative
Potency Factors (CRPF) approach, that combines the principles of dose addition3 and
response addition4 into one method to assess mixtures risk for multiple route exposures
(U.S. EPA, 2000a).
The CRPF approach is a component-based method for assessing health risks
that combines dose-response and exposure data for each individual chemical in the
mixture to estimate risk, as opposed to using data on the whole mixture. Oral dose-
response animal data exist for most of the major DBFs identified in the drinking water
for cancer, developmental and reproductive effects, and a number of systemic effects.
These data are too numerous to include here; numerous journal publications exist on
DBP toxicologic studies, as well as reviews of the literature (e.g., IPCS, 2000; Klinefelter
et al., 2001; U.S. EPA, 2000a). In contrast to the oral data, dermal and inhalation dose-
response animal data are relatively sparse. Thus, the development of DBP mixture risk
3Dose Addition is a chemical mixtures risk assessment method in which doses
are summed (after scaling for relative potency) across chemicals that have a similar
MOA; risk is then estimated using the combined total dose.
4Response addition is a chemical mixtures risk assessment method applied to
chemicals whose MOA are independent of each other (i.e., the presence of one
chemical in the body does not influence the effects caused by another chemical); risk of
a whole body effect (e.g., non-specific cancer), is then estimated by summing the risks
(e.g., skin cancer, liver cancer) of the individual chemicals.
-3-
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assessment approaches based primarily on the use of oral dose-response information
is a plausible research direction.
Three different measures of dose are presented with respect to possible
application of the CRPF approach (see Figure 1-1). The actual choice of dose metric, as
well as the temporal element of each exposure measure, is influenced by available
dose-response data. Contaminant exposures may be evaluated in one of three ways:
1) Exposures. In this measure, exposure is quantified separately for each exposure
route as the amount of an agent available at the exchange boundaries (e.g., skin,
lungs, intestinal tract). Exposure estimates are based on environmental
concentrations in the media and human activity patterns that affect the types of
water use and the amount of contact time with the drinking water.
2) Total Absorbed Doses (e.g., blood concentrations). Internal dose estimates are
developed based on the amount of a contaminant that is absorbed from all
exposure routes without regard to specific absorption processes.
3) Organ or Tissue Doses. In this measure, organ doses (e.g., doses experienced
by the kidney, liver, etc.) or tissue doses are estimated from all exposure routes
based on pharmacokinetic information.
The goal of this document is to examine the feasibility of conducting a cumulative
risk assessment for drinking water DBP mixtures by combining exposure modeling
results with the CRPF risk assessment approach. Section 2 of this document presents
the new CRPF approach, developed for application to the DBP complex mixture risk
problem (U.S. EPA, 2000a). To provide additional detail, Appendix 2 reproduces
Chapter 4 of the U.S. EPA (2000a) report that presents the CRPF approach as a
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Environmental Concentrations
Human Activity Ratteens
Dermal Exposure,
Inhalation; Exposure
Oral Exposure
Skin
Barrier
i
Lung
Intestinal Tract
Dermal AbsorbedTJose
-i-
i
i
Inhalation Absorbed Dose
i
i
H
.XDral Absorbed Dose
i
t
Total Absorbed Dose
(Internal Dose)
Pharmaciokinetics
Tissue/Organ Dose
FIGURE 1-1
Dose Metrics for Environmental Contaminants
-5-
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conceptual model. Section 3 presents a summary of the DBP exposure research
results (detailed report in Appendix 1) that provide human exposure estimates for 13
DBFs, accounting for oral, dermal and ingestion routes of exposure as well as human
activity patterns. Section 4 discusses the use of these newly developed exposure
estimates in the CRPF approach. Section 5 details the uncertainties and data gaps that
define future research needs and discusses the technical feasibility of completing a
cumulative risk assessment for DBP mixtures.
1.2. EXPOSURE MODELS
Different approaches exist for evaluating human exposures to environmental
contaminants. In this document two different mathematical models are employed to
evaluate human exposures. An Exposure Assessment Model generates estimates of
exposures at the body boundaries through human contact with the media, influenced by
human activity patterns. A Physiologically-Based Pharmacokinetic (PBPK) Model
predicts doses of DBFs experienced by relevant organs or target tissues.
Exposure assessment models are used in conjunction with exposure scenario
analyses to estimate contaminant concentrations in the media surrounding sources.
Exposure scenarios detail the assumptions concerning how humans might contact a
contaminant or a mixture of contaminants (Paustenbach, 2000). In the context of
drinking water contaminants analysis, exposure assessment models characterize
contaminant contact with body membranes (e.g., the tissues lining the gastrointestinal
tract, the lungs, and the derm is). An exposure scenario is constructed to characterize
sources of human exposure, physical properties of a building or room that influence the
dispersion of the contaminants from their sources into the indoor environment, and
human behavioral patterns that bring individuals into contact with the contaminated
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media. For example, an exposure assessment model can quantify the human
exposures that occur through the inhalation route due to a volatile drinking water
contaminant both during and following a shower.
PBPK models predict doses of contaminants that occur at the tissue or organ
level. PBPK models employ a series of mathematical formulae that quantify
pharmacokinetic processes. PBPK models predict the contaminant doses that pass
through the body's exterior membranes, the distribution of these contaminants through
body tissues, the metabolism of the contaminants, and their elimination. The rates at
which these processes occur change in part due to variations in predicted contaminant
absorbed doses in the tissues and organs modeled. Because PBPK models account for
the different rates at which these physiological and biochemical processes occur,
changes in tissue or organ doses can be predicted through extrapolation across species
based on measurements in a test species. PBPK models predict tissue doses based on
changes in exposures, changes in exposure routes, changes in exposure duration, and
interindividual variations (e.g., enzyme activity levels). These capabilities are significant
in the conduct of risk assessments because they quantify uncertain aspects of
extrapolation: across species (e.g., from test species to human); from high experimental
doses to lower environmental exposures; from experimental to environmental exposure
durations; and across different exposure routes (Clewell et al., 2002).
PBPK models are useful not only for assessing individual chemicals, but also for
conducting cumulative risk assessments. Cumulative risk assessments, as defined in
this effort, evaluate human exposures to multiple contaminants through multiple
exposure routes. Mumtaz et al. (1993) noted that PBPK models can be used to estimate
absorbed doses of chemicals and their metabolites (a mixture) through multiple
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exposure routes, to evaluate competition of different chemicals for a specific receptor
(e.g., glutathione) or target, and to examine increases or decreases in metabolic
enzyme activity due to the presence of a second contaminant.
1.3. GUIDANCE ON CUMULATIVE RISK
The EPA is developing a number of approaches for conducting cumulative risk
assessments. The Risk Assessment Forum, under the Office of Research and
Development, is currently drafting a Framework for Cumulative Risk Assessment (U.S.
EPA, 2002a) that will serve as the basis for a future cumulative risk assessment
guidelines document. The Office of Pesticides Programs (OPP) has also been actively
involved in response to a mandate within the Food Quality Protection Act of 1996 which
calls for the multiple route risk assessment of pesticide mixtures that act by a common
mechanism of toxicity. OPP has produced guidance in this area as well as a preliminary
cumulative assessment of the organophosphorous pesticides using Relative Potency
Factors (U.S. EPA, 2001, 2002b). A general definition of cumulative risk is, "the
combined risks from multiple routes of exposure to multiple agents or stressors", which
can include non-chemical stressors (U.S. EPA, 2002a). For the DBP complex mixture,
cumulative risk is defined for use in this document in a more limited way as "the
combined risks from exposure to multiple chemicals via multiple exposure routes over
time." Other stressors, such as smoking, that could influence outcomes associated with
DBPs (e.g., gastrointestinal cancer, adverse pregnancy outcomes) are not evaluated.
The starting point for approaching this cumulative risk problem is to apply the premises
put forth in existing Agency guidelines and guidance documents on chemical mixtures
risk assessment.
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The U.S. EPA has published methods to perform health risk assessments of
chemical mixtures (U.S. EPA, 1986, 2000b), in which three approaches to quantifying
health risk are recommended depending on the type of data available to the risk
assessor: data on the complex mixture of concern; data on a sufficiently similar mixture;
or, data on the individual components of the mixture or on their interactions. Figure 1-2
shows that different aspects of the risk actually posed can be evaluated using these
three types of data; data collection efforts can be targeted for use in these risk
assessment approaches (Teuschler and Simmons, 2002). In the top row of Figure 1-2,
data are available on the complex mixture of concern, in this case, real-world drinking
water samples. The quantitative risk assessment is done directly from these data,
including either epidemiologic or toxicologic data. In the middle row of Figure 1-2, data
are available on a "sufficiently similar" mixture, e.g., finished drinking water samples
created in the laboratory that are representative of specified treatment processes and
source waters. Two mixtures are thought to be sufficiently similar for use in risk
assessment when differences in their major chemical components and their relative
proportions are small (U.S. EPA, 2000b). In practice, existing toxicity data on a
sufficiently similar mixture may be used to estimate the expected toxicity of finished
drinking water produced by the same treatment process and source water.
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Mixtures Risk
Assessment Guidance
Data Available
on Complex
Mixture
of Concern
Data Available
on Sufficiently
Similar Mixture
Data Available
on Components;
Interactions Data
Approaches to Health
Effects Data Collection
Real World
Samples
Epidemiology
Studies
Created Samples
Representative of
Various Finished
Drinking Waters
Single Chemicals,
Defined Mixtures
Risk Assessment Uses
Extrapolation of Animal
Toxicity Data to Human
Health Risk Estimation
Evaluate Human Health
Risks Directly From Data
Extrapolate Toxicity Results
Across Similar Treatment
Processes and Source Waters
Evaluate Joint Toxic Action of
DBFs; Compare with Whole
Mixtures data to Estimate
Toxicity of Unknown DBFs
FIGURE 1-2
Mapping of Risk Assessment Approaches to Drinking Water Studies
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In the bottom row of Figure 1-2, data are available on single chemicals and
defined mixtures of DBFs to evaluate the mixture through an analysis of its
components. For example, there is precedent for using dose addition to estimate the
risk of systemic effects and response addition for estimates of cancer risk (U.S. EPA,
1989, 2000b). These particular procedures include a general assumption that
interaction effects (i.e., effects that are greater than or less than those expected under
additivity) at low exposure levels either do not occur at all or are small enough to be
insignificant to the risk characterization.5 For DBFs, toxicity and exposure data on the
components of a mixture can be combined and added (depending on the assumption
used) to estimate mixtures risk.
Thus, Figure 1-2 highlights several risk assessment issues of concern to
managers responsible for ensuring safe drinking water for the public. The first issue is to
evaluate the association between DBF mixture exposures and human health outcomes
and thereby establish whether or not human health risks are a significant concern.
Because the evaluations of this association are inconclusive and human health effects
from DBF exposures are possible, some drinking water regulations have been
promulgated and others posed with the goal of controlling levels of DBFs in the drinking
water (e.g., U.S. EPA, 1979, 1994a, 1998b). As rules go into effect, alternative drinking
water treatment technologies are developed to meet these new standards. Thus, a
5For exposures at low doses with low component risks, the likelihood of
significant interaction is usually considered to be low. Interaction arguments based on
saturation of metabolic pathways or competition for cellular sites usually imply an
increasing interaction effect with dose, so that the importance at low doses is probably
small. The default component procedure at low exposure levels is then to assume
response addition when the component toxicological processes are assumed to act
independently, and dose (or concentration) addition when the component toxicological
processes are similar (U.S. EPA, 2000b).
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second important issue is to choose among treatment options by evaluating potential
changes in exposure and health risk(s) across various drinking water treatment systems
and source waters. A third issue for evaluation of DBP mixtures is that approximately
50% of the DBP mass consists of unidentified total organic halide material, the toxicity
of which is largely unknown (Weinberg, 1999). By comparing whole mixture toxicity
data with data on the mixture components, the toxicity of the unknown fraction of the
DBP complex mixture can be evaluated.
In a preliminary health risk assessment of DBPs, toxicity and exposure data on
the components of a mixture were combined and added, assuming response addition,
to estimate mixtures risk (Teuschler et al., 2001; U.S. EPA, 2000a). To perform a
cumulative risk assessment, however, the DBP assessment must be broadened to take
into account dermal, oral, and inhalation exposure routes and patterns of human
behavior that affect water usage and contact time with the drinking water. The method
proposed in this document using the CRPF approach is a component based approach,
based on Agency guidance, and incorporates improved exposure information on the
DBPs.
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2. CUMULATIVE RELATIVE POTENCY FACTORS
This section describes the CRPF approach (U.S. EPA, 2000a) as it may be
applied to DBP mixture exposures. (A detailed description of the CRPF approach is
given in Appendix 2.) Section 4 will discuss how the exposure estimates presented in
Section 3 can be used to conduct a DBP cumulative risk assessment by applying the
CRPF approach. As discussed in Section 1.1, application of the CRPF approach will
vary depending on the choice of dose metric for the analysis (i.e., external exposures,
total absorbed doses, and tissue or organ doses). The use of these dose metrics is
discussed below and further developed in Section 4.
2.1. RELATIVE POTENCY FACTORS
U.S. EPA developed the Relative Potency Factor (RPF) approach to assess risks
posed by mixtures that are comprised of chemical components exhibiting a common
mode of action6 (MOA) for a toxic effect (U.S. EPA, 2000b). The RPF approach is based
on the concept of dose addition. Mixture components are grouped for the purpose of
developing an RPF Set by factors such as membership in a chemical class (relating to
observed toxicity), and common toxicologic effects, exposure routes, exposure
durations, or dose ranges. To implement the approach, the toxicity of each component
is predicted by scaling its exposure level by a measure of the component's relative
6The terms mechanism of toxicity (or mechanism of toxic action) and mode of
action represent a continuum of understanding regarding a toxicodynamic process.
Knowledge of a chemical's mechanism of toxicity or mechanism of toxic action
implies that the molecular and cellular events leading to a toxicologic outcome are
described and well-understood. A toxicologic outcome is considered to be damage to
the organism at any level of biological organization (i.e., molecular, cellular, tissue,...).
Knowledge of a chemical's mode of action implies a general understanding of the key
toxicodynamic events that occur at a tissue level, but not a detailed description of these
events at the cellular or molecular level.
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toxicity. This scaling factor, the RPF, is based on a comparison of a component's
toxicity with similar measures of toxicity for a selected index chemical, a toxicologically
well-studied component of the mixture
(i.e., of the RPF Set). The product of the
measured exposure dose of each mixture
component and its RPF is defined as an
Index Chemical Equivalent Dose (ICED).
The ICEDs of all the mixture components
are summed to express the total mixture
exposure in terms of an equivalent
exposure to the index chemical. The risk
posed by the mixture is quantified by
comparing a mixture's total ICED to the
dose-response assessment of the index
chemical. (The mathematical formulae for
the RPF are detailed in Text Box 2-1.)
Appropriate application of the
RPF method requires a judgment that the
mixture components share a common
mechanism of action or a common mode
of action and evidence that the
components have similarly shaped dose
response curves. For the first
assumption, the term Common
TEXT BOX 2-1
Mathematical Representations and RPF
Formulas
d., = dose of chemical 1 present in a mixture
(units not specified)
d2 = dose of chemical 2 present in a mixture
(units not specified; must be consistent with
those
pot., = potency estimate (e.g., a slope factor) for
chemical 1 (risk per unit of dose specified for d.,)
pot2 = potency estimate (e.g., a slope factor) for
chemical 2 (risk per unit of dose specified for d2)
ICED = index chemical equivalent dose based
on relative potency estimates (units consistent
with d., and d2)
f,(*) = dose-response function of the index
chemical for the response(s) common to
chemical 1 and chemical 2 (units consistent with
d., and d2)
h(d1,d2) = mixture hazard or risk from joint
exposure of dose d., to chemical 1 and dose d2
to chemical 2
[ED10].| = dose of chemical 1 that results in a
10% response, either as a fraction of exposed
test animals that respond, or as a fractional
change in a measured physiological value.
[ED10]2 = dose of chemical 2 that also results in
the same 10% response
Then, designating chemical 1 as the index
chemical in the RPF approach,
RPF2 = [ED^ / [ED10]2 ,
(or equivalently = pot2 / pot,)
ICED = d! + (RPF2* d2)
h(d1,d2) = f1(ICED)
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Mechanism of Action implies that the chemicals in a mixture exhibit a common
toxicologic outcome when tested and that the underlying molecular and cellular
toxicodynamic events leading to this outcome are the same for each chemical, after
they reach the target site. (Toxicodynamic events include the initial interaction of a
toxicant with its molecular or cellular target and subsequent responses to the toxic
insult.) The term Common Mode of Action implies that chemicals in a mixture exhibit a
common toxicologic outcome when tested but that the toxicodynamic events leading to
this common outcome after the chemicals reach the target site are not well understood.
Because detailed toxicodynamic data are not abundant for most chemical mixtures,
analysts typically must judge whether or not the chemicals in a mixture exhibiting a
common toxicologic outcome share a common MOA. The second assumption of
similarly shaped dose-response functions includes their expected shape in the low dose
region including the region that may lie below the lowest dose tested in the relevant
toxicological bioassay. Evidence that a chemical class fulfills one of these requirements
does not necessarily imply that the second requirement is fulfilled.
RPFs are based on comparisons with an index chemical, and the mixture risk is
estimated using the dose response function of the index chemical. Criteria pertaining to
the inclusion of compounds in an RPF Set apply to the index chemical. The index
chemical should be a well-studied member of the RPF Set; studies on the index
chemical need to provide exposure data for routes of interest and health assessment
data for health endpoints of interest. To estimate relative potency, toxicity studies of
compounds in the RPF Set need to be comparable to studies conducted on the index
chemical. (See Appendix 2 for a quantitative example of the RPF process.)
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2.2. THE CRPF APPROACH
The CRPF approach groups DBFs with a common MOA into RPF Sets called
subclasses. The MOA differ across the subclasses, but the toxicological endpoint (or
outcome) is the same. A dose-addition analysis is conducted within each subclass for
the toxicologic outcome common across subclasses using the RPF approach (U.S.
EPA, 2000b). Each resulting subclass risk estimate represents the risk for this common
endpoint. However, these subclass risks are independent of each other (i.e., the toxicity
caused by one subclass does not influence the toxicity caused by the other subclass
because their respective MOA are different), thus meeting the criteria required to apply
response addition; the subclass risk estimates are added to yield a risk estimate for the
total DBF mixture.
Figure 2-1 illustrates this integration of dose addition and response addition using
two subclasses to estimate risk from exposure to the mixture. Based on available data,
the components are considered to have two different MOA and are subdivided into two
subclasses for development of RPFs. For each subclass, an index chemical is
determined and an ICED is calculated using RPFs. The ICED is an important concept
for the CRPF method that is employed at two levels:
1) Component ICED - refers to the ICED for an individual chemical within a
subclass.
2) Subclass ICED - refers to the ICED for all chemicals within the subclass,
computed by summing their Component ICEDs.
Figure 2-1 is illustrated in this paragraph using a hypothetical non-cancer
example. In this example, the presence of amino acids in urine of test animals during
separate chronic rodent bioassays of two chemicals indicates that chronic exposure to
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Component
ICED for Index
Chemical Al =
DoseAl*!
Component
ICED for
Chemical A2 =
DoseA2*RPFA2
Component
ICED for
Chemical A3 =
DoseA3*RPFA3
Dose Addition for
Chemicals in Set A
Component ICED
for Chemical C1
= DoseCl*RPFCl
Component ICED
for Chemical C2
= DoseC2*RPFC2
Component ICED
for Index Chemical C3
= DoseC3*l
Dose Addition for
Chemicals in Set C
Subclass ICED for set A
D-R Curve of
Chemical Al
Subclass ICED
D-R Curve of
Chemical C3
Subclass ICED
Subclass ICED for set C
Response Addition for
Total Mixture Risk
Risk for Set A
Evaluated at
Subclass ICED
Risk for Set C
Evaluated at
Subclass ICED>
Total Mixture
Risk as Sum of
Risks for Set A
and Set C
FIGURE 2-1
CRPF Approach : Integration of Dose Addition and Response Addition to Estimate Mixture Risk
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each chemical alters renal function. Additional toxicologic evidence indicates that the
two renal toxicants exhibit different MOA. The first chemical causes cellular injury to
glomerular endothelial cells through an MOA similar to that of cyclosporine. The second
causes cellular injury to proximal tubule segments 3 and 4 of the nephron through an
MOA similar to that of mercuric chloride. Each chemical is selected as an index
chemical for a subclass; limited evidence suggests that members of each subclass
share a common MOA with their respective index chemicals. Low environmental
concentrations of the mixture of chemicals in the two subclasses result in predicted
human exposures in the low dose region where component interactions are not
significant (i.e., synergistic or antagonistic interactions among components are not
expected to occur, so the RPF approach based on dose-addition within each subclass
of renal toxicant is appropriate). Because the MOA data indicate independence of
toxicologic action between the subclasses, response addition is appropriate for
combining risks across the subclasses. A risk estimate for adverse renal effects is
made for each subclass from its index chemical's dose-response curve at the Subclass
ICED. The subclass risks are added using the assumption of response addition to
estimate the total mixture risk of adverse renal effects.
2.2.1. Theory of the RPF Approach. The RPF approach has been proposed for
characterizing health risks associated with mixtures of chemical compounds that have
data indicating they are toxicologically similar (U.S. EPA, 2000b). To develop an RPF-
based risk estimate for a class of chemicals, good toxicological data are needed for at
least for one component of the mixture (referred to as the index chemical). Scientific
judgment and analysis of available data are used to assess the relative toxicity of the
other individual components in the mixture. Based on available data, the RPF Set can
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be limited to specific exposure routes, specific health endpoints, or specific members of
a class of compounds that have similar pharmacodynamic and possibly
pharmacokinetic properties. Application of an RPF approach when conducting a
cumulative risk assessment allows the analyst to 1) subdivide a class of chemicals that
exhibit a common toxic endpoint but different Pharmacodynamic properties into
toxicologically appropriate subclasses; 2) incorporate differences in toxicity based on
exposure route and exposure time frame into this subdivision; and 3) appropriately limit
the cumulative risk assessment to certain health endpoints based on available data. To
the extent that data are available, division of the DBFs into RPF Sets called subclasses
is performed by incorporating all relevant biological information regarding toxicant-target
interactions and response processes (e.g., it would be important to distinguish between
carcinogens that directly interact with and damage DMA versus those that operate
through epigenetic or nonmutagenic mechanisms such as receptor-mediated pathways
and hormonal or physiological disturbances). The RPF method requires that a
quantitative uncertainty analysis or qualitative description of uncertainty be included in
the risk characterization.
2.2.2. RPF Calculations Using Exposures from Exposure Assessment Models.
Human exposures may be estimated using exposure assessment models that take into
account concentrations of chemicals in the media, human activity patterns, physical
properties of the chemicals, etc. (see Section 1.1. and Figure 1-1). To apply the RPF
approach to one subclass (m) of DBPs and one exposure route (w) using these
exposure estimates, the basic model is as follows:
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where:
Rmw(k) = subclass m risk (unitless) for a specified endpoint and exposure
route w as a function of index chemical k
fi^ = dose response function of index chemical k for the specified
endpoint and exposure route w
Cmw(k) = Subclass ICED of index chemical k for the specified endpoint and
exposure route w.
The Subclass ICED is developed when the component exposures are expressed
as Component ICEDs by developing scaling factors, i.e., RPFs. Then, the Subclass
ICED is estimated as:
_ (2-2)
where:
Cmw(k) = Subclass ICED of index chemical k for the specified
endpoint and exposure route w.
n = number of components in the subclass
RPFiw = proportionality constant (unitless) relative to the toxic potency of
the index chemical, k, for the ith mixture component, exposure
route w
Ciw = exposure estimate of the ith mixture component by exposure
route w
RPFiw* Ciw = Component ICED for the ith mixture component, exposure route
w.
Calculation of an RPF, involves estimating the relative potency of each
component compared with the index chemical (see Appendix 2 for an example
calculation). To illustrate, one method is to calculate the ratio of effective dose levels,
e.g., the ratio of the index chemical's ED10 to the ith chemical's ED10 to estimate an RPF,
for that chemical (see Text Box 2-1). A second method is to calculate the ratio of
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potency estimates (e.g., cancer slope factors). The calculation of the RPF, requires that
the chemicals in the subclass have similarly shaped dose-response curves, at least in
the region of exposures relevant to the risk assessment.
2.2.3. RPF Calculations Using Internal Doses from PBPK Models. Internal doses
(e.g., blood, tissue and organ doses) may be estimated using PBPK models that take
into account exposures and pharmacokinetic processes (see Section 1.1. and Figure
1-1). RPFs can be applied to a DBP subclass for multiple exposure routes using
measures of total absorbed dose or total tissue/organ doses from PBPK modeling for
evaluating risks posed to internal organs, providing that no portal-of-entry effects are
involved. For chemicals exhibiting portal of entry effects, PBPK models may be used to
refine the tissue dosimetry estimates. The basic model for subclass m for internal dose
across multiple route exposures is as follows:
where:
Rm(k) = subclass risk (unitless) of a specified endpoint as a function of index
chemical k
fk = oral dose response function of index chemical k for the specified
endpoint (adjusted to be relevant to internal doses using a
bioavailability factor for index chemical k)
Cm(k) = Subclass ICED of chemical k for internal doses accumulated across
multiple route exposures.
The Subclass ICED is developed when the internal doses of the components are
expressed as Component ICEDs by developing scaling factors, i.e., RPFs. Then, the
Subclass ICED is estimated as:
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where:
Cm(k) = Subclass ICED of chemical k for internal doses accumulated
across multiple route exposures
n = number of components in the subclass
RPF, = proportionality constant (unitless) relative to the toxic potency of
the index chemical, k, for the ith mixture component and the oral
exposure route
C, = internal dose of the ith mixture component accumulated across
multiple route exposures.
RPF* C, = Component ICED for the ith mixture component
Calculation of an RPF, for internal doses representing multiple route exposures
involves making an estimate of relative potency for each chemical compared with the
index chemical from oral dose-response information that is adjusted to internal doses
using bioavailability factors. Using the adjusted oral dose-response information, one
method is to calculate the ratio of the ED10 (or other effect level relevant to the risk
assessment) of the index chemical to the ith chemical's ED10to provide an RPF, for that
chemical. A second method is to calculate the ratio of potency estimates (e.g., cancer
slope factors). The calculation of the RPF, requires that the chemicals in the subclass
have similarly shaped dose-response curves, at least in the region of exposures
relevant to the risk assessment.
2.3. CRPF CALCULATIONS
The CRPF combines the RPF-based risk estimates under response addition
based on the assumption that the subclasses were accurately formed and
independence of action holds. The RPF approach yields a single risk estimate for each
subclass of toxicologically similar chemicals for a specified endpoint and time frame.
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The total mixture risk for endpoint h (expressed as RTh) is calculated as a sum of the
subclass risks (expressed as RJ:
When exposures are estimated using exposure assessment models, Equation
2-5 sums subclass risks that represent not only different MOA, but also different
exposure routes. A dose-response curve for each exposure route, or at least some
minimal effect level information, is required for each mixture component to develop
RPFs.
When internal doses (e.g., blood, tissue or organ doses) are estimated using
PBPK models, Equation 2-5 sums subclass risks that represent different MOA and
account for exposures from multiple routes. Only oral dose-response information is
required for each mixture component, along with bioavailability factors to adjust
laboratory administered doses to internal doses (see Section 4).
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3. DEVELOPMENT OF DBP MULTIPLE ROUTE EXPOSURE ESTIMATES
The research presented in this document suggests that both exposures and
internal doses can be estimated through modeling procedures, incorporating chemical
properties of the DBFs, physical characteristics of the indoor environment, and
behavioral and physiological characteristics of the exposed individuals relative to the
occurrence of the contaminant in the indoor environment. Such estimates may be
combined with dose-response data to estimate cumulative risk using the CRPF
approach (Section 2). This section describes the results of a research project to
develop multiple route exposures and internal dose estimates for DBFs. The full report
is provided in Appendix 1. It should be noted that some of the text in this chapter has
been taken directly from Appendix 1, but has been reorganized and edited to provide
the reader with a summary of the information provided in that report.
3.1. BACKGROUND ON DBP EXPOSURE ESTIMATION
The goal of an exposure assessment is to quantify the uptake of an agent or a
group of agents that results from an individual's or a population's contact with
environmental media (U.S. EPA, 1992; Paustenbauch, 2000). U.S. EPA (1992) defines
exposure assessment as the qualitative or quantitative "determination or estimation of
the magnitude, frequency, duration, and route of exposure." Exposure assessments
involve three general steps:
Estimation of the occurrence and concentrations of an agent or group of
agents in various media that individuals contact
Characterization of specific contact rates with the media
Calculation of the likelihood of an exposure, the resulting uptake and
biologically relevant dose rates, e.g., average daily exposure in terms of
mg/kg/day, peak exposure, or cumulative exposure.
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Occurrence data for routinely measured DBFs are available as concentration
measurements in drinking water samples taken at water treatment plants, at the
consumer's tap or simulated in laboratory studies (e.g., Krasner et al., 1989;
Richardson, 1998; U.S. EPA, 1996a). In-home concentrations have been measured in
tap water and indoor air; some DBFs in tap water (e.g., chloroform) volatilize through
heating during cooking, showering, etc. (e.g., Olin, 1999; Weisel and Chen, 1994;
Giardino and Andelman, 1996). As a result, DBP exposures can occur through
ingestion, inhalation, and dermal absorption. The inhalation exposure for volatile DBFs
and dermal exposure to highly lipophilic DBFs can result in exposures equivalent to
ingestion for median water uses. Thus, when comparing risks from different water
sources and treatment practices (which may result in different DBFs and
concentrations), it is critical to include all exposure routes.
Exposure assessment models have been developed for each exposure route;
several of these are summarized specifically for drinking water inhalation and dermal
exposures in Olin (1999). Paustenbauch (2000) provides a general review of exposure
assessment and describes ingestion, inhalation and dermal exposure. The models
predict exposures based on such factors as the physical and chemical properties of
DBFs in water and assumptions concerning human activity patterns, as well as air
exchange rates in buildings and room dimensions (Olin, 1999). Studies of human
activity patterns in the U.S., such as tap water consumption distributions (including
heated tap water consumption), showering and bathing frequency and duration
distributions, provide contact rate estimates for important exposure media (U.S. EPA,
1997b; Johnson et al., 1999). These data can be aggregated and used in exposure
modeling to estimate DBP contact rates for the three primary exposure routes.
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PBPK models have been developed to estimate the absorbed doses from oral,
inhalation, and dermal routes. Absorbed dose is defined by the U.S. EPA (1997b) as the
amount crossing a specific absorption barrier through uptake processes. In an oral
exposure model, DBP exposure is a function of the concentration in water and the daily
quantity of water ingested; a bioavailability parameter may also be included (U.S. EPA,
2000b) (See Key Definitions). Both U.S. EPA (1994b) and Wilkes (1998), among
others, describe inhalation exposure models. Wilkes (1998) describes a model for
estimating the absorbed dose of drinking water contaminants including DBPs. The
model estimates absorbed doses via inhalation of aerosols and vapors. These may be
generated from a number of household uses including showers, clothes washers,
dishwashers, and toilets. Bunge and McDougal (1999) describe two broad classes of
dermal penetration models: membrane models and pharmacokinetic models. Both
types of models can be used to estimate absorbed doses of relevant DBPs.
Further development of these (or similar) models and extensions to other
trihalomethanes (THMs) as well as to other DBP classes such as the haloacetic acids
(HAAs) and haloacetonitriles (HANs) is useful both in refining human exposure and
absorbed dose estimates and in obtaining more relevant information from
epidemiological studies. The development of DBP exposure data derived from exposure
assessment and PBPK models of human exposures from multiple exposure routes will
provide contextual support for both toxicology data and epidemiology data. This
research need has been described in two EPA reports. The Risk Assessment of
Mixtures of Disinfectant Byproducts (DBPs) for Drinking Water Treatment Systems
(U.S. EPA, 2000a) and Feasibility of Attaining/Constructing Refined DBP Exposure
Information for Extant Cancer Epidemiologic Studies (U.S. EPA, 2000c). The goal of this
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research effort is to develop exposure and internal dose estimates for several DBFs
using exposure assessment and PBPK models.
3.2. RESEARCH RESULTS REGARDING MULTIPLE ROUTE DBP ESTIMATES
A comprehensive exposure modeling effort was implemented to estimate
population-based exposures and absorbed doses for 15 DBFs, incorporating
parameters for chemical volatilization, human activity patterns, water use behaviors,
ingestion characteristics, building characteristics, physiological measurements, and
chemical concentrations in the water supply. The DBFs targeted for evaluation are
listed in Table 3-1. Estimates were made for a three person family based on data from
women and men of reproductive age (ages 15-45) and children (age 6).
The exposure assessment model for this effort was the Total Exposure Model
(TEM) developed by Wilkes Technologies (Wilkes, 1998). The PBPK Model used was
the Exposure Related Dose Estimating Model (ERDEM) (Blancato et al., 2000, 2002;
Knaak et al., 2002; U.S. EPA, 2002d). This model, formerly known as DEEM (Dose
Estimating Exposure Model), was developed by Anteon Corporation in collaboration
with the Human Exposure Research Branch of EPA's National Environmental Research
Laboratory in Las Vegas. Combining these two models into one analysis provided the
ability to evaluate target tissue dose (estimated using ERDEM) as a function of a variety
of behaviors, environmental factors, and other exposure related parameters (estimated
by TEM). Figure 3-1 illustrates the flow of information in and out of the two models. Of
particular note is that TEM is used to develop 24-hour exposure time histories for the
demographic groups of interest; this output data set becomes input data to the PBPK
model. Also, both models are capable of producing estimates of total absorbed dose,
although the ERDEM model does so using more specific physiological functions than
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TABLE 3-1
List of Chemicals for Exposure and Internal Dose Assessment
DBP Subclass
Trihalomethanes
(THMs)
Haloacetic Acids
(HAAs)
Haloacetonitriles
(HANs)
Miscellaneous
Chemical Name
Chloroform (CHCI3)
Bromodichloromethane
(BDCM)
Dibromochloromethane
(DBCM)
Bromoform (CHBr3)
Chloroacetic acid (CAA)
Dichloroacetic acid (DCA)
Trichloroacetic acid (TCA)
Bromoacetic acid (MBA)
Dibromoacetic acid (DBA)
Bromochloroacetic acid
(BCA)
Dichloroacetonitrile
(DCAN)
Trichloroacetonitrile
(TCAN)
Bromochloroacetonitrile
(BCAN)
Dibromoacetonitrile
(DBAN)
Bromate
CAS Number
67-66-3
75-27-4
124-48-1
75-25-2
79-11-8
79-43-6
76-03-9
79-08-3
631-64-1
5589-96-8
3018-12-0
545-06-2
83463-62-1
3252-43-5
15541-45-4
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TEM Modeling of Input Data on Chemical Properties, Human
Activity Patterns, Human Intake Parameters, Building Characteristics
24 Hour Exposure Time Histories
Simulated by TEM for a human of a specified age and sex
ERDEM Modeling of Input
Data on Physiological Parameters
Input DC
Measure
Concent
Drinking
At the F
i
jja
dDBP
rations In
I Water
aucet
r
Estimated DBF
Concentrations
In Household Air
DBF Oral
External
Exposure
Estimates
DBF Dermal
External
Exposure
Estimates
DBF
Inhalation
External
Exposure
Estimates
DBF Oral
Absorbed
Dose
Estimates
DBF Dermal
Absorbed
Dose
Estimates
DBF
Inhalation
Absorbed
Dose
Estimates
i
DBF
Multiple
Route
Total
Absorbed
Dose
Estimates*
*
DBF Multiple Route
Tissue and Organ
Dose Estimates
- AUC Kidney
- AUCTestes
- AUC Liver
- AUC Venous Blood
L
*Note: Both models can
produce Total Absorbed
Dose Estimates
FIGURE 3-1
Linking TEM Exposure Assessment Modeling with ERDEM PBPK Modeling
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TEM. Only ERDEM produces the organ and tissue doses. The research report
showing all details of the DBP analysis (Appendix 1) includes the following information:
Detailed Information on the model parameter inputs for both TEM and
ERDEM
Estimates of absorbed dose for oral, dermal, and inhalation routes of
exposure and total absorbed dose for 13 (of 15) DBFs using TEM
Estimates of total absorbed dose and tissue doses for the kidney, liver,
venous blood and testes/ovaries for 4 (of 15) DBFs using the PBPK model
ERDEM.
A sensitivity analysis of the combined models for a selected set of
parameters.
In the TEM analysis, oral ingestion is subdivided into direct and indirect
consumption. Direct consumption of drinking water represents the number of drinks
and volumes consumed, either assuming that the contaminant level remains constant
from tap to glass to body, or considering that some contaminant volatilized during air
contact. Indirect water consumption represents the quantity found in foods or
reconstituted drinks and also considers whether the fraction of the contaminant
remaining in the drink or food after volatilization and preparation is still significant
enough to be included in the exposure calculation.
3.2.1. Model Inputs for TEM. TEM has been applied to several modeling studies
examining the exposure and dose to waterborne contaminants as a result of household
water use. Wilkes et al. (1992) examined typical exposures for a three person family to
trichloroethylene (TCE) from normal water uses. An analysis of behavioral factors
leading to inhalation exposure quantified the importance of time spent in the bathroom
and in showering and bathing activities (Wilkes et al., 1996). A study comparing the
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exposure to DBFs to that of TCE as a result of constructing a municipal treatment
facility analyzed whether the remediation lowered the carcinogenic risk to the
community (Wilkes and Giardino, 1999; Giardino and Wilkes, 1999). As part of an
International Life Sciences Institute (ILSI/RSI) working group entitled "Working Group on
Estimation of Dermal and Inhalation Exposures to Contaminants in Drinking Water", a
modeling study demonstrating the application of TEM to produce multiple route,
population-based estimates of exposure and uptake to three contaminants (CHCI3,
methyl parathion, and chromium) was conducted and presented as a case study
(Wilkes, 1998).
TEM is an indoor-air-quality human exposure model that combines probabilistic
and deterministic principles in a single framework7. The input and output data for the
TEM application to DBFs are shown in Figure 3-2. This framework combines a Monte
Carlo simulation of variable parameters, such as water use behaviors and other
behaviors affecting exposure, with point estimates of parameters representing physical
and chemical processes, resulting in a prediction of the air and water concentrations at
the interface with the exposed individuals. The deterministic framework uses the
activities generated by the probabilistic algorithms to predict the release of
contaminants, the fate and transport of the contaminants within the building, and finally,
the resulting exposures. In the case of volatilization of DBFs during water use, the
Probabilistic analysis is conducted using simulation techniques, randomly
sampling values for parameters that have natural variability or uncertainty using
distributional data for those parameters. Deterministic analysis is conducted by solving
equations, calculating parameter values from known relationships (i.e., calculations
based on physical and chemical processes) and using point estimates for various
parameters.
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Physico-Chemical Properties, e.g.,
- Mass transfer coefficients
- Henry's Law constants
- Octanol/water partition coefficients
- Chemical concentrations in water
- Volatilization rates
Human Behavioral Characteristics, e,g,
- Frequencies/duration of water uses
for showering, bathing, faucets,
dishwashers, toilets, and clothes washer
- Appliance factors, such as flow rates,
water temperatures, cycles, etc.
Human Intake Characteristics, e.g.,
- Tap water consumption by age & sex
- Skin permeability coefficients
- Breathing rates
Building Characteristics, e.g.,
- Household air volume
- Air volumes for water-use zones
- Whole house air exchange rates
- Air flow between water-use zones
Distributions of Absorbed Dose Estimates
- Dermal
Uptake calculations represent steady-
state and non steady-state periods.
- Inhalation
Uptake calculations employ an
equilibrium calculation between inhaled
air and the bloodstream
- Ingestion, direct and indirect
Uptake calculations assume all of the
DBF in consumed water is absorbed
into the bloodstream.
- Total Absorbed Dose
24 Hour Exposure Time Histories
to be Used by ERDEM
- Breathing zone concentrations
- Respiratory Rates
- Dermal exposures
- Skin contact area
- Ingestion exposures
Outputs from TEM
FIGURE 3-2
TEM Modeling of Indoor Air Concentrations, Exposure and Absorbed Dose Estimates
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deterministic framework incorporates realistic models for predicting the transfer from the
liquid phase to the gas phase during household water uses. Additionally, route specific
uptake models are used to estimate the transfer of the chemical to the exposed
individual. The TEM model input parameters, shown here with an indication of where
they are discussed in Appendix 1, include the following:
Parameters needed for implementation of volatilization model (Section
3.1., Appendix 1)
Human behavior characteristics that drive the activity model, including
location and water use behaviors (Section 3.2., Appendix 1)
Ingestion characteristics (Section 3.3., Appendix 1)
Building characteristics (Section 3.4., Appendix 1)
Chemical concentrations in water supply (Section 3.5., Appendix 1)
Not all of these parameters are discussed here; the reader is referred to the appropriate
section of Appendix 1 for additional details.
One factor that limited the exposure modeling results to 13 of the 15 chemicals
was lack of data on specific chemical properties. A literature search was performed to
identify reliable values for the desired chemical properties (Section 3.1.2., Appendix 1).
For those with data gaps, prediction methods were employed to estimate parameter
values (Section 3.1.3., Appendix 1). The properties of interest were Henry's law
constant, liquid phase diffusivity, gas phase diffusivity, octanol/water partition
coefficient, and molecular weight. Boiling point and volatility were additional properties
of value for the study. A number of DBP-specific data gaps were identified as follows:
Bromochloroacetic Acid (BCA) - Henry's law constant, vapor pressure,
liquid and gas phase diffusivities
Dichloroacetic Acid (DCA) - Liquid and gas phase diffusivities
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Trichloroacetic Acid (TCA) - Liquid and gas phase diffusivities
Bromoacetic Acid (MBA) - Liquid and gas phase diffusivities
Dibromoacetic Acid (DBA) - Vapor pressure, liquid and gas phase
diffusivities
Bromochloroacetonitrile (BCAN) - Henry's law constant, Kow, boiling point,
vapor pressure, liquid and gas phase diffusivities
Bromodichloromethane (BDCM) - Henry's law constant for the desired
temperatures
Dichloroacetonitrile (DCAN) - Liquid and gas phase diffusivities
Trichloroacetonitrile (TCAN) - Liquid and gas phase diffusivities
Dibromoacetonitrile (DBAN) - Liquid and gas phase diffusivities
Bromate - Henry's law constant, Kow, boiling point, vapor pressure, liquid
and gas phase diffusivities
Prediction methods were used to supplement the literature review for chemical
properties that were not found. Values for the dermal permeability coefficients (Kp) were
calculated based on biological and physicochemical characteristics of human skin and
test chemicals, respectively. Computations were based on the method published by
Poulin and Krishnan (2001), in which the value for the partition coefficient of the
chemical for human skin and the value for the diffusion coefficient of the chemical for
lipid are combined with the fractional lipid and water composition of human skin.
Separate values were calculated based on the range of lipid and water contents for
human skin, accounting for the range of Kp values demonstrated. Values for the liquid
and gas phase diffusivity, the dimensionless Henry's Law Constant, and the overall
mass transfer coefficient were predicted for many of the DBFs. However, data were
insufficient to estimate chemical properties for BCAN and bromate; thus, exposure
estimates were not modeled for these two DBFs.
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The water-use behavior parameters needed for TEM were developed from the
data presented in the National Human Activity Patterns Survey (NHAPS), the
Residential End Use Water Survey (REUWS), Residential Energy Consumption Survey
(REGS), in appliance manufacturer data, and supplemented, as necessary, by best
judgment. (See Section 3.2.1. of Appendix 1 for additional details on these data bases.)
3.2.2. Model Inputs for ERDEM. ERDEM is a PBPK model consisting of
compartments representing different tissue types within the body (Figure 3-3). Rather
than make individual compartments for every organ in the body, the models are
constructed to include groups of tissues, which are grouped based on the similarity of
their tissue composition, metabolic activity and blood flows. These are often the lung
(where inhalation exposures occur at the blood:air interface in the alveolus), the liver
(modeled usually as the site of chemical metabolism), the richly perfused tissue group,
the poorly perfused tissue group and fat (adipose tissue). When the model is developed
to account for concentrations of toxicants in specific organs or tissues not usually
modeled separately, their tissue mass and blood flow is subtracted from their typical
compartment placement, and a new compartment is added to the model and is given
descriptions of tissue mass, blood flow, blood:tissue partition coefficient value, and,
where appropriate, metabolic activity. The present model exemplifies this, as the
compartments for ovaries and testes were isolated from the richly perfused tissue
group. Just as in the intact system (the whole body), these compartments differ in
biochemical composition, reflected in their being assigned different blood:tissue partition
coefficients (unique for each chemical), representing the ability of chemicals to move
from blood into tissue perfused with that blood, and the compartments are tied together
with blood flow. Thus, PBPK models are developed to accommodate
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Inputs
Bolus Dose
Ingestions
Rate
Ingestions
Skin Surface
Water
Inhalation
K
•ST.IN
KIN,
FEC
ST Stomach
IN Intestine
SP Spleen 0.00
QB
KST.PB
Portal Blood
Fecal
Elimination
LV Liver
QB
LV
CR Carcass
QB
KD Kidney
QB
FT Fat
QB
SL Slowly Perfused
QB
RP Rapidly Perfused
QB
RP
DR Dermal
QB
DR
BR Brain
QB
BR
VB Venous
QA
PU Static Lung
Exhalation
Liver
Metabolites
Renal
Elimination
FIGURE 3-3
Compartmental Design of ERDEM PBPK Model
-36-
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differences in chemical transport between blood and various tissues across dose
ranges, and to accurately simulate tissue doses of chemicals resulting from exposures
via the oral, dermal, and inhalation routes.
Chemicals are encountered by experimental animals and humans through the
oral, dermal and inhalation routes. These routes are each important, and previously,
PBPK models have been developed, validated and published accounting for exposure
for each of these routes. In the case of ERDEM, the models have discrete input portals
for each of the three exposure pathways, with systemic blood then serving as an
internal exposure conduit to tissues removed from the portal of entry (lung, Gl tract,
skin). Within the PBPK model, each compartment is linked with the others via the blood
compartment, which is described by both blood:air and blood:tissue partition coefficients
and flow to the various tissue groups, proportionate to the flow to various tissues in the
body. For instance, the lung compartment gets 100% of the cardiac output, while the
liver compartment gets approximately 20% of the cardiac output.
PBPK models are comprised of a series of differential equations which describe
the movement of a chemical into blood and from blood into tissues over the course of
chemical exposure. Models such as this are constructed so that they accurately portray
route-specific absorption of chemical across the skin by including biochemical constants
governing dermal transport, absorption into the blood in the alveolus by including the
blood:air partition coefficient value and rates of alveolar ventilation, and absorption from
the gut into the blood by including specific information on water solubility, lipid solubility
and ionization characteristics. Once in the systemic circulation, these models are
constructed to describe the partitioning of the chemical from blood into the various
tissue types of the body, the metabolism of the chemical, urinary elimination of the
-37-
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chemical as parent chemical and/or metabolite, and the exhalation of the chemical or
metabolite in expired air. This is accomplished by developing several chemical-specific
biochemical measures in vitro (tissue:air partition coefficients and/or blood:tissue
partition coefficients). These values are integrated with values describing blood flow to
the various tissue compartments and estimates of metabolic rate constants, and
through an iterative process, fitting PBPK model predictions to a set of values for
measured tissue time-course doses.
The input and output data for the ERDEM application to DBFs are shown in
Figure 3-4. Input parameters are treated in the analysis as point estimates. The
volumes and blood flows are required for each compartment or sub-section of a
compartment. The breathing rates, the gastrointestinal absorption rates, and the skin
permeation coefficients, in part, determine the absorbed dose of chemical into the body.
Partition coefficients for tissue to blood, tissue to air, and blood to air, determine how
much of the chemical remains and how much passes to the next state. Metabolic
constants determine the amount of chemical that is converted to metabolites. The
greatest difficulty is determining values for the various parameters needed for a species
and chemical; generic values for volumes and blood flows for a set of compartments or
sub-compartments is not adequate. Each type of chemical that is modeled may require
the use of a different set of compartments. Some compartments may be combined, or
others may be broken up into multiple subcompartments. The chemically-dependent
parameters are determined from many sources, or are estimated using various
techniques, such as QSARs (Quantitative Structure-Activity Relationships). The choices
are made based on the state of the science for the chemicals, their metabolism
pathways, and the type of chemical. The ERDEM model input parameters are
-38-
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24 Hour Exposure Time Histories from
TEM for Dermal, Inhalation, Ingestion
- Breathing zone concentrations
- Respiratory Rates
- Dermal exposures
- Skin contact area
- Ingestion exposures
Physiological Parameters, e.g.,
- Compartment volumes (kidney, liver,
blood, fat, etc.) by demographic group
- Breathing rates by activity and
demographic group
- Compartment blood flows by activity
and demographic group
- Inputs for each chemical:
- Skin permeability coefficients
- Rate constants for gastrointestinal tract
- Partition coefficients, e.g., skin:blood,
airblood, liverblood, kidney:blood.
- Metabolism pathways and rate constants
e.g., V-Max, Km.
Inputs to ERDEM
ERDEM
Distributions of Tissue and
Organ Dose Estimates
- AUC Kidney
- AUCTestes
- AUC Liver
- AUC Venous Blood
- Total Absorbed Dose
Outputs from ERDEM
FIGURE 3-4
ERDEM Modeling of Tissue and Organ Level Absorbed Dose Estimates
-39-
-------
developed in Section 3.6. of Appendix 1. These input parameters, with an indication of
where they are discussion in Appendix 1, include the following:
Compartment volumes by demographic group (Section 3.6.1.,
Appendix 1)
Breathing rates by activity and demographic group (Section 3.6.2.,
Appendix 1)
Compartment blood flows by activity and demographic group (Section
3.6.3., Appendix 1)
Definition of the exposure scenarios for each exposure route (Section
3.6.4., Appendix 1) (24-hour exposure time histories supplied by TEM)
Skin permeability coefficients for each chemical (Section 3.6.5.,
Appendix 1)
Rate constants for the gastrointestinal model for each chemical (Section
3.6.6., Appendix 1)
Compartment-to-blood partition coefficients (Section 3.6.7., Appendix 1)
Metabolism pathways for each parent chemical (Section 3.6.8.,
Appendix 1)
The metabolism rate constants, or the V-Max and the Michaelis Menten
constants for each metabolism to be modeled (Section 3.6.8., Appendix 1)
The elimination rate constants for the urine, feces, and any other required
compartments, by chemical (Section 3.6.9., Appendix 1)
Data for these parameters were found using a number of sources including the
peer reviewed literature, the EPA's Exposure Factors Handbook (U.S. EPA, 1997b),
personal communications from scientists working in this scientific area, estimates from
modeling predictions, and estimates extrapolated using values from other compounds in
the same class. Of particular note for the DBP analysis, however, is that the definition
of the exposure scenarios for each exposure route (Section 3.6.4., Appendix 1) is the
set of parameters that is supplied by TEM in the form of 24-hour exposure time
-40-
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histories. Study size limitations for this effort resulted in the selection of four DBFs for
PBPK modeling, CHCL3, BDCM, DCA and TCA.
3.2.3. Modeling Results. TEM was initiated using the inputs on chemical specific
properties, building-related model parameters and water-use behaviors, identifying the
structure of the household, the characteristics and locations of the water appliances,
and the population groups for the three-person household. For each simulated period of
24 hours, activity patterns were sampled from the NHAPS for the three defined
population groups, the activities were mapped into the household, and the appropriate
water uses were simulated consistent with the activity patterns. The model was
executed for 1000 simulations.
Subsequent to executing the exposure model, the results were interfaced with
the PBPK model, ERDEM (Figures 3-2 and 3-4). This was accomplished by creating 24-
hour exposure time histories containing information on breathing zone concentrations,
respiratory rates, dermal exposures, skin contact area, ingestion exposures as a
function of time for each of the simulations. These results were input into ERDEM for
250 of the simulations to predict blood and organ concentrations.
3.2.3.1. TEM Modeling Results — Simulation results of the TEM modeling
include distributions of absorbed dose estimates for the dermal, ingestion (direct and
indirect), and inhalation exposure routes and total absorbed dose. In Appendix 1, a
table is presented for each of the 13 DBPs, containing the absorbed doses for a
24-hour period as a function of route, population group, and percentile of the population.
Table 3-2 shows an example of the absorbed dose estimates for BDCM. Table 3-3
shows the 50th percentile absorbed dose estimates for all 13 DBPs. In addition to these
tables for the 13 DBPs, Appendix 1 provides plots of their respective
-41-
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TABLE 3-2
TEM Output for BDCM: Absorbed Dose Estimates (mg) for a 24-Hour Exposure
Percentile
Total3
Dermal
Ingestion
Direct
Indirect
Total3
Inhalation
Female, Age 15-45
1
5
10
25
50
75
90
95
99
7.20E-03
1.35E-02
1.92E-02
3.96E-02
8.00E-02
1.66E-01
2.79E-01
4.13E-01
2.41 E+00
Ob
Ob
1.54E-04
3.71 E-04
2.70E-03
5.21 E-03
8.67E-03
1.21E-02
1.87E-02
1.03E-03
1.83E-03
2.46E-03
4.19E-03
7.73E-03
1.51E-02
2.76E-02
3.50E-02
8.49E-02
5.64E-04
7.64E-04
8.86E-04
1.23E-03
1.71 E-03
2.37E-03
3.18E-03
3.61 E-03
5.05E-03
2.49E-03
3.51 E-03
4.14E-03
6.05E-03
9.72E-03
1.69E-02
2.95E-02
3.70E-02
8.60E-02
1.12E-04
2.66E-03
8.78E-03
2.35E-02
6.12E-02
1.42E-01
2.64E-01
3.88E-01
2.38E+00
Male, Age 15-45
1
5
10
25
50
75
90
95
99
6.25E-03
1.27E-02
1.97E-02
3.88E-02
8.43E-02
1.64E-01
2.95E-01
4.36E-01
1.93E+00
Ob
Ob
Ob
3.09E-04
2.90E-03
5.57E-03
8.73E-03
1.13E-02
1.84E-02
7.64E-04
1.55E-03
2.14E-03
4.05E-03
7.98E-03
1.55E-02
2.91 E-02
4.31 E-02
7.14E-02
2.79E-04
4.95E-04
6.49E-04
1.05E-03
1.85E-03
3.37E-03
5.67E-03
7.93E-03
1.31 E-02
2.18E-03
3.42E-03
4.35E-03
6.52E-03
1.11 E-02
1.86E-02
3.19E-02
4.68E-02
7.28E-02
1.01 E-04
2.64E-03
6.07E-03
1.89E-02
6.05E-02
1.46E-01
2.74E-01
4.23E-01
1.91 E+00
-42-
-------
TABLE 3-2 cont.
Percentile
Total3
Dermal
Ingestion
Direct
Indirect
Total3
Inhalation
Child, Age 6
1
5
10
25
50
75
90
95
99
3.51 E-03
6.98E-03
1.00E-02
1.95E-02
4.38E-02
9.48E-02
1.81E-01
2.29E-01
3.58E-01
Ob
Ob
Ob
9.26E-05
2.66E-04
2.67E-03
4.48E-03
5.63E-03
8.03E-03
4.66E-04
8.66E-04
1.17E-03
2.07E-03
4.02E-03
7.68E-03
1.32E-02
1.75E-02
3.25E-02
1.13E-04
2.26E-04
3.28E-04
6.03E-04
1.07E-03
2.17E-03
3.80E-03
5.37E-03
8.16E-03
1.10E-03
1.73E-03
2.27E-03
3.50E-03
6.03E-03
9.89E-03
1.53E-02
1.88E-02
3.54E-02
5.71 E-05
1.13E-03
2.98E-03
1.07E-02
3.36E-02
8.56E-02
1.73E-01
2.19E-01
3.51 E-01
3Note that total absorbed dose (by ingestion or by all three routes) is not equal to the
sum of the doses in each row. This occurs because each simulation provides a new
data point to each of the dose estimates represented in the columns; the percentiles are
then produced for each dose estimate (column) independently of each other.
Furthermore, because the total absorbed dose is the sum of independent random
variables, its variance is less than what is obtained when specific percentiles are
summed.
bThe zeroes entered in the dermal category represent the portion of the population that
has no dermal contact with the water supply during the simulated day. For the female
(age 15-45) population group, 6.9% had no dermal contact. For the male (age 15-45)
population group, 6.9% had no dermal contact. For the child (age 6) population group,
11.2% had no dermal contact.
-43-
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TABLE 3-3
50th Percentile 24-Hour Absorbed Dose Estimates (mg) Output by TEM
Chemical
Total*
Dermal
Ingestion
Direct
Indirect
Total*
Inhalation
Female, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
3.00E-01
8.00E-02
5.12E-02
2.65E-02
4.45E-01
2.73E-02
2.90E-02
8.73E-03
3.76E-03
7.95E-03
1.83E-03
1.26E-04
7.09E-04
2.51 E-02
2.70E-03
2.47E-03
1.60E-03
1.16E-04
1.05E-05
1.71E-05
2.32E-04
1.06E-04
2.18E-04
4.08E-05
4.18E-06
1.79E-05
2.09E-02
7.73E-03
5.33E-03
2.88E-03
1.91E-03
1.20E-02
1.27E-02
3.74E-03
1.61E-03
3.40E-03
7.48E-04
5.23E-05
3.03E-04
3.76E-03
1.71E-03
1.40E-03
3.00E-03
1.99E-03
1.25E-02
1.32E-02
3.89E-03
1.67E-03
3.54E-03
7.79E-04
5.45E-05
3.15E-04
2.52E-02
9.72E-03
7.03E-03
6.55E-03
4.34E-03
2.72E-02
2.89E-02
8.51 E-03
3.66E-03
7.74E-03
1.70E-03
1.19E-04
6.89E-04
2.19E-01
6.12E-02
3.73E-02
1 .63E-02
1.15E-06
5.46E-06
9.27E-06
1.79E-06
4.33E-07
2.09E-06
4.39E-05
9.73E-07
1.88E-06
Male, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
3.02E-01
8.43E-02
5.49E-02
3.00E-02
5.09E-03
3.14E-02
3.34E-02
9.97E-03
4.29E-03
2.62E-02
2.90E-03
2.64E-03
1.70E-03
1.25E-04
1.16E-05
1.88E-05
2.50E-04
1.14E-04
2.16E-02
7.98E-03
5.50E-03
2.97E-03
1.97E-03
1.23E-02
1.31 E-02
3.86E-03
1.66E-03
4.00E-03
1.85E-03
1.52E-03
3.24E-03
2.14E-03
1.35E-02
1 .43E-02
4.20E-03
1.81E-03
2.84E-02
1.11E-02
8.10E-03
7.55E-03
5.00E-03
3.14E-02
3.33E-02
9.81 E-03
4.22E-03
2.13E-01
6.05E-02
3.79E-02
1 .68E-02
1.33E-06
6.20E-06
1.09E-05
1.99E-06
5.04E-07
-44-
-------
TABLE 3-3 cont.
Chemical
Total*
Dermal
Ingestion
Direct
Indirect
Total*
Inhalation
Male, Age 15-45
BCA
DCAN
TCAN
DBAN
9.08E-03
2.09E-03
1.45E-04
8.13E-04
2.35E-04
4.46E-05
4.47E-06
1.94E-05
3.51 E-03
7.72E-04
5.40E-05
3.12E-04
3.82E-03
8.41 E-04
5.88E-05
3.40E-04
8.93E-03
1.96E-03
1.37E-04
7.94E-04
2.35E-06
4.26E-05
1 .OOE-06
1 .99E-06
Child, Age 6
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
1.56E-01
4.38E-02
2.91 E-02
1.34E-02
1.84E-03
1.12E-02
1.19E-02
3.61 E-03
1.56E-03
3.29E-03
7.72E-04
5.20E-05
2.94E-04
1.87E-03
2.66E-04
2.59E-04
1.73E-04
1.35E-05
1 .26E-06
2.06E-06
2.70E-05
1.22E-05
2.53E-05
4.84E-06
4.76E-07
2.10E-06
1.09E-02
4. 02 E-03
2.77E-03
1.50E-03
9.92E-04
6.22E-03
6.61 E-03
1.95E-03
8.36E-04
1.77E-03
3.89E-04
2.72E-05
1.58E-04
9.19E-04
1.07E-03
7.72E-04
7.42E-03
4.92E-04
3.08E-03
3.28E-03
9.64E-04
4.14E-04
8.77E-04
1.93E-04
1.35E-05
7.81 E-05
1.26E-02
6.03E-03
4.18E-03
2.70E-03
1.79E-03
1.12E-02
1.19E-02
3.50E-03
1.51 E-03
3.19E-03
7.01 E-04
4.91 E-05
2.84E-04
1.19E-01
3.36E-02
2.21 E-02
8.77E-03
6.29E-07
3.01 E-06
5.22E-06
1.01 E-06
2.37E-07
1 .26E-06
2.57E-05
5.57E-07
1.07E-06
aNote that total absorbed dose (by ingestion or by all three routes) is not equal to the sum of the
doses in each row. This occurs because each simulation provides a new data point to each of
the dose estimates represented in the columns; the percentiles are then produced for each
dose estimate (column) independently of each other. Furthermore, because the total absorbed
dose is the sum of independent random variables, its variance is less than what is obtained
when specific percentiles are summed.
-45-
-------
cumulative distribution functions and histograms for the dose estimates (see Section
4.2.2., Appendix 1).
The results of the uptake modeling provide information for comparing and
contrasting uptake as a function of the chemical, the population group and behavior,
and the route of exposure. General conclusions about the importance of each route for
a given chemical can be made by comparing the uptake for each route. However,
specific conclusions can be problematic due to large uncertainties in some of the model
parameters, most notably the dermal permeability coefficient. A large range of
uncertainty exists in the dermal estimates that make it difficult to compare the dermal
route to the inhalation and ingestion routes. This is because the skin permeability rates
(Section 3.6.5. of Appendix 1) are generally poorly quantified. The values presented in
the table are estimated based on correlation with other chemical properties, and there
are few measured values for this parameter to serve as a validation. As a result, the
uncertainty in this parameter is quite large. The impact of this uncertainty is examined
by calculating the dermal uptake at the minimum and maximum values of the identified
range (Section 4.2.3. of Appendix 1).
The THMs are the most volatile class of chemicals in this study, and the
inhalation route clearly dominates the absorbed dose estimates. The contribution of the
ingestion and dermal routes are similar, and given the uncertainty of the parameters, it
is unclear which route provides the larger dose. The contribution of the dose by route of
exposure/uptake is presented for each chemical for the 50th and 95th percentiles of
each population group (Tables 3-4 and 3-5). The relative contribution of the inhalation
pathway to the total absorbed dose for BDCM is higher than that for chloroform. This
may be attributed to the significantly higher blood:air partition coefficient of BDCM
-46-
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TABLE 3-4
Summary of 24-Hour Absorbed Dose by Route for 50th Percentile of the Population
Chemical
Total Absorbed
Dose (mg/day)
Contribution to Total by Route
Dermal
Ingestion
Inhalation
Female, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
3.00E-01
8.00E-02
5.12E-02
2.65E-02
4.45E-01
2.73E-02
2.90E-02
8.73E-03
3.76E-03
7.95E-03
1.83E-03
1.26E-04
7.09E-04
9%
4%
5%
7%
3%
0%
0%
3%
3%
3%
2%
3%
3%
9%
13%
15%
27%
97%
100%
100%
97%
97%
97%
95%
96%
97%
81%
83%
80%
67%
0%
0%
0%
0%
0%
0%
2%
1%
0%
Male, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
3.02E-01
8.43E-02
5.49E-02
3.00E-02
5.09E-03
3.14E-02
3.34E-02
9.97E-03
4.29E-03
10%
4%
5%
7%
2%
0%
0%
2%
3%
11%
15%
17%
29%
98%
100%
100%
97%
97%
80%
81%
78%
64%
0%
0%
0%
0%
0%
-47-
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TABLE 3-4 cont.
Chemical
BCA
DCAN
TCAN
DBAN
Total Absorbed
Dose (mg/day)
9.08E-03
2.09E-03
1.45E-04
8.13E-04
Contribution to Total by Route
Dermal
3%
2%
3%
2%
Ingestion
97%
96%
96%
97%
Inhalation
0%
2%
1%
0%
Child, Age 6
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
1.56E-01
4.38E-02
2.91 E-02
1.34E-02
1.84E-03
1.12E-02
1.19E-02
3.61 E-03
1.56E-03
3.29E-03
7.72E-04
5.20E-05
2.94E-04
1%
1%
1%
1%
1%
0%
0%
1%
1%
1%
1%
1%
1%
9%
15%
16%
23%
99%
100%
100%
99%
99%
99%
96%
98%
99%
89%
84%
83%
75%
0%
0%
0%
0%
0%
0%
4%
1%
0%
-48-
-------
TABLE 3-5
Summary of 24-Hour Absorbed Dose by Route for 95th Percentile of the Population
Chemical
Total Absorbed
Dose (mg/day)
Contribution to Total by Route
Dermal
Ingestion
Inhalation
Female, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
1.52E+00
4.13E-01
2.56E-01
1.12E-01
1.13E-02
7.03E-02
7.48E-02
2.22E-02
9.54E-03
2.02E-02
4.53E-03
3.13E-04
1.80E-03
8%
3%
4%
5%
3%
0%
0%
3%
4%
4%
3%
5%
3%
6%
8%
10%
14%
97%
100%
100%
97%
96%
96%
92%
94%
96%
86%
89%
87%
81%
0%
0%
0%
0%
0%
0%
5%
2%
1%
Male, Age 15-45
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
1.56E+00
4.36E-01
2.68E-01
1.20E-01
1 .66E-02
1.03E-01
1.10E-01
3.25E-02
1.40E-02
7%
2%
3%
4%
2%
0%
0%
2%
3%
7%
10%
11%
18%
98%
100%
100%
98%
97%
86%
88%
85%
78%
0%
0%
0%
0%
0%
-49-
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TABLE 3-5 cont.
Chemical
BCA
DCAN
TCAN
DBAN
Total Absorbed
Dose (mg/day)
2.95E-02
6.51 E-03
4.55E-04
2.63E-03
Contribution to Total by Route
Dermal
2%
2%
3%
2%
Ingestion
97%
94%
96%
97%
Inhalation
0%
4%
1%
0%
Child, Age 6
CHCI3
BDCM
DBCM
CHBr3
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
8.63E-01
2.29E-01
1.55E-01
6.28E-02
5.51 E-03
3.38E-02
3.60E-02
1.08E-02
4.65E-03
9.85E-03
2.26E-03
1.54E-04
8.76E-04
7%
2%
3%
4%
3%
0%
0%
3%
4%
4%
3%
4%
3%
5%
8%
8%
12%
97%
100%
100%
97%
96%
96%
90%
93%
96%
88%
90%
89%
84%
0%
0%
0%
0%
0%
0%
7%
2%
1%
-50-
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(6.11) versus that for chloroform (3.94). These values indicate that for equal amounts of
BDCM and chloroform in inspired air, blood will absorb 55% more BDCM than
chloroform.
The HAAs and HANs are much less volatile, and therefore the inhalation route
has the least contribution to the absorbed dose. Given the large uncertainty in the
dermal parameters, it is unclear whether ingestion or dermal is the largest contributor to
the total absorbed dose. In general, for less volatile compounds, dermal absorption is
less than ingestion, but is generally within an order of magnitude. This summary further
illustrates that multiple exposure route analysis is important because exposures are
dependent upon chemical properties, particularly a chemical's volatility. In addition, this
summary further underscores the importance of understanding the uncertainties
associated with individual exposure routes relative to the predicted exposures. In the
case of the dermal route, the summary also shows the importance of understanding this
uncertainty to identify the importance of the dermal route. Given the large uncertainty in
the dermal parameters, the dermal route cannot be dismissed as unimportant even
though the results indicate it is of lesser importance. Other analyses not conducted as
a part of this research could have benefits. A very intensive evaluation of the results
would allow an understanding of the impact of each activity and the range of behavior
across a population. An analysis of the relationship between water-use behavior and
resultant exposure and dose would be useful in identifying and potentially modifying
exposure related behaviors. In addition, the impact of a multitude of other factors, such
as air exchange rates, water use rates, and water temperature, could be evaluated.
3.2.3.2. ERDEM Modeling Results — TEM was used to produce 24-hour
exposure time histories for use by ERDEM; 250 simulations of exposure conditions
-51-
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were generated. These exposure conditions were the results of Monte Carlo
simulations of individual water-related activity. For instance, if hand washing occurred at
a finite frequency between 3 and 7 times per day, and for a duration of between 30
seconds and three minutes, the model would randomly select a frequency and a
duration 250 times, and pair that exposure with random selections from data describing
exposures from other water-related activities. Water use patterns were separately
developed for the adult male, the adult female and the male child. Each of these
exposures was combined in TEM to produce a total of 250 individual daily exposure
patterns. This same panel of exposure patterns was used with chemical-specific
physicochemical characteristics to determine "secondary" measures of water exposure,
e.g., the concentration of chloroform in air following showering activity. Once
completed, the 250 individual exposure patterns developed from simulations of water
use activities were used as an "input" for the PBPK modeling of internal dose (Figure
3-3), accomplished via ERDEM. In the next phase, the chemical of interest was
selected, and exposure patterns simulated by TEM were used as input values upon
which ERDEM based the exposure scenarios for simulations of tissue doses. The
estimation of tissue doses was accomplished by programming and operating a
previously validated PBPK model for each chemical of interest. These models were
standardized, so that flows and tissue volumes were consistent across the different
chemicals. ERDEM was constructed to simulate tissue doses of parent chemical in
several different tissues, identified as potential target organs of toxicity. ERDEM
estimated exposure metrics as area under the concentration-time curve (AUC) for liver,
kidney, venous blood, ovaries and testes averaged over two days. This differs from the
TEM modeling, in which results are presented as AUC averaged over a single 24-hour
-52-
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exposure period. Differences, if any, between the AUC values calculated by TEM and
separately by ERDEM, thus, will reflect "carry-over", or the residual chemical present
from the first 24-hour period at the time the second 24-hour exposure period was
initiated. Results for BDCM (Table 3-6), CHCI3 (Table 3-7), DCA (Table 3-8) and TCA
(Table 3-9) are presented for three different age-dependent models: the adult male, the
adult female and the 6-year-old male child. Results are configured so that variance in
water use patterns governing exposure via the oral, dermal and inhalation routes is
demonstrated as variance in the AUC for a given tissue or organ. While TEM identified
250 independent exposure scenarios, the PBPK models employed by ERDEM utilized
point estimates for partition coefficients and metabolic parameters taken from within
distributions of values, either previously determined or developed through professional
judgment. A sensitivity analysis (Section 3.2.4.) demonstrated the impact of variance of
these values with respect to different pharmacokinetic outcomes of interest. For the
pharmacokinetic outcome of interest (determined by the results of toxicity studies: for
instance, if kidney toxicity is the result of a metabolite, the appropriate pharmacokinetic
outcome would be the amount of the metabolite present in kidney), the sensitivity of that
outcome was measured and is presented as a function of variance of the parameter
(e.g., blood:kidney partition coefficient) being investigated. AUC values are presented
as mg/L*hr. In this simulation, the model was not constructed to simulate water use
patterns in the form necessary to capture peak exposures, as these are highly
influenced by the placement of an individual in juxtaposition to a water use portal and
the timing of discrete and often independent water uses. Instead, the model does
capture AUC values, which are useful in estimating toxicity, and are based on the
-53-
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TABLE 3-6
48-Hour PBPK Modeled Absorbed Doses for BDCM for the Adult Male, Adult Female and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
10th
50th
90th
95th
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
0.00230
0.00450
0.455
0.00043
0.00176
0.00681
0.0134
1.31
0.00119
0.00517
9.98
9.98
10.0
9.95
9.96
0.0919
0.180
17.7
0.0161
0.0698
8.56E-06
1 .68E-05
0.00730
1.11E-05
9.04E-06
6.72E-05
0.000132
0.0201
2.73E-05
5.52E-05
9.58E-05
0.000188
0.0340
4.26E-05
8. 11 E-05
0.000884
0.00173
0.184
0.000188
0.000682
0.00386
0.00757
0.732
0.000714
0.00294
0.00643
0.0126
1.25
0.00114
0.00490
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
Demographic Group
0.00269
0.00372
0.457
0.000525
0.00203
Average
0.00721
0.00995
1.20
0.00133
0.00540
Standard
6.23
6.22
6.24
6.23
6.22
Skewness
0.0640
0.0883
10.6
0.0118
0.0479
Max
1 .02E-05
1 .4E-05
0.00793
1 .51 E-05
1.11E-05
Min
5.36E-05
7.39E-05
0.0206
3.33E-05
4.85E-05
5th
0.00013
0.00018
0.0328
4.41 E-05
0.000107
10th
0.00103
0.00142
0.177
0.000217
0.000778
50th
0.00424
0.00584
0.703
0.000794
0.00319
90th
0.00723
0.00994
1.22
0.00135
0.00539
95th
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
0.00132
0.00258
0.175
0.000377
0.00104
0.00149
0.00291
0.190
0.000392
0.00117
2.18
2.18
2.19
2.20
2.19
0.00899
0.0176
1.16
0.00244
0.00710
3.86E-06
7.57E-06
0.00174
6.51 E-06
4.38E-06
4. 85 E-05
9.52E-05
0.0126
4.3E-05
4.54E-05
0.000142
0.000279
0.0232
5.94E-05
0.000119
0.000815
0.00160
0.113
0.000251
0.000653
0.00342
0.00670
0.437
0.000921
0.00268
0.00440
0.00864
0.567
0.00118
0.00345
-54-
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TABLE 3-7
48-Hour PBPK Modeled Absorbed Doses for CHCL3 for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.01118
0.0141
1.57
0.00120
0.00576
0.0319
0.0407
4.43
0.00331
0.0163
9.70
9.70
9.74
9.71
9.67
0.426
0.544
59.2
0.0443
0.218
1 .68E-05
2.14E-05
0.0175
2.24E-05
1 .07E-05
0.000251
0.000321
0.0650
6.32E-05
0.000142
0.000552
0.000704
0.1070
0.000106
0.000325
0.00445
0.00568
0.658
0.000522
0.00234
0.0187
0.0239
2.560
0.00194
0.00960
0.0316
0.0403
4.61
0.00339
0.0164
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0117
0.0114
1.58
0.001428
0.00629
0.0311
0.0302
4.10
0.00362
0.0164
6.16
6.16
6.18
6.17
6.15
0.275
0.267
36.4
0.0321
0.145
1 .39E-05
1 .35E-05
0.0214
3.17E-05
1.119E-05
0.000194
0.000189
0.0622
7.74E-05
0.000117
0.000532
0.000519
0.104
0.000116
0.00031 1
0.00448
0.00436
0.609
0.000585
0.00241
0.0183
0.0178
2.45
0.00219
0.00980
0.0314
0.0305
4.24
0.00366
0.0166
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00586
0.00772
0.601
0.0010
0.00319
0.00666
0.00878
0.657
0.00117
0.00358
2.23
2.23
2.24
2.26
2.24
0.0422
0.0556
4.20
0.00758
0.0229
5.19E-06
6.84E-06
0.00466
1 .43E-05
4.19E-06
0.000137
0.000180
0.0354
9.92E-05
9.23E-05
0.000622
0.000821
0.0796
0.000165
0.000347
0.00364
0.00480
0.391
0.000738
0.00198
0.0152
0.0201
1.5
0.00272
0.00823
0.0201
0.0265
1.99
0.00354
0.0108
-55-
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TABLE 3-8
48-Hour PBPK Modeled Absorbed Doses for DCA for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00399
0.00493
0.0693
0.00404
0.00498
0.00318
0.00393
0.0542
0.00322
0.00397
3.54
3.54
3.26
3.52
3.54
0.0310
0.0384
0.509
0.0313
0.0388
0.000392
0.000485
0.00633
0.000396
0.00049
0.00123
0.00152
0.0217
0.001248
0.00154
0.00141
0.00174
0.0242
0.001426
0.00176
0.00317
0.00392
0.0544
0.00321
0.00396
0.00733
0.00907
0.127
0.00743
0.00917
0.00983
0.0122
0.175
0.00997
0.0123
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00221
0.00262
0.0339
0.00224
0.00272
0.00227
0.00270
0.0347
0.00232
0.00281
4.30
4.30
4.22
4.31
4.30
0.0215
0.0255
0.325
0.0218
0.0265
0.000284
0.000337
0.00422
0.000288
0.000351
0.00048
0.000569
0.00726
0.000486
0.000592
0.000612
0.000726
0.00934
0.000620
0.000755
0.00159
0.00189
0.0243
0.00162
0.00196
0.00449
0.00533
0.0675
0.00456
0.00555
0.00547
0.00649
0.0848
0.00557
0.00675
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00248
0.00307
0.0161
0.00249
0.00310
0.00208
0.00257
0.0133
0.00208
0.00259
2.46
2.46
2.46
2.46
2.46
0.0168
0.0208
0.109
0.0169
0.0211
0.000195
0.000241
0.00133
0.000196
0.000243
0.000559
0.000692
0.0036
0.000562
0.000699
0.000664
0.000821
0.00433
0.000667
0.000830
0.00185
0.00229
0.0121
0.00186
0.00232
0.00507
0.00627
0.0333
0.00510
0.00633
0.00617
0.00763
0.0389
0.00619
0.00771
-56-
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TABLE 3-9
48-Hour PBPK Modeled Absorbed Doses for TCA for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0201
0.0317
0.0737
0.0205
0.0305
0.0165
0.0260
0.0576
0.0167
0.0250
3.86
3.86
3.26
3.85
3.86
0.166
0.263
0.541
0.169
0.253
0.00216
0.00341
0.00673
0.00219
0.00328
0.00585
0.00923
0.0231
0.00597
0.00888
0.00729
0.0115
0.0257
0.00746
0.0111
0.0160
0.0252
0.0578
0.0163
0.0242
0.0375
0.0592
0.135
0.0382
0.0570
0.0488
0.0770
0.186
0.0497
0.0740
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0118
0.0176
0.0360
0.0120
0.0177
0.0123
0.0183
0.0369
0.0126
0.0185
4.43
4.43
4.22
4.45
4.43
0.117
0.174
0.346
0.119
0.175
0.00151
0.00224
0.00449
0.00153
0.00226
0.00251
0.00373
0.00772
0.00255
0.00376
0.00330
0.00489
0.00992
0.00335
0.00494
0.00848
0.0126
0.0258
0.00861
0.0127
0.0237
0.0352
0.0718
0.0242
0.0356
0.0293
0.0435
0.0901
0.0298
0.0439
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0154
0.0243
0.0171
0.0155
0.0234
0.0131
0.0206
0.0141
0.0131
0.0198
2.47
2.47
2.46
2.47
2.47
0.106
0.166
0.115
0.106
0.160
0.0011
0.00174
0.00142
0.00111
0.00167
0.00344
0.00543
0.00382
0.00347
0.00522
0.00405
0.00638
0.00459
0.00408
0.00614
0.0114
0.0180
0.0129
0.0115
0.0173
0.0308
0.0485
0.0354
0.0310
0.0466
0.0413
0.0652
0.0414
0.0416
0.0627
-57-
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default assumption that Haber's law (the toxic response is proportionate to the metric,
"concentration times duration") holds for the toxicities and risk associated with these
toxicants. For example, the AUC for BDCM in kidney of the adult male, under these
conditions, expressed an average value of 0.00230 mg/L*hr, with values at the 5th and
95th percentile of the distribution of 6.72E-6 and 0.00643, respectively. In comparison,
the AUC in the kidney of the adult female demonstrated an average value of 0.00269,
and values of 5.36E-5 and 0.00723 mg/L*hr, respectively, at the 5th and 95th percentiles
of the distribution. Finally, values for BDCM AUC in the kidney of the male child
demonstrated an average value of 0.00132, and values at the 5th and 95th percentiles of
the distribution of 4.85E-5 and 0.00440mg/L*hr, respectively. Because these values
represent risk-relevant values within each tissue or organ, and because the PBPK
model has already accounted for body mass, the values presented in Tables 3-6
through 3-9 are directly comparable without further adjustment. The variance in these
values resulted from variance in water use patterns, travel between indoor locations
with and without embedded water use appliances, in-home ventilation, and specific
anatomic, biochemical and physiologic properties of the adult male, adult female and
male child. Variance presented in these tables does not reflect variance of embedded
model values (i.e., point estimates used for metabolic rates, tissue partition coefficients)
which are well known to vary among individuals due to the biochemical individuality
characteristic of outbred species. While their variance may appear quite high to those
accustomed to reviewing PBPK model simulations from well-characterized chemical
exposures, these simulations are intended to integrate the variance of water use
patterns and other in-home variables in estimating internal (tissue-specific) doses, here
presented as AUC values. Thus, while it may at first seem that differences in tissue
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doses observed e.g., between the adult male and the male child may have their basis in
age-related Pharmacokinetic differences, these results also are based on age-
dependent differences in water use patterns, and time spent in various locations within
and without the home during water use activities.
3.2.4. Sensitivity Analysis. The values of the parameters defining the modeling
problem ultimately determine the predicted exposures and doses. The uncertainty in the
estimated parameter values varies depending upon the parameter. For example, many
estimated parameter values, such as water flow rate, water volume, house and room
volumes, etc. are known within a reasonable and definable range. Other parameter
estimates, such as those for skin permeability coefficients and various behavioral
parameters may have uncertainties of an order of magnitude or higher.
Both sensitivity and uncertainty analyses were considered for evaluation.
However, due to the difficulty of separating uncertainty and variability in many of the
behavioral parameters, it was concluded that it would be more meaningful to conduct a
screening-level sensitivity analysis to identify the parameters having the most significant
impact (U.S. EPA, 1997c). Therefore, neither Monte Carlo simulation nor uncertainty
analyses were conducted; however sensitivity analysis characterized the importance of
each parameter, allowing a qualitative judgment of the importance of a parameter's
uncertainty. The sensitivity analysis was conducted by first establishing a base-case
scenario, consisting of a base-case set of activities and model parameters. To evaluate
the sensitivity of a particular parameter, the value of that parameter was varied by 10%
from its base-case value. The impact of this change was then evaluated by comparing
the relative change in the chosen dose metrics. It was recognized that due to the sheer
number of model parameters and the large uncertainty in some of the parameter values,
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the results of the analysis would provide guidance in selecting the set of important
parameters, but a more refined study would be necessary. In addition, the sensitivity of
the various parameters was expected to be similar for each of the three modeled
subjects, so the analysis was limited to the adult male. Some results were presented for
the adult female and the child to demonstrate this similarity. In addition, the sensitivity
analysis was limited to a subset of two DBFs, CHCI3 and DCA. The analysis evaluated
the two modeling components separately: (1) the exposure and uptake model
components, and (2) the physiological model components. (See Section 5.0 of
Appendix 1 for complete details.)
3.2.4.1. TEM Sensitivity Analysis Results — The TEM sensitivity analysis
identified a number of important results (Section 5.4.1. of Appendix 1). From the
analysis, it was clear that the conclusions are not consistent across chemicals.
Parameters were ranked by their absolute value of relative sensitivity. Table 3-10
summarizes these results. For volatile chemicals, as represented by CHCI3, the
parameters influencing the air concentrations had the most significant impact. These
parameters included the overall mass transfer coefficients, air exchange rates, zone
volumes, water flowrates, and duration of water uses. The air exchange rates and zone
volumes were inversely related to the absorbed dose because of they lower airborne
concentrations. The overall mass transfer coefficient was the most sensitive parameter
for CHCI3, consistent with the inhalation route having the largest dose, causing
approximately an 8% change in the total absorbed dose for a 10% change in the overall
mass transfer coefficient. Although the mass transfer coefficients were examined as a
group, it was clear that larger inhalation exposure events, such as showering, would be
more sensitive to this parameter. For the volatile chemical, CHCI3, the model was
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TABLE 3-10
Average Relative Sensitivity Analysis of Total Absorbed Dose for Water Use, Environmental and Chemical Parameters
for CHCI3 and DCA, Ranked by Absolute Value
Parameter*
Overall Mass
Transfer
Coefficient (KOLA)
Air Exchange
Rate (hr-1) and
Interzonal Air
Flows (m3/hr)
Shower
Flowrate, gal/min
Shower Mean
Duration, min
House and Zone
Volumes (m3)
Kitchen Faucet
Flowrate, gal/min
Kitchen Faucet
mean Duration,
min
CHCI3
Relative Sensitivity, % (Rank)
Male (15-45)
80.17 (1)
-57.56 (2)
34.08 (3)
33.03 (4)
-26.63 (5)
8.19 (6)
6.63 (7)
Female (15-45)
80.37 (1)
-70.70 (2)
31.91 (3)
26.04 (4)
-12.07 (5)
4.90 (8)
2.98 (10)
Child (6 years)
73.48 (1)
-59.77 (2)
4.65 (8)
2.91 (13)
-23.38 (3)
7.71 (6)
5.76 (7)
Parameter*
Consumption
Volume, L/day
Shower Mean
Duration, min
Henry's Law
Constant
Overall Mass
Transfer
Coefficient
(KOLA)
Air Exchange
Rate (hr-1) and
Interzonal Air
Flows (mVhr)
Shower
Flowrate, gal/min
House and Zone
Volumes (m3)
DCA
Relative Sensitivity, % (Rank)
Male (15-45)
99.96 (1)
0.0135 (2)
0.0129 (3)
0.00299 (4)
-0.00177 (5)
0.00165 (6)
-0.00137 (7)
Female (15-45)
99.94 (1)
0.0197 (2)
0.0188 (3)
0.00383 (6)
-0.00874 (4)
0.00208 (8)
0.00485 (5)
Child (6 years)
99.96 (1)
5.54E-4 (12)
0.00385 (5)
0.00650 (3)
-0.00548 (4)
2.01E-4 (13)
-0.00133 (7)
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TABLE 3-1 Ocont.
Parameter*
Bathroom Faucet
Mean Duration,
min
Consumption
Volume, L/day
Clothes Washer
Mean Duration,
min
Henry's Law
Constant
Dishwasher
Volume, gal
Clothes Washer
Volume, gal
Bath Flowrate,
gal/min
Bath Mean
Duration, min
Bath Volume, gal
Dishwasher
Mean Duration,
min
Toilet Volume,
gal/flush
CHCI3
Relative Sensitivity, % (Rank)
Male (15-45)
5.84 (9)
5.51 (10)
3.23 (11)
2.64 (12)
1.39 (13)
1.33 (14)
0.90 (15)
0.86 (16)
0.27 (17)
0.10 (18)
0.00 (19)
Female (15-45)
7.78 (7)
3.27 (9)
2.56 (12)
2.83 (11)
1.24 (13)
1.06 (14)
0.276 (16)
0.28 (15)
0.08 (18)
0.12 (17)
0.00 (19)
Child (6 years)
2.76 (14)
4.40 (9)
3.11 (12)
1.98 (15)
1.18 (17)
1.30 (16)
17.20 (5)
19.55 (4)
4.27 (10)
0.11 (18)
0.00 (19)
Parameter*
Bathroom Faucet
Mean Duration,
min
Kitchen Faucet
Flowrate, gal/min
Bathroom Faucet
Flowrate, gal/min
Clothes Washer
Mean Duration,
min
Dishwasher
Mean Duration,
min
Bath Mean
Duration, min
Bath Flowrate,
gal/min
Dishwasher
Volume, gal
Bath Volume, gal
Clothes Washer
Volume, gal
Toilet Volume,
gal/flush
DCA
Relative Sensitivity, % (Rank)
Male (15-45)
0.00103 (9)
6.79E-4 (10)
1.64E-4 (11)
1.06E-4 (12)
9.36E-5 (13)
8.81E-5 (14)
7.57E-5 (15)
4.75E-9 (16)
4.71E-10(17)
1.76E-10(18)
0.00 (19)
Female (15-45)
0.00214 (7)
6.67E-4 (10)
4.62E-4 (11)
1.41E-4 (13)
1.84E-4 (12)
3.77E-5 (14)
2.94E-4 (15)
8.50E-9 (16)
1.86E-10 (18)
2.33E-10 (17)
0.00 (19)
Child (6 years)
7.01E-4 (10)
8.01 E-4 (9)
0.000191 (14)
1.30E-4 (16)
1.30E-4(15)
0.0108 (2)
0.00211 (6)
6.03E-09(17)
1.18E-08(16)
2.18E-10(18)
0.00 (19)
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relatively insensitive to the actual volume of non-flowing type water appliances (e.g.,
bath volume, dishwasher volume, clothes washer volume, toilet volume, etc.) with less
than a 0.2% change in dose for a 10% change in the volume parameter. In addition, the
model was relatively insensitive to Henry's law constant (H), yielding a relative change
of less than 0.3% for a 10% change in H.
For low volatility chemicals, as represented by DCA, consumption and dermal
contact played the most significant roles. Consumption was by far the most sensitive
parameter, changing the total absorbed dose approximately 10% for a 10% change in
the consumption volume. The dermal influence, though much less significant, was
evident in the shower duration for the adults and in the bath duration for the child.
Although the inhalation route's contribution to total absorbed dose was small relative to
the other routes, it was interesting to note that, with the exception of Henry's law
constant, the sensitivity of the inhalation parameters were in the same sequential order
as for CHCI3. The increased relative influence of Henry's law constant as compared to
the mass transfer coefficient is due to the dynamics of the equilibrium relationship as
defined by Henry's law. The concentration in the air is limited to the equilibrium
condition, as defined by Henry's law, which is approached in the vicinity of the water
appliance during water uses of duration longer than a few minutes, thereby attenuating
the mass transfer rate. For this reason, Henry's law constant is the most sensitive
parameter for the inhalation route.
Although CHCI3 is a volatile chemical and DCA is a low volatility chemical, and as
such they are generally representative of chemicals with similar chemical properties,
many other factors affect the exposure and uptake of a chemical. Factors such as skin
permeability are not highly correlated with volatility, and therefore the fractional dermal
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uptake can be very different for chemicals with similar volatility. Therefore, the
conclusions reached based on the sensitivity analysis for these two chemicals would
have to consider the effect of the other chemical properties which impact uptake.
3.2.4.2. ERDEM Sensitivity Analysis Results — The ERDEM sensitivity
analysis identified a number of highly sensitive parameters, but also identified numerous
insensitive parameters. Table 3-11 presents a summary of the most sensitive model
parameters for each dose metric for CHCI3 and DCA. In some cases, the change in the
dose metric variables, due to the perturbation of an input variable, was less than the
relative error in the integration process. For these cases, the results were not reported.
The relative sensitivities for liver AUC and testes AUC dose metrics were evaluated for
CHCI3 and DCA. For CHCI3, the AUC estimates for the liver differed by a factor of
around 10 from the estimates for the testes. But, for DCA, the values of AUC were very
similar for liver versus testes. The volumes of the body, fat, and the slowly perfused
tissue showed a high relative sensitivity in the liver but not in the testes. Liver
metabolism was sensitive in the liver, but not in the testes.
The peak concentration of liver and testes dose metrics were also evaluated for
CHCI3. The input parameters exhibiting high relative sensitivity were: volume of the
body, alveolar ventilation rate, cardiac output, the blood flows to the liver and slowly
perfused tissue, and the partition coefficients for the static lung/air and static lung/blood.
The peak concentration of liver and testes dose metrics were also evaluated for
CHCI3. The input parameters exhibiting high relative sensitivity were: volume of the
body, alveolar ventilation rate, cardiac output, the blood flows to the liver and slowly
perfused tissue, and the partition coefficients for the static lung/air and static lung/blood.
However, the volumes of the dermis, fat, rapidly perfused tissue, and slowly perfused
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TABLE 3-1 1
Summary of the Most Sensitive Model Parameters for Each Dose Metric
Dose Metrics
Absorbed Dose at 24
hours (mg)
Amount Metabolized in
Liver at 24 hours (mg)
AUC in Liver at 24 hours
(mg*h/L)
AUC in Testes at 24 hours
(mg*h/L)
Concentration in Liver
(mg/L)
Concentration in Testes
(mg/L)
Most Sensitive Model Parameters (Relative Sensitivity)
CHCI3
Alveolar Ventilation Rate
(89.83%)
Alveolar Ventilation Rate
(52.98%)
Liver Metabolism Vmax
(-107.34%)
BloodTestes Partition
Coefficient (100.21%)
Liver Metabolism Vmax
(-108.98%)
Testes: Blood Partition
Coefficient (99.50%)
DCA
Blood Flow in Kidney
(4.88%)
N/A
Body Mass
(-89.07%)
BloodTestes Partition
Coefficient (100.56%)
Rate of Absorption into
Portal Blood from Stomach
(75.16%)
BloodTestes Partition
Coefficient (99.41%)
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tissue, and the partition coefficient of rapidly perfused tissue/blood were sensitive in the
liver but not in the testes. The partition coefficient of testes/blood was sensitive in the
testes only. In a similar manner to the results shown for CHCI3, the relative sensitivities
were examined for each dose metric for DCA. The dose metric - absorbed dose - had
negligible relative sensitivity for all 34 input parameters for DCA, while for CHCI3 the
absorbed dose was most sensitive to alveolar ventilation rate (relative sensitivity of
89.38%).
3.2.4.3. Parameters Not Evaluated — Several model parameters were not
explicitly examined as a part of this study, including the following:
Location behavior of exposed individuals relative to sources of DBP
exposures
Impact of other occupants (family size, behavior of other occupants, etc.)
Impact of mechanical systems (e.g., the heating/air conditioning system,
other fans, etc.)
Impact of changing physical conditions in the house (e.g., opening and
closing of doors and windows)
Impact of weather
Water temperature
Model appropriateness (mass balance model, uptake models, behavioral
models, etc.)
Although these parameters were not explicitly studied, the impacts of several of the
parameters were indirectly addressed. The impact of changing physical conditions and
weather were addressed indirectly by looking at the effect of increasing the whole house
air exchange rate and inter-zonal airflows. In general, changes causing increased
ventilation would lower peak concentrations at the source. However, while opening an
interior door would decrease the peak concentration at the source, it increases the
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concentrations at other locations in the home, thereby potentially providing additional
exposure to the occupants in those locations. Similarly, the use of a mechanical system
would encourage mixing in the house, causing lower exposures near the source but
potentially higher exposures at other locations. The impact of water temperature and
other chemical properties were also indirectly examined by looking at the effect of
changing the overall mass transfer coefficient. Water temperature impacts chemical
diffusivity in water, and for chemicals whose volatilization is limited by liquid phase mass
transfer, an increased water temperature will increase the overall mass transfer
coefficient. The liquid and gas phase diffusivities will have a similar effect subject to the
phase that provides the greatest resistance to mass transfer.
The impact of behavioral characteristics of the occupants clearly has the
potential for causing the greatest variation. Wilkes et al. (1992) showed that, for TCE,
someone taking a second shower immediately following another person's shower would
be exposed to much greater air concentrations, and receive a higher absorbed dose.
For the scenario examined by Wilkes et al., the second shower was estimated to
provide approximately a 50% higher dose than the first shower of identical length and
conditions due to the elevated air concentrations. Wilkes et al. (1996) showed, for TCE,
a high degree of correlation between behavior and predicted dose, with the most
important predictors being shower duration, bath duration, time spent in the bathroom,
and total household water use. Wilkes et al. (1992) also compared the estimated
exposures of single occupant households to two occupant households. The two person
households showed a mean increase in the potential inhalation dose of 38% for the
male population group and 11% for the female population group.
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4. COMBINING THE CRPF METHOD WITH EXPOSURE
ESTIMATES TO CONDUCT A DBP CUMULATIVE RISK ASSESSMENT
This report proposes that the CRPF method (described in Section 2.0 and
Appendix 2) is a feasible approach for conducting cumulative risk assessments for
DBFs. Data from exposure assessment and PBPK models can be used to estimate
contaminant exposures and the resulting doses to internal tissues over time. The
exposure data may be combined with relevant dose-response data and models to
estimate risk posed by a contaminant mixture through multiple exposure routes over
varying exposure time periods. This section envisions how the exposure estimates
(described in Section 3 and in Appendix 1) may be combined with dose-response
information under the CRPF approach, describing those steps necessary to perform
such an assessment.
4.1. STRATEGY FOR CONDUCTING THE CRPF-BASED ASSESSMENT
Because animal dose-response data are typically available for only a single
exposure route (usually oral), practical implementation of the CRPF approach for
multiple exposure routes requires route extrapolations. Few inhalation or dermal toxicity
data are available for the DBFs. Thus, although the CRPF analysis may be conducted
using separate exposures for each route, it is more logical to develop the approach so it
can be implemented using dose-response information on the oral route only. (PBPK
models may also be useful in constructing physiologically-based extrapolations across
different exposure routes.) The text that follows in this section focuses on the use of
internal doses based on human exposures to all three routes. Working with the 13
DBFs for which example exposure and dose estimates have been developed (Appendix
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1), it is envisioned that the following steps may be followed to conduct the CRPF-based
assessment.
Group DBPs into Subclasses by Common MOA
1) Collect, evaluate and select the highest quality data for each of the 13 DBPs,
including data on MOA and dose-response toxicology data.
2) Using the exposure assessment and PBPK modeling results and the MOA data,
determine the best measure of a biologically effective dose. The PBPK modeling
will provide improved understanding of the relationship between toxicity and
doses in the target tissue. The analyst has several options for dose analysis:
• Analysis of contaminants as exposures. (Not discussed further in this
section. A CRPF analysis using separate exposures for each route
requires dose-response information for each route. Thus, this option is
not practical given the current state of the DBP toxicity data base.)
• Analysis of contaminants as a total absorbed dose (e.g., blood
concentrations).
• Analysis of contaminants as tissue or organ doses.
3) Identify subclasses of the 13 DBPs, grouping them by similar toxic MOA for each
endpoint of interest (e.g., cancer, developmental effects, reproductive effects).
4) Determine the appropriate dose metric based on MOA and available dose-
response data. (Analyses may be based upon dose metrics such as area under
the curve for absorbed and tissue doses or the maximum concentration.)
Conduct Dose Response Modeling of Toxicology Data
1) Beginning with data from an oral toxicology study, adjust the administered animal
doses to internal animal doses using bioavailability factors.
2) Adjust the internal animal doses to internal human equivalent doses using
allometric scaling or PBPK modeling.
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3) Using these internal human equivalent doses, develop dose-response curves for
the 13 DBFs individually.
4) Re-evaluate subclass groupings based on the requirement of the RPF method
that the members of the subclass are to have similarly shaped dose-response
curves within the exposure region of interest.
Develop RPF Estimates for Each Subclass and Combine Using the CRPF
Approach
1) For each subclass, choose an index chemical and estimate RPFs for each
member of the subclass relative to the index chemical.
2) Using the multiple route internal doses for the 13 DBFs, multiply each component
dose by its RPF to obtain the Component ICED. Sum the Component ICEDs to
generate an index chemical equivalent dose for each subclass (i.e., a Subclass
ICED).
3) Use the dose-response curve for the index chemical to estimate risk for its
subclass.
4) Based on response addition, sum the subclass risks to estimate the total multiple
route mixtures risk for the DBFs.
5) Develop a full risk characterization for the analysis, including an analysis of
uncertainty.
4.2. GROUP DBFS INTO SUBCLASSES BY COMMON MOA
The 15 DBFs evaluated in this report are fairly well studied, providing varying
degrees of understanding of MOA, but the data are not sufficient to establish a
consensus on MOA among researchers for most of these DBFs. For purposes of
illustration, however, Table 4-1 offers one division into subclasses that can be loosely
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TABLE 4-1
Example: DBFs Grouped into Subclasses by Common MOA*
Genotoxic Carcinogens
Non-Genotoxic Carcinogens
Bromodichloromethane (glutathione
transferase activation, adduct formation
is a distinct possibility)
Bromoform (mechanism unknown,
potentially oxidative damage, glutathione
transferase activation)
Chlorodibromomethane (mechanism
unknown)
Chloroform (gross tissue damage and
regeneration, "necrotic" foci)
Dichloroacetic Acid (no observed
necrotic foci)
Trichloroacetic Acid (no observed
necrotic foci)
Developmental Toxicants -
Primary Effect is Cardiovascular,
Developmental Toxicants -
Primary Effect is for Whole Organism
Monochloroacetic Acid (heart)
Dichloroacetic Acid (heart)
Trichloroacetic Acid (heart)
Monobromoacetic Acid (heart)
Trichloroacetonitrile (heart)
Dibromoacetic Acid (delayed parturition)
Bromochloroacetic Acid (reduced pup
viability)
Dichloroacetonitrile (reduced pup
viability)
Bromodichloromethane (full litter
resorption)
Bromoform (full litter resorption)
Chloroform (reduced pup weight)
Reproductive Toxicants -
Primary Effect in Testis and Sperm
Dichloroacetic Acid (testicular effects)
Dibromoacetic Acid (testicular effects)
Bromochloroacetic Acid (sperm effects)
Bromodichloromethane (sperm effects)
Information summarized based on data presented in Klinefelter et al. (2001) and U.S.
EPA (2000a).
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supported by the toxicology data (see Klinefelter et al., 2001; U.S. EPA, 2000a). The
carcinogens are divided into those that are thought to be genotoxic and non-genotoxic.
The developmental/reproductive toxicants are divided into groups for which the primary
effect is either cardiovascular defects, effects on the fetus or litter, or male reproductive
effects. The scope of this report is not to debate the example classifications shown in
Table 4-1, but to show that such an analysis can be performed, thus, allowing for the
development of RPF estimates of risk for each of these five subclasses.
4.2.1. Developmental and Reproductive Effects from Exposure to DBFs. The
HAAs, HANs, THMs and other DBFs have been shown to adversely affect reproduction
and development in animals (Klinefelter et al., 2001). Studies of reproductive and
developmental toxicity effects of DBFs have demonstrated alterations in sperm
morphology, motility and count; decreased levels of fertility; spontaneous resorptions;
decreased fetal body weight; and visceral, cardiovascular and craniofacial
malformations (U.S. EPA, 2000a). The groups in Table 4-1 were formed from
evaluations of these data sets, suggesting three general categories of effects that are
the most sensitive endpoints in common for these chemicals:
1) Cardiovascular Effects (e.g., interventricular septal defects, defects between
ascending aorta and right ventricle, and levocardia)
2) Effects on Fetus/Litter (e.g., decreased fetal body weight and crown-rump length,
full litter resorption)
3) Male Reproductive Effects (e.g., sperm alterations, testicular effects).
Data on the actual toxicological mechanisms causing these effects are generally not
available, so the groups in Table 4-1 were formed to reflect the primary effect observed
in animal studies for each DBP.
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4.2.2. Carcinogenicity from Exposure to DBFs. The divisions in Table 4-1 are based
on mechanistic evidence in the toxicological literature of whether each DBP is genotoxic
or non-genotoxic. For the THMs, kidney tumors were seen in male rats exposed to
CHCI3, whose MOA is thought to be cytolethality (cell death) and cellular regeneration,
that is, CHCI3 is thought to be non-genotoxic. The MOA for the other three THMs are
less clear. BDCM is structurally similar to other known animal carcinogens, is
mutagenic, and produced tumors at multiple sites in multiple species. DBCM is
mutagenic and produced liver tumors in female mice only at doses that also produced
liver damage. CHBr3 is genotoxic and induced neoplastic lesions in the large intestines
in rats [see EPA's Integrated Risk Information System (IRIS) for this information (U.S.
EPA, 2002c)]. Although there may be a cytolethality component to the MOA for these
three THMs, evidence exists showing these three brominated THMs are genotoxic
(Landi et al., 1999). It is noteworthy that, although the decisions in Table 4-1 were
made in accord with these data on glutathione transferase and cytolethality, other
opinions on MOA exist. For example, Fawell (2000) provides a discussion of MOA for
the THMs, concluding that all four THMs are non-genotoxic.
An increased incidence of hepatocellular adenoma and carcinomas was found in
male and female mice exposed to DCA, and although TCA produced tumors in male
and female mice, there is no evidence of carcinogenicity in rats [see IRIS for this
information (U.S. EPA, 2002c)]. DCA and TCA promote the outgrowth of tumors with
distinct genetic effects, thus their MOA may not be the same, but both appear to be
tumor promoters. Evidence of genotoxicity is observed at concentrations greatly in
excess of those anticipated to occur in humans. Thus, although DCA and TCA may
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each have a dose-dependent genotoxic and a non-genotoxic MOA, the most relevant
MOA for human exposures is non-genotoxic.
4.3. CONDUCT DOSE RESPONSE MODELING OF TOXICITY DATA
The multiple route exposure doses developed in Appendix 1 are combined to
estimate internal doses for the human. In general, an internal dose is that amount of
DBP, expressed as mg/kg body weight, which travels from the external environment
(air, water) to the internal environment in the animal species under investigation. The
total absorbed dose does not reflect concentration of DBFs in any given tissue, but
represents the total amount of DBFs entering the body. To use the estimates of human
total absorbed dose or tissue doses in a risk assessment based on animal toxicology
data, the external animal dose must undergo two conversions: 1) the dose which the
animal encounters in the external environment (air, water) must be converted to an
internal dose, and 2) the animal's internal dose must be adjusted to an internal human
equivalent dose (HED) to account for animal:human differences in response. Figure 4-1
illustrates these two conversions and shows that the dose response model is then built
using the internal HED and the animal response data. This step assumes that the
uptake in affected tissues is the same across species and that the animal response
adequately characterizes the human response. Estimates can then be made using this
dose-response function of single DBP human health risks, toxicity values (e.g., an ED10)
or slope factors for use in calculating RPFs, and index chemical human health risks (to
be summed across subclasses to developing risk estimates by the CRPF
approach).
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Animal Toxicology Study
TEM & ERDEM Modeling
Animal Administered Doses
Adjust to Internal
Animal Dose -
Administered
Dose Times %
Bioavailable;
Adjust to Internal
HED (Human
Equivalent
Dose) - Use
Allometric Scaling
Animal Response Data
Build Human Dose
Response Model
Estimate
Human Risks,
EDIO's, etc.
Internal HED
Human Exposures
in the Home via
Oral, Dermal and
Inhalation Routes
Human Total
Absorbed Dose
and Tissue/Organ
Dose Estimates
Estimate Individual DBF
Risks, Index Chemical
Risks, Relative Potency
Factors
FIGURE 4-1
Dose-Response Development, Human Risk Estimates and RPF Calculations for Each Single DBP
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The first conversion, from administered animal dose to an internal dose, is
accomplished using an estimate of oral bioavailability (assuming a 100% oral
bioavailability when data on this factor are lacking). When data on oral bioavailability
are available for the chemical of interest under the relevant study conditions, then the
measured oral bioavailability should be used to adjust the internal animal dose based on
the administered dose. DBP toxicity results are rarely available from animal studies
conducted via the inhalation and dermal routes. When these data become available,
however, increased effort will be required to determine the internal dose based on the
encountered dose, as was done for the human. This requires information on route-
specific absorption of encountered concentrations of toxicants in the species. These
are developed through applying assumptions and employing a PBPK model. The PBPK
model was applied to the human (Appendix 1) to simulate an internal dose following
oral, dermal and inhalation exposures to contaminants in air and water from delivery of
the contaminants via disinfected drinking water, and oral, dermal and inhalation
exposures to these contaminants in water or volatilized from water into air.
One method to perform the second conversion to an internal HED is to using
body weight scaling to calculate equivalent doses across species. Equation (4-1),
below, is based on allometric scaling laws that relate a biologic measure of physiology
to body weight raised to a power (U.S. EPA, 1980, 1996b):
1/4
Clh Cla
W
(4-1)
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where:
dh = internal human equivalent dose (mg/kg/day)
da = internal animal dose (mg/kg/day)
wh = adult human body weight (e.g., 70 kg for a male adult human)
wa = animal body weight (e.g., 0.35 kg fora male adult rat)
A second approach to scaling animal to human doses is to use PBPK models
when data for humans and animals are available. The use of PBPK models can provide
a credible scientific forum to do conversions based on target tissue dose extrapolation
between animals and humans. Such an extrapolation can account for physiological
differences which are not necessarily identified in the allometric method. Hence,
reconstruction of the dose-response relationship for humans can be done using PBPK
methods that will relate toxicity to target tissue concentrations.
For each DBP, and for each toxic event (response), dose-response functions are
fitted to the internal HED to facilitate a comparison with the human internal doses
derived from the multiple route exposure assessment. The oral dose-response function
observed in research animals is appropriate for comparison with the internal dose
derived in humans from the multiple route exposure estimates, based on several
factors: 1) the toxic endpoints of concern are systemic, they are not manifest as portal
of entry (route-specific) effects, 2) the systemic levels of toxicants are determined
independent of route of exposure, and 3) there are no data describing the responses
observed in research animals as a function of tissue-specific dose (i.e., concentration of
toxicant in the testes or ovary) upon which a sound estimate of human risk can be
made.
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Once the dose response functions are modeled for each DBP, then the subclass
designations should be revisited to ensure the dose-response curves are similarly
shaped within a subclass. This is a requirement of the RPF method (U.S. EPA, 2000b)
that allows use of the index chemical dose-response function to estimate risk for the
subclass. Statistical methods are under development to test for similarity of dose
response curves (e.g., Chen et al., 2001, 2002; U.S. EPA, 2001); at a minimum,
graphical displays of dose-response functions for the DBPs within a subclass should be
compared within the exposure region of interest.
This procedure for applying the CRPF approach will be illustrated throughout the
remaining text in this Section 4 for the cancer endpoint only and utilizing the two
subclasses that are hypothesized for cancer in Table 4-1. The basic schematic that will
be followed for this illustration is shown in Figure 4-2.
Table 4-2 shows some dose response modeling results and RPF calculations
that will be used to illustrate the steps in a CRPF analysis of the genotoxic and non-
genotoxic subclasses shown in Table 4-1. With the exception of DCA, TCA, and
chloroform, oral upper bound slope estimates for the cancer endpoint were taken
directly from IRIS (U.S. EPA, 2002c) for BDCM, DBCM, and CHBr3. These values were
computed for excess risk, using the linearized multistage model that assumes a low
dose linear response. The mean slope estimates for these chemicals were computed
by re-running the linearized multistage model on the IRIS data sets and taking the
Maximum Likelihood Estimate (MLE) value.
As noted in Table 4-2, "under the Proposed Guidelines for Carcinogen Risk
Assessment (U.S. EPA, 1996, 1999), chloroform is likely to be carcinogenic to humans
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Genotoxic
DBFs:
BDCM (Index
Chemical),
DBCM, CHBrS
SFDBCM
SFBDCM
SFcHBrS
^FBDCM
DBCM
Total
Absorbed
Dose
CHBrS
Total
Absorbed
Dose
Non-Genotoxic
DBFs:
DCA (Index
Chemical), TCA
DBCM ICED +
CHBrS ICED +
BDCM Dose =
'
Equivalent Dose
of BDCM
(ma/Kg day)
o>
w
E.
o
Q
W
0>
o:
._
"-jy^^
^^j
BDCM Dose
TCAICED +
DCA Dose =
Equivalent Dose
of DCA
(mg/Kg day)
o:
Tumor Risk for
Genotoxic DBFs
at Subclass ICED
Tumor Risk for
Non-Genotoxic
DBFs at
Subclass ICED
DCA Dose
Mixture
Risk Estimate
Determine Subclasses;
Select Index Chemical
Develop Component ICEDs Develop
Develop RPFs Component Doses Subclass ICEDs
Estimate Subclass
Risks Using Index
Chemical Dose-Response
Sum Subclass Risks
To Estimate Total
Mixture Risk
FIGURE 4-2
Schematic of CRPF Approach for Illustration of DBP Mixture Cancer Risk
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TABLE 4-2
Incremental Cancer Risk per mg/kg-day
DBP
Slope Factor (SF)
(mg/kg-d)-1
MLE
95%
RPF Calculations
Group
RPF
(SFI/SF1)
Cancer Weight of Evidence
IRIS, U.S. EPA, 2002c
B2 = Probable Human Carcinogen
C = Possible Human Carcinogen
BDCMa
5.7 x10'3
6.2 x10'2
Genotoxic
(Index
Chemical)
1
B2. Based on inadequate human data and sufficient evidence
of carcinogenicity in two animal species (mice and rats) as
shown by increased incidence of kidney tumors and tumors of
the large intestine in male and female rats, kidney tumors in
male mice, and liver tumors in female mice.
DBCMa
7.2x10-'
8.4 x10'2
Genotoxic
1.35
C. Based on inadequate human data and limited evidence of
carcinogenicity in animals; namely, positive carcinogenic
evidence in B6C3FI mice (males and females), together with
positive mutagenicity data, and structural similarity to other
trihalomethanes, which are known animal carcinogens.
CHBr3a
3.4x10"
7.9 x10'3
Genotoxic
0.13
B2. Based on inadequate human data and sufficient evidence
of carcinogenicity in animals, namely an increased incidence of
tumors after oral administration of bromoform in rats and
intraperitoneal administration in mice. Bromoform is genotoxic
in several assay systems. Also, bromoform is structurally
related to other trihalomethanes (e.g., chloroform,
bromodichloromethane, dibromochloromethane) which have
been verified as either probable or possible carcinogens.
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TABLE 4-2 cont.
DBP
Slope Factor (SF)
(mg/kg-d)-1
MLE
95%
RPF Calculations
Group
RPF
(SFI/SF1)
Cancer Weight of Evidence
IRIS, U.S. EPA, 2002c
B2 = Probable Human Carcinogen
C = Possible Human Carcinogen
DCAb
1.4x10-;
1.0x10-1
Non-
Genotoxic
(Index
Chemical)
1
B2. Based on a lack of human carcinogenicity data and
increased incidence of hepatocellular adenomas and
carcinomas in male and female mice. Hyperplastic liver
nodules, which are expected to progress into hepatocellular
adenomas and carcinomas, were increased in both rats and
mice.
TCAb
4.9 x10'2
8.4 x10'2
Non-
Genotoxic
0.84
C. The classification is based on a lack of human data and
limited evidence of an increased incidence of liver neoplasms
in both sexes of one strain of mice. No evidence of
carcinogenicity was found in rats. Results from genotoxicity
studies are mixed; trichloroacetic acid does not appear to be a
point mutagen.
CHCI3a
RfD =
1.0x10-2
RfD =
1.0x1Q-2
Non-
Genotoxic
B2. Under the Proposed Guidelines for Carcinogen Risk
Assessment (U.S. EPA, 1996, 1999), chloroform is likely to be
carcinogenic to humans by all routes of exposure under
high-exposure conditions that lead to cytotoxicity and
regenerative hyperplasia in susceptible tissues (U.S. EPA,
1998a,b). Chloroform is not likely to be carcinogenic to
humans by any route of exposure under exposure conditions
that do not cause cytotoxicity and cell regeneration.
aSlope factors are from IRIS, (U.S. EPA, 2002c). MLE slope factors are from the same dose-response model as the 95% upper
bound slope factors.
"Slope factors are derived from data presented in Bull and Kopfler (1991). They are included here to illustrate the CRPF approach
only and are not representative of EPA peer-reviewed, endorsed values.
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by all routes of exposure under high-exposure conditions that lead to cytotoxicity and
regenerative hyperplasia in susceptible tissues (U.S. EPA, 1998a,b). Chloroform is not
likely to be carcinogenic to humans by any route of exposure under exposure conditions
that do not cause cytotoxicity and cell regeneration." Thus, this illustration assumes that
exposures below chloroform's Reference Dose (RfD) of 0.01 mg/kg/day do not
contribute to carcinogenicity.
For DCA and TCA, quantitative cancer estimates are not available on IRIS, but
qualitative assessments there list B2 and C cancer classifications, respectively. For
purposes of this illustration only, the upper bound and mean (MLE) slope factors for
DCA and TCA were derived from risk levels given in Bull and Kopfler (1991). As was
done for chloroform, DCA was reviewed by an expert panel regarding its mechanism of
action. The panel concluded there was insufficient evidence that tumors occur at low
doses of DCA in animal studies (U.S. EPA, 1998b); thus it is questionable whether the
mechanism of action for cancer is active at the low levels to which humans are
exposed. However, the Agency position on DCA falls short of employing the RfD
methodology as was applied in the case of chloroform, leaving open the question of low
dose mechanism. Thus, DCA was kept as part of this quantitative illustration.
The two dose conversions discussed above in this section are accounted for in
the Table 4-2 information. There is an implicit assumption here that each of these
DBPs is 100% bioavailable to the experimental animal, so the administered animal dose
is assumed to be equivalent to the absorbed animal dose. This assumption is a
significant source of uncertainty that is only made here to simplify the illustration. The
second conversion from internal animal dose to internal human equivalent dose is done
for these chemicals using allometric scaling.
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4.4. DEVELOP RPF ESTIMATES FOR EACH SUBCLASS AND COMBINE USING
THE CRPF APPROACH
Once the dose conversions, individual DBP dose-response modeling and
subclass designations are completed, the RPF methodology found in Agency guidance
can be applied (U.S. EPA, 2000b). For each subclass, an index chemical is chosen.
(Table 4-2 indicates that BDCM and DCA are the index chemicals for the genotoxic
subclass and non-genotoxic subclasses, respectively.) The index chemical is generally
a well-studied chemical with a well defined dose-response curve for the effect of interest
and whose toxicologic similarity to the other chemicals in the subclass can be
substantiated. RPFs are then calculated for each member of the subclass relative to
the index chemical using the dose-response functions generated for the individual
DBPs. (Table 4-2 shows the RPFs for each DBP, where the calculation was conducted
using a ratio of slope factors.) Then, within each subclass, the multiple route internal
exposure estimate for each DBP is multiplied by its RPF to calculate a Component
ICED for each member of the subclass; these estimates are summed to yield a total
Subclass ICED. The dose-response curve for the index chemical is used to estimate
risk for that subclass at the Subclass ICED.
The subclasses were developed based on the assumption that the MOA for each
subclass was truly different from the other subclasses. Thus, by design, it can be
assumed that the toxic action of each subclass will be toxicologically independent of the
other subclasses (i.e., the toxic action caused by one subclass would not affect the
toxicity caused by the other subclasses). This assumption of independence of action is
the basis for using response addition to calculate risk from exposure to a mixture.
Under this assumption, the total risk of an overall effect, such as the risk of
developmental toxicity, can be calculated by summing the risks across the subclasses.
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As an example, for the hypothetical groupings presented in Table 4-1, developmental
effect risks would be summed across the three subclasses to yield an overall risk of
developmental effects. The end result is a multiple route, DBP mixtures risk estimate.
Table 4-3 provides an illustration of the cancer risk calculations that could be
made for a 70 kg adult male by combining the dose-response information in Table 4-2
with the TEM total absorbed dose estimates shown in Table 3-4. The 50th percentile
doses (mg/day) from Table 3-4 are converted to mg/kg/day doses (dividing by 70 kg)
and then multiplied by the RPF for each DBP to obtain Component ICEDs. The sum of
the Component ICEDs form the Subclass ICEDs. The product of each Subclass ICED
and the MLE slope factor for the subclass index chemical provides an estimate of the
average cancer risk for that subclass. The subclass risks are then added to obtain the
final total average cancer risk for the whole mixture.
It is noteworthy that a strength of the CRPF approach is that it can be applied
more broadly and expanded beyond this simple illustration using only six well-studied
DBPs. In this hypothetical example, the toxicity of each chemical was well
characterized. However, this approach can accommodate other DBPs for which fewer
toxicity data exist. For example, other genotoxic carcinogens exhibiting similar MOA to
BDCM may be present in the mixture. Although in vivo data may not be available,
RPFs can be derived using other measures of potency (e.g., in vitro genotoxicity data),
providing these data are relevant to the endpoint of interest and also exist for the index
chemical. Clearly, exposure estimates would also need to be developed for the CRPF
approach to be implemented.
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TABLE 4-3
Illustration of CRPF Approach for Average Cancer Risk Calculations
(Includes assumption of 100% bioavailability)
DBP
95% Upper
Bound Slope
Factor (SF)
RPF
(SF/SF.r
Total Absorbed Dose
for 70 kg Male
50%
mg/day
50%
mg/kg/day
Component
ICED
mg/kg/day
Subclass
ICED
mg/kg/day
Subclass
Risk
MLE Slope
Factor times
Subclass
ICED
Genotoxic Subclass
BDCMb
DBCM
CHBr3
6.20E-02
8.40E-02
7.90E-03
1.00
1.35
0.13
8.43E-02
5.49E-02
3.00E-02
1.20E-03
7.84E-04
4.29E-04
1.20E-03
1.06E-03
5.46E-05
2.32E-03
1.32E-05
Non-Genotoxic Subclass
DCAC
TCA
CHCI3
1.00E-01
8.40E-02
RfD=0.01
1.00
0.84
~
3.14E-02
3.34E-02
3.02E-01
4.49E-04
4.77E-04
4.31 E-03
4.49E-04
4.01E-04
~
8.49E-04
Total Mixture Average Cancer Risk
1.19E-06
1 .44E-05
aSF1 is slope factor for index chemical; SF, is slope factor for ith chemical in the subclass.
"Genotoxic Subclass Index Chemical, Maximum Likelihood Estimate (MLE) of Cancer Slope Factor (SF) = 5.7E-3
cNon-Genotoxic Subclass Index Chemical, MLE SF = 1.4E-3
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The final step of such an effort is to fully characterize the uncertainties that exist
as a product of the analysis. This risk characterization should include uncertainties in
the CRPF process, including discussions regarding subclass development, choice of
index chemical, and the strength of the exposure assessment. In this illustration of the
CRPF approach for estimating DBP cancer risk, there are a number of uncertainties.
Several key uncertainties are listed below.
Based upon expected differences in toxicodynamic MOA, the carcinogenic
DBPs considered were categorized into 2 broad groups: genotoxic
carcinogens and non-genotoxic carcinogens. The genotoxic carcinogens
are assumed to share a common MOA and the non-genotoxic
carcinogens also are assumed to share a common MOA (for CA and TCA
this may, in fact, be unlikely). The genotoxic and non-genotoxic modes of
action are assumed to be independent. The outcomes (i.e., cancer) are
assumed to be statistically independent. The common outcome being
modeled through the CRPF approach in the human is cancer that results
from DBP multiroute exposures. The target organ is not specified.
Calculated slope factors for the individual chemicals are assumed to be an
appropriate basis for relative potency factors. In this example, upper
bound (95th percentile) confidence limits of the maximum likelihood
estimate were employed. While the slope factors as presented on IRIS
(U.S. EPA, 2002c) and by Bull and Kopfler (1991) were used in this
example, other estimates of slope such as the MLE, which represents the
best estimate of dose response, may be more appropriate measures on
which to base an evaluation of relative potency. Additionally, the slope
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factors are based on test animal responses and the original study doses
were transformed to human equivalent doses for purposes of calculating
the slope factors.
The slope factors used to estimate relative potency factors were derived
from studies that had broad dose intervals. The use of slope factors
derived from these studies to estimate RPFs assumes that the chemicals'
MOA does not change over the range of study doses. Some RPFs are
based on ratios of ED10 to avoid this potential problem.
For calculating doses in the bioassay data, the individual DBFs were
assumed to be 100% bioavailable. Multiroute human exposures to DBFs
were estimated as total absorbed doses. The study doses were assumed
to be equivalent to the estimated total absorbed dose in the human. A
more detailed approach that estimates absorbed doses in the rodent
bioassays would reduce the uncertainty associated with the assumption.
RPFs could also be based on animal absorbed doses; this would eliminate
some pharmacokinetic uncertainty in the estimation of the RPFs.
This example is based on total absorbed dose without further
consideration of pharmacokinetic differences between chemicals, target
tissue dosimetry, and is based on the assumption that target tissue
dosimetry at these doses is similar in rodents and humans.
The RPFs were developed from rodent studies and are applied to
humans. This assumes that the MOA for individual chemicals are the
similar for humans and rodents. This also assumes that the between-
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chemical differences in pharmacokinetics are similar between humans and
rodents.
These RPFs were developed from single chemical bioassays. The RPF
approach does not account for pharmacokinetic interactions (e.g.,
competition for metabolizing enzymes or inhibition of elimination
mechanisms). These interactions may significantly influence tissue
dosimetry of the individual chemicals when the exposure occurs to the
mixture. As a result, the assessment of relative potency and risk may not
be consistent between model predictions and observations of toxicity
when rodents are exposed to the whole mixture.
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5. FEASIBILITY OF CUMULATIVE RISK ASSESSMENT
FOR COMPLEX DBP MIXTURES
Exposure modeling techniques and risk assessment methods are available to
formulate CRA estimates for specified groups of DBFs. This analysis illustrates that
multiple route exposure estimates can be developed that account for human activity
patterns affecting contact time with identified DBFs in tap water by developing internal
dose estimates for selected DBFs. Although important data gaps still exist (e.g.,
chemical properties of some DBFs such as bromate, MOA data for appropriately
assigning DBFs into subclasses), additional data on these chemicals continue to be
developed by many researchers. Application of this approach may provide a more
scientific basis for evaluating risks posed by different mixtures of DBFs than
comparisons developed based on concentrations of individual DBFs and single route
risk analyses. With sufficient data, applications of this approach should provide a more
useful comparison to epidemiologic studies than analyses based on concentrations of
individual DBFs and single routes of exposure. Cumulative risk estimates developed
using these approaches can be compared across different types of treatments of the
same source water or across geographic areas. These estimates of risk should be
compared on a relative basis, rather than an absolute basis. For example, a Hazard
Index or other component based mixtures risk assessment approach may be applied
(see U.S. EPA, 2000b) using cumulative dose estimates. For more difficult problems,
such as predicting actual risks from exposure to chlorinated drinking water (e.g.,
number of cases of cancer for a population served by a particular system), additional
research will be required before credible CRAs can be implemented. To improve upon
the current effort, the following information still needs to be developed:
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1) A careful treatment is needed to determine MOA for the major DBFs of
concern for health risk assessment. At a minimum, MOA should be
determined for cancer, developmental effects and reproductive effects.
2) Dose response models need to be developed for the major DBFs of
concern for all relevant endpoints. Although some initial work has been
done in the 1990's (U.S. EPA, 2000a), this research should be updated to
include the current literature base. In addition, issues to be carefully
considered in the development of new dose response models include
consideration of vehicle effects, non-linear responses at low doses,
different MOA at low and high doses, background response rates, and
litter effects.
3) The exposure and PBPK model predictions used in this analysis need to
be further evaluated against independent data sets.
4) Improved quantitative skin permeability rates need to be developed. A
large range of uncertainty exists in the dermal estimates that make it
difficult to compare the dermal route to the inhalation and ingestion routes.
Similarly, much uncertainty associated with inhalation exposures could be
reduced through better estimation of volatilization.
5) A factor that limited the exposure modeling results to 13 of the 15
chemicals was lack of data on chemical properties, e.g., Henry's law
constant, Kow, boiling point, vapor pressure, liquid and gas phase
diffusivities (see Section 3 for a chemical-specific detailed list). This is a
important data gap, particularly because bromate was not included in the
exposure modeling estimates. (Bromate, a suspected carcinogen, is of
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concern for high bromide source waters where ozonation is the primary
disinfectant for the treatment system.)
6) Some physiological parameters are still needed for improved PBPK
modeling. The sensitivity analysis (based on CHCI3 and DCA) indicated
that certain parameters could produce relatively large changes in the
exposure estimates. These included alveolar ventilation rates, blood flow
in the kidney, volume in the liver, liver metabolism Vmax, volume in the
body, the partition coefficient for testes/blood, and stomach to portal blood
rate.
7) Future exposure modeling efforts should ensure that a complete
uncertainty analysis be conducted and that the sensitivity analyses include
all modeled chemicals and demographic groups in the study.
8) Research needs to be conducted to determine whether populations
sensitive to particular DBFs or DBP classes exist. Sensitivity may arise
through different activity patterns among people (e.g., long vs. short
shower durations), toxicokinetic differences among individuals, and
toxicodynamic differences between individuals.
9) Approximately 50% of DBFs in the finished drinking water consists of
unidentified material. EPA has conducted research to identify these DBPs
(Richardson, 1998), to estimate the potential toxicity of these chemicals
(Moudgal et al., 2000; Woo et al., 2002), and to estimate the additional
health risk from exposure to this unknown fraction of DBPs (Teuschler et
al., 2001; U.S. EPA, 2000a). Research needs to be conducted to
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enhance the CRPF approach to account for the potential toxicity of the
unknown fraction.
While comprehensive lists of needed research are useful, they generally provide
little insight as to which of the research needs are of the highest priority. The current
understanding of the risks that DBFs pose through multiple exposure routes would be
improved ultimately through the successful conduct of any research listed here. To
determine which areas of research would be most useful in refining risk estimates,
quantitative human health risk estimates for DBFs need to be developed, including
detailed analyses of uncertainly and variability. The research needs could be evaluated
based on the expected improvement in the confidence in estimated DBP risks. This
evaluation could serve as a ranking approach for DBP research needs.
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Online, http://www.epa.gov/iris
U.S. EPA. 2002d. Exposure Related Dose Estimating Model (ERDEM) for Assessing
Human Exposure and Dose. Office of Research and Development, Washington, DC.
EPA/600/R-00/011.
Weinberg, H. 1999. Disinfection byproducts in drinking water: The analytic challenge.
Anal. Chem. News Features. 4(12): 801A-808A.
Weisel, C. and W. Chen. 1994. Exposure to chlorination by-products from hot water
uses. Risk Anal. 14(1):101-106.
Wilkes, C. 1998. Case Study. In: Exposure to Contaminants in Drinking Water:
Estimating Uptake through the Skin and by Inhalation, S. Olin, Ed. CRC Press,
Washington, DC. p. 183-224.
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Wilkes, C.R. and N.J. Giardino. 1999. A Community Comparison of Exposures and
Risks from TCE in a Contaminated Groundwater Supply vs. DBFs in a Municipal Water
Supply; Part I: Risk Assessment. Proc. The Eighth International Conference on Indoor
Air Quality and Climate. Edinburgh, Scotland. Vol. 2, p. 800-817.
Wilkes, C.R., M.J. Small, J.B. Andelman, N.J. Giardino and J. Marshall. 1992.
Inhalation exposure model for volatile chemicals from indoor uses of water. Atmos.
Environ. 26A(12):2227-2236.
Wilkes, C.R., M.J. Small, C.I. Davidson and J.B. Andelman. 1996. Modeling the effects
of water usage and co-behavior on inhalation exposures to contaminants volatilized
from household water. J. Exp. Anal. Environ. Epidemiol. 6(4):393-412.
Woo, Y., D. Lai, J.L. McClain, M.K. Manibusan and V. Dellarco. 2002. Use of
mechanism-based structure-activity relationships analysis in carcinogenic potential
ranking for drinking water disinfection by-products. Environ. Health Perspect.
10(1): 75-87.
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Developing Individual Human Exposure Estimates for Individual DBFs
Developing Exposure Estimates
Final Report
September 2002
Prepared by: Charles R. Wilkes1, Andrea D. Mason, Laura L. Niang, and Harry E. Rector
Wilkes Technologies, Inc.
10126 Parkwood Terrace
Bethesda, MD20814
Fred W. Power1, Andy M. Tsang, Peter M. Stephan, and Lyn S. Harrison
Anteon Corporation
4220 Maryland Parkway
Las Vegas, NV 89119
Jerry N. Blancato and Stephen C. Hern
Human Exposure Research Branch
U.S. Environmental Protection Agency
Las Vegas, Nevada 89193
Prepared for: National Center for Environmental Assessment
U.S. Environmental Protection Agency
26 W. Martin Luther King Drive
Cincinnati, OH 45268
EPA Project Officer: John Lipscomb
GS A Contract No. GS-1OF-0154K
1 Primary authors
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Notice
This is the final report of a three-task effort to model exposures to disinfection byproducts for a typical three-
person US family. The research presented herein was compiled under the following tasks: Task 1, Identifying
an Appropriate Mathematical Exposure Model and Developing Model Parameters; Task 2, Developing
Individual Human Exposure Estimates; and Task 3, Report on Sensitivity and Uncertainty Analysis.
The study conducted and described in this report is meant to demonstrate route specific exposure and uptake of
15 relatively common disinfection byproducts. For many of the chemicals evaluated in this report, there are
significant gaps in the understanding of the specific chemical parameters impacting exposure and uptake, such
as the overall mass transfer coefficients, skin permeability rates and partition coefficients. In some cases the
validity of these parameter estimates are not well understood. This document presents a combination of
approaches based on best available data and methods, primarily from peer-reviewed publications. Any new data
or advances in methods should be considered when using the results of this analysis.
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Table of Contents
1.0 Introduction 1
1.1 Project Objectives 2
2.0 Model Selection 5
2.1 Exposure and Dose Model 5
2.2 Pharmacokinetic Model for Estimation of Relevant Dose 7
2.2.1 Physiologically Based Pharmacokinetic Modeling 8
2.2.2 The Exposure Related Dose Estimating Model (ERDEM) 8
2.2.3 The ERDEM Front End 9
2.2.4 Exposure Time History Input 9
3.0 Model Parameters 11
3.1 Volatilization Model Parameters 11
3.1.1 Method for Estimating Overall Mass Transfer Coefficient 13
3.1.2 Literature Review of Chemical Properties 14
3.1.3 Estimating Chemical Properties 17
3.2 Behavioral Characteristics 24
3.2.1 Activity Patterns 24
3.2.2 Water Use Behaviors for Groups of Interest 26
3.3 Ingestion Characteristics 36
3.3.1 Available Data Sources 36
3.3.2 Ingestion Behavior for the Three Populations: Results of Analysis 38
3.4 Building Characteristics 41
3.4.1 Representation of Household Volumes 41
3.4.2 Representation of Whole House Air Exchange Rates and Interzonal Airflows 44
3.4.3 Model Representation of Building 46
3.5 Concentrations in Water Supply 46
3.5.1 DBFs (Excluding Bromate) 48
3.5.2 Bromate 49
3.5.3 Water Concentrations Selected as Model Inputs 50
3.5.4 Estimated Concentrations in Consumed Tap Water 51
3.6 Physiological Parameters 56
3.6.1 Compartment Volumes by Demographic Group 56
3.6.2 Breathing Rates by Activity and Demographic Group 57
3.6.3 Compartment Blood Flows by Activity and Demographic Group 58
3.6.4 Definition of the Exposure Scenarios for Each Exposure Route 58
3.6.5 Skin Permeability Coefficients for Each Chemical 59
3.6.6 Rate Constants for the Gastro-Intestinal Model for Each Chemical 59
3.6.7 Partition Coefficients for Each Chemical 60
3.6.8 Metabolism Pathways and Rate Constants 61
3.6.9 Elimination Parameters 62
3.7 Uptake Calculations 62
4.0 Modeling Results 65
4.1 Model Execution 65
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4.2 Exposure and Uptake Modeling Results 65
4.2.1 Analysis of Results of Water Use Behavior 68
4.2.2 Uptake Modeling Results 68
4.2.3 Analysis of the Impact of the Uncertainty in the Dermal Parameters 135
4.2.4 Discussion of Uptake Modeling Results 139
4.3 Pharmacokinetic Modeling Results 143
4.3.1 Meaning of Exposure Time Histories 143
4.3.2 Choosing Dose Metrics 144
4.3.3 Variability of PBPK Model Results Due to Variability of Exposure Time Histories 144
5.0 Sensitivity Analysis 152
5.1 Overview 152
5.2 Methods 153
5.2.1 Sensitivity Analysis Framework 153
5.2.2 Model Parameters 154
5.2.3 Dose Metrics in Sensitivity Analysis 156
5.3 Results 757
5.3.1 Results of Sensitivity Analysis Using TEM 157
5.3.2 Results of Sensitivity Analysis Using ERDEM 157
5.4 Discussion 757
5.4.1 Discussion on Sensitivity Analysis Using TEM 158
5.4.2 Discussion on Sensitivity Analysis Using EDREM 159
5.4.3 Other Model Sensitivity Issues 159
5.4.4 Conclusions 160
6.0 Quality Assurance 196
7.0 References: 200
Appendix A: Figures Presenting Results of Pharmacokinetic Modeling A-l
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List of Tables
Table 1. List of Chemicals for Exposure Assessment 3
Table 2. Physical Properties of Chemicals of Interest 15
Table 3. Data Gaps for Chemical Properties 17
Table 4. Estimated Values for Liquid Phase Diffusivity, Gas Phase Diffusivity, and Dimensionless
Henry's Law Constant 19
Table 5. Predictor Chemicals for DBFs used to Estimate Mass Transfer Coefficients 21
Table 6: Estimated Values for Overall Mass Transfer Coefficient (KOLA) 23
Table 7. Shower Frequency Values from NHAPS and REUWS Analyses 27
Table 8. Summary Statistics for Shower Duration, Volume and Flowrate from REUWS Analyses 27
Table 9. Selected Model Parameters for Showers 27
Table 10. Bath Frequency and Duration Values from NHAPS Analyses 28
Table 11. Bath Volume and Flowrate Values from REUWS Analyses 28
Table 12. Selected Model Parameters for Bathing 28
Table 13. Frequency of Clothes Washer Use for 3-Person Households: RECS 29
Table 14. Typical Clothes Washer Parameters: Based on REUWS and Experimental Data 30
Table 15. Selected Model Parameters for Clothes Washer Use 31
Table 16. Frequency of Dishwasher Use for 3-person Households: RECS, 1997 31
Table 17. Manufacturer Supplied Dishwasher Information Summary 32
Table 18. Selected Model Parameters for Dishwasher Use 33
Table 19. Summary of Reported Toilet Use Characteristics from Literature 33
Table 20. Statistics for Toilet Flushes from REUWS 34
Table 21. Selected Parameters for Toilet Use 34
Table 22. Summary of Reported Faucet Frequency and Volume of Use Characteristics in Literature 35
Table 23. Summary Statistics for Faucet Use from REUWS 35
Table 24. Selected Parameters for Faucet Use 36
Table 25. Tapwater consumption characteristics 37
Table 26. Parameters of Fitted Lognormal Distribution for Water Ingestion in the United States 39
Table 27. Comparison of Consumption for Raw Data and Fitted Distributions based on CSFII Data 40
Table 28. Selected Parameters for Tapwater Consumption Modeling Study 40
Table 29. Analysis of RECS for Total House Volume for 3-Person U.S. Households (RECS 1997) 42
Table 30. Dimensions of Water-Use Zones 44
Table 31. Summary Statistics for US Residential Air Exchange Rates 45
Table 32. Summary of DBP Concentrations Reported by Miltneretal. (1990) 48
Table 33. Mean and 95th Percentile Concentrations for Identified DBFs 49
Table 34. Estimated Bromate Formation in Ohio River Water by Ozonation, from Three Studies 50
Table 35. List of Selected Concentrations for Chemicals in Modeling Study 50
Table 36. Chemical Properties of Compounds Studied by Howard and Corsi (24° C) 52
Table 37. Chemical properties of Compounds Being Modeled (24° C) 52
Table 3 8. Summary of Experimental Stripping Efficiencies for Cyclohexane, Toluene , and Acetone 52
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Table 39. Estimated Rate Constants from Batterman et. al 53
Table 40. Estimated Fractional Volatilization as a Function of Time for THMs for Cold, Room
Temperature, and Hot Water 53
Table 41. THM Consumption Scenarios 55
Table 42. Recommended Consumption Model Inputs for the THMs, DCA, and TCA 56
Table 43. Volumes of Compartments by Percentage for PBPK Modeling with ERDEM 57
Table 44. Alveolar Ventilation Rates by Demographic Group and Activity 58
Table 45. Blood Flows to Compartments by Percentage for PBPK Modeling with ERDEM 58
Table 46. Skin Permeability Coefficients 59
Table 47. Gastro-Intestinal Permeation Rate Constants 60
Table 48. Partition Coefficients Required for Fundamental Uptake Modeling in TEM 60
Table 49. Partition Coefficients Used by ERDEM 61
Table 50. Metabolism Rate Constants 62
Table 51. Elimination Rate Constants 62
Table 52. Summary of Chemical Specific Model Parameters 66
Table 53. Summary of Water-Use Behavioral Model Inputs 67
Table 54. Summary of Building Related Model Inputs 68
Table 55. Chloroform Absorbed Dose Results 71
Table 56. BDCM Absorbed Dose Results 76
Table 57. DBCM Absorbed Dose Results 81
Table 58. Bromoform Absorbed Dose Results 86
Table 59. MCA Absorbed Dose Results 91
Table 60. DCA Absorbed Dose Results 96
Table 61. TCA Absorbed Dose Results 101
Table 62. MBA Absorbed Dose Results 106
Table 63. DBA Absorbed Dose Results Ill
Table 64. BCA Absorbed Dose Results 116
Table 65. DCAN Absorbed Dose Results 121
Table 66. TCAN Absorbed Dose Results 126
Table 67. DBAN Absorbed Dose Results 131
Table 68. Summary of Absorbed Dose by Route for the 50th Percentile of the Population 140
Table 69. Summary of Absorbed Dose by Route for the 95th Percentile of the Population 141
Table 70. Analysis of PBPK Model Results for Bromodichloromethane for the Adult Male, Adult
Female, and Male Child 145
Table 71. Statistics for Chloroform Simulations for the Adult Male, Adult Female, and Male Child 146
Table 72. Statistics for Dichloroacetic Acid Simulations for the Adult Male, Adult Female, and Male
Child 147
Table 73. Statistics for Trichloroacetic Acid Simulations for the Adult Male, Adult Female, and Male
Child 148
Table 74. Analysis of ERDEM Model Simulations for Four Chemicals and Three Demographic Groups 149
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Table 75. Largest 90th Percentile Concentrations for Chloroform and Bromodichloromethane for Three
Demographic Groups 150
Table 76. Base-Case Activity and Water Use Patterns for Adult Male (Ages 15 - 45) Used in Sensitivity
Analysis 162
Table 77. Base-Case Activity and Water Use Patterns for Adult Female (Ages 15 - 45) Used in
Sensitivity Analysis 163
Table 78. Base-Case Activity and Water Use Patterns for the Child (Age 6) Used in Sensitivity Analysis.... 164
Table 79. Summary of Base-Case Water Uses Used in Sensitivity Analysis 165
Table 80 Base-Case Consumption Activity Patterns Used in Sensitivity Analysis 166
Table 81. Master List of Water Use Variables in TEM for Sensitivity Analysis 167
Table 82. Master List of Environmental and Chemical Parameters in TEM for Sensitivity Analysis 168
Table 83. List of Gender Specific Physiological Parameters in ERDEM for Sensitivity Analysis 169
Table 84. List of General Physiological Parameters in ERDEM for Sensitivity Analysis 170
Table 88. Relative Sensitivity Analysis of Potential and Absorbed Dose for Water Use Parameters for
Chloroform 180
Table 88. Relative Sensitivity Analysis of Potential and Absorbed Dose for Water Use Parameters for
Chloroform (Continued) 181
Table 89. Six Dose Metric Outputs with All Parameters at Baseline Values 181
Table 90. Chloroform- Relative Sensitivity of Absorbed Dose at 24 hrs 182
Table 91. Chloroform - Relative Sensitivity of Amount Metabolized in the Liver at 24 hrs 182
Table 92. Chloroform - Relative Sensitivity of Area Under the Curve for Liver at 24 hrs 183
Table 93. Chloroform - Relative Sensitivity of Area Under the Curve for Testes at 24 hrs 184
Table 94. Chloroform - Relative Sensitivity of Peak Concentration in Liver at 7.35 hrs 185
Table 95. Chloroform - Relative Sensitivity of Peak Concentration in Testes at 7.35 hrs 186
Table 96. DCA - Relative Sensitivity of Amount Eliminated in the Liver at 24 hrs 187
Table 97. DCA - Relative Sensitivity of Area Under the Curve for Liver at 24 hrs 188
Table 98. DCA - Relative Sensitivity of Area Under the Curve for Testes at 24 hrs 189
Table 99. DCA - Relative Sensitivity of Peak Concentration in Liver at 8.75 hrs 190
Table 100. DCA - Relative Sensitivity of Peak Concentration in Testes at 19.8 hrs 191
Table 101. Average Relative Sensitivity Analysis of Absorbed Total Dose for Water Use, Environmental
and Chemical Parameters for Chloroform and DCA, Ranked by Absolute Value 192
Table 102. Summary of the Most Sensitive Model Parameters for Each Dose Metric 193
Table 103. Chloroform - Sensitive Input Model Parameters 194
Table 104. DCA - Sensitive Input Model Parameters 195
Table 105. Categories of Data Sources and Models 196
Table 106. Quality and Sources of Data Used in the Models 197
Table 107. Categories of Model Approaches and Algorithms 199
Table 108. Quality of Modeling Approaches and Algorithms 199
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List of Figures
Figure 1. Estimation of Population Exposures to Compounds Originating in the Water Supply
Figure 2. ERDEM System Flow Chart-With Static Lung/Stomach/Intestine Inputs 10
Figure 3. Cumulative Distribution Function of Volume for 3-Person Households 42
Figure 4. Comparison of RECS Data and the Fitted Probability Density Function of Volume for 3-
Person Households 43
Figure 5. Schematic Representation of House Interzonal Air Flows 47
Figure 6. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
Chloroform Dose for Females, Males and Children 72
Figure 7. Histogram for Absorbed Dermal Chloroform Dose for Females, Males and Children 73
Figure 8. Histogram for Absorbed Inhalation Chloroform Dose for Females, Males and Children 73
Figure 9. Histograms for the Absorbed Chloroform Ingestion Dose for Females, Males and Children 74
Figure 10. Histogram for the Total Absorbed Chloroform Dose for Females, Males and Children 75
Figure 11. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
BDCM Dose for Females, Males and Children 77
Figure 12. Histogram for Absorbed Dermal BDCM Dose for Females, Males and Children 78
Figure 13. Histogram for Absorbed Inhalation BDCM Dose for Females, Males and Children 78
Figure 14. Histograms for the Absorbed BDCM Ingestion Dose for Females, Males and Children 79
Figure 15. Histogram for the Total Absorbed BDCM Dose for Females, Males and Children 80
Figure 16. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
DBCM Dose for Females, Males and Children 82
Figure 17. Histogram for Absorbed Dermal DBCM Dose for Females, Males and Children 83
Figure 18. Histogram for Absorbed Inhalation DBCM Dose for Females, Males and Children 83
Figure 19. Histograms for the Absorbed DBCM Ingestion Dose for Females, Males and Children 84
Figure 20. Histogram for the Total Absorbed DBCM Dose for Females, Males and Children 85
Figure 21. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
Bromoform Dose for Females, Males and Children 87
Figure 22. Histogram for Absorbed Dermal Bromoform Dose for Females, Males and Children 88
Figure 23. Histogram for Absorbed Inhalation Bromoform Dose for Females, Males and Children 88
Figure 24. Histograms for the Absorbed Bromoform Ingestion Dose for Females, Males and Children 89
Figure 25. Histogram for the Total Absorbed Bromoform Dose for Females, Males and Children 90
Figure 26. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
MCA Dose for Females, Males and Children 92
Figure 27. Histogram for Absorbed Dermal MCA Dose for Females, Males and Children 93
Figure 28. Histogram for Absorbed Inhalation MCA Dose for Females, Males and Children 93
Figure 29. Histograms for the Absorbed MCA Ingestion Dose for Females, Males and Children 94
Figure 30. Histogram for the Total Absorbed MCA Dose for Females, Males and Children 95
Figure 31. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
DCA Dose for Females, Males and Children 97
Figure 32. Histogram for Absorbed Dermal DCA Dose for Females, Males and Children 98
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Figure 33. Histogram for Absorbed Inhalation DCA Dose for Females, Males and Children 98
Figure 34. Histograms for the Absorbed DCA Ingestion Dose for Females, Males and Children 99
Figure 35. Histogram for the Total Absorbed DCA Dose for Females, Males and Children 100
Figure 36. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
TCA Dose for Females, Males and Children 102
Figure 37. Histogram for Absorbed Dermal TCA Dose for Females, Males and Children 103
Figure 38. Histogram for Absorbed Inhalation TCA Dose for Females, Males and Children 103
Figure 39. Histograms for the Absorbed TCA Ingestion Dose for Females, Males and Children 104
Figure 40. Histogram for the Total Absorbed TCA Dose for Females, Males and Children 105
Figure 41. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
MBA Dose for Females, Males and Children 107
Figure 42. Histogram for Absorbed Dermal MBA Dose for Females, Males and Children 108
Figure 43. Histogram for Absorbed Inhalation MBA Dose for Females, Males and Children 108
Figure 44. Histograms for the Absorbed MBA Ingestion Dose for Females, Males and Children 109
Figure 45. Histogram for the Total Absorbed MBA Dose for Females, Males and Children 110
Figure 46. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
DBA Dose for Females, Males and Children 112
Figure 47. Histogram for Absorbed Dermal DBA Dose for Females, Males and Children 113
Figure 48. Histogram for Absorbed Inhalation DBA Dose for Females, Males and Children 113
Figure 49. Histograms for the Absorbed DBA Ingestion Dose for Females, Males and Children 114
Figure 50. Histogram for the Total Absorbed DBA Dose for Females, Males and Children 115
Figure 51. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
BCA Dose for Females, Males and Children 117
Figure 52. Histogram for Absorbed Dermal BCA Dose for Females, Males and Children 118
Figure 53. Histogram for Absorbed Inhalation BCA Dose for Females, Males and Children 118
Figure 54. Histograms for the Absorbed BCA Ingestion Dose for Females, Males and Children 119
Figure 55. Histogram for the Total Absorbed BCA Dose for Females, Males and Children 120
Figure 56. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
DCAN Dose for Females, Males and Children 122
Figure 57. Histogram for Absorbed Dermal DCAN Dose for Females, Males and Children 123
Figure 5 8. Histogram for Absorbed Inhalation DCAN Dose for Females, Males and Children 123
Figure 59. Histograms for the Absorbed DCAN Ingestion Dose for Females, Males and Children 124
Figure 60. Histogram for the Total Absorbed DCAN Dose for Females, Males and Children 125
Figure 61. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
TCAN Dose for Females, Males and Children 127
Figure 62. Histogram for Absorbed Dermal TCAN Dose for Females, Males and Children 128
Figure 63. Histogram for Absorbed Inhalation TCAN Dose for Females, Males and Children 128
Figure 64. Histograms for the Absorbed TCAN Ingestion Dose for Females, Males and Children 129
Figure 65. Histogram for the Total Absorbed TCAN Dose for Females, Males and Children 130
Figure 66. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and Total Absorbed
DBAN Dose for Females, Males and Children 132
Figure 67. Histogram for Absorbed Dermal DBAN Dose for Females, Males and Children 133
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Figure 68. Histogram for Absorbed Inhalation DBAN Dose for Females, Males and Children 133
Figure 69. Histograms for the Absorbed DBAN Ingestion Dose for Females, Males and Children 134
Figure 70. Histogram for the Total Absorbed DBAN Dose for Females, Males and Children 135
Figure 71. Comparison of Estimated Dermal Absorbed Chloroform Dose Across the Range of
Uncertainty in the Permeability Coefficient 136
Figure 72. Comparison of Estimated Dermal Absorbed DCA Dose Across the Range of Uncertainty in
the Permeability Coefficient 137
Figure 73. Comparison of Estimated Dermal Absorbed TCA Dose Across the Range of Uncertainty in the
Permeability Coefficient 138
Figure 74. Schematic Representation of House Interzonal Air Flows for the Base-Case Sensitivity
Analysis 161
Figure 75. Predicted Chloroform Air Concentrations for the Base-case Scenario 171
Figure 76. Predicted DCA Air Concentrations for the Base-case Scenario 172
Figures 77a and b. Dose Metric Curves for Chloroform: Absorbed Dose and Amount Metabolized in
the Liver 173
Figures 77c and d. Dose Metric Curves for Chloroform: AUC in the Liver and AUC in the Testes 174
Figures 77e and f. Dose Metric Curves for Chloroform: Concentration in the Liver and Concentration
in the Testes 175
Figures 78a and b. Dose Metric Curves for DCA: Absorbed Dose and Amount Eliminated in the Liver.... 176
Figures 78c and d. Dose Metric Curves for DCA: AUC in the Liver and AUC in the Testes 177
Figures 78e and f. Dose Metric Curves for DCA: Concentration in the Liver and Concentration in the
Testes 178
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1.0 Introduction
Disinfection of drinking water is widely recognized for its significant role in reducing illness caused by
waterborne pathogens which are responsible for numerous diseases. Although disinfection is necessary
for the elimination of these pathogenic organisms, it can also lead to the generation of a variety of
chemicals, known as disinfection byproducts (DBFs), which are formed as a result of reactions of the
disinfectant with organic matter in the water. In the U.S., where the primary form of disinfection is
chlorination, public drinking water contains low levels of many DBFs and is a potential source of
exposure to these compounds. The potential for exposure is significant by ingestion, but has also been
shown to be significant through inhalation and through contact with the skin. The importance of each
route varies with chemical characteristics, use patterns, physiological characteristics, and a variety of
other factors (Wilkes et al., 1996; Olin, 1999). For example, exposure to a volatile chemical, such as
chloroform, occurs most significantly during large household water uses, such as showering, bathing, and
clothes washing activities. Although all three primary routes can be significant, typically inhalation
dominates the exposure for these volatile compounds. For the less volatile compounds, ingestion and
dermal contact play more significant roles in exposure and uptake.
In the early 1970s, advances in gas chromatography and mass spectrometry led to improvements in the
detection of various DBFs in drinking water. In 1974, Rook (1974) and Bellar et al. (1974) showed that
trihalomethanes (THMs) result from the chlorination process. Subsequently, a significant amount of
research identified THM formation pathways as complicated reactions involving aqueous halogen species
and natural aquatic humic substances, particularly humic and fulvic acids (Glaze et al., 1979; Peters et al.
1980; Urano et al., 1983). In addition, more recent research has identified the formation of haloacetic
acids (HAAs), haloacetonitriles (HANs), and a variety of other DBFs and verified their existence in water
supplies (Krasner et al., 1989, Westrick et al., 1984, Miller et al., 1990, Richardson, 1998).
Based on data collected under the information collection rule, U.S. EPA reported that mean
concentrations of dichloroacetic acid in the distribution system ranged from 0.4 to 36 (ig/L, and mean
concentrations of trichloroacetic acid ranged from 0.2 to 28(ig/L (U.S. EPA, 2001). In areas where
naturally-occurring bromine ion is present in surface water, significant amounts of bromo- and
chlorobromo acetic acids can form (Ireland et al., 1988). In addition to HAA, several haloacetonitriles
(HAN) (dichloroacetonitrile, trichloroacetonitrile, dibromoacetonitrile, bromochloroacetonitrile) can form
in chlorinated drinking water. In addition to THM, HAA, and HAN, two haloketones (1,1-
dichloropropanone and 1,1,1-trichloropropanone), chloropicrin, and trichloroacetaldehyde monohydrate
(chloral hydrate) have all received some attention as potential DBFs. Alternative forms of disinfection can
also produce DBFs. For example, ozonation has been shown to lead to the formation of aldehydes and
ketones (Miltner et al., 1992). A study involving the ozonation of humic substances revealed the
formation of mutagenic compounds, primarly glyoxal and glyoxylic acids (Matsuda et al., 1992).
In 1979, the U.S. EPA issued the National Interim Primary Drinking Water Regulations, which
established a maximum contaminant level (MCL) of 100 (ig/L for total trihalomethanes (TTHM) in
drinking water. In 1986, Congress passed amendments to the Safe Drinking Water Act (SDWA), an
action that required the U.S. EPA to establish regulations for a wide range of drinking water
contaminants. In 1988, the U.S. EPA published the Drinking Water Priority List (DWPL), and revised the
list in 1991. The DWPL includes THMs, as well as several of the other DBFs described above. In 1998,
U. S. EPA issued National Primary Drinking Water Regulations: Disinfectants and Disinfection
Byproducts; Final Rule, which lowered the MCL for TTHMs to 80 (ig/L. In addition, maximum
contaminant level goals (MCLGs) were set for each of the four THMs, with the MCLG for chloroform,
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report, Revision 2
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bromodichloromethane, and bromoform set at zero, and the MCLG for dibromochloromethane set at 60
MCLs were also set for other disinfection by-products. For the haloacetic acids (HAAs), an MCL 60
(ig/L was set for the sum of five HAAs (monochloroacetic acid, dichloroacetic acid, trichloroacetic acid,
monobromoacetic acid, and dibromoacetic acid; referred to as HAAS). MCLGs were set for two HAAs,
dichloroacetic acid at zero, and trichloroacetic acid at 300 (ig/L. For bromate, the MCL was set at 10
(ig/L and the MCLG was set at zero.
Exposure to DBFs originating in the drinking water is a very complex problem, influenced by a multitude
of factors, including chemical properties of the contaminant, physical characteristics of the indoor
environment, behavior of the individual relative to the contaminant, and behavioral and physiological
characteristics of the exposed population. Previous modeling studies have demonstrated the considerable
impact human behavior has on an individual's exposure to waterborne contaminants (Wilkes et al., 1996;
Wilkes, 1999), demonstrating that differences in behavior can produce exposures varying across more
than an order of magnitude. Mathematical exposure and uptake models represent a realistic, cost-effective
means for estimating human exposure. Mathematical models within a probabilistic framework allow a
close examination of the factors that lead to exposures and provide a basis for addressing higher risk
populations. However, in the case of exposure to waterborne contaminants, previous modeling studies
(Wilkes et al., 1996; Wilkes, 1999) have shown that a strictly probabilistic framework would fail to
capture the effect of an individual's activities on his or her exposure. The ideal model would therefore
combine a probabilistic representation of human behavior related to water use and exposure with a
deterministic calculation of the concentrations in the contact media leading to the exposure (i.e. in the
water and air). Such modeling frameworks also offer the ability to evaluate the impacts of parameter
uncertainty and variability, such that results may be incorporated into meaningful and useful sets of
outcomes.
1.1 Project Objectives
The goal of this project is to implement a comprehensive exposure model to estimate population-based
exposures and doses to various DBFs. This project is limited to considering the factors and processes
affecting exposure and uptake to waterborne contaminants from the point where the contaminants enter
the considered household at a specific water appliance through the uptake by the exposed individual. As
such, this project does not consider the nature of the raw water supply, the treatment processes, the
transport of the water to the household, or any of the chemical and physical processes that occur during
the treatment and transport of the water supply. In addition, this project does not consider factors that
occur in the household prior to use of the water, such as chemical reactions that occur in the hot water
heater.
The DBFs of concern in this project are listed in Table 1. The populations of concern in this project are
the following: (a) women of reproductive age (ages 15-45); (b) men of similar age (ages 15-45); and (c)
children (age 6). To begin the process of estimating the exposure of these populations to the given DBFs,
we chose the Total Exposure Model (TEM) as our modeling tool and identified, collected, and
summarized all the model parameters necessary to set up the modeling study. This report presents and
discusses these various model parameters needed for running TEM, specifically those related to chemical
volatilization, human activity patterns, ingestion, building characteristics, and chemical concentration in
the water supply. Furthermore, to assess the population doses associated with the resultant exposures, the
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report, Revision 2
July 2002, Page 2
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PBPK model ERDEM will be adjoined with TEM. This report also presents and discusses the model
parameters necessary for ERDEM.
Table 1. List of Chemicals for Exposure Assessment
DBF Subclass
Trihalomethane s
(THMs)
Haloacetic Acids
(HAAs)
Haloacetonitrile s
(HANs)
Miscellaneous
Chemical Name
Chloroform
Bromodichloromethane (BDCM)
Dibromochloromethane (DBCM)
Bromoform
Chloroacetic acid (CAA)
Dichloroacetic acid (DCA)
Trichloroacetic acid (TCA)
Bromoacetic acid (MBA)
Dibromoacetic acid (DBA)
Bromochloroacetic acid (BCA)
Dichloroacetonitrile (DCAN)
Trichloroacetonitrile (TCAN)
Bromochloroacetonitrile (BCAN)
Dibromoacetonitrile (DBAN)
Bromate
CAS Number
67-66-3
75-27-4
124-48-1
75-25-2
79-11-8
79-43-6
76-03-9
79-08-3
631-64-1
5589-96-8
3018-12-0
545-06-2
83463-62-1
3252-43-5
15541.45.4
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2.0 Model Selection
The exposure and dose model chosen for this study is the Total Exposure Model (TEM), developed by
Wilkes Technologies. The PBPK model chosen for this study is the Exposure Related Dose Estimating
Model (ERDEM, formerly DEEM) developed by Anteon Corporation in collaboration with the Human
Exposure Research Branch of the National Environmental Research Laboratory of the U.S. EPA in Las
Vegas, Nevada.
2.1 Exposure and Dose Model
TEM is an indoor-air-quality human exposure model that combines probabilistic and deterministic
principles in a single framework. This framework combines probabilistic sampling of parameters that
have natural variability, such as water use behavior and other behavior affecting exposure, with
deterministic representation of the physical and chemical processes, resulting in a prediction of the air and
water concentrations at the interface with the exposed individuals. The deterministic framework uses the
activities generated by the probabilistic algorithms to predict the release of contaminants, the fate and
transport of the contaminants within the building, and finally the resulting exposures. In the case of
volatilization of DBFs during water use, the deterministic framework incorporates realistic models for
predicting the transfer from the liquid phase to the gas phase during household water uses. Additionally,
route specific uptake models are used to estimate the transfer of the chemical to the exposed individual.
TEM was chosen because it provides the following capabilities:
1. Sources and chemicals: TEM will deterministically represent the emission of DBFs during household
water uses. The emission models are based on fundamental theory (i.e., two-film volatilization
theory) and include source-specific representation (i.e., the model has explicit representations of the
various water appliances and fixtures, such as the clothes washer, toilet, and shower). The models
shall account for both the emissions into the air as well as the resulting concentrations in the water for
"pool-type" water uses, such as bathtubs. The model is capable of addressing chemicals with a wide
range of volatilities.
2. Building, transport and removal: The model will deterministically represent transport and removal of
chemical contaminants resulting from the use of household water. The transport component will
represent multiple zones, such that each room in a house with a water-using appliance or fixture can
be individually represented. This capability is vital, since research has demonstrated the importance
of behavior and location relative to the water-use for volatile compounds (Wilkes et al., 1996).
3. Human activities and water uses: The model will sample activity patterns from the human activity
pattern databases, such as the National Human Activity Pattern Survey (NHAPS). The model will
simulate water uses appropriate to the sampled individual, and the selected activity pattern, based on
analysis of actual water use behavior and deterministically incorporate the emissions resulting from
these uses to predict the resulting air and water concentrations.
4. Exposure: The model will merge the probabilistic behavior with the deterministic predictions of
contaminant concentrations in contact with the exposed individual to estimate the exposure.
TEM is a PC based model written in C++ by Dr. Charles Wilkes, utilizing a combination of probabilistic
and deterministic techniques, and has been applied in a number of exposure assessment research projects.
The original model, entitled Model for the Analysis of Volatiles and Residential Indoor air Quality
(MAVRIQ), was developed as part of a research project at Carnegie Mellon University (Wilkes, 1994),
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report, Revision 2
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where the showering emission and finite difference algorithms were validated against analytical solutions
and field data. The results of the calculations and the analytical solution were virtually identical. For the
field validation, the air flows in the model were set using tracer gas data, and the resulting model
predictions for airborne TCE concentrations were compared to the field measured data. The predictions
of the model compared very favorably to the field measurements as shown in the following figure
(Wilkes, 1994).
CONCENTRATION (mg/m3)
2.5
— Bathroom Predictions (MERAff)
+ Bathroom Data
Shower Predictions (MIRAN)
• Shower Daa
TJME (minutes)
Comparison of Experimental Data and Predictions by MAVRIQ for the Vanport TCE Data. (Reproduced
from Wilkes PhD Thesis, Wilkes, 1994).
The TEM model has been further developed under a contract with the US Air Force and the US
Environmental Protection Agency. This model can be executed on a personal computer (PC) under the
Microsoft Windows 95 or later operating system, and is capable of simulating a wide variety of exposure
scenarios. The model performs finite-difference calculations to predict air emissions and air
concentrations for each location in the modeled household. The model parameters can be either pre-
defined or sampled from distributions characterizing the parameters. Physical parameters, such as room
volumes, house configuration, number of family members, and water flow rates will be sampled from
databases or characteristic distributions.
The source model will involve applications of fundamental mass transfer kinetics and two-phase mass
balances to estimate the volatilization of DBFs during various residential water uses. The rate of
volatilization from water to adjacent air is typically modeled based on the two-film volatilization theory
(Lewis and Whitman, 1924). See Equation 1 (Section 3.1).
The human behavior model allows the use of two approaches for modeling human activities. The first
approach allows the activity pattern for the individual to be pre-defined, allowing a complete description
of a particular case of interest. When using this approach, all activities and water uses will be explicitly
described for the time period of interest using a computer-generated, graphical input screen that facilitates
parameter input through the use of drop-down menus and other tools. The IAQ model then makes use of
this information to calculate concentration versus time profiles. It then combines concentration
predictions with the location behavior to estimate inhalation exposure.
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ACTIVITY
PATTERN
DATABASE
ID START_TIMELOC_CODE ACT_CODE
^ 11"^" "0"3?S" V"." 5" W-ffi
DISTRIBUTION OF
WATER USES
MAP TO
MODEL
LOCATIONS
SIMULATE
WATER USES
TEM
MODEL INDOOR AIR
CONCENTRATIONS AND
CALCULATE EXPOSURE
MANY REPETITIONS
HOUSE,
VENTILATION,
SOURCE
CHARACTERIZATION
DBF concentration in watei
POPULATION DISTRIBUTION OF
EXPOSURES/BODY BURDEN
Figure 1. Estimation of Population
Exposures to Compounds
Originating in the Water
Supply
The second approach functions stochastically and
deterministically, and develops a distribution of likely
exposures for a population group. This is
accomplished by sampling model parameters and
executing the model for many repetitions. This
Monte-Carlo technique includes sampling of activity
databases such as the National Human Activity
Patterns Survey (NHAPS) and the California Air
Resources Board database of human activity patterns
(California Air Resources Board, 1991). The Monte-
Carlo technique also includes known or estimated
distributions for other behavioral parameters such as
water-use characteristics, as illustrated in Figure 1.
The model simulates water uses appropriate to the
sampled activity pattern based on the characteristics
of the population group. The model will estimate the
distribution of exposures to a population by
repeatedly sampling from the specified databases and
parameter distributions, executing the model, and
estimating the resultant exposure. The model will
also be capable of evaluating the co-exposure effects
(interaction of multiple individuals) to evaluate the
impact of an individual's behavior on other family
members.
TEM has been applied to several modeling studies examining the exposure and dose to waterborne
contaminants as a result of household water use. Wilkes et al. (1992) examined a typical exposure for a
three person family to trichloroethylene (TCE) from normal water uses. An analysis of behavioral factors
leading to inhalation exposure quantified the importance of time spent in the bathroom and in showering
and bathing activities (Wilkes et al., 1996). A study comparing the exposure to DBFs to that of TCE as a
result of constructing a municipal treatment facility analyzed whether the remediation lowered the
carcinogenic risk to the community (Wilkes and Giardino, 1999; Giardino and Wilkes, 1999). As part of
an International Life Sciences Institute (ILSI/RSI) Working group entitled "Working Group on
Estimation of Dermal and Inhalation Exposures to Contaminants in Drinking Water", a modeling study
demonstrating the application of TEM to produce population-based estimates of exposure and uptake to 3
contaminants (chloroform, methyl parathion, and chromium) was conducted and is presented as a case
study (Wilkes, 1999). Many of these same strategies will be utilized in this project as discussed below.
2.2 Pharmacokinetic Model for Estimation of Relevant Dose
The physiologically based pharmacokinetic (PBPK) model ERDEM (Exposure Related Dose Estimating
Model) was chosen to model the determination of a relevant dose to certain organs of the human body.
See Figure 2. This model, formerly known as DEEM (Dose Estimating Exposure Model), has been in
development for many years by Jerry Blancato of the U.S. EPA, and by Jerry Elig and Fred Power of
Anteon Corporation. Results have been reported at five meetings of the Society of Toxicology and at the
year 2000 International Society of Exposure Analysis meeting . It uses the time proven ACSL (Advanced
Continuous Simulation Language) for which the health application rights were acquired by AEgis
Technology Group.
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2.2.1 Physiologically Based Pharmacokinetic Modeling
The physiologically based PBPK model consists of a group of compartments representing different parts
of the body. These are tied together with blood flow, membranes, chemical interactions, and exposure
routes into the body. The models may be flow limited or diffusion limited. The volumes and blood flows
are required for each compartment or sub-section for a compartment. The breathing rates, the gastro-
intestinal absorption rates, and the skin permeation coefficients, in part, determine the absorbed dose of
chemical into the body. Partition coefficients for tissue to blood, tissue to air, and blood to air, determine
how much of the chemical remains and how much passes to the next state. Metabolic constants
determine the amount of chemical that is converted to metabolites. The greatest difficulty is determining
values for the various parameters needed for a species and chemical. Just having values for volumes and
blood flows for a set of compartments or sub-compartments is not enough. Each type of chemical that is
modeled may require the use of a different set of compartments. Some compartments may be combined,
or others may be broken up into multiple sub compartments. The chemically dependent parameters are
determined from many sources, or are estimated using various techniques, such as QSARs (Quantitative
Structure-Activity Relationships). The choices are made based on the state of the science for the
chemicals, their metabolism pathways, and the type of chemical.
A PBPK model can be used to extrapolate from low dose to high dose, to compare exposures for one
exposure route to another. The exposure scenarios can be varied from a single exposure to multiple
exposures and can even take exposure time history input. Some models handle multiple exposures to
chemicals and their metabolites.
This application of PBPK modeling, for DBFs in the water, is to determine the dose metric variability
arising from the variation of the exposure due to the different activities of subjects in their indoor
environment.
2.2.2 The Exposure Related Dose Estimating Model (ERDEM)
The Exposure related Dose Estimating Model, a PBPK model, is designed to model the exposure of a
species to multiple chemicals, determine the dose of the exposure chemicals and their metabolites to each
compartment or sub-compartment of the chemical species. ERDEM models up to eight different exposure
inputs. Multiple chemicals may be included in each exposure scenario and up to nine different scenarios
may be defined. Time histories may be input for inhalation, dermal and rate ingestion input. The parent
exposure chemicals may have multiple metabolites and these metabolites may have metabolites, etc. All
metabolites and parent chemicals may circulate. ERDEM consists of the compartments: Arterial Blood,
Brain, Carcass, Derma, Fat, Intestine, Kidney, Liver, Ovaries, Rapidly Perfused Tissue, Slowly Perfused
Tissue, Spleen, Static Lung, Stomach, Testes, and Venous Blood. The Static Lung models breathing
using a partition coefficient blood-air exchange. See Figure 2 for the ERDEM system flow chart.
The Breathing Lung utilizes the following compartments: Alveoli, Lower Dead Space, Lung Tissue,
Pulmonary Capillaries, and Upper Dead Space. The full gastro-intestinal model consists of the Wall and
Lumen for the Stomach, Duodenum, Lower Small Intestine, and Colon with Lymph Pool and Portal
Blood compartments included. Bile flow is treated as an output from the Liver to the Duodenum Lumen.
Chylomicron flow is modeled between the Lymph Pool and selected compartments.
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Each of the compartments Brain, Carcass, Fat, Kidney, Liver, Lung Tissue, Ovaries, Rapidly and Slowly
Perfused Tissues, Spleen, Static Lung, and Testes have two forms of elimination, an equilibrium binding
process, and multiple metabolites. The Gastro-Intestinal Walls of the Stomach, Duodenum, Lower Small
Intestine, and Colon have metabolism but no added elimination or binding. The Arterial Blood,
Pulmonary Capillaries, Portal Blood, and Venous Blood have binding.
2.2.3 The ERDEM Front End
The inputs to such a PBPK model are very complicated and many of the necessary inputs can be easily
missed. A graphical-user-interface (GUI) front end has been written (currently as Beta 3.3) to aid the user
in data preparation and file management. Each set of data for a model is called a Model Data Set (MDS).
Each type of input is specified in a window. After inputting all of the necessary parameter values the user
runs an export, which converts the data to the command file format required by the ACSL (Advanced
Continuous Simulation Language) model engine. One of the main menu items is entitled "Model" where
details are entered to set up of the model, including choosing the subsystem models, specifying
compartment volumes, and specifying the scaling and the reference body volume. An Activity menu is
provided where the user specifies one or more activities - differentiated by changing Cardiac output for
each activity. The Alveolar Ventilation Rate is specified for each activity, and the blood flow to each
compartment is input for each activity. The chemical menu is used for input of the exposure chemicals,
and their metabolites. The metabolism pathways are defined for each chemical. The Chemical
Compartments menu provides for input of chemically specific information for each compartment active
for a specific chemical. This includes the partition coefficients, elimination and binding constants, and
Stomach/Intestine absorption rates. The metabolism constants are input for each metabolism defined in
the Chemical menu for each compartment that has been specified as having metabolism. The Exposure
menu provides the options for choosing the exposure routes, the chemicals active for which exposure
routes, the exposure scenarios, and the exposure concentration for each exposure route, chemical and
scenario specified.
2.2.4 Exposure Time History Input
ERDEM has the ability to handle exposure time histories by using the Table function in ACSL. The
exposure time histories generated by the TEM model have been formatted as ASCII files for input to
special ERDEM subroutines for preprocessing. When a user generates time histories they need to follow
a particular format including limitations on the number of time steps in an exposure period and the
minimum step between data points. For special cases subroutines can be written to convert the time
histories to a format suitable for input to ERDEM. The Dermal, Open Chamber Inhalation, and Rate
Ingestion Oral exposures have been implemented. Currently, the user can define input time histories for
up to five different exposure chemicals.
These time histories can be generated for multiple variations of the TEM exposure model inputs to
provide a measure of the uncertainty in model results due to exposure variation. Then sensitivity and
Monte Carlo analyses can be performed on the input PBPK model parameters using the mean exposures
from TEM and thus estimate the uncertainties in the relevant dose due to the PBPK input model
parameters.
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Inputs
ST
Stomach
Ks^T KIN.H.TT
IN
Intestine
Metabolites
•->carcass Metabolites
4.79
DR Dermal
14.29
>,
Bolus Dose
Injections
Infusions
>L
•Percent of Total Blood
Flow
"Percent of Body Volume
VB Venous 5.19
QAl
1
1
f W p
Y
Intestinal
Elimination
There are N chemicals
modeled in each of the
compartments: Liver, Kidney,
Fat, Brain, Slowly Perfused,
Perfused Tissue and Spleen.
The Static Lung, and Lung are
modeled with elimination and
metabolism.
There are up to N chemicals,
which include the original
chemicals as well as their
metabolites. There is binding
in the Arterial Blood and
Venus Blood.
Open Chamber
Exhalation
)
j Open Chamber j
1 Inhalation I
•->
CC Closed
Chamber Inhalation
Lung
Metabolites
Figure 2. ERDEM System Flow Chart - With Static Lung/Stomach/Intestine Inputs
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3.0 Model Parameters
For each identified parameter, the values have either been collected from published literature or
estimated. An attempt has been made to identify parameter values from multiple sources to assist in the
execution of the sensitivity and uncertainty analysis. The collected values are evaluated and a judgment
made to select the most appropriate value(s) for use in the model execution.
The TEM model input parameters include the following:
• Parameters needed for implementation of volatilization model
• Human behavior characteristics that drive the activity model, including location and water use
behaviors
• Ingestion characteristics
• Building characteristics
• Chemical concentrations in water supply
The ERDEM model input parameters include the following:
• Compartment volumes by demographic group
• Compartment blood flows by activity for each demographic group
• Definition of the exposure scenarios for each exposure route for each chemical
• The compartment-to-blood partition coefficients for each chemical
• The skin permeation coefficients for each chemical
• The rate constants for the gastro-intestinal model to be used for each chemical
• The lung-to-blood and blood-to-air partition coefficients for the lung model for each chemical
• The metabolism pathways for each parent chemical
• The metabolism rate constants, or the V-Max and the Michaelis Menten constants for each
metabolism to be modeled
• The elimination rate constants for the urine, feces, and any other required compartments, by
chemical
• The binding input parameters for those chemicals as needed
3.1 Volatilization Model Parameters
Each of the water-using appliances or fixtures, when operated, represents an opportunity for emission of
waterborne chemicals. The emission behavior during a given water use is a function of a variety of
chemical and physical factors, including water temperature, surface area, concentration, chemical
diffusivities, and Henry's Law constant.
To facilitate prediction of water and air concentrations, the emission behavior is idealized using two types
of models: the plug flow model (PFM) and the completely mixed flow model (CMFM). The derivations
of these models are presented elsewhere (Little and Chiu, 1999).
The plug flow model is derived assuming a constant uniform flow and a volume and surface area that
remains essentially constant. The PFM is appropriate for use in representing emissions during continuous
flowing water uses such as faucets and showers. Emissions for sources idealized as plug flow are
represented by the following equation:
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QL
(1)
(2)
(3)
KOLA KLA HKGA
where: S = source emission rate (mass/time)
Kv = volatilization coefficient (volume/time)
C{ = contaminant concentration in the water supply prior to volatilization
(mass/volume)
Cg = concentration in the air surrounding the water stream (mass/volume)
H = dimensionless Henry's Law constant
QL = volumetric flow rate of the water (volume/time)
KQL = overall mass transfer coefficient (L/time)
A = interface area between water and air (L2)
KL = liquid phase mass transfer coefficient (L/time)
KG = gas phase mass transfer coefficient (L/time)
The rate of volatilization is maximized if Cg/H is negligible relative to Q. Conversely, if Cg/H approaches
Q, a state of chemical equilibrium may be achieved with a corresponding suppression of volatilization.
This equilibrium condition may occur for sources that include a headspace with poor air exchange (e.g.,
dishwashers) or that involve chemicals with low Henry's law constants. The concentration of a
contaminant in the liquid phase may be effectively spatially uniform (e.g., in well-mixed systems such as
washing machines), or may vary with space (e.g., the flowing water film or droplets associated with
showers). The interfacial area, A, is typically difficult, if not impossible, to determine for residential water
uses. This is particularly true when significant amounts of splashing occur (e.g., in kitchen wash basin),
disintegrated films or droplets occur (e.g., showers and dishwashers), and/or when entrained air bubbles
are present (e.g., during the filling of bathtubs). Thus, interfacial area and overall mass transfer
coefficients are typically combined (KoiA).
The completely mixed flow model assumes a well-mixed volume of water with a constant surface area,
and is appropriate for use in representing emissions from standing water-type water uses. An example of
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a CMFM type source is a filled bathtub. Emissions for sources idealized as CMFM are represented by the
following equation:
f (C "
S=KOTA\ C,-\ -*-
OL ,
(5)
The volatilization coefficient represents the rate of transfer across the liquid/gas interface where the water
is in contact with the air, while Henry's Law constant is used to quantify the concentration gradient
relative to equilibrium.
3.1.1 Method for Estimating Overall Mass Transfer Coefficient
The volatilization coefficient, a function of the overall mass-transfer coefficient (K0i), is primarily a
function of the water temperature, surface area, and the chemical's diffusion coefficients in water and air.
Using a power relationship between liquid-phase and gas-phase diffusivities and the liquid-phase and gas-
phase mass transfer coefficients (KL ^DLP and KG ^DGq), Little (1992) derived the following equation
for predicting the overall mass-transfer coefficient for a desired chemical based on the measured
coefficient for a reference chemical:
DLr
1 D,
Or
(KOLA\ (KLA\ { DLl ) (KGA\
D
(6)
where: DL = Liquid-phase diffusivity (L2/T)
DG = Gas-phase diffusivity (L2/T)
/' = Chemical for which the overall mass-transfer coefficient is being estimated
r = Reference chemical
p, q = power constants
Using this relationship and the observations of previous researchers that the ratio of KG/KL is
approximately constant for a given mass-transfer system (Little, 1992, Corsi and Howard, 1998), Corsi
and Howard rearranged Equation 6 to obtain the following equation:
(KOLA\ (DLr
1 +
(7)
The above equation provides a means for estimation of KoiA for a chemical based on measurements for
another chemical based on the diffusivities, Henry's Law constants, and the ratio of KG/KL for the given
system. Corsi and Howard (1998) conducted a series of laboratory experiments to determine the values of
KOLA and KG/KL. The experiments were conducted for 5 reference chemicals (acetone, ethyl acetate,
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toluene, ethylbenzene, and cyclohexane) and for 5 water-use types (sinks, showers, bathtubs, wash
machines, and dishwashers) covering a significant range of Henry's law constants and diffusivities.
Using the measured values, Corsi and Howard present a method for estimating the product of the overall
mass transfer coefficient and the interfacial surface area (KOLA). Evaluation of liquid phase concentration
is complicated for acids since only fully-protonated molecules can volatilize from water. For haloacetic
acids, the pK values are significantly lower than the typical range of pH for drinking water, and therefore
it is unlikely that significant quantities of HAA are generally available for volatilization.
3.1.2 Literature Review of Chemical Properties
The chemicals of interest for this study are the Trihalomethanes (THMs), Haloacetic Acids (HAAs),
Haloacetonitriles (HANs), and Bromate, as listed in Table 1, above, and in Table 2, below. The properties
of interest are Henry's law constant, liquid phase diffusivity, gas phase diffusivity, octanol/water partition
coefficient, and molecular weight. Boiling point and volatility are additional properties of value for the
study.
3.1.2.1 Literature Search
The literature was searched to identify reliable values of the desired chemical properties. Values were
obtained from chemical handbooks and dictionaries or online data banks. The results of the search are
summarized in Table 2. References to the relevant journal articles have been provided where available.
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Table 2. Physical Properties of Chemicals of Interest
Chemical
TRIHALOMETHANES (THMs)
Chloroform
(CAS: 67-66-3) CHC13
Bromodichloromethane
(CAS: 75-27-4) CHBrCl2
Dibromochloromethane
(Chlorodibromomethane)
(CAS: 1 24-48- l)CHBr2Cl
Bromoform
(CAS: 75-25-2) CHBr3
Henry's
Dimensionless
H
0.150
0.150 (a)
0.151 (a)
0.163 (a)
0.0656
0.0866 (a)
0.065
0.095
0.085
0.102
0.037
0.034
0.056
0.0219
0.0219 (a)
0.0255 (a)
0.0240 (a)
Law
G
a.
!
H
25
24
25
25
25
25
25
25
25
25
25
25
25
25
25
25
HALOACETIC ACIDS (HAAs)
Chloroacetic Acid (MCA)
(CAS: 79-11-8) C2H3C1O2
Dichloroacetic Acid (DCA)
(CAS: 79-43-6) C2H2C1202
Trichloroacetic Acid (TCA)
(CAS: 76-03-9) C2HC13O2
Bromoacetic Acid (MBA)
(CAS: 79-08-3) C2H3BrO2
Dibromoacetic Acid (DBA)
(Dibromoethanoic Acid)
(CAS: 631-64-1) C2H2Br2O2
Bromochloroacetic Acid (BCA)
(CAS: 5589-96-8) C2H2BrClO2
2.66 x 10-6(a)
3.71 x 10'7 (a)
3.41 x 10'7(a)
5.52x 10'7(a)
2.72 xlO'7 (a)
1.78 x 10-7(a)
25
25
25
25
25
25
Constant
AH
RT
o
( K)
4500
4700
4700
5200
9700
8000
8700
9300
8900
Reference
i
2
5a
5b
1
2
5d
5d
5d
5e
5d
5d
5e
1
2
5a
5b
1
5c
5c
5c
5c
5c
Diffusivit?
Water
Dw
(cm2/s)
1.0 x 10'5
1.06 x 10'5
1.05 x 10'5
1.03 x 10'5
. .
1.21 x 1Q-5
yin
G
1
H
Reference
i
i
i
i
i
Diffush
Ai
DA
(cm2/s)
0.1040
0.0298
0.0196
0.0149
^^1
0.0733
dty ii
r
G
a.
!
H
25
Reference
i
i
i
i
Octanol/H2(
Partition Cc
LogK,,w
1.96
1.97
1.88
2.00
2.09
2.16
2.24
2.30
2.40
)
.ef.
1
1
1
i
2
1
2
1
2
5
1
2
Molecular
Weight
119.38
163.83
208.28
252.73
Boiling
Point
Tb
(°Q
61.17
90
90
120
119-120
149.1
150-151
Reference
3
3
3
4
3
4
Vapor Pre
Pvp
(mmHg)
160
(v.den. 4.12)
50
76
5.6
(v.den. 8.7)
ssur
G
1
H
20
20
20
25
Reference
4
6g
4
4
i
0.22
0.92
0.92
1.33
1.33
0.41
0.70
0.61
2
1
2
1
2
6a
67b
6b
94.50
128.95
163.39
138.95
217.84
173.39
189.3
189
194
194
196.5
196
208
195 (@250
mmHg)
130 (@16
mmHg)
215
3
4
3
4
3
4
3
3
3
1
0.1787
1
0.06
0.1185
43
25
51
25
25
4
6c
4
6d
6d
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 15
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Table 2. Physical Properties of Chemicals of Interest
Chemical
Henry's
Dimensionless
H
Law
G
a.
!
H
Constant
AH
RT
o
( K)
Reference
Diffusivit?
Water
Dw
(cm2/s)
yin
G
1
H
Reference
Diffush
Ai
DA
(cm2/s)
dty ii
r
G
a.
!
H
Reference
Octanol/H2<
Partition Cc
LogK,,w
)
.ef.
1
1
1
Molecular
Weight
Boiling
Point
Tb
(°Q
Reference
Vapor Pre
Pvp
(mmHg)
ssur
Q
1
H
Reference
HALOACETONITRILES (HANs)
Dichloroacetonitrile (DCAN)
(CAS: 3018-12-0) C2HC12N
Trichloroacetonitrile (TCAN)
(CAS: 545-06-2) C2C13N
Bromochloroacetonitrile
(BCAN)
(CAS: 83463-62-1) C2HBrClN
Dibromoacetonitrile (DBAN)
(CAS: 3252-43-5) C2HBr2N
1.55 x W4
(a,b)
5.48 x 10'5
(a,b)
1.66 x 10'5
(a,b)
25
25
25
6b
6b
6b
0.29
2.09
0.42
0.47
6b
6a
4
6b
109.94
144.39
154.39
198.84
MISCELLANEOUS
Bromate
(CAS: 15541-45-4) BrO3
0.63
7
79.90
112.5
110-112
85.7
83-84
169
169-170
3
4
3
4
3
4
2.82
58
74.12
0.301
25
20
25
25
6e
5
6f
6e
NOTES:
(a) Henry's law constant is reported in the literature with concentration and partial pressure units. The value reported in the table was converted to dimensionless H.
(b) Estimated
REFERENCES:
1. Risk Assessment Information System, Oak Ridge National Laboratory. Accessed 22 January 2001. .
2. Hazardous Substances Data Bank (HSDB), at National Library of Medicine. Accessed 18 January 2001. .
3. CRC Handbook of Chemistry and Physics. Online. Copyright 2000, CRC Press. At the following URL, click Chemical References/Handbook of Chemistry and Physics. Accessed 19 January 2001.
.
4. Gangolli, Sharat, ed. The Dictionary of Substances and Their Effects, 2nd ed. 7volumes. Cambridge, U.K.: Royal Society of Chemistry, 1999. (Ref. for partition coefficients: J. Sangster, J. Phys Chem RefData
1989, 18(3): 1111-1229)
5. Sander, Rolf. Compilation of Henry's Law Constants for Inorganic and Organic Species of Potential Importance in Environmental Chemistry. At the following URL, click Henry's Law Constants/Downloading
Instructions. Accessed 24 January 2001. .
5a Mackay, D., and W.Y. Shiu. A critical review of Henry's law constants for chemicals of environmental interest. J. Phys. Chem. Ref. Data 10: 1175-1199 (1981).
5b Staudinger, J., and P.V. Roberts. A critical review of Henry's law constants for environmental applications. Crit. Rev. Environ. Sci. Technol. 26: 205-297 (1996).
5c Bowden, D.J., S.L. Clegg, and P. Brimblecombe. The Henry's law constants of the haloacetic acids. J. Atmos. Chem. 29 (1): 85-107 (1998).
5d Nicholson, B. C., B. P. Maguire, and D. B. Bursill, Henry's law constants for the trihalomethanes: Effects of water composition and temerature, Environ. Sci. Technol., 18, 518-521, 1984.
5e Moore, R. M., C. E. Geen, and V. K. Tail, Determination of Henry's law constants for a suite of naturally occuring halogenated methanes in seawater, Chemosphere, 30, 1183-1191, 1995.
6. Syracuse Research Corporation. Physical properties electronic database. Accessed 29 January 2001. http://esc.syrres.com/interkow/physdemo.htm. The full reference citations are NOT available here.
6a Ref. Hansch, C. et al. (1995); 6b Meylan, WM & Howard, PH (1995); 6c Ref. Daubert, RE & Danner, RP (1989); 6d Ref. Perry, RH & Green, D (1984); 6e Ref. Neely, WB & Blau, GE
(1985); 6f Ref. Boublik, T. et al. (1984); 6g Ref. Callahan, MA et al. (1997a)
7. Chandrika J. Moudgal, National Center for Environmental Assess, USEPA, 26 W. Martin Luther King Dr., ML 117, Cincinnati, OH 45268. Phone: (513) 569-7078; e-mail: moudgal.chandrika@epa.gov
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 16
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3.1.2.2 Missing information
The literature search identified many of the needed chemical properties, however the following
properties, displayed in Table 3, are unavailable:
Table 3. Data Gaps for Chemical Properties
Chemical
Bromochloroacetic Acid (BCA)
Dichloroacetic Acid (DCA)
Trichloroacetic Acid (TCA)
Bromoacetic Acid (MBA)
Dibromoacetic Acid (DBA)
Bromochloroacetonitrile (BCAN)
Bromodichloromethane
Dichloroacetonitrile (DCAN)
Trichloroacetonitrile (TCAN)
Dibromoacetonitrile (DBAN)
Bromate
Data Gaps for Chemical Properties
Henry's law constant, vapor pressure, liquid
and gas phase diffusivities
Liquid and gas phase diffusivities
Liquid and gas phase diffusivities
Liquid and gas phase diffusivities
Vapor pressure, liquid and gas phase
difrusivities
Henry's law constant, Kow, boiling point,
vapor pressure, liquid and gas phase
difrusivities
Henry's law constant for the desired
temperatures
Liquid and gas phase diffusivities
Liquid and gas phase diffusivities
Liquid and gas phase diffusivities
Henry's law constant, Kow, boiling point,
vapor pressure, liquid and gas phase
difrusivities
3.1.3 Estimating Chemical Properties
Prediction methods are used to supplement the literature review for chemical properties that were
not found in the literature. Values for the liquid and gas phase diffusivitiy, the dimensionless
Henry's Law Constant, and the overall mass transfer coefficient are predicted and discussed in
the following subsections.
3.1.3.1 Estimating Liquid and Gas Phase Diffusivity and Henry's Law Constant
The liquid phase diffusivity is predicted using the Hayduk and Laudie method (Lyman et al.,
1990, pp 17-20). This method is reasonably accurate for a wide range of compounds and has
been validated using compiled measured data. The method uses the molal volume as predicted by
the LaBas method and the viscosity of water to predict the liquid phase diffusivity as a function
of temperature. Similarly, the gas phase diffusivity is predicted using the Wilke and Lee method
(Lyman et al., 1990). This method was found to have an absolute average error of 4.3% when
compared to measured values for approximately 150 compounds. This method uses the molecular
weight, boiling point, the molal volume, and properties of air to predict the chemical's diffusivity
in air. The estimated values for liquid and gas phase diffusivities are given in Table 4.
Henry's Law Constant can be found in current literature for most chemicals, but often not at the
temperature of interest. Therefore, a method to adjust H to the designated temperature is
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 17
-------
necessary. The following equation is used to adjust Henry's law constant for temperature
dependence.
(8)
R T
Where:
H = Henry's law constant at desired temperature
He = Henry's law constant at standard conditions
AH = enthalpy of solution
R = gas constant = 0.082057 L-attn
°Kmol
T = temperature (°K)
9 = denotes standard condition (298.1 5 °K)
The values for Henry's law constant adjusted for temperature are presented in Table 4.
3.1.3.2. Estimating Overall Mass Transfer Coefficients
Modeling emissions of disinfection byproducts during water usage requires knowledge of the
overall mass transfer coefficient (KoiA) as a function of the appliance, the water temperature, the
water flowrate, and the chemical. The KoiA for each of the 15 DBFs and each type of water use
are estimated applying the methods discussed in Section 3.1.1. This estimation method requires
using measured data as a means for estimating parameters for the case of interest. Although the
uncertainties of estimates arrived at by methods described in Section 3.1.1 and Equation 7 have
not be robustly quantified, it is clear that this method is greatly influenced factors such as the
chemical behavior and the physical conditions of the water use. For this reason, the measured
data upon which the estimates are based should similar to the conditions being represented.
In selecting the predictor chemicals, an effort was made to select to measured data gathered under
similar conditions as those being modeled. The measured data are taken from the set of
chemicals studied by Corsi and Howard (1998). Corsi and Howard conducted laboratory
experiments and estimated the overall mass transfer coefficients for common household water
appliances for the following five chemicals: Acetone, Ethylacetate, Toluene, Ethylbenzene, and
Cyclohexane. Using Equation 7, these chemicals are used as predictor chemicals for the
chemicals modeled in this study. Since the mass transfer behavior of a given chemical is related
to its liquid and gas diffusivities and Henry's Law Constant, the predictor chemical was chosen
such that these values were most similar to the desired chemical. The predictor chemicals used in
this estimation for each of the 15 DBFs are presented in Table 5. For each water use, the
measured data from the most similar set of physical condition were used. In some cases, the
desired condition was outside of the range encompassed by the measured data. For example,
The estimated values of the KoiA for each of the 15 DBFs, derived from this predictor process,
are given in Table 6. The values presented in Table 6 are estimated assuming a water temperature
and hydrodynamic conditions similar to those under which the experiments were conducted (e.g.
dropsize distribution, water flowrate, air turbulence, etc.). Temperature is another important
factor, affecting mass transfer and uptake kinetics. There is a great deal of uncertainty in the
understanding of temperature and temperature effects, and this is an area where future research is
warranted.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 18
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Table 4. Estimated Values for Liquid Phase Diffusivity, Gas Phase Diffusivity, and Dimensionless Henry's Law Constant
Temp
°C
6
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Cl
D,/(lE-6)
8.200
8.443
8.699
8.951
9.206
9.465
9.726
9.992
10.260
10.532
10.807
11.085
11.368
11.653
11.942
12.233
12.527
12.825
13.126
13.431
13.739
14.050
14.362
14.680
15.001
15.324
15.649
15.976
16.310
16.644
16.981
17.322
17.668
18.012
18.362
Uorofon
D,
0.0894
0.09
0.0906
0.0912
0.0917
0.0923
0.0929
0.0935
0.0941
0.0947
0.0953
0.0959
0.0965
0.0971
0.0977
0.0983
0.0989
0.0995
0.1002
0.1008
0.1014
0.102
0.1026
0.1032
0.1039
0.1045
0.1051
0.1057
0.1063
0.107
0.1076
0.1082
0.1089
0.1095
0.1101
m
Hb
0.1086
0.1105
0.1123
0.1142
0.1161
0.1236
0.131
0.1384
0.1459
0.1533
0.1617
0.1701
0.1785
0.1869
0.1953
0.2037
0.2122
0.2207
0.2291
0.2376
0.2475
0.2575
0.2674
0.2773
0.2872
0.2981
0.3093
0.3209
0.3328
0.3451
0.3577
0.3707
0.3841
0.3979
0.4121
D,/(lE-6)
8.077
8.316
8.5685
8.8162
9.0674
9.3226
9.5802
9.8418
10.106
10.374
10.645
10.919
11.197
11.478
11.763
12.05
12.339
12.633
12.929
13.229
13.533
13.839
14.147
14.46
14.776
15.094
15.415
15.736
16.065
16.394
16.727
17.062
17.403
17.742
18.086
BDCM
D,
0.0849
0.0854
0.086
0.0866
0.0871
0.0877
0.0882
0.0888
0.0894
0.09
0.0905
0.0911
0.0917
0.0922
0.0928
0.0934
0.094
0.0946
0.0951
0.0957
0.0963
0.0969
0.0975
0.0981
0.0987
0.0993
0.0999
0.1005
0.1011
0.1017
0.1023
0.1029
0.1035
0.1041
0.1047
H
0.0586
0.0618
0.0651
0.0686
0.0722
0.076
0.0799
0.0841
0.0884
0.0929
0.0976
0.1025
0.1076
0.1129
0.1185
0.1243
0.1303
0.1366
0.1431
0.1499
0.157
0.1643
0.172
0.1799
0.1882
0.1968
0.2057
0.2149
0.2245
0.2345
0.2449
0.2556
0.2667
0.2782
0.2902
D,/(lE-6)
7.9588
8.1943
8.4431
8.6872
8.9347
9.1862
9.44
9.6978
9.9579
10.222
10.489
10.759
11.034
11.31
11.59
11.873
12.159
12.448
12.74
13.035
13.335
13.637
13.94
14.248
14.559
14.873
15.189
15.506
15.83
16.154
16.482
16.812
17.148
17.482
17.822
DBCM
D,
0.0814
0.082
0.0825
0.0831
0.0836
0.0841
0.0847
0.0852
0.0858
0.0863
0.0869
0.0874
0.088
0.0885
0.0891
0.0897
0.0902
0.0908
0.0913
0.0919
0.0925
0.093
0.0936
0.0942
0.0947
0.0953
0.0959
0.0965
0.097
0.0976
0.0982
0.0988
0.0994
0.0999
0.1005
H
0.0245
0.0259
0.0274
0.0289
0.0305
0.0322
0.0339
0.0358
0.0377
0.0397
0.0418
0.044
0.0463
0.0487
0.0512
0.0538
0.0565
0.0594
0.0624
0.0654
0.0687
0.072
0.0756
0.0792
0.083
0.087
0.0911
0.0954
0.0999
0.1045
0.1093
0.1143
0.1195
0.1249
0.1306
Bi
D,/(lE-6)
7.8452
8.0773
8.3226
8.5632
8.8072
9.055
9.3052
9.5593
9.8157
10.076
10.339
10.606
10.876
11.149
11.425
11.704
11.985
12.27
12.558
12.849
13.145
13.442
13.741
14.045
14.352
14.661
14.972
15.285
15.604
15.924
16.247
16.572
16.903
17.233
17.567
•omofori
D,
0.0787
0.0792
0.0797
0.0802
0.0808
0.0813
0.0818
0.0823
0.0829
0.0834
0.0839
0.0845
0.085
0.0856
0.0861
0.0866
0.0872
0.0877
0.0883
0.0888
0.0894
0.0899
0.0905
0.091
0.0916
0.0921
0.0927
0.0932
0.0938
0.0944
0.0949
0.0955
0.0961
0.0966
0.0972
11
H
0.0134
0.0142
0.0151
0.016
0.017
0.018
0.0191
0.0203
0.0214
0.0227
0.024
0.0254
0.0269
0.0284
0.03
0.0317
0.0335
0.0353
0.0373
0.0393
0.0415
0.0437
0.0461
0.0485
0.0511
0.0538
0.0566
0.0595
0.0625
0.0657
0.0691
0.0726
0.0762
0.08
0.084
D,/(lE-6)
8.3612
8.6086
8.87
9.1264
9.3865
9.6506
9.9173
10.188
10.461
10.739
11.019
11.303
11.591
11.882
12.176
12.474
12.774
13.077
13.384
13.694
14.009
14.326
14.644
14.968
15.296
15.625
15.957
16.29
16.63
16.971
17.315
17.662
18.015
18.366
18.723
MCA
D,
0.0869
0.0874
0.088
0.0886
0.0892
0.0898
0.0904
0.091
0.0915
0.0921
0.0927
0.0933
0.0939
0.0945
0.0951
0.0957
0.0963
0.0969
0.0975
0.0981
0.0987
0.0994
0.1
0.1006
0.1012
0.1018
0.1024
0.1031
0.1037
0.1043
0.1049
0.1055
0.1062
0.1068
0.1074
H
1.4E-07
1.6E-07
1.7E-07
1.9E-07
2.2E-07
2.4E-07
2.7E-07
3E-07
3.3E-07
3.7E-07
4.1E-07
4.6E-07
5.1E-07
5.6E-07
6.2E-07
6.9E-07
7.7E-07
8.5E-07
9.4E-07
1E-06
1.1E-06
1.3E-06
1.4E-06
1.5E-06
1.7E-06
1.8E-06
2E-06
2.2E-06
2.5E-06
2.7E-06
3E-06
3.2E-06
3.5E-06
3.9E-06
4.2E-06
D,/(lE-6)
7.3871
7.6057
7.8366
8.0632
8.293
8.5263
8.7619
9.0012
9.2426
9.4879
9.7356
9.9863
10.241
10.498
10.758
11.021
11.285
11.554
11.825
12.099
12.377
12.657
12.938
13.225
13.514
13.805
14.098
14.392
14.693
14.994
15.298
15.604
15.916
16.226
16.541
DCA
D,
0.0773
0.0778
0.0784
0.0789
0.0794
0.0799
0.0804
0.081
0.0815
0.082
0.0825
0.0831
0.0836
0.0841
0.0847
0.0852
0.0857
0.0863
0.0868
0.0874
0.0879
0.0884
0.089
0.0895
0.0901
0.0906
0.0912
0.0917
0.0923
0.0928
0.0934
0.094
0.0945
0.0951
0.0956
H
1.5E-07
1.7E-07
1.8E-07
2E-07
2.2E-07
2.4E-07
2.6E-07
2.9E-07
3.1E-07
3.4E-07
3.7E-07
4E-07
4.4E-07
4.8E-07
5.2E-07
5.7E-07
6.2E-07
6.7E-07
7.3E-07
7.9E-07
8.5E-07
9.2E-07
1E-06
1.1E-06
1.2E-06
1.3E-06
1.4E-06
1.5E-06
1.6E-06
1.7E-06
1.9E-06
2E-06
2.2E-06
2.3E-06
2.5E-06
D,/(lE-6)
6.6689
6.8662
7.0747
7.2792
7.4866
7.6973
7.91
8.126
8.3439
8.5654
8.789
9.0153
9.2452
9.4771
9.7119
9.949
10.188
10.43
10.675
10.923
11.174
11.426
11.68
11.939
12.2
12.463
12.727
12.993
13.264
13.536
13.811
14.087
14.369
14.649
14.933
TCA
D,
0.0706
0.0711
0.0716
0.072
0.0725
0.073
0.0735
0.074
0.0744
0.0749
0.0754
0.0759
0.0764
0.0769
0.0773
0.0778
0.0783
0.0788
0.0793
0.0798
0.0803
0.0808
0.0813
0.0818
0.0823
0.0828
0.0833
0.0838
0.0843
0.0848
0.0853
0.0858
0.0863
0.0869
0.0874
H
2.3E-07
2.5E-07
2.8E-07
3.1E-07
3.4E-07
3.8E-07
4.1E-07
4.6E-07
5E-07
5.5E-07
6.1E-07
6.7E-07
7.3E-07
8E-07
8.8E-07
9.6E-07
1.1E-06
1.2E-06
1.3E-06
1.4E-06
1.5E-06
1.6E-06
1.8E-06
2E-06
2.1E-06
2.3E-06
2.5E-06
2.7E-06
3E-06
3.2E-06
3.5E-06
3.8E-06
4.1E-06
4.5E-06
4.9E-06
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 19
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Table 4 (continued). Estimated Values for Liquid Phase Diffusivity, Gas Phase Diffusivity, and Dimensionless Henry's Law Constant
Temp
°C
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
D,/(lE-6)
8.2316
8.4751
8.7325
8.985
9.241
9.5011
9.7636
10.03
10.299
10.573
10.849
11.128
11.412
11.698
11.988
12.28
12.576
12.875
13.177
13.482
13.792
14.104
14.417
14.736
15.059
15.383
15.71
16.038
16.373
16.708
17.047
17.388
17.736
18.081
18.432
MBA
Dz
0.0821
0.0827
0.0832
0.0838
0.0844
0.0849
0.0855
0.086
0.0866
0.0871
0.0877
0.0883
0.0888
0.0894
0.09
0.0905
0.0911
0.0917
0.0923
0.0928
0.0934
0.094
0.0946
0.0951
0.0957
0.0963
0.0969
0.0975
0.0981
0.0987
0.0993
0.0999
0.1004
0.101
0.1016
H
1.1E-07
1.2E-07
1.3E-07
1.5E-07
1.6E-07
1.8E-07
2E-07
2.2E-07
2.5E-07
2.7E-07
3E-07
3.3E-07
3.7E-07
4.1E-07
4.5E-07
4.9E-07
5.4E-07
6E-07
6.6E-07
7.3E-07
8E-07
8.8E-07
9.6E-07
1.1E-06
1.2E-06
1.3E-06
1.4E-06
1.5E-06
1.7E-06
1.8E-06
2E-06
2.2E-06
2.4E-06
2.6E-06
2.8E-06
D,/(lE-6)
7.2039
7.417
7.6423
7.8633
8.0873
8.3149
8.5446
8.7779
9.0134
9.2526
9.4942
9.7386
9.987
10.238
10.491
10.747
11.006
11.267
11.532
11.799
12.07
12.343
12.617
12.897
13.179
13.463
13.748
14.035
14.329
14.622
14.919
15.217
15.521
15.824
16.131
DBA
D,
0.0732
0.0737
0.0742
0.0747
0.0752
0.0757
0.0762
0.0767
0.0772
0.0777
0.0782
0.0787
0.0792
0.0797
0.0802
0.0807
0.0812
0.0817
0.0822
0.0827
0.0832
0.0837
0.0843
0.0848
0.0853
0.0858
0.0863
0.0869
0.0874
0.0879
0.0884
0.089
0.0895
0.09
0.0906
H
7.2E-08
8E-08
8.9E-08
9.8E-08
1.1E-07
1.2E-07
1.3E-07
1.5E-07
1.6E-07
1.8E-07
2E-07
2.2E-07
2.4E-07
2.6E-07
2.9E-07
3.1E-07
3.4E-07
3.8E-07
4.1E-07
4.5E-07
5E-07
5.4E-07
5.9E-07
6.5E-07
7.1E-07
7.7E-07
8.4E-07
9.2E-07
1E-06
1.1E-06
1.2E-06
1.3E-06
1.4E-06
1.5E-06
1.7E-06
D,/(lE-6)
7.294
7.5098
7.7378
7.9615
8.1884
8.4188
8.6514
8.8877
9.126
9.3683
9.6128
9.8604
10.112
10.365
10.622
10.882
11.143
11.408
11.676
11.947
12.221
12.498
12.775
13.058
13.343
13.631
13.92
14.211
14.508
14.805
15.105
15.408
15.715
16.022
16.333
BCA
D,
0.0742
0.0747
0.0752
0.0757
0.0762
0.0767
0.0772
0.0777
0.0782
0.0787
0.0792
0.0798
0.0803
0.0808
0.0813
0.0818
0.0823
0.0828
0.0834
0.0839
0.0844
0.0849
0.0855
0.086
0.0865
0.087
0.0876
0.0881
0.0886
0.0892
0.0897
0.0902
0.0908
0.0913
0.0919
Ha
1.31E-06
D,/(lE-6)
7.9109
8.145
8.3923
8.635
8.881
9.1309
9.3832
9.6394
9.8979
10.161
10.426
10.694
10.967
11.242
11.521
11.802
12.086
12.373
12.664
12.957
13.255
13.555
13.856
14.162
14.472
14.784
15.098
15.413
15.735
16.057
16.383
16.711
17.045
17.377
17.714
DCAN
Dz
0.0855
0.0861
0.0867
0.0872
0.0878
0.0884
0.0889
0.0895
0.0901
0.0907
0.0913
0.0918
0.0924
0.093
0.0936
0.0942
0.0947
0.0953
0.0959
0.0965
0.0971
0.0977
0.0983
0.0989
0.0995
0.1001
0.1007
0.1013
0.1019
0.1025
0.1031
0.1037
0.1043
0.1049
0.1056
Ha
1.55E-04
D,/(lE-6)
7.0603
7.2692
7.4899
7.7065
7.9261
8.1491
8.3743
8.6029
8.8337
9.0682
9.3049
9.5445
9.7879
10.033
10.282
10.533
10.786
11.043
11.302
11.564
11.83
12.097
12.366
12.64
12.916
13.194
13.474
13.756
14.043
14.331
14.621
14.914
15.212
15.509
15.81
TCAN
Dz
0.0784
0.0789
0.0794
0.0799
0.0805
0.081
0.0815
0.082
0.0825
0.0831
0.0836
0.0841
0.0846
0.0852
0.0857
0.0862
0.0868
0.0873
0.0879
0.0884
0.0889
0.0895
0.09
0.0906
0.0911
0.0917
0.0922
0.0928
0.0933
0.0939
0.0944
0.095
0.0955
0.0961
0.0966
Ha
5.48E-05
D,/(lE-6)
7.0603
7.2692
7.4899
7.7065
7.9261
8.1491
8.3743
8.6029
8.8337
9.0682
9.3049
9.5445
9.7879
10.033
10.282
10.533
10.786
11.043
11.302
11.564
11.83
12.097
12.366
12.64
12.916
13.194
13.474
13.756
14.043
14.331
14.621
14.914
15.212
15.509
15.81
BCAN
Dz
H
5.32E-04
Dibro
D,/(lE-6)
7.691
7.919
8.159
8.395
8.635
8.878
9.123
9.372
9.623
9.879
10.137
10.398
10.663
10.930
11.201
11.475
11.750
12.030
12.312
12.598
12.887
13.179
13.471
13.769
14.070
14.374
14.679
14.985
15.298
15.612
15.928
16.247
16.572
16.895
17.223
moaceto
Dz
0.0779
0.0784
0.0789
0.0795
0.08
0.0805
0.081
0.0816
0.0821
0.0826
0.0831
0.0837
0.0842
0.0847
0.0853
0.0858
0.0863
0.0869
0.0874
0.088
0.0885
0.0891
0.0896
0.0902
0.0907
0.0913
0.0918
0.0924
0.0929
0.0935
0.094
0.0946
0.0952
0.0957
0.0963
Mtrile
H
1.66E-05
Value predicted at 25 °C. Because of extremely low value, this value will be used as H for all temperatures
Henry's law constants in this table are based on combination of literature reported values and estimates derived from procedures presented in Section 3.1
,3.1.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 20
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Table 5. Predictor Chemicals for DBFs used to Estimate Mass
Transfer Coefficients
Disinfection Byproduct
Chloroform
Bromodichloromethane (BDCM)
Dibromochloromethane (DBCM)
Bromoform
Chloroacetic acid (MCA)
Dichloroacetic acid (DCA)
Trichloroacetic acid (TCA)
Bromoacetic acid (MBA)
Dibromoacetic acid (DBA)
Bromochloroacetic acid (BCA)
Dichloroacetonitrile (DCAN)
Trichloroacetonitrile (TCAN)
Bromochloroacetonitrile (BCAN)
Dibromoacetonitrile (DBAN)
Bromate
Predictor Chemical
Toluene
Toluene
Toluene
Toluene
Toluene
Toluene
Ethylbenzene
Cyclohexane
Toluene
Toluene
EA
Toluene
Toluene
Toluene
The predictor chemical was chosen based on the minimum sum of the normalized difference
between the predictor and desired chemical's liquid diffusivity, gas diffusivity, and Henry's law
constants at 20 C and 40 C. A sample calculation for identifying the predictor chemical for
chloroform is as follows:
Relevant Chemical Properties for Chloroform
Desired Chemical:
Liquid Diffusivity (cm2/sec):
Gas Diffusivity (cm2/sec):
Henry's Law Constant:
Chloroform
9.21E-06 (20° C); 1.5E-05 (40° C)
0.09175 (20 °C); 0.10386 (40° C)
0.1161(20° C); 0.2872 (40° C)
Relevant Predictor Chemical Properties
Property
Liquid Diffusivity @ 20 ° C
(cm2/sec):
Liquid Diffusivity @ 40° C
(cm2/sec):
Gas Diffusivity @ 20 ° C
(cm2/sec):
Gas Diffusivity @ 40 ° C
(cm2/sec):
Henry's Law Const @ 20 ° C:
Henry's Law Const @ 40 ° C:
Predictor C
Acetone
1.05E-05
1.71E-05
0.110
0.124
0.0011
0.00298
Chemical
Ethylacetate
8.36E-06
1.36E-05
0.0880
0.0997
0.00445
0.0132
Toluene
7.96E-06
1.30E-05
0.0831
0.0942
0.215
0.456
Ethylbenzene
7.19E-06
1.17E-05
0.0753
0.0853
0.252
0.642
Cyclohexane
7.96E-06
1.30E-05
0.0853
0.0966
6.18
11.62
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 21
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The normalized difference between the chemical properties for each predictor chemical and
chloroform is calculated as follows:
_ (Predictor Chemical Property t -Chloroform Chemical Property^ inno/
''J Chloroform Chemical Property.
Where:
NDjj = Normalized difference between the predictor chemical property /' and the
chloroform property /'.
/ = chemical property
j = predictor chemical
EXAMPLE CALCULATION (For Acetone, Liquid Diffusivity at 20 C):
*100
ND
Liquid Diffusivity, Acetone
Liquid DiffusivityAcetone - Liquid DiffusivityCUoroform
ND
Liquid Diffusivitychloroform
LOSE-05 - 9.21E-06
L iqiiid Diffusivity,Acetone
9.21E-06
*100 = 14%
Summary of Normalized Difference for Between Chloroform and Each Predictor Chemical
Property
Liquid Diffusivity
Gas Diffusivity
Henry's Law Constant
Average Normalized Difference
Acetone
14%
19%
99%
Ethylacetate
9%
4%
96%
Toluene
13%
9%
72%
Ethylbenzene
22%
18%
120%
Cyclohexane
14%
7%
> 200%
Based the Average Absolute Difference, Toluene is chosen as the predictor chemical for
Chloroform.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 22
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Table 6: Estimated Values for Overall Mass Transfer Coefficient (KOLA)
Appliance
Shower
Bath:
Fill
Bathing
Clothes
Washer: Fill
Wash
Rinse
Toilets
Faucets:
Kitchen
Bathroom
Laundry
Room
Temp
°C
40
35
35
35
35
35
25
35
35
30
Chloro-
form
0.432
0.245
0.0780
0.317
0.113
0.403
0.00468
0.128
0.128
0.117
BDCM
0.428
0.228
0.0735
0.265
0.0637
0.265
0.00368
0.116
0.116
0.104
DBCM
0.415
0.186
0.0625
0.174
0.0293
0.122
0.00312
0.0913
0.0913
0.0792
Bromo-
form
0.402
0.153
0.0531
0.124
0.0177
0.0735
0.00265
0.0731
0.0731
0.0613
MCA
4.49E-04
1.05E-05
4.64E-06
5.24E-06
5.21E-07
2.16E-06
2.32E-07
5.07E-06
5.07E-06
3.01E-06
DCA
4.37E-04
7.42E-06
3.27E-06
3.69E-06
3.67E-07
1.52E-06
1.63E-07
3.58E-06
3.58E-06
2.32E-06
Estimate
TCA
4.53E-04
1.22E-05
5.39E-06
6.08E-06
6.05E-07
2.51E-06
2.69E-07
5.89E-06
5.89E-06
3.68E-06
;dKOLA
MBA
4.41E-04
1.33E-05
3.56E-06
3.54E-06
2.69E-07
1.14E-06
1.78E-07
4.26E-06
4.26E-06
2.58E-06
(m3/hr)
DBA
4.28E-04
4.12E-06
1.81E-06
2.05E-06
2.04E-07
8.46E-07
9.06E-08
1.99E-06
1.99E-06
1.23E-06
BCA
4.39E-04
1.20E-05
5.28E-06
5.97E-06
5.94E-07
2.46E-06
2.64E-07
5.78E-06
5.78E-06
5.67E-06
DCAN
0.00381
0.00290
7.71E-04
7.73E-04
8.95E-05
2.51E-04
3.26E-05
9.28E-04
9.28E-04
9.08E-04
TCAN
0.00143
5.18E-04
2.28E-04
2.59E-04
2.58E-05
1.07E-04
1.14E-05
2.50E-04
2.50E-04
2.44E-04
BCAN
DBAN
7.24E-04
1.57E-04
6.90E-05
7.81E-05
7.78E-06
3.23E-05
3.45E-06
7.58E-05
7.58E-05
7.41E-05
Brom-
ate
Note: Dishwashers are modeled as equilibrium sources and therefore do not require a K0iA for modeling.
Temperature of water for the various water appliances are selected based on judgement.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 23
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3.2 Behavioral Characteristics
Activity patterns and water use behavior have been shown to have a significant impact on
predicted exposure (Wilkes et. al., 1996). TEM represents the influence of behavior by using
activity pattern databases and analysis of other behaviors that influence contaminant release and
subsequent human exposure. The activity pattern database is queried to obtain a subset of records
having the desired demographic characteristics. This subset is randomly sampled to obtain an
activity pattern record, and this record is used to specify locations within the household and
opportunities for conducting activities that may result in exposure. The actual water uses are
simulated based on parameters defined from analysis of other water-use studies. This results in
occupant driven water uses, which ultimately lead to exposure to the waterborne contaminants.
As discussed in Section 1.1, the chosen population for this exposure estimation modeling study is
a three-person family in which both parents are within their reproductive years. The family
consists of one male between the ages of 15 and 45, one female between the ages of 15 and 45,
and one child of approximately six years old. Because there are few records in the database
reflecting six year-olds, the child is characterized by sampling the database for children between
the ages of one and nine. Although it is recognized that there is significant difference in behavior
between a toddler and a nine year old, it was necessary to represent the child as a range of ages to
allow a reasonable sample size in the database. It is not entirely clear what the impact of this
assumption is on the ultimate exposure to DBFs. Younger children likely spend a greater fraction
of their day at home, and for higher volatility chemicals this may increase their exposure. For
less volatile chemicals, the impact of inhalation exposure is minimal, and the resultant exposure is
highly dependent upon the child's water-use behavior.
3.2.1 Activity Patterns
In order to most accurately represent individuals' exposure to waterborne contaminants, it is
necessary to understand the frequency of each type of water use (e.g. how often they shower), and
the duration of the events (e.g. minutes occupant spends in shower). In this study, the frequency
and duration are described for each of the six water-use activities most important to exposure,
including showering, bathing, and using the clothes washer, dishwasher, toilet, and faucet. For
some of these events, the frequencies and/or durations are described as distributions from which
individual usages will be sampled, in other cases (e.g. dishwasher duration), the parameters are
specified as the best available estimate.
The water-use behavior parameters needed for TEM have been developed from the data presented
in the National Human Activity Patterns Survey (NHAPS), the Residential End Use Water Study
(REUWS), Residential Energy Consumption Survey (RECS), in appliance manufacturer data, and
supplemented, as necessary, by best judgement. These databases are described below.
3.2.1.1. Available Activity Pattern Databases
NHAPS
The NHAPS database contains the results from a two-year nationwide activity pattern survey
commissioned by the U.S. EPA National Exposure Research Laboratory. During the period from
October 1992 through September 1994, 9,386 persons residing in the 48 contiguous United States
were chosen using a telephone random-digit dial method and interviewed over the phone (Tsang
and Klepeis, 1996). First, respondents were asked to recall their activities and locations for the
previous 24 hours. The locations and activities were recorded as codes chosen from a list of 83
possible locations and 91 possible activities. This diary section had minimal information
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 24
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regarding water use. The only activity choice that specifically pertained to water-use was
"bathing." All of the other activities are more generally defined such as "food clean-up", "plant
care", "personal care".
Then the respondents were asked a series of multiple-choice questions. Every respondent was
asked for specific demographic information, including date of birth, gender, race, geographical
region, level of education, etc., and they were asked a multitude of questions, asking for
demographic information as well as information about various activities, most relating to possible
exposure to contaminants in the air and water, such as "How long did you spend in the shower?"
or "Was a dishwasher used yesterday when you were home?" Not everyone was asked the same
questions as there were two versions of the questionnaire. NHAPS did not acquire information on
toilet use, and acquired only limited information on faucet use.
REUWS
The REUWS database contains water use data obtained from 1,188 volunteer households
throughout North America (Mayer et al, 1998). The REUWS study was funded by the American
Waterworks Association Research Foundation (AWWARF). During the period from May 1996
through March 1998, approximately 100 single-family detached homes in each of 12 different
municipalities (located in California, Colorado, Oregon, Washington, Florida, Arizona, and
Ontario) were outfitted with a data-logging device (Meter Master 100 EL, manufactured by
Brainard Co., Burlington, NJ) attached to their household water meter (on only magnetic driven
water meters). The data logger recorded the water flows at 10-second intervals for a total of four
weeks (two in warm weather and two in cool weather) at each household. Following the study,
the data was retrieved and analyzed by a flow trace analysis software program, called Trace
Wizard, developed by Aquacraft, Inc., Boulder, CO, which disaggregated the total flows into
individual end uses (i.e. toilet, shower, faucet, dishwasher, clothes washer, etc) (Mayer et.al.
1998). In addition to identifying the type of water use (e.g. shower, faucet, toilet), Trace Wizard
identified the event durations, volumes, peakflows, and mode measurements for each water-using
event.
The REUWS database includes demographic information on each household based on a mail-in
survey. This information includes employment status (unemployed, part-time, full-time),
education level of the primary wage earner (less than high school, high school graduate, some
college, Bachelor's, Master's, Doctoral), and household income. It does not give information on
age or gender.
RECS
The Residential Energy Consumption Survey (RECS), conducted nationwide in 1997, contains
energy usage characteristics of 5,900 residential housing units. The information was acquired
through on-site personal interviews with residents; telephone interviews with rental agents of
units where energy use is included in the rent; and mail questionnaires to energy suppliers to the
units. The database contains information on physical characteristics of the housing units,
demographic information of the residents, heating and cooling appliances used, fuel types, and
energy consumption.
3.2.1.2. Modeling Activity Patterns
NHAPS represents the most comprehensive survey of activities of U.S. residents available.
However, water use behavior data associated with the survey data is sparse and incomplete. The
24-hour record of locations and activities contains general locations (e.g. kitchen, bathroom, etc.)
and activities (e.g. personal care, cooking , cleaning, etc.). However, the 24-hour activity record
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 25
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does not specify actual water use events such as dishwasher use, clothes washer use, and
showering. To model the activity patterns, TEM samples a 24-hour record from NHAPS and,
using a transition matrix, places the occupant in the modeled house such that his/her location is
consistent with the recorded activity and location in the NHAPS database. Information on water
use behavior gathered from other sources is then used to simulate appropriate water use activities.
Water use occurrences are simulated as a Poisson process using frequency data obtained from
analyses of NHAPS, REUWS, and RECS. The water-use activity duration is also simulated based
on, typically, a lognormal distribution, also resulting from analyses of NHAPS, REUWS, and
RECS. For more information on how the activities are mapped to model locations and how the
water use simulation is implemented, see Wilkes, 1999.
3.2.2 Water Use Behaviors for Groups of Interest
Release of airborne contaminants occurs as a result of typical household water uses. In addition,
dermal contact occurs during some household water uses like showers and baths. For this reason,
it is imperative to represent these water uses as accurately as is reasonable within the daily
activity patterns of the model occupants. From a population exposure point of view, the water use
activities that have a significant impact are use of showers, baths, clothes washers, dishwashers,
toilets, and faucets. For each of these water uses, the published literature and other data sources
such as survey data have been reviewed, analyzed, and summarized in the following sections.
After analysis, it was concluded that NHAPS provides reliable data on frequency of occasional
water-use events (e.g. showering and bathing), but is believed to provide poor estimates of the
event durations because the values were based on recall (Wilkes et. al., 2002a). The respondents
tended to estimate event durations around 5 minute intervals, and the values were not consistent
with published literature (Wilkes et al. 2002a). In contrast, because REUWS is derived from
direct water meter measurements, REUWS provides reasonable data on the durations and
volumes of some water-use events, particularly showers, clothes washers, and toilets. However,
since REUWS is based on the entire household water use, personal frequencies of water use
events for individual persons cannot be reliably discerned. In regard to clothes washer
frequencies, RECS provides the best data for our purposes.
3.2.2.1. Showers
The model uses shower frequency, duration, water flowrate and temperature to represent
occupant showering behavior and subsequent contaminant release and occupant exposure. A
Poisson process is used to simulate shower occurrence, and a lognormal distribution is sampled to
simulate the duration. Analysis has shown that showering characteristics vary among
demographic groups. A number of shower studies have been done throughout the United States to
determine typical shower frequency, durations, and volumes. These studies include a study of 162
U.S. households by Brown and Caldwell (1984), a study was conducted of 25 homes in Tampa,
Florida (Konen and Anderson, 1993), and a study of 25 homes in Oakland, California (Aher et
al., 1991). In general, these studies revealed an average frequency of around 5 showers per week
and a duration ranging from 6.3 to 10.4 minutes. The flowrates measured in the Tampa and
Oakland studies ranged from 1.5 to 2.5 gpm.
In addition to the above studies, NHAPS and REUWS have been analyzed for showering
characteristics, as discussed above. The analysis conducted by Wilkes et al. (2002a) concluded
that NHAPS provided the most reasonable basis for specifying shower use frequency, and
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 26
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REUWS provided the most reasonable basis for specifying shower duration characteristics. The
results of the frequency analyses from both NHAPS and REUWS are presented in Table 7. The
results of the duration, volume and flowrate analyses from REUWS are presented in Table 8. For
a more detailed discussion of these data sources and analyses, refer to Wilkes et al., 2002a. The
actual selected parameter values for showering frequency, duration and flowrate used in the
modeling study discussed in this report are presented in Table 9.
Table 7. Shower Frequency Values from NHAPS and REUWS Analyses
Statistic
Shower
Frequency per
person-day
Children
5-12 years
(NHAPS)
0.55
Men
18-48 years
(NHAPS)
1.24
Populatioi
Women
18-48 years
(NHAPS)
1.12
i
All
Households
(NHAPS)
0.98
All Households
(REUWS)
0.82
Table 8. Summary Statistics for Shower Duration, Volume and Flowrate from REUWS
Analyses
Statistic for All Households
(REUWS)
Shower Duration
Shower Volume (adults only)
Shower Flowrate
Geometric Mean
6.8 minutes
15.80 gallons/shower
2.00 gallons/minute
Geometric
Standard
Deviation
0.493
0.560
0.455
Arithmetic Mean
7.65 minutes
19.30 gallons/shower
2.40 gallons/minute
Table 9. Selected Model Parameters for Showers
Statistic
Shower Frequency per person per day
Children 6 years
Men 15-45 years
Women 15-45 years
Shower Duration
Shower Flowrate
Value
0.55
1.24
1.12
7.65 minutes
2.40 gallons/minute
12.2.2. Baths
The model uses bath frequency, duration and water volume and temperature to represent occupant
bathing behavior and subsequent contaminant release and occupant exposure. A Poisson process
is used to simulate bath occurrence, and a lognormal distribution is sampled to simulate the
duration. Relatively few studies have been conducted in the United States to determine typical
bath frequencies, duration, and volumes. The Brown and Caldwell study in 1981-83 found that
people who only bathe (do not shower) take about 2.9 baths per week. The NHAPS database is
analyzed for bathing frequencies and duration. Although the bathing durations given in NHAPS
tended to cluster around 5 minute intervals, and are based on recall, it is the best available data.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 27
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The REUWS database does not provide bathing durations, only the amount of time it took to fill
the tub. The results of the NHAPS bathing frequencies and durations for the three subpopulations
of interest are provided in Table 10. The results of the REUWS analysis to determine bath
flowrate is presented in Table 11. The bathtub emission model uses a bathtub water volume, a fill
duration, and a bath duration. Although no studies have analyzed the volume of water used in
bathing, Brown and Caldwell (1984) estimated 50 gallons (189L) based on the physical size of a
typical bathtub. The fill duration was set at 8 minutes, which is consistent with the amount of
time required to fill a 50-gallon bathtub, based on a mean flowrate of 25 L/minute (6.6
gal/minute). This mean bath fill flowrate was derived by evaluating both field measurements and
the REUWS data. The flowrate in two independent field measurements in household bathtubs
were 8.9 and 9.3 gallons/minute (Wilkes, 2002b). The REUWS analysis resulted in a mean bath
fill flowrate of 4.9 gallons/minute, with a standard deviation of 2.1 gallons/minute. The selected
bath fill flowrate value of 6.6 gallons/minute is consistent with the REUWS study at
approximately the 85th percentile. The actual parameter values used in the modeling study are
presented in Table 12.
Table 10. Bath Frequency and Duration Values from NHAPS Analyses
Statistic
(NHAPS)
Bath frequency per person per
day
Bath Duration
Geometric Mean (minutes)
Geometric
Standard Deviation
Arithmetic Mean (minutes)
Men
18-48 years
0.21
17.15
0.694
20.75
Popu
Women
18-48 years
0.38
17.75
0.718
21.48
ation
Children
5-12 years
0.48
18.60
0.511
20.80
All
Households
0.32
17.60
0.633
20.90
Table 11. Bath Volume and Flowrate Values from REUWS Analyses
Statistic for All Households
(REUWS)
Bath Flowrate
Geometric Mean
4.40 gallons/minute
Geometric
Standard
Deviation
0.537
Arithmetic Mean
4.90 gallons/minute
Table 12. Selected Model Parameters for Bathing
Statistic
Bathing Frequency
per person per day
Bathing Duration
Bath Volume
Bath Fill Duration
Men
15-45 years
0.21
20.75 minutes
50 gallons
8 minutes
Women
15-45 years
0.38
21.48 minutes
50 gallons
8 minutes
Children
6 years
0.48
20.80 minutes
50 gallons
8 minutes
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 28
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3.2.2.3. Clothes washers
The model uses clothes washer frequency, the number of cycles and information about each
cycle, including fill duration, agitation duration, water volume and water temperature to represent
occupant use of clothes washers and subsequent contaminant release and occupant exposure. A
Poisson process is used to simulate clothes washer use. Both the NHAPS and the RECS surveys
asked respondents questions about their clothes washer use. The two questions asked in NHAPS
were: "How often do you wash clothes in a machine?" and "How many separate loads of laundry
were done when you were home?" The answers for the first question were recorded as: Almost
every day, 3-5 times a week, 1-2 times a week, Less often, or Don't know. The answers for the
second question were recorded as actual number of loads under 10, or "over 10". The problem
with the first question was that the frequency range in the choices is too broad, and the question is
unclear whether it refers to how many actual loads or how many days per week they did laundry
regardless of how many sequential loads they did in one day. The major problem with the second
question is that it required the individual to be at home during the event. In the RECS survey, the
question relating to clothes washer use was more specific; however, the answer choices likewise
offered a range. The RECS question was: "In an average week, how many loads of laundry are
washed in your clothes washer?" The answer choices were: 1 load or less each week, 2 to 4 loads,
5 to 9 loads, 10 to 15 loads, More than 15 loads, or Don't know.
RECS was analyzed for clothes washer frequency behavior (Wilkes 2002a) because the
questionnaire was less ambiguous than the one used for NHAPS. The results for three-person
families are presented in Table 13. The analysis of three-person families excluded families with
individuals over the age of 65 because we were attempting to represent families with children.
The REUWS and experimental data are analyzed for clothes washer volume and durations of the
various wash and rinse fills, and agitation cycles. The results of the analysis are presented in
Table 14. Table 15 presents selected parameters to be used in modeling clothes washer use.
Table 13. Frequency of Clothes Washer Use for 3-Person Households: RECS
Frequency
15+ loads/wk
10-15 loads/wk
5-9 loads/wk
2-4 loads/wk
1 load or less/wk
Total
Estimated Mean Frequency
3-Person
%
3.00
15.10
50.50
28.60
2.80
100.00
Family
N
370,834
1,847,105
6,189,132
3,501,403
337,711
12,246,185
6.74 loads/wk
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 14. Typical Clothes Washer Parameters: Based on REUWS and Experimental Data
Parameter
Number of Cycles
2.2
Comments8
Average Number of Fills (REUWS)
Cyclel: Wash
Volume
Time to fill
Time to Agitate
16.6
3.3
7.4
gallons
minutes
minutes
Mean Volume for First Fills (REUWS)
Mean Volume/Mean Mode Flow Rate of 5. 01
gallons per minute (REUWS)
Based on REUWS time btwn 1st and 2nd fill (14.7
min)-typical drain/spin (4 min)-wash time (3.3
min)
Cycle 2: Rinse
Volume
Time to fill
Time to Agitate
15.2
3.5
4.0
gallons
minutes
minutes
Mean Volume for Second Fills (REUWS)
Mean Volume/Mean Mode Flow Rate of 4. 3 6
gallons per minute (REUWS)
Based on Experimental Data on Time to Agitate
for a typical rinse cycle
Cycle 3: Rinse
Volume
Time to fill
Time to Agitate
15.3
3.4
4.0
gallons
minutes
minutes
Mean Volume for Third Fills (REUWS)
Mean Volume/Mean Mode Flow Rate of 4.5 1
gallons per minute (REUWS)
Based on Experimental Data on Time to Agitate
for a typical rinse cycle
Spin Rinse
Volume
Duration
2.8
unknown
gallons
Mean Volume of Small Fills (REUWS) (includes
events with 0 gal spritzes)
The duration of spin rinse varies significantly
across machines and is difficult to quantify
Totals for Clothes Washer Events
Volume
Duration
37.4
25.2
29.2
gallons
minutes
minutes
Time until end of last fill
Estimated time through last agitation (spin cycle
follows)
Note: Cycle 2 is 100% likely to occur
Cycle 3 is 18.7% likely to occur
Cycle 4 is 0.8% likely to occur
a. Values based on REUWS data and experimental data (Wilkes et. al. 2002a and 2002b)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 30
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Table 15. Selected Model Parameters for Clothes Washer Use
Parameter
Temperature
Wash
Fill Duration
Agitation Duration
Volume
Rinse
Fill Duration
Agitation Duration
Volume
Frequency
Value Used in Modeling
35 °C
3.3 minutes
7.4 minutes
16. 6 gallons
4.2 minutes
9.8 minutes (5 min. added
for spin rinse)
21.0 gallons
0.99 events per day for 3 person household
Note: The model is currently set up to handle 2 complete cycles. The first event is the wash
cycle, consisting of the wash fill and the wash agitation and drain, the second event is a
combination of all the rinse activities, which are represented as 1.2 rinse cycles.
3.2.2.4. Dishwashers
The model uses dishwasher frequency, the number of cycles and information about each cycle,
including cycle duration, water volume and water temperature to represent occupant use of
dishwashers and subsequent contaminant release and occupant exposure. A Poisson process is
used to simulate dishwasher use. There are very few studies on the water use characteristics of
dishwasher use. In 1994, a US Department of Housing and Urban Development study (Brown
and Caldwell, 1994) reported that people generally used the dishwasher 3.7 times per household
per week, or 1.2 times per person per week. A 1983 Consumer Reports study (reported in Brown
and Caldwell, 1994) found that dishwashers at the time were using from 8.5 to 12 gallons per
load, and older dishwashers were using 14 gallons per load. Similar to the NHAPS clothes washer
data, the NHAPS dishwasher data is likewise unreliable as the questions pertaining to
dishwashers were ambiguous. The NHAPS questions relating to dishwashers were, "How often
does (respondent) use the dishwasher?" This does not indicate how often the family used the
dishwasher. However, the RECS respondents were asked, "Which category best describes how
often your household actually uses the automatic dishwasher in an average week?" Their answer
choices were as follows: Less than 4 times a week, 4 to 6 times a week, or At least once each day.
The RECS data were analyzed for three person households, excluding all families with a member
over 65 years old in order to best represent families with a child. The results are presented in
Table 16.
Table 16. Frequency of Dishwasher Use for 3-person Households: RECS, 1997
Frequency
Daily
4-6 times/wk
Less than 4 times/week
Total
Estimated Mean Frequency
3-Pe
%
17.70
29.90
52.40
100.00
rson Family
N
1,459,081
2,473,849
4,328,473
8,261,403
3.78 times/wk
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 31
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The most reliable data on dishwasher cycle volumes and durations were obtained from the
manufacturers. These data are presented in Tables 17 and 18.
Table 17. Manufacturer Supplied Dishwasher Information Summary
Condition
Dishwasher Model:
Rinse Only ~ Heavy Soil
Rinse Only - Light Soil
Quick Wash - Heavy Soil
Quick Wash - Light Soil
China - Heavy Soil
China - Light Soil
Low Energy - Heavy Soil
Low Energy - Light Soil
Normal - Heavy Soil
Normal - Medium Soil*
Normal - Light Soil
Heavy - Heavy Soil
Heavy - Medium Soil
Heavy - Light Soil
Dishwasher Model:
Rinse Only
Low Energy/China
Normal*
Heavy
Pots-N-Pans
Dishwasher Model:
Rinse Only
Light Wash
Normal*
Pots-N-Pans
Dishwasher Model:
Rinse and Hold
Short Wash
Water Saver Cycle
China/Crystal Cycle
Light Wash Cycle
Normal Wash Cycle*
Potscrubber Cycle
Total Volume,
gal
Number of
Fills
Whirlpool GU980SCG a
4.3
2.2
6.9
4.8
8.6
6.5
8.6
6.5
10.8
8.6
6.9
10.8
10.8
8.6
2
2
2
2
3
3
3
3
3 or 4
3 or 4
3 or 4
5
5
5
Whirlpool DU920PFG a
2.2
6.5
6.9
8.6
8.6
2
3
3
5
5
Whirlpool DU850DWG a
2.9
5.8
7.2
8.6
2
4
5
6
GE Potscrubber b
3
7
6.1
7.3
7
8.5
10.1
2
5
4
5
5
6
7
Average Volume
per Fill, gal
2.15
1.1
3.45
2.4
2.87
2.17
2.87
2.17
3.60-2.7
2.87-2.15
2.30- 1.725
2.16
2.16
1.72
1.1
2.17
2.3
1.72
1.72
1.45
1.45
1.44
1.43
1.5
1.4
1.53
1.46
1.4
1.42
1.44
a. whirlpoolfgiin-response.com 9/2000
b. answerctrfgjexchange.appl. ge.com 2001
* Normal cycles used for calculations in following table of selected model parameters.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 18. Selected Model Parameters for Dishwasher Use
Characteristic
Volume of Water
Number of Cycles (without drying)
Volume of Water per Cycle
Duration per Cycle
Frequency
Average *
8.5 gallons
2 Cycles
4.25 gallons
30 minutes
0.54 events per day for 3
person households
* Based on the average of the "normal" cycles of selected dishwashers
12.2.5. Toilets
The model uses the frequency of flushing to incorporate toilet use into the sampled activity
pattern. Once a toilet flush has occurred the emission models also require the volume of water for
the flush. For modeling purposes, it is assumed that a flush duration is instantaneous.
Several recent studies reported toilet flush frequency and volume. These studies focused on the
performance of ultra-low toilets, contrasting their performance after retrofit with the performance
of the low flow and older non-conserving toilets they replaced. The Tampa Florida study (Konen
and Anderson, 1993) retrofitted the showers and toilets in 25 single-family homes with ultra-low
flow devices and monitored their water usage for 30 days before and 30 days after retrofit. The
Oakland California study (Aher et.al., 1991) retrofitted 25 single-family homes with ultra-low
flow toilets and monitored their water usage for 21 days before and 21 days after retrofit. The
Dept. of Housing and Urban Development study (Brown and Caldwell, 1984) monitored 196
households with 545 persons found that people flushed toilets approximately 4 times per day. The
results from these studies are presented in Table 19.
Table 19. Summary of Reported Toilet Use Characteristics from Literature
Toilet Type
Low-Flow
(Avg. 3.6gpf)
Ultra-low Flow
(rated 1.6gpf)
Low-Flow
(Avg. 4.0 gpf)
Ultra-low Flow
(rated 1.6 gpf)
Variety of toilets
(33% low volume
models or devices)
Reported
Frequency
(fpcd)a
Mean = 3. 8
Min=1.8
Max =8. 4
Mean = 4.5
Min=1.7
Max = 12.8
Mean = 3.2 or
12.8fphdb
Mean = 3.7
or 15.9fphd
Mean = 4.0
Volume
(gal/flush)
Mean =3. 6
Min= 1.7
Max = 5. 6
Mean= 1.6
Min= 1.1
Max =3.0
Mean = 4.0
Mean= 1.8
Min= 1.34
Max = 2.44
Population/
Sample Size
Tampa, Florida,
25 single family
homes
Tampa, Florida
25 single family
homes
Oakland, California,
25 single family
homes
Oakland, California,
25 single family
homes
CA, CO, D.C., VA,
WA, 196 households,
545 persons, 356
toilets
Reference
Konen and
Anderson,
March 1993
Konen and
Anderson,
March 1993
Aher et al.,
Oct. 1991
Aher et al.,
Oct. 1991
Brown and
Caldwell,
U.S. HUD,
June 1984
Special Study
Conditions
Study
comparison of
low flow to
ultra-low flow
retrofit
(average 2.9
persons/home)
Study
comparison of
low flow to
ultra-low flow
retrofit
(average. 4.4
persons/home)
Study subjects
recorded toilet
flush counts.
a. fpcd: Flushes per capita day
b. fphd: Flushes per home per day
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 33
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REUWS also provides toilet use data. The data were derived from an analysis of household water
meter monitoring. Because the water meters record total water use for the household, it is
impossible to attribute each flush to any given individual. Therefore, the average frequency of
toilet use in REUWS was derived by analyzing the total frequency of use for each family divided
by number of persons in the household. The data contained in REUWS has been analyzed for
frequency of toilet use and water volume characteristics. For a complete description of the
analysis of REUWS refer to Wilkes et al., 2002a.
The frequency of toilet use will be modeled as Poisson process with a mean frequency of 5.23
flushes per person per day. The volume per flush was found to best represented as a normal
distribution with a mean of 3.5 gallons and a standard deviation of 1.2 gallons. The results of the
REUWS analysis are presented in Table 20. The actual toilet use frequency and volume values
used in the DBF modeling study are presented in Table 21.
Table 20. Statistics for Toilet Flushes from REUWS
Minimum
Maximum
Mean
Standard
Deviation
Number of
Records or
Households b
All Fh
Frequency
(flushes/person/day)
0.03
42.73
5.23
3.15
2,145a
ishes
Family
Size
0.00
9.00
2.76
1.37
2,158
Sampling
Days
1.00
16.00
10.65
1.63
2,158
Duration of
Tank Fill
(seconds)
10.00
2,720.00
71.43
29.77
245,328
Single Flusl
Volume
(gallons)
0.29
9.77
3.48
1.18
245,331
ics Only
Mode Flow
(gallons per minute)
0.00
14.10
3.89
1.31
245,331
a. 13 surveys indicated "0" for Q.31 or Q.30 regarding the number of people in selected age groups (households aggregated from
295,660 records).
b. Number of households reflects the combined total of homes participating in the first sampling period (1,173) and second
sampling period (985).
Table 21. Selected Parameters for Toilet Use
Statistics
Frequency
Volume of water used per flush
Value
6 flushes/person/day
3.5 gallons/flush
Note: model assumes instant filling
3.2.2.6. Faucets
Faucet use characteristics for bathrooms and kitchens were researched in a study of 25 homes in
the City of Tampa (Konen and Anderson, 1993). The mean water flowrate was 2.4 gpm from the
kitchen faucet and 3.4 gpm from the bathroom faucet, each with the faucets were fully open.
Brown and Caldwell (June 1984) estimated that faucet use in the homes they studied was 9.0
gallons/person/day. The frequency of faucet use was not given. These data are presented in Table
22.
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March 2002, Page 34
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The faucet use characteristics reported in REUWS are analyzed and reported in Table 23. The
REUWS database should be used with caution in respect to faucet use, since the techniques used
to acquire the data in REUWS are unreliable, and it is expected that many uses labeled as faucets
are misclassified and that many of the uses labeled as "leaks" and "unknown" could be faucets.
For a complete discussion of the analysis, refer to Wilkes et. al., 2002a. The actual faucet use
parameter values selected for use in the DBF modeling study are presented in Table 24. The
frequency and duration values were adjusted from those in the REUWS analysis because the
room locations and activity patterns sampled from NHAPS do not typically provide adequate
opportunity for the frequency of faucet use reflected in the analysis of REUWS. Most probably
resulting from the fact that people don't often report being in the locations of faucet use, they tend
to under-report bathroom visits, and small water uses overall. In addition, there is no reasonable
information on which household faucet is being used (eg. bathroom, laundry, kitchen). Therefore,
to compensate for the discrepancies (i.e., interface with activity patterns), the faucet frequencies
were adjusted downward, while the durations were increased. The frequency and mean duration
used in the study, 15.5 events per day and 1.1 to 1.7 minutes mean duration, as reported in Table
24, was chosen through iterative modeling trials to best represent the actual total desired daily
duration of faucet use. The combination chosen allowed the model to simulate reasonable faucet
use by the occupants which resulted in total faucet use (duration of summed faucet uses) similar
to the parameters reported in Table 23.
Table 22. Summary of Reported Faucet Frequency and Volume of Use Characteristics
in Literature
Type of
Appliance
Conventional
Conventional
Conventional
Location
Kitchen
Bathroom
Not given
Frequency
Not given
Not given
Not given
Volume
(gpm)
Maximum flow a
Mean =2.4
Min= 1.5
Max = 3. 8
Maximum flow a
Mean = 3.4
Min = 0.9
Max = 7.9
9.0 gal/pers/day b
Population/
Sample Size
Tampa, Florida,
25 single family
homes (avg. 2.9
persons/home)
Tampa, Florida,
25 single family
homes (avg. 2.9
persons/home)
Nationwide
Reference
Konen and
Anderson,
March 1993
Konen and
Anderson,
March 1993
Brown and
Caldwell,
June 1984
a. Measured flowrates with faucets in fully open position
b. Estimated value
Table 23. Summary Statistics for Faucet Use from REUWS
Minimum
Maximum
Mean
Standard Deviation
Number of Records
Duration
(minutes)
0.00
90.00
0.57
0.76
1,150,867
Volume
(gallons)
0.10
37.60
0.65
0.98
1,150,872
Mode
Flow
(gpm)
0.00
10.70
1.20
0.68
1,150,871
Frequency of Use
per day per
person
0.89
227.25
20.64
15.40
1,185 (households)
Frequency of Faucet
Use by household
14.00
5508.00
969.56
655.19
1,150,872
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Table 24. Selected Parameters for Faucet Use
Statistic
Faucet Use Duration
Flowrate
Frequency of Faucet Use
Value
Range from 1.1 to 1.7 minutes
1 .20 gallons per minute
15.5 events per day
3.3 Ingestion Characteristics
The most obvious route of human exposure to waterborne contaminants is via ingestion. Every
day, people drink water directly and consume water indirectly in juices, sodas, soups, foods,
coffee, tea, etc. In order to assess a person's ingestion exposure to chemicals found in the water
system, it is important to appropriately represent and estimate the amount of water the person
consumes, and from what sources. In order to understand the dynamics of exposure uptake and
distribution in the body, we must first consider the dynamics of direct and indirect consumption
from an exposure perspective. For direct consumption, we must develop a methodology for
representing the number of drinks and volumes consumed, either assuming that the contaminant
level remains constant from tap to glass to body, or consider that some contaminant volatilized
during air contact. For indirect water consumption, such as via food or reconstituted drinks, we
also need to consider the quantity consumed, and also evaluate whether the fraction of the
contaminant remaining in the drink or food after volatilization and preparation is still significant
or should the drink or food be ignored in the exposure calculation.
3.3.1 Available Data Sources
Currently, the U.S. EPA typically assumes that adults consume an upper-percentile quantity of 2
liters of tap water per day and infants (body mass of 10 kg. or less) consume 1 liter per day
(USEPA, 1997a). These rates include the tap water consumed directly and the tap water
consumed in other drinks like juices, coffee, etc. Prior to 1995, the primary survey used to
estimate tap water intake in the U.S. was the USDA's 1977-1978 National Food Consumption
Survey, Ershow and Cantor, 1989 in Exposure Factors Handbook (U.S. EPA, 1997a)). However,
newer studies have been conducted that better reflect consumption behavior for modern times,
reflecting our changed habits such as drinking more bottled or filtered water, and drinking more
soda and other canned drinks. Furthermore, water intake is assumed to vary with levels of
physical activity and outdoor temperatures and Americans are exercising more than ever.
There are two major recent surveys that prove useful when estimating the amount of water people
ingest per day. One is NHAPS and the other is the Combined 1994-1996 Continuing Survey of
Food Intake by Individuals (CSFII) (Jacobs et al., 2000) conducted by the U.S. Department of
Agriculture (USDA). There are also a few other studies presented in the Exposure Factors
Handbook (EFH Vol. 1, USEPA, August 1997).
3.3.1.1 Ingestion: Exposure Factors Handbook
The Exposure Factors Handbook, Volume 1, Chapter 3 (U.S. EPA, 1997a) presents the key and
relevant drinking water intake studies prior to 1995. These surveys and studies include the
following: 1981 Tapwater Consumption in Canada study by the Canada Department of Health
and Welfare; 1977-78 Nationwide Food Consumption Survey by the US Department of
Agriculture, analysis by Ershow and Cantor; 1978 Drinking Water Consumption in Great Britain,
analysis by Hopkins and Ellis; 1987 Bladder Cancer, Drinking Water Source, and Tapwater
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Consumption study by the National Cancer Institute, analysis by Cantor et al.; and the 1992-1994
National Human Activity Patterns Survey (NHAPS) analysis by Tsang and Klepeis. For a more
complete discussion of these studies, see Wilkes et al., 2002a. The tapwater consumption data
from these studies are summarized in Table 25, specifically for the subpopulations that most
closely represent the three groups of interest identified in Section 1.1.
Table 25. Tapwater consumption characteristics
Population
Average Consumption (units)
Canadian Department of Health a: 970 individuals, 295 households
Children, 3-5 Years
Children, 6- 17 Years
Females, 18-34 Years
Females, 35-54 years
Males, 18-54 Years
Average Daily Consumption, (All)
90th Percentile
48 mL/kg
26 mL/kg
23 mL/kg
25 mL/kg
19 mL/kg
1.34L/day
2.36 L/day
1978 Drinking Water Consumption in Great Britain : N = 3,564 People
Females, 5-11 Years
Females, 18-30 Years
Females, 3 1-54 Years
Males, 5-11 Years
Males, 18-30 Years
Males, 3 1-54 Years
0.533 L/day
0.991 L/day
1.091 L/day
0.550 L/day
.006 L/day
.201 L/day
1987 National Cancer Institute Study c: N = 8,000 White Adults
Females, 21-84 Years
Males, 2 1-84 Years
Females and Males, 18-44 Years
.35 L/day
.4 L/day
.3 L/day
1977 - 78 USDA Nationwide Food Consumption Survey (NFCS) d: N = 26,000
Adults, 20 to 75 or older Years
90th Percentile
Adults, 15- 19 Years6
Adults, 20-44 Years e
Children, 4-6 Years e
Pregnant Women
Lactating Women
Non-Pregnant, Non-Lactating
Women, 15 -49 Years f
1.2 L/day
2.1 L/day
999mL/day (N = 2998)
1,255 mL/day(N = 7171)
37.9 mL/kg-day (N = 1702)
2,076 mL/day(N= 188)
2,242 mL/day (N = 77)
1,940 mL/day(N = 6201)
All references discussed and cited in Exposure Factors Handbook, U.S. EPA, 1997a
a. Canadian Ministry of National Health and Welfare, 1981 b. Hopkins and Ellis, 1980
c. Cantor et al., 1987 d. Ershow and Cantor, 1989
e. Ershow and Cantor, 1989 f. Ershow and Cantor, 1991
3.3.1.2 1994-1996 USDA 's Continuing Survey of Food Intake by Individuals (CSFII)
The 1994-96 USDA's Continuing Survey of Food Intake by Individuals (CSFII) is the most
recent and comprehensive consumption database available. CSFII was conducted over the three-
year period between January 1994 and January 1997. More than 15,000 persons in the United
States were interviewed on two non-consecutive days with questions about what drinks and foods
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they consumed in the previous 24 hours. The U.S. EPA report, Estimated Per Capita Water
Ingestion in the United States (Jacobs et al., 2000), presents estimates of per capita water
ingestion based on the CSFII data for direct and indirect water intake.
The study uses the following definitions:
• Direct water: plain water consumed directly as a beverage.
• Indirect Water: water used to prepare foods and beverages at home or in a restaurant.
• Intrinsic Water: water contained in foods and beverages at the time of market purchase
before home or restaurant preparation. Intrinsic water includes both the "biological
water" of raw foods and any "commercial water" added during manufacturing or
processing.
In the survey, respondents were asked:
• What is the main source of water used for cooking? (Community water, private well, spring
bottled, other?)
• What is the main source of water used for preparing beverages? (same)
• What is the main source of plain drinking water? (same)
• How many fluid ounces of plain drinking water did you drink yesterday?
• How much of this plain drinking water came from your home? (All, most, some, none)
• What was the main source of plain drinking water that did not come from your home?
(Tap or drinking fountain, bottled, other, don't know)
• Recall everything they ate over the past 24 hours. Where was the food obtained?
3.3.2 Ingestion Behavior for the Three Populations: Results of Analysis
Of the available references providing water consumption data on the subpopulation groups of
interest for our study, the CSFII survey was chosen as the most useful because of its current
relevance and its comprehensive specification of water intake in its various forms. The intakes for
the two days of the survey were averaged for each person, providing the estimated mean two-day
average. Table 26 lists the distribution parameters (geometric mean and standard deviation) for
direct and indirect tapwater consumption in the U.S. for women and men over 20 and children
between one and ten from the CSFII study. Table 27 shows a comparison of the consumption
percentiles for the data set and the fitted lognormal distributions for each of the demographic
groups. The actual parameters selected for use in this DBP modeling study are presented in Table
28.
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Table 26. Parameters of Fitted Lognormal Distribution for Water Ingestion in the
United States
Population
Women, direct (20+ years)
Women, indirect (20+ years)
Men, direct (20+ years)
Men, indirect (20+ years)
Children, direct (1-10 years)
Children, indirect (1-10 years)
All ages, direct
All ages, indirect
Geometric Mean
ml/day
394
384
389
418
188
97
321
290
Geometric Standard
Deviation
2.52
2.20
2.69
2.33
2.50
2.51
2.79
2.53
Source: Fitted to data from Table Al in Jacobs et.al. 2000.
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Table 27. Comparison of Consumption for Raw Data and Fitted Distributions based on CSFII Data
Percentile
1
5
10
50
90
95
99
Men, 2(
Direct
Consumption
(ml/d)
Data1
—
—
—
352
1,450
1,891
3,773
Fitted
Lognormal
Distribution
39
77
110
390
1,380
1,980
3,897
)+ years
Indirect
Consumption
(ml/d)
Data1
—
—
—
412
1,210
1,597
3,094
Fitted
Lognormal
Distribution
58
104
142
419
1,235
1,682
3,000
Women, ,
Direct
Consumption
(ml/d)
Data1
—
—
—
349
1,395
1,865
3,062
Fitted
Lognormal
Distribution
46
86
121
394
1,285
1,799
3,386
ZO+ years
Indirect
Consumption
(ml/d)
Data1
—
—
—
365
1,080
1,394
2,367
Fitted
Lognormal
Distribution
61
105
140
385
1,057
1,410
2,421
Children,
Direct
Consumption
(ml/d)
Data1
—
—
—
174
696
919
1,415
Fitted
Lognormal
Distribution
22
42
58
189
611
854
1,601
1-10 years
Indirect
Consumption
(ml/d)
Data1
—
—
—
84
352
457
734
Fitted
Lognormal
Distribution
11
21
30
97
316
441
828
Total Po
Direct
Consumption
(ml/d)
Data1
—
—
—
290
1,270
1,769
3,240
Fitted
Lognormal
Distribution
30
60
87
322
1,193
1,734
3,499
pulation
Indirect
Consumption
(ml/d)
Data1
—
—
—
262
1,008
1,334
2,373
Fitted
Lognormal
Distribution
33
63
88
290
952
1,336
2,523
1. Data taken from CSFII
Table 28. Selected Parameters for Tapwater Consumption Modeling Study
Statistic
Volume
Geometric Mean (Liters/day)
Geometric Standard Deviation
Duration (time to consume water)
Geometric Mean (minutes)
Geometric Standard Deviation
Arithmetic Mean (minutes)
Arithmetic Standard Deviation (min.)
Mean Frequency
Time of Day
Men (age
Direct
Consumption
0.390
0.988
2.236
1.269
5
10
8
5 am- 10 pm
15-45 years)
Indirect
Consumption
0.419
0.8449
3.162
1.517
10
30
8
5 am - 10 pm
Women (age
Direct
Consumption
0.394
0.9228
2.236
1.269
5
10
8
5 am- 10 pm
15-45 years)
Indirect
Consumption
0.385
0.4894
3.162
1.517
10
30
8
5 am - 10 pm
Child (ag
Direct
Consumption
0.189
0.9173
2.236
1.269
5
10
8
5 am - 10 pm
e 6 years)
Indirect
Consumption
0.097
0.9187
3.162
1.517
10
30
8
5 am - 10 pm
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3.3.2.1 Methodology for distributing water consumption is distributed throughout day.
No studies were identified that quantify the manner in which water consumption is distributed
throughout the day. A reasonable, common sense approach is being adopted for implementing
this distribution. The water consumption will be distributed into a specified number of
consumption events represented by a Poisson process. The consumption volume is sampled from
the appropriate lognormal distribution as identified in Section 3.3.2, with the total volume
randomly placed among the consumption events.
3.4 Building Characteristics
Housing characteristics, including zonal volumes, interzonal airflows, and whole house air
exchange rates, also have a significant impact on the estimated exposures. The important building
parameters are volumes of the whole house, volumes of the individual water-use zones, whole
house air exchange rates, and interzonal airflows.
TEM will model each subject residence as a collection of individual water-use zones in flow
communication with a "Rest-of-House" (ROH) zone that aggregates the zones that are free of
water-use sources. In order to execute TEM for typical conditions and building characteristics,
information related to indoor volume and airflows is needed.
3.4.1 Representation of Household Volumes
The Exposure Factors Handbook (U.S. EPA 1997b) recommends using 369 m3 as the central
estimate of volume for American residences. If an underlying normal distribution is assumed, it
would have a standard deviation of 258 m3, giving 209 m3 as the most reliable conservative
estimate. These estimates are based on peer-reviewed data appraisals drawn from statistically
representative surveys of American households through the Residential Energy Consumption
Survey (RECS). RECS was first conducted in 1978 and was updated on a biennial basis until
1984, after which the survey was conducted periodically, every three or four years. In addition to
data related to energy consumption, RECS solicits information on demographics, building
characteristics, and other factors that relate to the needs of TEM. The distribution of indoor
residential volume contained in the Exposure Factors Handbook was calculated based on the
estimated floor area assuming 8-foot (2.44 m) ceiling height.
Estimates for total house volume contained in the Exposure Factors Handbook were derived
primarily from RECS data collected in 1993 and published in 1995 (U.S. DOE, 1995). Results of
the 1997 survey (U.S. DOE, 1999) only became available after the Exposure Factors Handbook
was updated. Initial reviews of the 1997 RECS data indicate that total house volume estimates
derived from the 1997 RECS data would be very similar to the earlier data. The RECS data was
analyzed and the representativeness of several distributions was evaluated. Based on the fit, a
lognormal distribution was chosen to represent the distribution of volumes, as shown in Figure 3.
The probability density function for the chosen lognormal distribution is compared to a histogram
of housing volumes in Figure 4. The volume of the median 3-bedroom American home from the
1997 RECS data is characterized by a total volume of 317 m3 (Table 29, Figure 3). Such housing
corresponds to a modest (-1400 ft2) residence occupied by 3 or 4 people. In addition to expected
general appliances, all such homes are equipped with a kitchen (which usually contains an
automatic dishwasher), and nearly all have 2 baths plus a laundry, as well as a basement. The
"average" house has a central forced-air system to support heating and cooling needs.
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Selection of Total House Volume: Total house volume for 3-bedroom cases are selected
from the statistical distribution derived from the 1997 RECS data (Table 29, below). The
distribution of total volume for 3-bedroom homes is lognormal (Figure 3), and is
characterized by a geometric mean volume of 317 m3 (11,195 ft3) and geometric standard
deviation of 0.4218.
Table 29. Analysis of RECS for Total House Volume for 3-Person U.S. Households
(RECS 1997).
Percentile
4.1
22.3
60.4
79.7
90.5
96.6
Area, ft2
0-600
601 -999
1000 - 1599
1600 - 1999
2000 - 2399
2400 - 2999
Area, m2
55.7
92.8
148.6
185.7
222.9
278.6
Volume, ft3
4800
7992
12792
15992
19192
23992
Volume, m3 a
135.9
226.3
362.3
452.9
543.5
679.5
a. Volumes were calculated by assuming an 8 ft ceiling height
500
1000
1500
Floor Area, ft2
2000 2500
3000
3500
4000
•a
c
01
E
Ol
a.
100
80
60
40
20
Properties of the Fitted Lognormal Distribution:
Geometric Mean = 5.758 (= 317 m3)
Geometric STDEV = 0.4218
Percentile Volume (m3)
1
5
10
25
50
75
90
95
99
119
158
185
238
317
421
543
634
846
Volumes are calculated from the fitted distribution.
100 200 300 400 500
House Volume, m
600
3
700
800
900
1,000
Figure 3. Cumulative Distribution Function of Volume for 3-Person Households.
Source: Analysis of RECS 1997 data
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2.0E+06
1.8E+06
"Probability Density Function (PDF)
Geometric Mean = 5.758 (= 317 m3)
.Geometric STDEV = 0.4218
NOTES:
1. The area of the housing were surveyed and reported in RECS in ranges of
square feet. The values were converted to volumes by assuming an 8 ft
ceiling height (2.44 m). The values are reported in the following ranges, along
with the corresponding assumed volumes:
FLOOR AREA CALCULATED HOUSE VOLUME
0 - 600 ft (0 - 55.7 m )
600-1000 ft2 (55.7-92.9m2)
1000 -1600 ft2 (92.9 - 148.6 m2)
1600 - 2000 ft2 (148.6 -185.8 m2)
2000 - 2400 ft2 (185.8 - 223.0 m2)
2400 - 3000 ft2 (223.0 - 278.9 m2)
• 3000 ft2 (> 278.9 m2)
0-135.9m
135.9-226.5m3
226.5 - 362.5 m3
362.5 - 453.1 m3
453.1 - 543.7 m3
543.7 - 679.6 m3
> 679.6 m3
2. The values are averaged in each category for display purposes
2.0E+05
O.OE+00
Values reported as > 680 m3(> 3000 ft2)
Assumed distribution for the purposes of
plotting.
200
400
600
Volume, m
800
1000
1200
Figure 4. Comparison of RECS Data and the Fitted Probability Density Function of
Volume for 3-Person Households.
Source: Analysis of RECS 1997 data
The RECS data does not identify volumes for individual water-use zones. Given that indoor
spaces are designed to meet specific patterns of use, the Architectural Graphics Standards
published through the American Institute of Architects (Hoke, 1988, 1994) provides a basis for
assigning floor areas to specific zones. This resource summarizes the range of basic dimensions
for key zones for various sized households. The range of kitchen dimensions is keyed to the
number of people in the household. Table 30 summarizes this range for a household composed on
3-4 people (the predominant household size for 3-bedroom US homes). Bathroom dimensions, on
the other hand, are largely independent of the number of people. Floor areas have been
transformed to volume estimates assuming 8-foot (2.44 m) ceiling height.
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Table 30. Dimensions of Water-Use Zones
Zone
Hall Bath
Master Bath
Kitchen
Laundry
Shower
Dimension
Area (m2)
Volume (m3)
Area (m2)
Volume (m3)
Area (m2)
Volume (m3)
Area (m2)
Volume (m3)
Area (m2)
Volume (m3)
Low End
3.2
7.9
2.0
4.9
6.3
15.4
5.5
13.5
1.2
2.9
High End
6.1
14.9
3.5
8.5
7.4
18.1
10.4
25.4
1.8
4.5
Source: Hoke 1988, 1994
This range of zonal volumes is largely unverified in the professional literature, but the values in
Table 30 have the intuitive appeal of being derived from an authoritative source that guides
residential design. Residential laundry facilities, for the most part, are installed in a host space
rather than taking up a separate room. In homes featuring a heated basement, the laundry should
be positioned in that zone. In homes built to slab-on-grade and crawlspace designs, the laundry is
usually assigned to the kitchen, and the kitchen-laundry zone should be sized to accept both uses.
Selection of Indoor Volumes for Water-Use Zones: The range for zonal sizes are defined
from the Architectural Graphics Standards. For each type of water-use zone, each range
listed in Table 30 (above) will be used to define zone-specific uniform distributions.
Values assigned to individual model cases will be randomly selected from these
distributions within TEM.
3.4.2 Representation of Whole House Air Exchange Rates and Interzonal Airflows
The Exposure Factors Handbook (U.S. EPA 1997b) recommends using 0.45 ACH as the
"typical" value for air exchange in American residences. The national distribution of residential
air exchange is described in the Exposure Factors Handbook and summarized in Table 31. In the
absence of comprehensive measurement surveys, the distribution in Table 31 was derived from
analysis of perfluorocarbon tracer (PFT) data collected for a number of research programs since
the early 1980s (Koontz and Rector 1995).
Selection of Air Exchange Rate: The national distribution of residential air exchange rates
are defined from the Exposure Factors Handbook (See Table 31). Values assigned to
individual model cases will be randomly selected from this distribution within TEM.
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Table 31. Summary Statistics for US Residential Air Exchange Rates.
Arithmetic Mean (h"1)
Arithmetic Standard
Deviation (h"1)
Geometric Mean (h"1)
Geometric Standard
Deviation
10th Percentile (h'1)
50th Percentile (h'1)
90th Percentile (h'1)
Maximum (h"1)
West
Region
0.66
0.87
0.47
2.11
0.20
0.43
1.25
23.32
North Central
Region
0.57
0.63
0.39
2.36
0.16
0.35
1.49
4.52
Northeast
Region
0.71
0.60
0.54
2.14
0.23
0.49
1.33
5.49
South Region
0.61
0.51
0.46
2.28
0.16
0.49
1.21
3.44
All Regions
0.63
0.65
0.46
2.25
0.18
0.45
1.26
23.32
Source: U.S. EPA, 1997b
Given the simplified scenarios envisioned for initial model runs, interzonal airflows can be
assigned through the air exchange rate. That is, interzonal airflows would be sized by the air
exchange terms. The next level of complexity could utilize the algorithms developed by Koontz
and Rector (1995) from their analysis of the PFT data cited above. Under this scheme, the
normalized interzonal airflow (QN, h"1) for any zonal pair is defined as a function of the flow
from zone 1 to zone 2 (Qi2), flow from zone 2 to zone 1 (Q2i), and total (V, m3) such that:
n _ (612 + 621) 1
U»- 2 >
(9)
While the analysis showed differences in the correlation equations, the practical differences are
negligible in that both estimators produce a normalized interzonal airflow term of 0.22 h-1 at an
air exchange rate (I, h-1 ) of 0.45:
Bedroom: (^=0.078 + 0.317
Kitchen: Ow =0.046 + 0.397
(10)
(11)
It is expected that bathrooms are used with the door closed. Relatively little direct data exists to
define airflows. Experimental work by Giardino et al. (1996) provides useful values published in
a peer-reviewed journal. For a 13 m3 bath, these determinations found exiting airflow from the
bath to the adjacent hallway to be 4.2 m3 h"1 with the door closed and 15.1 m3 h"1 with the door
open. Similarly, entering airflows from the hallway to the bath were found to be 16.3 m3 h"1 with
the door closed and 47.9 m3 h"1 with the door open. These flows were utilized in subsequent
residential exposure modeling of radon volatilized from various water-use scenarios (Rector,
Wilkes, and Giardino, 1996)
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At higher levels of complexity, dynamic and engineering estimators can be applied to recognize
the influences of weather and operation of the heating/cooling system. These strategies are
discussed in a recent model strategies report (Rector et al., 2001).
A modeling study conducted by researchers at the National Institute of Standards and Technology
(NIST) developed simplified approaches to modeling interzonal dispersal of indoor contaminants
in homes served by central air-conditioning/heating systems (Persily, 1998). Under the NIST
study, patterns of fan operation were defined by the following rules:
• Airflows were assumed to be 50 L s"1 (180 m3 h"1) at major supply registers and 25 L s"1
(90 m3 h"1) at minor supply registers when the central air handler was running. (These
values are consistent with standard guidance in ASHRAE 1992).
• System on-time was assumed to be 60 percent (of the total timeframe) at design
conditions, (i.e., the highest temperature reached 98-99 percent of the time during the
cooling months, or the lowest outdoor temperature reached 98-99 percent of the time
during the heating months).
The NIST study also addressed local exhaust fans operating in the kitchen and bathrooms under
user control. Based on analysis of commercially-available equipment and engineering judgement,
kitchen exhaust flows were assigned to be 170 m3 h"1 (100 cfm), and bath exhaust flows in the
NIST study were assigned to be 80 m3 h"1 (47 cfm).
Selection of Interzonal and Exhaust Airflows: Interzonal airflows are scaled by the air
exchange rate using the algorithm developed by Koontz and Rector (1995). Exhaust
flows for the kitchen and bathrooms will be assigned in conformance with the NIST
study (170 m3 h-1 in the kitchen, 80 m3 h-1 in each bath, under user control). These
flows will be superimposed on the airflows that prevail when the fans are not operating.
3.4.3 Model Representation of Building
As described in Section 3.4.1, the house is idealized as a collection of compartments where water-
use zones are explicitly represented and the remaining indoor zones are lumped into a common
zone called "Rest of House", ROH. The volume parameters and the air exchange rate parameters
are specified in accordance with Sections 3.4.1 and 3.4.2. The idealized representation of the
house is presented in Figure 5.
3.5 Concentrations in Water Supply
The concentrations of DBFs in U.S. water supplies varies significantly across utilities. Several
surveys have reported the concentrations of some DBFs (Krasner et al., 1989, Westrick et al.,
1984, Miller et al., 1990, Richardson, 1998). In addition, a recent case study in two U.S.
municipal water systems shows wide variation across the system (Lynberg et al., 2000, Miles et
al., 2000). Also, USEPA has recently completed analysis of the Information Collection Rule
(ICR). U.S. EPA had collected data required by the Information Collection Rule (ICR) from
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Rest of House (ROH)
V=LN (316.7m3, 0.4218)-
Z Water Using Zones
Q = (0.078 + 0.31* WHACH) * 24
H >
*
_i
o
O
Water-Using Zones
Laundry
V = U (13.5, 25.4)
Kitchen
V = U(15.4, 18.1)
Q = (0.078 + 0.31* WHACH) * 24
Hall Bath
V = U (7.9, 14.9)
Q = (0.078 + 0.31* WHACH) * 24
Master Bath
V = U(4.9,8.5)
o
o
-------
drinking water utilities to support future regulation of disinfectants, and disinfection byproducts.
The rule intended to provide U.S. EPA with information on chemical byproducts that form when
disinfectants react with chemicals already present in source water. The following sections present
the results of various studies identifying concentrations in the water supplies of the 15
disinfection byproducts of interest listed in Table 1. The results from these studies are presented
in the following Tables 32 through 34. The results from these studies serve to help define a set of
concentrations to be used in this modeling study (Table 35).
With the exception of bromate, the results reported by Miltner et al. (1990) may be used to
quantify DBF concentrations in a distribution system. Section 3.5.1 discusses the concentration
of identified DBFs reported by Miltner et al. Section 3.5.2 discusses results from Miltner et al.
(1992), which modeled ozonation, and thus could be used to quantify bromate concentrations.
Table 32 summarizes the assumed concentration distributions identified by Miltner et al. (1990;
1992). Note that in all cases, it is assumed that the distributions describing the concentration of
each DBF is normal.
Table 32. Summary of DBF Concentrations Reported by Miltner et al. (1990)
Chemical
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
DBAN
Bromate
Filte
Mean
(ug/L)
55.50
24.40
10.20
0.35
1.44
30.85
20.10
0.29
1.50
8.50
3.50
0.20
1.90
0.15
0.00
r-Cl
Standard
Deviation
(ug/L)
2.01
1.52
0.85
0.30
0.10
1.49
0.97
0.02
0.12
0.06
0.43
0.06
0.24
0.07
0.00
03-F
Mean
(ug/L)
39.55
21.10
13.00
1.50
1.46
19.30
10.00
0.28
1.98
6.70
2.60
0.05
1.65
0.55
4.00
ilter-Cl
Standard
Deviation
(ug/L)
2.95
0.18
0.49
0.18
0.05
0.79
0.73
0.04
0.13
0.12
0.24
0.00
0.12
0.14
0.36
a. Based on Miltner et al. (1990; 1992)
b. The concentration of each DBF is assumed to be normal.
c. The standard deviation was calculated using mean and 95th percentile values developed below, along with the
assumption of normality.
d. Bromate is not an organic halogen and therefore this fraction is zero.
3.5.1 DBFs (Excluding Bromate)
U.S. EPA has performed a series of studies in its pilot water treatment plant in Cincinnati, Ohio to
quantify the impact of chemical disinfectants on DBF concentrations. Miltner et al. (1990)
describe the plant and its operation descriptions in detail. For this study, raw Ohio River water
was trucked to the USEPA and treated at 1.7 gpm. For the O3-filter-Cl treatment train, ozone was
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applied so that the transferred ozone/TOC (total organic carbon) ratio was approximately 80%.
Chlorine was applied in the clear well after filtration to yield a free residual near 0.2 mg/L in
samples taken from the clear wells and stored for 3 days to simulate distribution. Chlorine doses
were in the range of 2.8 to 3.0 mg/L, resulting in free chlorine residuals in clear well effluents
near 1.2 mg/L. Detention time in the clear wells was approximately 9.5 hours.
The mean and 95th percentile values listed in Table 33 were developed from data provided by
Miltner et al. (1990). Note that these statistics differ slightly from the distributions published by
Miltner et al. (1990) because of a recalculation of the means and confidence limits assuming a
normal distribution and substituting half the detection limit for non-detects in the Miltner et al.
data rather than replacing non-detects with zero, as in the original publication.
Table 33. Mean and
(Excluding
95th Percentile Concentrations for Identified DBFs
Bromate) from Miltner et al. (1990)
Chemical
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
DBAN
Bromate
Mean
(ng/L)
55.50
24.40
10.20
0.35
1.44
30.85
20.10
0.29
1.50
8.50
3.50
0.20
1.90
0.15
0.00
Filter-Cl
5th
percentile
(ng/L)
52.20
21.90
8.80
0.00
1.30
28.40
18.60
0.24
1.30
8.30
2.70
0.05
1.50
0.03
0.00
95th
percentile
(ng/L)
58.80
26.90
11.60
0.84
1.60
33.30
21.70
0.33
1.70
8.60
4.20
0.30
2.30
0.27
0.00
Mean
(ng/L)
39.55
21.10
13.00
1.50
1.46
19.30
10.00
0.28
1.98
6.70
2.60
0.05
1.65
0.55
4.00
O3-Filter-Cl
5th
percentile
(ng/L)
34.70
20.90
12.20
1.10
1.37
18.00
8.90
0.22
1.74
6.50
2.20
0.05
1.44
0.31
3.40
95th
percentile
(ng/L)
44.40
21.40
13.80
1.80
1.54
20.60
11.20
0.34
2.20
6.90
3.00
0.05
1.85
0.78
4.60
3.5.2 Bromate
Under water treatment plant conditions, chlorine will not react with bromide to form bromate.
Rather, chlorine reacts with bromide to form bromine, which reacts with organic compounds to
form brominated DBFs. Hence, in the case of the filter-Cl treatment train, the assumed bromate
concentration was zero.
Data from Miltner et al. (1992) were used to estimate bromate levels generated by the O3-filter-Cl
treatment train. Transfer efficiencies, gas/liquid ratios, liquid depths, ozone-to-TOC or DOC
ratios, pHs, and temperatures were similar to the corresponding conditions reported by Miltner et
al. (1990). Miltner et al. (1992) reported an ambient bromide concentration of 37 (ig/L. At
ozone/TOC ratios below 1 mg/mg, there was no measurable bromate (when the bromate detection
level was 7 (ig/L). In Shukairy et al. (1994), the ambient bromide concentration was 50.7 (ig/L.
At an ozone/TOC ratio near 0.8 mg/mg and a dissolved ozone residual near 0.6 mg/L, the
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bromate concentration was near 4 (ig/L. Thus, the estimate for bromate formation in this study
would be near 4 (ig/L, a level that is below the proposed MCL of 10 (ig/L. Replication data
described in U.S. EPA Method 300.1 for bromate suggests that the expected deviation at 4 (ig/L
would be ± 0.6 (ig/L. Table 34 describes the basis for the estimate.
Table 34. Estimated Bromate Formation in Ohio River Water by Ozonation, from Three
Studies
Parameter
Ozone/TOC, mg/mg
PH
Temperature, °C
Residual ozone, mg/L
Bromide, mg/L
Bromate, mg/L
Miltner et al., 1990
0.8
7.4-8.1
26-28
0.47
37- 50.7 b
4±0.6c'd
Study a
Miltner et al., 1992
<1
7.8-8.1
23-24
<0.47
37
<7
Shukairy et al., 1994
0.81
7.4-7.65
23-24
0.6
50.7
4
a. All studies utilize same contractor, similar conditions
b. Assumed
c. Estimated
d. Deviation based on replication data presented in U.S. EPA method 300.1
3.5.3 Water Concentrations Selected as Model Inputs
Table 35 presents the selected water concentrations that are used as inputs for the modeling study.
The concentration values were selected based on data presented in the "Stage 2 Occurrence and
Exposure Assessment for Disinfectants and Disinfection Byproducts (D/DBPs)" (The Cadmus
Group, Inc. 2001). For each chemical, the value was selected based on the 90th percentile
concentration for surface water supply systems.
Table 35. List of Selected Concentrations for Chemicals in Modeling Study
DBF Subclass
Trihalomethanes
(THMs)
Haloacetic Acids
(HAAs)
Haloacetonitriles
(HANs)
Miscellaneous
Chemical Name
Chloroform
Bromodichloromethane (BDCM)
Chlorodibromomethane (DBCM)
Bromoform
Chloroacetic acid (MCA)
Dichloroacetic acid (DCA)
Trichloroacetic acid (TCA)
Bromoacetic acid (MBA)
Dibromoacetic acid (DBA)
Bromochloroacetic acid (BCA)
Dichloroacertonitrile (DCAN)
Trichloroacetonitrile (TCAN)
Bromochloroacetonitrile (BCAN)
Dibromoacertonitrile (DBAN)
Bromate
Concentration
(nig/Liter)
0.070
0.023
0.015
0.0077
0.0051
0.032
0.034
0.01 (Guess)
0.0043
0.0091
0.0020
0.00014
0.0011
0.00081
0.0074
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3.5.4 Estimated Concentrations in Consumed Tap Water
This section presents the development of reasonable representations of the chemical
concentrations in consumed tap water for the 15 chemicals identified in Table 1. The
volatilization of contaminant occurs during the filling activity, from the water surface while
sitting in a glass or storage and as a result of any processing action. Each of these is analyzed
below, and a combined volatilization is calculated for a number of scenarios. The results of this
calculation are used to recommend estimated fractional volatilization and first order removal rate
constants for each chemical.
3.5.4.1 Volatilization During Filling
Volatilization during a filling activity occurs in much the same way as during any other faucet
use. There are differences in the volatilization occurring in the pool of water in a partially filled
glass of water and the film of water in the bottom of a sink.
The experiments from Howard and Corsi (1996) as well as those performed by Batterman et. al.
(2000) attempt to quantify this volatilization. Batterman et. al. implement an experiment meant
to represent an "experimental procedure portray (ing) the filling of a pitcher from the tap and then
the filling of a glass from the pitcher." The authors describe the procedure as follows: "The THM
stock solution (2 mg/mL of each THM) was diluted in a filled 4 L black bottle to obtain the test
mixture containing 100 (ig/L of each THM compound and then transferred to a typical covered
water pitcher (Rubbermaid, capacity = 2.34 L, filled to 1.96 L, height = 21.7 cm, dia = 12.2 cm,
material = resin) and used to fill glasses and mugs." According to the authors, the "water transfers
were done quickly (3-5 seconds) and at a minimal (2 cm) pouring height."
Unfortunately, neither the quick filling nor the filling height is typical of filling a glass of water
for consumption. Filling 1.96 L in 3 to 5 seconds yields a flowrate in the range of 23.5 to 39.2
L/min. A typical faucet has a possible flowrate ranging from 0 (user controlled) to approximately
11 L/min, with a typical faucet use being in the range of about 2-8 L/min (Wilkes, 2002a). The
large flowrate used by Batterman et. al. would significantly lower the opportunity for
volatilization. Although no behavioral studies have been identified that quantify the distance the
water must travel, it seems likely from personal experience that 2 cm would represent a
reasonable minimum, and a reasonable maximum is probably on the order of 12 - 15 cm. The
combination of the large flowrate and low height of the filling in the Batterman et. al. experiment
has the effect of significantly lowering volatilization, and therefore this research is not useful in
estimating the volatilization during filling.
Howard and Corsi (1996) conducted experiments measuring the volatilization resulting from
using the kitchen faucet. The most consequential differences between the Howard and Corsi
experiments and the filling of a glass or pitcher for consumption are the larger height of the drop
and the potential splashing that could occur when the water lands in the sink. Both of these
differences lead to a higher volatilization rate. Howard and Corsi measured the fractional
volatilization for 3 compounds: cyclohexane, toluene, and acetone. The chemical properties
impacting the volatilization rate for the three compounds measured by Howard and Corsi are
given in Table 36. The chemical properties impacting volatilization for the compounds being
modeled are given in Table 37. Table 38 summarizes the stripping efficiency measured by
Howard and Corsi for the 3 compounds. Based on the low Henry's Law Constants, no significant
volitilization is likely to occur for the non-THM DBFs. Therefore, the analysis of volatilization
prior to consumption presented in the following sections, is limited to the THMs.
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Table 36. Chemical Properties of Compounds Studied by Howard and Corsi (24° C)
Chemical
Cyclohexane
Toluene
Acetone
H (unitless)
7.1
0.27
0.0012
D, (cm2/sec)a
9.0 E -6
9.1 E -6
1.1 E-5
D2 (cm2/sec)b
0.088
0.085
0.11
a. DI is estimated using the Hayduk and Laudie method (Lyman etal, 1990, pp 17-20)
b. Dg is estimated using the Wilke and Lee method (Lyman etal, pp 17-13).
Table 37. Chemical properties of Compounds Being Modeled (24° C)
Chemical
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
Dibromoacetonitrile
H (unitless)
0.15
0.088
0.038
0.021
3.3 E-7
3.1 E-7
5 E-7
2.5 E-7
1.6 E-7
1.3E-6
1.6E-4
5.5 E-5
5.3 E-4
1.7 E-5
D, (cm2/sec)a
1.03 E-5
1.01 E-5
9.96 E -6
9.82 E -6
1.05 E-5
9.2 E -6
8.3 E -6
1.03 E-5
9.0 E -6
9.1 E -6
9.9 E -6
8.8 E -6
8.8 E -6
9.6 E -6
Dg (cm2/sec)b
0.094
0.089
0.086
0.083
0.092
0.082
0.074
0.087
0.077
0.078
0.090
0.083
??
0.082
a. DI is estimated using the Hayduk and Laudie method (Lyman et. al., 1990, pp 17-20)
b. Dg is estimated using the Wilke and Lee method (Lyman et. al., pp 17-13).
Table 38. Summary of Experimental Stripping Efficiencies for Cyclohexane,
Toluene, and Acetone
Flowrate
4.8
7.9
4.8
7.9
4.8
6.3
7.9
Aerator
None
None
Screen
Screen
Bubble Aerator
Bubble Aerator
Bubble Aerator
Strip
Cyclohexane
24
19
19
18
33
35
44
)ing Efficiency ("
Toluene
21
17
13
14
23
22
23
/o)a
Acetone
4.9
2.2
1.7
1.1
1.4
1.5
1.6
a. Measured by Howard and Corsi for Kitchen Sink Experiments; water temperature approximately 23° C.
3.5.4.2 Volatilization During Storage
After preparation and prior to consumption, the water may sit in a pitcher in the refrigerator or in
a glass or cup on the table. During this period, volatilization occurs at the liquid/air interface.
Batterman et. al. studied the rate at which this occurred for the four trihalomethanes at a variety
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of temperatures (4, 25, 30, and 100 degrees C) and in two containers (tall glass, wide mouth
glass) for a two hour period. Batterman et. al. fit the resulting measurements to an exponential
decay model with good results (R2 values for chloroform ranged from 0.59 to 0.86). Table 39
summarizes these results. The recommended fractions volatilized as a function of time are
summarized in Table 40 for three conditions (cold water, room temperature water, and hot water).
Table 39. Estimated Rate Constants from Batterman et. al.
Condition
Tall glass, full, water at 4 °C
Tall glass, full, water at 25 °C
Tall glass, half full, water at 25 °C
Wide mouth glass, full, water at 25 °C
Tall glass, full, water at 30 °C
Tall glass, half full, water at 30 °C
Wide mouth glass, full, water at 30 °C
Coffee mug, full, water at 100 °C
Chlon
k (h-1)
0.088
0.055
0.070
0.180
0.183
0.248
0.411
1.50
jform
R2
0.77
0.63
0.77
0.59
0.69
0.83
0.62
0.86
BD(
k (h-1)
0.076
0.046
0.064
0.110
0.135
0.205
0.427
1.52
:M
R2
0.78
0.53
0.64
0.30
0.65
0.90
0.80
0.82
DB(
k (h-1)
0.080
0.047
0.063
0.108
0.142
0.177
0.392
1.41
:M
R2
0.75
0.47
0.76
0.61
0.74
0.90
0.82
0.80
Brom<
k (h-1)
0.080
0.044
0.062
0.140
0.158
0.193
0.332
1.40
jform
R2
0.84
0.33
0.56
0.71
0.85
0.89
0.76
0.85
Table 40. Estimated Fractional Volatilization as a Function of Time for THMs for Cold, Room
Temperature, and Hot Water
Condition
Cold Water
(4C)
Room Temp
(25 C)
Hot Water
(100 C)
Chemical
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Rate
Const, k
(h1)
0.09
0.076
0.080
0.080
0.18
0.11
0.108
0.14
1.50
1.52
1.41
1.40
Fraction Volatilized
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0.007
0.006
0.07
0.07
0.015
0.009
0.009
0.012
0.12
0.12
0.11
0.11
10
0.015
0.013
0.013
0.013
0.030
0.018
0.018
0.023
0.22
0.22
0.21
0.21
15
0.022
0.019
0.020
0.020
0.044
0.027
0.027
0.034
0.31
0.32
0.30
0.30
30
0.044
0.037
0.039
0.039
0.086
0.054
0.053
0.068
0.53
0.53
0.51
0.50
60
0.086
0.073
0.077
0.077
0.16
0.104
0.102
0.13
0.78
0.78
0.76
0.75
Time,
75
0.11
0.091
0.095
0.095
0.20
0.13
0.13
0.16
0.85
0.85
0.83
0.83
mini
90
0.13
0.11
0.11
0.11
0.24
0.15
0.15
0.19
0.89
0.90
0.88
0.88
ites
105
0.15
0.13
0.13
0.13
0.27
0.18
0.17
0.22
0.93
0.93
0.92
0.91
120
0.16
0.14
0.15
0.15
0.30
0.20
0.19
0.24
0.95
0.95
0.94
0.94
180
0.24
0.20
0.21
0.21
0.42
0.28
0.28
0.34
0.99
0.99
0.99
0.99
240
0.30
0.26
0.27
0.27
0.51
0.36
0.35
0.43
1.0
1.0
1.0
1.0
360
0.42
0.37
0.38
0.38
0.66
0.48
0.48
0.57
1.0
1.0
1.0
1.0
420
0.47
0.41
0.43
0.43
0.72
0.54
0.53
0.62
1.0
1.0
1.0
1.0
480
0.51
0.46
0.47
0.47
0.76
0.59
0.58
0.67
1.0
1.0
1.0
1.0
3.5.4.3 Volatilization During Processing
A wide variety of activities influence the removal of compounds from tap water. These activities
include primarily heating and mixing activities that occur when using the water to make coffee,
tea, other water based beverages, and in the process of preparing food. Beverages made from tap
water fall into 2 primary categories: heated and non heated beverages. The non heated beverages
undoubtedly have some volatilization due to the process of mixing the water with any additives,
such as orange juice from concentrate. These losses have not been quantified in the literature
sources identified above. The heating of water greatly reduces the concentration of volatile
constituents. Batterman et. al. report a average chloroform loss of 81% resulting from bringing
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water to 100 °C (presumably from room temperature, although this is not stated) in a kettle. After
pouring the water into a mug, the measured fraction volatilized is an average of 85%.
3.5.4.4 Recommendations
The volatilization during filling appears to be correlated with the chemicals Henry's Law
constant, the liquid phase diffusivity, and the gas phase diffusivity. Table 41 presents a variety of
consumption scenarios and estimated volatilization fraction as a result of each scenario for each
of the THMs. Table 42 presents recommended values for model inputs for the THMs, DCA, and
TCA. The model uses an initial fraction volatilized and a rate constant to estimate the amount of
contaminant remaining at the time of consumption. The values presented in Table 42 for the
fraction of the compound remaining prior to consumption or storage accounts for an estimate of
the average amount volatilized as a result of filling a container with tap water. The rate constant
is used by the model to estimate the volatilization during storage or while a glass of water is
consumed over an extended period (e.g., used to represent the volatilization from a glass of water
over a period like 30 minutes when someone slowly sips the water). Except for the THMs, the
compounds presented in Table 37 have extremely low Henry's Law constants, and therefore the
amount volatilized is expected to be negligible. For this reason, it is assumed that no
volatilization occurs prior to consumption.
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Table 41. THM Consumption Scenarios
Scenario
Glass of water, room
temperature, immediate
consumption (over 5-10
minutes)
Glass of water, room
temperature, consumption over
1 hour
Glass of ice water, immediate
consumption (over 5-10
minutes)
Glass of ice water, consumption
over 1 hour
Hot beverage (e.g., coffee or
tea), consumed immediately
Hot beverage (e.g., coffee or
tea), consumed immediately
(over 20 minutes)
Prepared and stored beverages
(e.g., pitcher of orange juice),
prepared, stored cold (assume
average - 4 hours), poured,
consumed over 5-10 minutes
Prepared and stored beverages
(e.g., pitcher of orange juice),
prepared, stored cold (assume
average - 4 hours), poured,
consumed over 30 minutes
Chemical
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Chloroform
BDCM
DBCM
Bromoform
Filling
0.12
0.075
0.044
0.035
0.12
0.075
0.044
0.035
0.12
0.075
0.044
0.035
0.12
0.075
0.044
0.035
0.12
0.075
0.044
0.035
0.12
0.075
0.044
0.035
0.12C 0.12d
0.075 c 0.075 d
0.044 c 0.044 d
0.035 c 0.035 d
0.12d 0.12d
0.075 c 0.075 d
0.044 c 0.044 d
0.035 c 0.035 d
Fraction Volai
Storage a
0.013
0.008
0.008
0.010
0.084
0.053
0.052
0.067
0.007
0.006
0.006
0.006
0.044
0.037
0.039
0.039
0.11
0.11
0.11
0.11
0.23
0.23
0.22
0.22
0.29 e 0.007 f
0.25 e 0.006 f
0.26 e 0.006 f
0.26 e 0.006 f
0.29 e 0.02 f
0.25 e 0.02 f
0.25 e 0.02 f
0.26 e 0.02 f
ilized
Processing
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.85s
0.80s
0.72s
0.63s
0.85s
0.80s
0.72s
0.63s
0
0
0
0
0
0
0
0
Total"
0.13
0.08
0.05
0.04
0.19
0.12
0.09
0.10
0.13
0.08
0.05
0.04
0.16
0.11
0.08
0.07
0.88
0.84
0.76
0.68
0.90
0.86
0.79
0.72
0.38
0.36
0.33
0.32
0.39
0.37
0.33
0.32
a. Calculated using weighted averages for the appropriate time categories, with fractional volatilization as given in Table 40;
b. Total is calculated in a consecutive manner by multiplying fraction remaining after each activity (i.e., for coffee, hot,
consumed immediately; the initial concentration is reduced for filling by 18% to yield 82%, then the 82% is reduced by
85% because of heating to yield 12.3%, and finally the 12.3% is reduced by 23% to account for storage losses to yield
9%, or a fractional volatilization of .91);
c. Volatilization attributed to preparation;
d. Volatilization attributed to pouring from the pitcher into the glass;
e. Volatilization attributed to storage in the pitcher; f. Volatilization while in the glass; g. Taken from Batterman et. al.
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Table 42. Recommended Consumption Model Inputs for the THMs, DCA, and TCA
Chemical
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
Dibromoacetonitrile
Average Fract
Prior to S
Consul
Direct
0.80
0.90
0.95
0.95
1
1
1
1
1
1
1
1
1
1
ion Remaining
torage or
nption
Indirect
0.15
0.2
0.25
0.3
1
1
1
1
1
1
1
1
1
1
Volatilization Ratt
Direct
0.07
0.06
0.06
0.06
0
0
0
0
0
0
0
0
0
0
; Constant (h -1)
Indirect
0.4
0.4
0.4
0.4
0
0
0
0
0
0
0
0
0
0
3.6 Physiological Parameters
The ERDEM model requires sets of input parameters by chemical, by exposure, by compartment,
by demographic group, and by activity.
3.6.1 Compartment Volumes by Demographic Group
The user chooses the compartments to be modeled in ERDEM based on the information available
for the exposure chemical(s) and the metabolites. The compartments used for a metabolite may
be a subset of those used for the parent chemical. The body volume is first chosen for each
demographic group. The compartment volumes are then usually chosen as a percentage of the
body volume. The normally suggested compartments are the Arterial Blood, Liver, Static Lung,
Kidney, Fat, Slowly Perfused Tissue (muscle), Rapidly Perfused Tissue, Ovaries or Testes, and
the Venous Blood. The volume percentages depend on the chosen compartments. Table 43
presents the values used for the PBPK modeling presented in this report.
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Table 43. Volumes of Compartments by Percentage for PBPK Modeling with ERDEM
Parameter/Compartment
Volume of the Body (L) a
Arterial Blood (%) (estimated)
Dermis (%) b
Fat (%) c
Kidney (%) b
Liver (%) d
Ovaries (%)
Rapidly Perfused Tissue (%) d
Slowly Perfused Tissue
(including Muscle) (%) f
Static Lung (%) d
Testes (%) 8
Venous Blood (%) (estimated)
Male
(Age 15 - 45)
77.6
3
9
17
0.4
2.6
—
4.6
55.95
1.4
0.046
6
Female
(Age 15 - 45)
63.8
3
9
23
0.4
2.6
0.0063
4.6
49.99
1.4
—
6
Child
(Age 6)
22.5
3
9
17
0.4
2.6
—
4.6
55.99
1.4
0.0074
6
a. Body volumes, calculated from the Exposure Factors Handbook, Tables 7.2 and 7.3 adjusted for weight of clothes.
b. Value from Corley, et al (1990)
c. Fat content based on measurements by Fisher, et al (1998).
d. Fisher, etal (1990).
e. The ovarian volume of 4g is presented for the adult woman (ages 15-45). This value is low for most women in our
population group of 15-45. The value of 4g is consistent with the ovaries volume for a very young woman
(approximately 15 years old), based on values reported in Publication 23 of the International Commission on
Radiological Protection (ICRP, 1974). This value represents an approximate minimum value for the selected
population group.
f. Value estimated from the Fat content using Fisher, et al (1998)
g. A value of 35.7g was used as the testes volume for the adult male (ages 15-45). This value is consistent with the
mean value reported in ICRP-23 (1974) for a 20 to 30 year old male. A value of 1.67g was used as the testes volume
for the male child (age ~6), the mean value reported by ICRP-23 (1974) for a male between the ages of 5 and 10.
In Table 43 above, a value of 4 grams is presented as the ovarian volume for the adult woman
(ages 15-45). This value is low for most women in our population group of 15-45. The value of
4g is consistent with the ovaries volume for a very young woman (approximately 15 years old),
based on values reported in Publication 23 of the International Commission on Radiological
Protection (ICRP, 1974). This value represents an approximate minimum value for the selected
population group. A value of 35.7g was used as the testes volume for the adult male (ages 15-45).
This value is consistent with the mean value reported in ICRP-23 (1974) for a 20 to 30 year old
male. A value of 1.67g was used as the testes volume for the male child (age ~6), the mean value
reported by ICRP-23 (1974) for a male between the ages of 5 and 10.
3.6.2 Breathing Rates by Activity and Demographic Group
The breathing rates (alveolar ventilation rates, QA) based on the Exposure Factors Handbook,
Table 5.6 (U.S. EPA, 1997b) are presented in Table 44 for an adult male and female (15 - 45
years old) and a child of approximately age six for two activity levels: resting and sedentary.
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Table 44. Alveolar Ventilation Rates by Demographic Group and Activity
Activity Level
Rest
Sedentary
Alveolar Ventilation Rate (Liters/Hour)8
Male
(Age 15 - 45)
540
600
Female
(Age 15 - 45)
430
480
Child
(Age 6)
410
435
a. From Exposure Factors Handbook, Table 5-6, U.S. EPA, 1997b
3.6.3 Compartment Blood Flows by Activity and Demographic Group
The Cardiac Output is chosen by activity for each demographic group. ERDEM can handle as
many as nine activity scenarios. Usually only one is modeled. The compartment blood flows are
usually chosen as a percentage of the Cardiac Output. The compartments requiring blood flow
input are the Liver, Kidney, Fat, Dermis, Ovaries, Slowly Perfused Tissue (muscle), the Rapidly
Perfused Tissue, and the Testes. The percentages depend on the chosen compartments. A
proposed table of values is given in Table 45. The blood flows as a percentage of the Cardiac
Output are the same for each of the two activities: resting and sedentary. In addition, the blood
flows for the female were not adjusted from the male except for differences due to the Testes and
Ovaries.
Table 45. Blood Flows to Compartments by Percentage for PBPK Modeling with ERDEM
Compartment
Cardiac Output (L/hr)
Dermis (%)
Fat (%)
Kidney (%)
Liver (%)
Ovaries d
Rapidly Perfused
Tissue (%)
Slowly Perfused Tissue
(including Muscle) (%)
Testes d
M
At Rest
461.34s
4.8
4.8
19.4
23.7
—
27.0
19.0
1.3
Blood Flo\»
ale
Sedentary
512.60 a
4.8
4.8
19.4
23.7
—
27.0
19.0
1.3
vs (Percenta
Fer
At Rest
423.55 b
4.8
4.8
19.6
24.0
0.02
27.58
19.2
—
ge of Cardia
dale
Sedentary
472.8 b
4.8
4.8
19.6
24.0
0.02
27.58
19.2
—
c Output) c
Chi
At Rest
350.28 a
4.8
4.8
19.6
24.0
27.39
19.2
0.21
lda
Sedentary
371.64s
4.8
4.8
19.6
24.0
27.39
19.2
0.21
a. The ratio of male Cardiac Output to Alveolar Ventilation Rate was 0.85434 in Fisher, et al, (1998). This is used
here to estimate male Cardiac Output.
"• The ratio of female Cardiac Output to Alveolar Ventilation Rate was 0.985 in Fisher, et al, (1998). This is used
here to estimate the female Cardiac Output.
c. The blood flow percentages for the male are from Fisher, et al, (1998). The female was not modified except for the
changes due to the Ovaries and Testes.-
d. The blood flow for the Ovaries and Testes was determined from their volume relative to body weight.
3.6.4 Definition of the Exposure Scenarios for Each Exposure Route
The ERDEM simulations for exposure modeling will use time histories output from the TEM
model. There will be dermal, inhalation, and ingestion time histories. In addition, there will be
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 5 8
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an activity time history that supplies the alveolar ventilation rate as a function of time. The same
blood flow percentages are used for each activity at this time. There may be up to nine different
values for alveolar ventilation rate supplied. This method of inputs from TEM to ERDEM is in
current use and has been completely tested.
3.6.5 Skin Permeability Coefficients for Each Chemical
The skin permeation coefficient, called the Permeability Coefficient of Stratum Corneum, Kp, is
required for each chemical to be modeled. Kp values were calculated bases on biological and
physiochemical characteristics of the skin and the chemicals, respectively. Computations were
based on the method published by Poulin and Krishnana (2001), in which the value for the
partition coefficient of the chemical for lipid is combined with the fractional lipid and water
composition of human skin. For each of the 15 chemicals of interest, the Kp values used for
TEM and ERDEM are given in Table 46. Separate values were calculated based on the range of
lipid and water contents for human skin, accounting for the of Kp values demonstrated.
Table 46. Skin Permeability Coefficients
Chemical
Name
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
DBAN
Bromate
Kp
(cm/hr)
(measured)
0.13
—
—
—
—
—
—
—
—
—
—
—
—
—
—
Kp
(cm/hr)
(Krishnan, 2001)
0.0156-0.0393
0.0184-0.0478
0.0215-0.0577
0.0247-0.0681
0.0034-0.0040
0.0036-0.0041
0.0062-0.0081
0.0036-0.0041
0.0039-0.0046
0.0037-0.0044
0.0029-0.0033
0.0051-0.0064
0.0031-0.0036
0.0033-0.0038
0.0049-0.0058
Kpa
(cm/hr)
(other predictions)
—
—
—
—
—
1.84E-6
3.58E-6
—
—
—
—
—
—
—
—
Kpb
(cm/hr)
(est. possible
range)
0.015-0.15
0.018-0.18
0.021-0.22
0.024-0.25
.8E-6-0.01
.8E-6-0.01
3.5 E-6- 0.01
E-6-0.01
.0 E-6 -0.01
.0 E-6 -0.01
.0 E-6 -0.01
.0 E-6 -0.01
.0 E-6 -0.01
.0 E-6 -0.01
.0 E-6 -0.01
Kpc
Value Used as
Model Input
(cm/hr)
0.13
0.0331
0.0396
0.0464
0.0037
1.84E-6
3.58E-6
0.00385
0.00425
0.00405
0.0031
0.00575
0.00335
0.00355
0.00535
a. Personal communications with James McDougal, 1999
b. Range of possible Kp values estimated based on predictions and on measured/predicted values for other
compounds in the same class. For classes other than the THMs, no measurements have been identified, so the
range itself is somewhat uncertain.
c. The midpoint of the estimate range by Krishnan was used unless alternative information was available.
3.6.6 Rate Constants for the Gastro-Intestinal Model for Each Chemical
There are two models for the gastro-intestinal (GI) tract. Normally a Stomach/Intestine model is
used that requires absorption rate constants for the transport of chemical from the stomach to the
intestine, stomach to portal blood, and intestine to portal blood. Often only the stomach to portal
blood parameter is supplied. A second model, called the Full GI model may be used if bile flow
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or chylomicron flow need to be modeled. The latter model would require blood flows for the GI
compartment walls and food flow for the lumen (Stomach, Duodenum, Lower Small Intestine,
and the Colon). One can use a subset of these compartments. The rate constants are, in general,
different for each chemical. The four chemicals presented in Table 47 are the chemicals being
evaluated by the PBPK model, ERDEM.
Table 47. Gastro-Intestinal Permeation Rate Constants.
Stomach to Portal
Blood Rate Constant
Stomach to Intestine
Rate Constant
Intestine to Portal
Blood Rate Constant
Chloroform
5.0a
2.0a
6.0a
BDCM
13.65b
0.044b
2.18b
DCA
13.65b
0.044b
2.18b
TCA
13.65b
0.044b
2.18b
a. Values used by Blancato, 2001 for CHCLg modeling
b. Values from Abbas and Fisher,1997 and modified based on Staats et.al, 1990
3.6.7 Partition Coefficients for Each Chemical
The partition coefficients between the skin and blood and between the blood and air are required
for the fundamental uptake modeling in TEM. Partition coefficients for each physiological
compartment are given in Table 48 for the 15 DBFs of interest.
Table 48. Partition Coefficients Required for Fundamental Uptake Modeling in TEM
Chemical Name
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
BCAN
DBAN
Bromate
Skin/Blood e
1.62a
2.0 b
3.82
5.51
0.96
0.43 c
0.52 d
0.96
0.97
0.97
0.96
1.02
0.96
0.96
0.97
Blood/Air e
7.43 a
6.11b
10.26
25.89
46845.95
22995.65
387756.34
163836.96
1514909.77
349330.57
4110.45
8467.35
18035.71
31960.96
0.5
a. Estimates for CHC13 from Corley et.al., 1990
b. Estimates from Krishnan, 2001 and Lipscomb, 2001
c. Estimates for DCA and TCA from Fisher et.al., 1998
d. These values were estimated. Compartments were not used by Fisher et.al., 1998 for TCA modeling.
e. All other estimates from personal communication with John Lipscomb, 2001
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The partition coefficients for each physiological compartment in relation to the blood is presented
in Table 49 for each of the four chemicals that are modeled by ERDEM.
Table 49. Partition Coefficients Used by ERDEM
Compartmental
Relationship to
Venous Blood
Dermis/Blood
Fat/Blood
Kidney/Blood
Liver/Blood
Ovaries/Blood d
Rapidly Perfused
Tissue/Blood
Slowly Perfused
Tissue/Blood
Static Lung/Air
Static Lung/Blood
Testes/Blood d
Chloroform3
1.62
37.69
1.48
2.29
1.37
2.29
1.62
7.43
1.0
1.89
BDCMb
2.0 e
16.75
1.05
0.975
1.45
0.975
0.395
6.11
1.0 (Est)
2.06
DCAC
0.43 f
2.8 f
0.8
0.8
0.95
0.8 f
0.43
NA
0.16
0.99
TCAC
0.52 f
0.5 f
0.66
0.66
0.98
0.66 f
0.52
NA
0.47
1.04
a. Estimates for CHC13 from Corley et.al.
b. Estimates for BDCM from Gargas et.al., 1989
c. Estimates for DCA and TCA from Fisher et.al., 1998
d. Ovaries/Blood and Testes/Blood estimates determined by Krishnan, 2001, and Lipscomb, 2001
e. Estimates from Krishnan, 2001, and Lipscomb, 2001
f. These values were estimated. Fisher, et al, 1998 did not use these Compartments for TCA and DCA
modeling.
3.6.8 Metabolism Pathways and Rate Constants
There may be many different pathways hypothesized for a given chemical. A particular
metabolism definition must be chosen for each chemical for modeling purposes. The metabolism
processes are defined by rate constants if the metabolism is linear, or V-Max and Km (Michaelis-
Menten constant) if the metabolism is saturable. In addition, there may be additional parameters
required if the metabolism is inhibited by another chemical. Usually the metabolism is modeled
in the Liver compartment but it may be important to model metabolism in other compartments
such as the Kidney or Static Lung compartments. Chloroform is modeled as metabolizing in the
liver and kidney to Phosgene (CG) and Carbon Dioxide (CO2). The metabolism rate constants for
the four chemicals modeled by ERDEM are presented in Table 50.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 50. Metabolism Rate Constants
Variable
Liver Linear Metabolism Rate
Constant (/hr/kg)
Kidney Linear Metabolism Rate
Constant (/hr/kg)
Liver Metabolism Vmax
(mg/hr/kg)
Kidney Metabolism Ratio of
Kidney to Liver Vmax (mg/hr/kg)
Liver Metabolism Michaelis-
Mentin Constant (mg/Liter)
Kidney Metabolism Michaelis-
Mentin Constant (mg/Liter)
Chloroform
Metab to CO2:a
0.39917
Metab. to CO2:a
0.001857
Metab. to CG: a'b
15.7
Metab. to CG: a'b
0.033
0.448 c
0.448 c
BDCM"
~
~
12.8 d
~
0.5 d
~
DCA
~
~
~
~
~
~
TCA
~
~
~
~
~
~
a. Dr. Jerry Blancato, personal communication.
b. Phosgene.
c. Corley, etal,(1990).
d. John Lipscomb, personal communication.
3.6.9 Elimination Parameters
Many chemicals will have measurable elimination in the kidney and a few from the feces. Often
an elimination process is defined by chemical for other compartments when a reaction occurs that
does not result in a chemical that must be modeled further (such as a metabolite that stays in the
current compartment and is of no further interest). The elimination is usually linear but it can
also be of the saturable form. The elimination rate constants for the four chemicals modeled by
ERDEM are presented in Table 51.
Table 51. Elimination Rate Constants
Variable
Urine
Elimination Rate
Constant (/hr/kg)
Liver
Elimination Rate
Constant (/hr/kg)
Chloroform
~
~
BDCM
~
~
DCA
0.023 a
20.5 c
TCA
2.169 b
0.5785 d
a. Clewell,etal(1997)
b. Estimated from urine measurement data from Fisher, et al, (1998)
c. Estimated from mouse data of Abbas and Fisher, (1997)
d. Power, personal communication, from TCA PBPK model results fitted to data from Fisher, et all, (1998). (to
be reported)
3.7
Uptake Calculations
The dermal uptake calculation implemented in TEM is based on membrane equations developed
by Cleek and Bunge (Olin, 1999). This representation uses two simple functions, representing
the non-steady-state and steady-state periods. The dermal uptake does not account for issues such
as skin hydration and skin temperature.
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The ingestion uptake calculation implemented in TEM is based on the estimated water
concentrations at the time the water is consumed, and assumes that the entire mass of the
chemical in the consumed water is absorbed into the bloodstream.
The inhalation uptake calculation implemented in TEM is based on the predicted air
concentrations in the breathing zone. TEM implements an equilibrium calculation between the
inhaled air and the bloodstream. This calculation is described in Wilkes, 1999.
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4.0 Modeling Results
4.1 Model Execution
The Total Exposure Model, TEM, was set up as described in the above sections. Table 52
presents a summary of the chemical specific model parameters, Table 53 presents a summary of
the behavioral model inputs, and Table 54 presents a summary of the building related parameters.
The model is initiated with the inputs described in these tables, identifying the structure of the
household, the characteristics and locations of the water appliances, and the population groups for
the three-person household. For each simulation, activity patterns are sampled from the NHAPS
for the three defined population groups, the activities are mapped into the household, and the
appropriate water uses are simulated consistent with the activity patterns, as described in Section
2.1. The model is executed for 1000 simulations.
Subsequent to executing the exposure model, the results were interfaced with the PBPK model,
ERDEM. This was accomplished by creating a series of transfer files containing information on
breathing zone concentrations, respiratory rates, skin contact concentrations, skin contact area,
ingestion concentrations and quantities as a function of time for each of the simulations. These
results are input into ERDEM for 250 of the simulations to predict blood and organ
concentrations.
The results of the exposure modeling study are presented in Section 4.2, and the results of the
PBPK modeling study are presented in Section 4.3.
4.2 Exposure and Uptake Modeling Results
The exposure model, TEM, was initiated as described in earlier sections, and executed. The
results are in several forms:
1) An MS-Access database containing the results of:
• Each sampled parameter (eg., building volumes, building interzonal,
etc.).
• Sampled activity pattern
• Simulated activities (eg., water uses simulated within each sampled
activity pattern, ingestion behavior, etc.)
• Predicted air and water concentrations
• Predicted exposure and potential dose
• Predicted absorbed dose.
2) Transfer files to be used as input to the PBPK model (ERDEM)
These results are analyzed and presented in the following sections.
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Table 52. Summary of Chemical Specific Model Parameters
Parameter
Henry's Law @ 25 °C
Hemy'sLaw @30°C
Henry's Law @ 35 ° C
Henry's Law @ 40 °C
KOLA Shower (m3/h)
KOLA Bath, Fill (nrVh)
KOLA Bath, Pool (m3/h)
KOLA Clothes Washer,
Fill (m3/h)
KOLA Clothes Washer,
Wash (nrVh)
KOLA Clothes Washer,
Rinse (m3/h)
KOLA Toilet (m3/h)
KQL A Faucets® 35 °C
(m3/h)
KOLA Faucets @ 30° C
(m3/h)
Blood/Air Partition Coeff.
Skin Permeability Coeff.,
KD (cm/h)
Skin/Blood Partition
Coefficient
Ingestion Direct: Initial
Fraction Volatilized a
Ingestion Indirect: Initial
Fraction Volatilized a
Ingestion Direct: Rate
Const, for Volatilization b
Ingestion Indirect: Rate
Const, for Volatilization b
Chloroform
0.153
0.195
0.238
0.287
0.432
0.243
0.078
0.317
0.113
0.403
0.00468
0.128
0.117
0.135
0.13
1.62
0.80
0.15
0.07
0.4
BDCM
0.0929
0.119
0.150
0.188
0.428
0.228
0.0735
0.265
0.0637
0.265
0.00368
0.116
0.104
0.164
0.0330
2.0
0.9
0.2
0.06
0.1
DBCM
0.0397
0.0512
0.0654
0.0830
0.415
0.186
0.0625
0.174
0.0293
0.122
0.00312
0.0913
0.0792
0.0975
0.0396
3.82
0.95
0.25
0.06
0.4
Bromoform
0.0227
0.030
0.0393
0.0511
0.402
0.153
0.0531
0.124
0.0177
0.0735
0.00265
0.0731
0.0613
0.0386
0.0464
5.51
0.95
0.3
0.06
0.4
MCA
3.7E-7
6.2E-7
l.OE-6
1.9E-6
4.49E-4
1.05E-5
4.64E-6
5.24E-6
5.21E-7
2.16E-6
2.32E-7
5.07E-6
3.01E-6
2.13E-5
0.00370
0.96
1
1
0
0
DCA
3.4E-7
5.2E-7
7.9E-7
1.2E-6
4.37E-4
7.42E-6
3.27E-6
3.69E-6
3.67E-7
1.52E-6
1.63E-7
3.58E-6
2.32E-6
4.35E-5
1.84E-6
0.43
1
1
0
0
TCA
5.5E-7
8.8E-7
1.4E-6
2.1E-6
4.53E-4
1.22E-5
5.39E-6
6.08E-6
6.05E-7
2.51E-6
2.69E-7
5.89E-6
3.68E-6
2.59E-6
3.58E-6
0.52
1
1
0
0
MBA
2.7E-7
4.5E-7
7.3E-7
1.2E-6
4.41E-4
1.33E-5
3.56E-6
3.54E-6
2.69E-7
1.14E-6
1.78E-7
4.26E-6
2.58E-6
6.1E-6
0.00385
0.96
1
1
0
0
DBA
1.8E-7
2.9E-7
4.5E-7
7.1E-7
4.28E-4
4.12E-6
1.81E-6
2.05E-6
2.04E-7
8.46E-7
9.06E-8
1.99E-6
1.23E-6
6.6E-7
0.00425
0.97
1
1
0
0
BCA
1.31E-6
C
C
C
4.39E-4
1.20E-5
5.28E-6
5.97E-6
5.94E-7
2.46E-6
2.64E-7
5.78E-6
5.67E-6
2.86E-6
0.00405
0.97
1
1
0
0
DCAN
1.55E-4
C
C
C
0.00381
0.00290
7.71E-4
7.73E-4
8.95E-5
2.51E-4
3.26E-5
9.28E-4
9.08E-4
2.43E-4
0.00310
0.96
1
1
0
0
TCAN
5.4E-7
C
C
C
0.00143
5.18E-4
2.28E-4
2.59E-4
2.58E-5
1.07E-4
1.14E-5
2.50E-4
2.44E-4
1.18E-4
0.00575
1.02
1
1
0
0
DBAN
1.66E-5
C
C
C
7.24E-4
1.57E-4
6.90E-5
7.81E-5
7.78E-6
3.23E-5
3.45E-6
7.58E-5
7.41E-4
3.13E-5
0.00355
0.96
1
1
0
0
NOTES: BCAN and Bromate were not modeled because of a lack of chemical parameters, a. The initial fraction volatilized is the assumed amount volatilized during the filling activity;
b. The rate constant for volatilization is the rate at which the chemical is assumed to volatilize during storage; c. These values are not available, therefore the values for H at 25 C are used.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 66
-------
Table 53. Summary of Water-Use Behavioral Model Inputs
Water Use
Water
Temperature
(°C)
Frequency
(events per person
per day)
Dun
Geometric
Mean
(min)
ition
Geometric
Std Dev
Flowrate
(gpm)
Volume
(gal)
Fill
Duration
(min)
Cycle
Duration
(min)
Female, Ages 15-45
Shower
Bath
Toilet
Faucet - Kitchen
Faucet - Bathroom
Faucet - Laundry
40
35
25
35
35
30
1.12
0.38
6
6.1
6.1
3.4
???
???
NA
???
???
???
???
???
NA
???
???
???
2.40
NA
NA
1.2
1.2
1.2
NA
50
3.5
NA
NA
NA
NA
8
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Male, Ages 15-45
Shower
Bath
Toilet
Faucet - Kitchen
Faucet - Bathroom
Faucet - Laundry
40
35
25
35
35
30
1.24
021
6
6.1
6.1
3.4
???
???
NA
???
???
???
???
???
NA
???
???
???
2.40
NA
NA
1.2
1.2
1.2
NA
50
3.5
NA
NA
NA
NA
8
NA
NA
NA
NA
NA
NA
NA
NA
Child, Age 6
Shower
Bath
Toilet
Faucet - Kitchen
Faucet - Bathroom
Faucet - Laundry
40
35
25
35
35
30
0.55
0.48
6
6.1
6.1
3.4
???
???
NA
???
???
???
???
???
NA
???
???
???
2.40
NA
NA
1.2
1.2
1.2
NA
50
3.5
NA
NA
NA
NA
8
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Household Water Uses
Clothes Washer
Dishwasher
35
35
0.99 events per 3
person household
per day
0.54 events per 3
person household
per day
NA
NA
NA
NA
NA
16.6 (Wash)
2 1.0 (Rinse)
4.25 (Wash)
4.25 (Rinse)
3.3 (Wash)
4.2 (Rinse)
NA
7.4 (Wash)
9.8 (Rinse)
30 (Wash)
30 (Rinse)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 67
-------
Table 54. Summary of Building Related Model Inputs
Parameter
Whole House Volume
Laundry Room Volume
Kitchen Room Volume
Hall Bath Room Volume
Master Bath Room Volume
Shower Room Volume
Rest of House (ROH) Volume
Whole House ACH (h'1)
ROH Airflow
ROH to Laundry Airflow
ROH to Kitchen Airflow
ROH to Hall Bath Airflow
ROH to Master Bath Airflow
Bath to Shower Airflow
Representation
Lognormal, Geometric Mean = 316.7 m3; Geometric Standard
Deviation = 0.42 18
Uniform Distribution, Minimum =13.5 m3; Maximum = 25.4 m3
Uniform Distribution, Minimum = 15.4 m3; Maximum = 18.1 m3
Uniform Distribution, Minimum = 7.9 m3; Maximum = 14.9 m3
Uniform Distribution, Minimum = 4.9 m3; Maximum = 8.5 m3
Uniform Distribution, Minimum = 2.9 m3; Maximum = 4.5 m3
Whole House Volume - SWater Using Zone Volumes
Lognormal, Geometric Mean = 0.46 h"1; Geometric Standard
Deviation = 2.25
Whole House ACH * Whole House Volume
0.078 +0.31 * Whole House ACH
0.078 +0.31 * Whole House ACH
0.078 +0.31 * Whole House ACH
0.078 +0.31 * Whole House ACH
0.078 +0.31 * Whole House ACH
4.2.1 Analysis of Results of Water Use Behavior
The water-use parameters presented in Section 3.2 are entered as model inputs to the exposure
model. Under ideal conditions, the model would simulate water uses with characteristics
essentially identical to these parameters. However, shortcomings or inconsistencies between these
specified water use characteristics and the behavioral characteristics recorded in the activity
patterns result in the inability to simulate all the desired water uses. For example, many activity
pattern records report virtually no bathroom visits, and many others never report entering the
kitchen.
TEM adjusts for activity patterns that have no opportunity for a particular water use to occur by
calculating a "conditional" frequency. The conditional frequency is calculated by pre-processing
the sampled activity patterns to determine the number that have an opportunity for each water use
to occur, and then adjusting the desired frequency to account for the records that do not have
eligible locations and activities. However, in many records, an opportunity exists, but the duration
is very brief, which also results in a lower simulated frequency, and duration of water use.
4.2.2 Uptake Modeling Results
The simulation results for absorbed dose are analyzed for each chemical as a function of route
(dermal, ingestion, and inhalation) and presented in the following sections. For each chemical, a
table containing the absorbed does is presented as a function of route, population group, and
percentile of the population. The route-specific and total absorbed dose given for various
percentiles of the population, are calculated for the specific route, and therefore, for a given
percentile, the member of the population is likely to be different for each route (e.g., the person
who has the 50th percentile absorbed dose by the inhalation route is not the same person as has the
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 68
-------
50th percentile dermal absorbed dose). For each chemical, the cumulative distribution function
for absorbed dose is plotted along with histograms of the route specific absorbed dose.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 69
-------
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 70
-------
4.2.2.1 Uptake Results for Chloroform
The following Table 55 presents the resultant absorbed dose of chloroform from the analysis of
the dermal, ingestion and inhalation exposure routes for each of the population groups: female
age 15-45, male age 15-45, and child age 6. Figure 6 presents the resultant cumulative
distribution function plots for the analysis of absorbed dose of chloroform and Figures 7, 8, and 9
present the histograms for absorbed dermal dose, inhalation dose, and ingestion dose,
respectively, for the female, male and child populations. Figure 10 presents the total absorbed
chloroform dose.
Table 55. Chloroform Absorbed Dose Results
Percentile
Total b
c
Dermal
Total
hloroform ^
Ingestion
Direct
Absorbed Do
Ingestion
Indirect
se, mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
1.95E-02
4.02E-02
6.40E-02
1.42E-01
3.00E-01
6.04E-01
1.03E+00
1.52E+00
8.56E+00
Oa
Oa
9.54E-04
2.44E-03
2.51E-02
5.13E-02
9.18E-02
1.31E-01
2.12E-01
2.79E-03
4.95E-03
6.64E-03
1.13E-02
2.09E-02
4.09E-02
7.45E-02
9.46E-02
2.30E-01
1.25E-03
1.67E-03
1.93E-03
2.65E-03
3.76E-03
5.16E-03
6.94E-03
7.88E-03
1.11E-02
6.04E-03
8.75E-03
1.05E-02
1.54E-02
2.52E-02
4.47E-02
7.86E-02
9.95E-02
2.32E-01
4.37E-04
l.OOE-02
3.09E-02
8.33E-02
2.19E-01
5.01E-01
9.41E-01
1.41E+00
8.47E+00
Male, Age 15-45
1
5
10
25
50
75
90
95
99
1.76E-02
3.76E-02
6.07E-02
1.43E-01
3.02E-01
6.17E-01
1.07E+00
1.56E+00
6.99E+00
Oa
Oa
Oa
2.04E-03
2.62E-02
5.40E-02
8.92E-02
1.18E-01
2.02E-01
2.07E-03
4.19E-03
5.78E-03
1.10E-02
2.16E-02
4.19E-02
7.87E-02
1.17E-01
1.93E-01
6.07E-04
1.10E-03
1.42E-03
2.26E-03
4.00E-03
7.29E-03
1.22E-02
1.75E-02
2.88E-02
5.31E-03
8.54E-03
1.10E-02
1.64E-02
2.84E-02
4.83E-02
8.42E-02
1.25E-01
1.96E-01
3.75E-04
9.69E-03
2.16E-02
6.70E-02
2.13E-01
5.20E-01
9.69E-01
1.52E+00
6.88E+00
Child, Age 6
1
5
10
25
50
75
90
95
99
8.96E-03
1.86E-02
3.07E-02
6.70E-02
1.56E-01
3.40E-01
6.55E-01
8.63E-01
1.32E+00
Oa
Oa
Oa
6.03E-04
1.87E-03
2.79E-02
4.84E-02
6.11E-02
8.89E-02
1.26E-03
2.34E-03
3.15E-03
5.60E-03
1.09E-02
2.08E-02
3.56E-02
4.73E-02
8.78E-02
9.66E-05
1.90E-04
2.76E-04
5.15E-04
9.19E-04
1.83E-03
3.20E-03
4.45E-03
6.99E-03
1.97E-03
3.39E-03
4.36E-03
7.11E-03
1.26E-02
2.24E-02
3.70E-02
4.80E-02
9.01E-02
2.17E-04
4.00E-03
1.03E-02
3.75E-02
1.19E-01
3.04E-01
6.18E-01
7.74E-01
1.30E+00
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 71
-------
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
234
Absorbed Chloroform Dose, mg
Dermal
Ingestion
nhalation
- - Total
234
Absorbed Chloroform Dose, mg
LJ_
8 50"
n -
f'f ^~~T^frs~
ll ^<^^
r^^"
;' /s'' Child, Age 6
/.' — D£
i /' In
t / • .
- - Tc
rmal
jestion
tal
//
'//
//
i
0.2 0.4 0.6 0.8
Absorbed Chloroform Dose, mg
1.2
Figure 6. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed Chloroform Dose for Females, Males and Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 72
-------
0.5
c
.g
"m n A
"5
a.
o
0-03
o
o
'•ft n 9
03
i_
LL.
0.1
n -
i
8
o
d
H
• Female,
DMale, Ag
D Child, Ac
Age 15-45
e 15-45
je6
f
? s
o o
ci ci
•
1 111 1 • Jl *-L •-, „ — m
r- CD l£> -*• CM T-
in r- en i- co in
O O O T- T- T-
ci ci ci ci ci ci
R §
T- If)
d d
Absorbed Chloroform Dose: Dermal, mg
Figure 7. Histogram for Absorbed Dermal Chloroform Dose for Females, Males and
Children.
0.5
O
*;o4
"5
a.
o
0-03 .
O
g
•K n 9
OJ
k.
LJ_
n 1
o
8
ci
o
o
•
L
f I Hi n t
O5 O5 O5 O5 OC
l^ T— ID CO C^
CM "3- in CD oc
ci ci ci ci c
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
*rfin_fl
i oo oo oo oo
I en ^ CM CM
i d ^ ^ ^
Absorbed Chloroform Dose: Inhalation, mg
Figure 8. Histogram for Absorbed Inhalation Chloroform Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 73
-------
0.5 -
c
o
"5
Q.
O
0-n,
:tion of
D C
J C
i±!
n
r
1
•
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
r
1 1' H
0.6
0.5 -
0.4
3
Q.
O
0.3
0.2
0.1
Absorbed Chloroform Dose: Ingestion, mg
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
-
|-|
1 1 ft IL IT m_ _ „ n
OCOLOCOT-'^'CDCnCM'^'^
Absorbed Chloroform Dose: Direct Ingestion, mg
0.5 -
c
.0
Q.
O
2: 0.3
o
c
.0
S
u.
r
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
j
_
•
O T- (N CO
8888
1
^r
8
-
l|
CD
8
.tltl^l
|^
8
CO O) O
885
rn
o
o
Absorbed Chloroform Dose: Indirect Ingestion, mg
Figure 9. Histograms for the Absorbed Chloroform Ingestion Dose for Females, Males
and Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 74
-------
0.5 -
c
0
"S n A
a.
o
°- 03
O
c
O
*• n 9
03
i_
LL.
01
n
8
o
o
-
co
K
O
-I
fc
CO
o
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
tl
111 1 1 1 li rh ^^ ^ IT
SS5S?8S^
U5I^O5-!-COl£)CDO5
cicici-^-^-^-^cb
Total Absorbed Chloroform Dose, mg
Figure 10. Histogram for the Total Absorbed Chloroform Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 75
-------
4.2.2.2 Uptake Results for BDCM
The following Table 56 presents the resultant absorbed dose of BDCM from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 11 presents the resultant cumulative distribution
function plots forthe analysis of absorbed dose of BDCM and Figures 12, 13, and 14 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 15 presents the total absorbed BDCM dose.
Table 56. BDCM Absorbed Dose Results
Percentile
Total b
Dermal
Total
BDCM Abs
Ingestion
Direct
>orbed Dose
Ingestion
Indirect
.nig
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
7.20E-03
1.35E-02
1.92E-02
3.96E-02
8.00E-02
1.66E-01
2.79E-01
4.13E-01
2.41E+00
Oa
Oa
1.54E-04
3.71E-04
2.70E-03
5.21E-03
8.67E-03
1.21E-02
1.87E-02
1.03E-03
1.83E-03
2.46E-03
4.19E-03
7.73E-03
1.51E-02
2.76E-02
3.50E-02
8.49E-02
5.64E-04
7.64E-04
8.86E-04
1.23E-03
1.71E-03
2.37E-03
3.18E-03
3.61E-03
5.05E-03
2.49E-03
3.51E-03
4.14E-03
6.05E-03
9.72E-03
1.69E-02
2.95E-02
3.70E-02
8.60E-02
1.12E-04
2.66E-03
8.78E-03
2.35E-02
6.12E-02
1.42E-01
2.64E-01
3.88E-01
2.38E+00
Male, Age 15-45
1
5
10
25
50
75
90
95
99
6.25E-03
1.27E-02
1.97E-02
3.88E-02
8.43E-02
1.64E-01
2.95E-01
4.36E-01
1.93E+00
Oa
Oa
Oa
3.09E-04
2.90E-03
5.57E-03
8.73E-03
1.13E-02
1.84E-02
7.64E-04
1.55E-03
2.14E-03
4.05E-03
7.98E-03
1.55E-02
2.91E-02
4.31E-02
7.14E-02
2.79E-04
4.95E-04
6.49E-04
1.05E-03
1.85E-03
3.37E-03
5.67E-03
7.93E-03
1.31E-02
2.18E-03
3.42E-03
4.35E-03
6.52E-03
1.11E-02
1.86E-02
3.19E-02
4.68E-02
7.28E-02
1.01E-04
2.64E-03
6.07E-03
1.89E-02
6.05E-02
1.46E-01
2.74E-01
4.23E-01
1.91E+00
Child, Age 6
1
5
10
25
50
75
90
95
99
3.51E-03
6.98E-03
l.OOE-02
1.95E-02
4.38E-02
9.48E-02
1.81E-01
2.29E-01
3.58E-01
Oa
Oa
Oa
9.26E-05
2.66E-04
2.67E-03
4.48E-03
5.63E-03
8.03E-03
4.66E-04
8.66E-04
1.17E-03
2.07E-03
4.02E-03
7.68E-03
1.32E-02
1.75E-02
3.25E-02
1.13E-04
2.26E-04
3.28E-04
6.03E-04
1.07E-03
2.17E-03
3.80E-03
5.37E-03
8.16E-03
1.10E-03
1.73E-03
2.27E-03
3.50E-03
6.03E-03
9.89E-03
1.53E-02
1.88E-02
3.54E-02
5.71E-05
1.13E-03
2.98E-03
1.07E-02
3.36E-02
8.56E-02
1.73E-01
2.19E-01
3.51E-01
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 76
-------
0.6 0.8 1 1.2
Absorbed BDCM Dose, mg
Male, Age 15-45
Dermal
Ingestion
Inhalation
— 'Total
0.4 0.6 0.8 1 1.2
Absorbed BDCM Dose, mg
1.4
1.6
100
90
80
70
60
u.
8 50
40
30
20
10
0
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
7L
1.8
0.05 0.1 0.15 0.2 0.25
Absorbed BDCM Dose, mg
0.3
0.35
Figure 11. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed BDCM Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 77
-------
c
o
.± n oc
is 0-Jb
5 n ^
o
n
n 9^
o
c n 9
.2
o 015_
it
n 1
n
J
1
c
o
-
CM
O
O
ci
I
• Female, Age 15-45
D Male, Age 15-45
D nhild Ag<= R
p. B_^
111 1 111 •-, •_.
ill i ill i ill lid., r
^~ 1"^ O) •*— OOLOOOOOO
8000000-??
ooooooooo
cicicicicicicicici
A
Absorbed BDCM Dose: Dermal, mg
Figure 12. Histogram for Absorbed Dermal BDCM Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A
c
•- 0 35 -
1 m
o
0-025 -
O
c n 9
.2
"§015 -
k.
LJ_
n 1
n nc;
o
o
CO
a
o
o
m
• I
f
1
1
l£>
9
O
O
•
IT.
f 1 f. ^
1 Hi firm
00 i- CO CD
R & s $
O O T- T-
ci ci ci ci
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
T
co ^ oo oo
T- T- C\i C\i
ci ci ci ci
A
Absorbed BDCM Dose: Inhalation, mg
Figure 13. Histogram for Absorbed Inhalation BDCM Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 78
-------
0.5 -
.o
s
Q.
O
%G
O
c
.0
5
u.
n -
r
f
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1
1 III 1 1 ft n. rt m^ _ rfl
Absorbed BDCM Dose: Ingestion, mg
0.5 -
.2
"5
Q.
O
D- 0 3
O
c
°
2
LJ-
n -
-
n
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
|-|
1 III 111 li ru i-u ^ „ H
Absorbed BDCM Dose: Direct Ingestion, mg
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
Absorbed BDCM Dose: Indirect Ingestion, mg
Figure 14. Histograms for the Absorbed BDCM Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 79
-------
n A^
OA
c
o
IS '
= no
o
^~ n 9c>
o
c n 9
.2
t> n HE
LJ_
0-1
n n^ -
n
._
i
• Female, Age 15-45
DMale, Age 15-45
H Child, Age 6
|
1 l~"l •>, tfl
1 in Illr liHd^«-^IL
CM
8
ci
CM
ci
O O O O O O
Total Absorbed BDCM Dose, mg
CO
ci
%
o
oq
A
Figure 15. Histogram for the Total Absorbed BDCM Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 80
-------
4.2.2.3 Uptake Results for DBCM
The following Table 57 presents the resultant absorbed dose of DBCM from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 16 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of DBCM and Figures 17, 18, and 19 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 20 presents the total absorbed DBCM dose.
Table 57. DBCM Absorbed Dose Results
Percentile
Total b
Dermal
Total
DBCM Abs
Ingestion
Direct
>orbed Dose
Ingestion
Indirect
.nig
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
5.02E-03
9.23E-03
1.32E-02
2.60E-02
5.12E-02
1.03E-01
1.74E-01
2.56E-01
1.38E+00
Oa
Oa
1.52E-04
3.64E-04
2.47E-03
4.48E-03
7.34E-03
9.88E-03
1.52E-02
7.11E-04
1.26E-03
1.69E-03
2.89E-03
5.33E-03
1.04E-02
1.90E-02
2.41E-02
5.85E-02
4.60E-04
6.24E-04
7.25E-04
1.01E-03
1.40E-03
1.94E-03
2.60E-03
2.95E-03
4.14E-03
1.90E-03
2.60E-03
3.07E-03
4.45E-03
7.03E-03
1.19E-02
2.05E-02
2.57E-02
5.94E-02
9.36E-05
1.67E-03
5.29E-03
1.43E-02
3.73E-02
8.58E-02
1.60E-01
2.31E-01
1.36E+00
Male, Age 15-45
1
5
10
25
50
75
90
95
99
4.56E-03
8.95E-03
1.34E-02
2.62E-02
5.49E-02
1.05E-01
1.79E-01
2.68E-01
1.04E+00
Oa
Oa
Oa
3.03E-04
2.64E-03
4.93E-03
7.34E-03
9.58E-03
1.51E-02
5.26E-04
1.07E-03
1.47E-03
2.79E-03
5.50E-03
1.07E-02
2.00E-02
2.97E-02
4.92E-02
2.28E-04
4.04E-04
5.30E-04
8.57E-04
1.52E-03
2.75E-03
4.63E-03
6.48E-03
1.07E-02
1.65E-03
2.54E-03
3.21E-03
4.71E-03
8.10E-03
1.34E-02
2.27E-02
3.24E-02
5.04E-02
6.59E-05
1.61E-03
4.06E-03
1.22E-02
3.79E-02
9.06E-02
1.63E-01
2.42E-01
1.03E+00
Child, Age 6
1
5
10
25
50
75
90
95
99
2.34E-03
4.83E-03
6.98E-03
1.35E-02
2.91E-02
6.35E-02
1.19E-01
1.55E-01
2.51E-01
Oa
Oa
Oa
9.05E-05
2.59E-04
2.26E-03
3.69E-03
4.68E-03
6.47E-03
3.21E-04
5.96E-04
8.02E-04
1.43E-03
2.77E-03
5.29E-03
9.06E-03
1.20E-02
2.24E-02
8.09E-05
1.63E-04
2.36E-04
4.33E-04
7.72E-04
1.56E-03
2.73E-03
3.88E-03
5.86E-03
7.74E-04
1.21E-03
1.58E-03
2.45E-03
4.18E-03
6.92E-03
1.05E-02
1.32E-02
2.44E-02
4.16E-05
6.75E-04
2.00E-03
6.91E-03
2.21E-02
5.64E-02
1.11E-01
1.48E-01
2.50E-01
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 81
-------
100 T
100
100
90
80
70
60
u.
8 50
40
30
20
10
0
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.4 0.6 0.8
Absorbed DBCM Dose, mg
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.4 0.6 0.8
Absorbed DBCM Dose, mg
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
7L
zz:
0.05 0.1 0.15
Absorbed DBCM Dose, mg
0.2
1.2
1.2
0.25
Figure 16. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed DBCM Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 82
-------
n A^
n A
c
o
.=. r\ oc
^O.db
5 n ^
o
n
n o^
o
c 09
.2
IS 015-
ul
0.1 -
0.05-
• Femal
nMale, i
HChild,
I
O
O
=
_
s, Age 15-45
*\ge 15-45
Age 6
i ..i ^i H n 1
§00 CM in en co i^-
o o o o o o
o o o o o o o
ci ci ci ci ci ci ci
CO
o
o
o
111
o
ci
-i
>0.0387
Absorbed DBCM Dose: Dermal, mg
Figure 17. Histogram for Absorbed Dermal DBCM Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
0.45-
0.4-
c
£ 0.35 -
_ns
Q. 0.3-
o
2:o.25-
o
I °-2~
LJ.
0.1 -
0.05-
0-
•
~
n
n
Female, Age 15-45
Male, Age 15-45
Child, Age 6
IF
1
O CM -fr
8 £ 8
o o o
ci ci ci
t
0.0457
TIT
dltn
lit nn
O5 i- CO CD
§CD T- CD
Is- C35 O
O O O T-
ci ci ci ci
J~
1
Hi In 1
CX3 O O
r\i co co
5 5 5
Absorbed DBCM Dose: Inhalation, mg
Figure 18. Histogram for Absorbed Inhalation DBCM Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 83
-------
Absorbed DBCM Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
USjkjtLJi H
Absorbed DBCM Dose: Direct Ingestion, mg
0.5 -
c
o
15 0.4 -
3
Q.
O
to.3-
O
c
o
t)0.2 -
s
u.
0.1 -
n
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
J"
1
1 1 I J1-, Jll _TL _
1
CD CD
8888888888S
ooooooooooo
A
Absorbed DBCM Dose: Indirect Ingestion, mg
Figure 19. Histograms for the Absorbed DBCM Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 84
-------
n A^
OA
c
o
IS '
= no
o
^~ n 9c>
o
c n 9
.2
t> n HE
LJ_
0-1
n n^ -
n -
o
8
g-
CD
1-^
H
iE
U5
• Female, Age 15-45
DMale, Age 15-45
H Child, Age 6
J-
|
1 III |~L |~i «_^ f\
i nil i in ir ri^«^^^iL
I^CMOOCOO^^OIO
CMOI"^LOCMOOOLO
OOT-COCDCnCMSIP
o o o o o o
Total Absorbed DBCM Dose, mg
CM
A
Figure 20. Histogram for the Total Absorbed DBCM Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 85
-------
4.2.2.4 Uptake Results for Bromoform
The following Table 58 presents the resultant absorbed dose of Bromoform from the analysis of
the dermal, ingestion and inhalation exposure routes for each of the population groups: female
age 15-45, male age 15-45, and child age 6. Figure 21 presents the resultant cumulative
distribution function plots for the analysis of absorbed dose of Bromoform and Figures 22, 23,
and 24 present the histograms for absorbed dermal dose, inhalation dose, and ingestion dose,
respectively, for the female, male and child populations. Figure 25 presents the total absorbed
Bromoform dose.
Table 58. Bromoform Absorbed Dose Results
Percentile
Total b
B
Dermal
Total
romoform £
Ingestion
Direct
Jbsorbed Do
Ingestion
Indirect
se, mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
4.55E-03
6.83E-03
9.23E-03
1.54E-02
2.65E-02
4.94E-02
7.79E-02
1.12E-01
6.28E-01
Oa
Oa
1.02E-04
2.43E-04
1.60E-03
2.84E-03
4.64E-03
6.24E-03
9.45E-03
3.84E-04
6.82E-04
9.14E-04
1.56E-03
2.88E-03
5.62E-03
1.03E-02
1.30E-02
3.16E-02
9.53E-04
1.34E-03
1.56E-03
2.18E-03
3.00E-03
4.16E-03
5.61E-03
6.28E-03
8.82E-03
2.15E-03
2.83E-03
3.43E-03
4.60E-03
6.55E-03
9.40E-03
1.39E-02
1.69E-02
3.43E-02
3.01E-05
6.89E-04
2.30E-03
6.46E-03
1.63E-02
3.63E-02
6.71E-02
9.85E-02
6.16E-01
Male, Age 15-45
1
5
10
25
50
75
90
95
99
3.97E-03
6.49E-03
9.21E-03
1.68E-02
3.00E-02
5.18E-02
8.26E-02
1.20E-01
4.87E-01
Oa
Oa
Oa
2.03E-04
1.70E-03
3.19E-03
4.59E-03
5.99E-03
9.33E-03
2.84E-04
5.76E-04
7.95E-04
1.51E-03
2.97E-03
5.77E-03
1.08E-02
1.60E-02
2.66E-02
4.82E-04
8.86E-04
1.15E-03
1.84E-03
3.24E-03
5.84E-03
l.OOE-02
1.38E-02
2.28E-02
1.70E-03
2.60E-03
3.29E-03
4.82E-03
7.55E-03
1.18E-02
1.91E-02
2.48E-02
3.50E-02
2.99E-05
6.76E-04
1.69E-03
5.45E-03
1.68E-02
3.79E-02
6.90E-02
1.07E-01
4.73E-01
Child, Age 6
1
5
10
25
50
75
90
95
99
1.45E-03
2.75E-03
3.79E-03
6.99E-03
1.34E-02
2.64E-02
4.90E-02
6.28E-02
9.18E-02
Oa
Oa
Oa
6.01E-05
1.73E-04
1.42E-03
2.30E-03
2.91E-03
4.03E-03
1.73E-04
3.22E-04
4.34E-04
7.70E-04
1.50E-03
2.86E-03
4.90E-03
6.51E-03
1.21E-02
7.65E-05
1.59E-04
2.22E-04
4.21E-04
7.42E-04
1.51E-03
2.62E-03
3.73E-03
5.58E-03
5.15E-04
8.69E-04
1.06E-03
1.61E-03
2.70E-03
4.43E-03
6.57E-03
8.14E-03
1.32E-02
1.63E-05
2.86E-04
8.37E-04
2.85E-03
8.77E-03
2.22E-02
4.41E-02
5.84E-02
9.00E-02
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 86
-------
100 T
100
100
90
80
70
60
u.
8 50
40
30
20
10
0
177
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.2 0.3
Absorbed Bromoform Dose, mg
0.4
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.1 0.2 0.3
Absorbed Bromoform Dose, mg
0.4
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.01 0.02 0.03 0.04 0.05 0.06
Absorbed Bromoform Dose, mg
0.07
0.5
0.5
0.08
Figure 21. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed Bromoform Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 87
-------
0.5
c
g
"en n A
a.
o
0-03
o
c
o
•ti n 9
2
LJ.
0.1
flf
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
1
o o c
ci ci o
i J j«H n ti h
1-^ O5 CM
O O O
O O O
ci ci ci
•* CD en T-
T— T— T— CM
O O O O
o o o o
ci ci ci ci
>0.0235
Absorbed Bromoform Dose: Dermal, mg
Figure 22. Histogram for Absorbed Dermal Bromoform Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
0.5
g
"m n A
a.
o
"S0'3
c
o
*• n 9
LL!
01
n
8
8
ci
-
8
o
o
•
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
lai
1 1 ill
88888-?!??
S8S88&888
cicicicicicicicici
Absorbed Bromoform Dose: Inhalation, mg
Figure 23. Histogram for Absorbed Inhalation Bromoform Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 88
-------
0.5 -
°
"5
Q.
O
0-n-j
tion of
D C
O C
s
u.
n
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 11 ti n
111 1 111 1 1 1 • r^ 1
Absorbed Bromoform Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL m_ m_ H
Absorbed Bromoform Dose: Direct Ingestion, mg
Fraction of Population
o o o o o
D -^ ho GJ 4^ O1
_[
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
n
i
,r
LlL Jl -n .
i-i
8888888888S
ooooooooooo
A
Absorbed Bromoform Dose: Indirect Ingestion, mg
Figure 24. Histograms for the Absorbed Bromoform Ingestion Dose for Females, Males
and Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 89
-------
0.5 -
c
o
"S n A
a.
o
°- 03
o
c
o
*• n 9
2
LL.
01
n
r
1
O CO
O CN
-
CD
§
M
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1
1 lirbrkru^^rL
COO)1*— COCOCOOO)
(~) (~) (~) (~) (~) (~) T — CN
o o o o o o o
Total Absorbed Bromoform Dose, mg
Figure 25.
Histogram for the Total Absorbed Bromoform Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 90
-------
4.2.2.5 Uptake Results for MCA
The following Table 59 presents the resultant absorbed dose of MCA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 26 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of MCA and Figures 27, 28, and 29 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 30 presents the total absorbed MCA dose.
Table 59. MCA Absorbed Dose Results
Percentile
Total b
Dermal
Total
MCA Absi
Ingestion
Direct
jrbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
1.52E-03
1.99E-03
2.40E-03
3.21E-03
4.45E-03
6.32E-03
9.38E-03
1.13E-02
2.27E-02
Oa
Oa
7.93E-06
1.89E-05
1.16E-04
1.98E-04
3.13E-04
3.91E-04
5.78E-04
2.54E-04
4.51E-04
6.05E-04
1.03E-03
1.91E-03
3.73E-03
6.79E-03
8.63E-03
2.09E-02
6.31E-04
8.88E-04
1.03E-03
1.44E-03
1.99E-03
2.76E-03
3.71E-03
4.16E-03
5.84E-03
1.42E-03
1.88E-03
2.27E-03
3.05E-03
4.34E-03
6.22E-03
9.24E-03
1.12E-02
2.27E-02
1.94E-09
3.54E-08
1.30E-07
4.74E-07
1.15E-06
2.34E-06
3.86E-06
5.29E-06
3.18E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
1.27E-03
1.84E-03
2.27E-03
3.35E-03
5.09E-03
7.93E-03
1.30E-02
1.66E-02
2.33E-02
Oa
Oa
Oa
1.58E-05
1.25E-04
2.20E-04
3.12E-04
3.93E-04
5.82E-04
1.88E-04
3.82E-04
5.27E-04
l.OOE-03
1.97E-03
3.82E-03
7.17E-03
1.06E-02
1.76E-02
3.19E-04
5.87E-04
7.60E-04
1.22E-03
2.14E-03
3.87E-03
6.62E-03
9.16E-03
1.51E-02
1.13E-03
1.72E-03
2.18E-03
3.19E-03
5.00E-03
7.82E-03
1.27E-02
1.64E-02
2.32E-02
2.11E-09
3.86E-08
9.84E-08
4.40E-07
1.33E-06
2.39E-06
3.99E-06
5.97E-06
2.74E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
4.22E-04
6.27E-04
7.71E-04
1.14E-03
1.84E-03
2.99E-03
4.39E-03
5.51E-03
8.76E-03
Oa
Oa
Oa
4.69E-06
1.35E-05
9.49E-05
1.49E-04
1.86E-04
2.54E-04
1.15E-04
2.13E-04
2.87E-04
5.10E-04
9.92E-04
1.89E-03
3.24E-03
4.31E-03
8.00E-03
5.07E-05
1.05E-04
1.47E-04
2.79E-04
4.92E-04
9.97E-04
1.73E-03
2.47E-03
3.70E-03
3.41E-04
5.75E-04
7.00E-04
1.07E-03
1.79E-03
2.94E-03
4.35E-03
5.39E-03
8.75E-03
1.10E-09
1.80E-08
5.05E-08
1.81E-07
6.29E-07
1.50E-06
2.56E-06
3.43E-06
5.20E-06
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 91
-------
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01 0.015
Absorbed MCA Dose, mg
0.02
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01 0.015
Absorbed MCA Dose, mg
0.02
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.001 0.002 0.003 0.004 0.005
Absorbed MCA Dose, mg
0.006 0.007 0.008
Figure 26. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed MCA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 92
-------
n A^
04 -
c U'H
o
5U-J&
3 n ^
O
n
O
O
^015-
"- 01
n n^
n
n
J
i
0.000000
CO
c^
c
o
n
m r
t"
§ 1
O O
ci ci
1 •
i Iflr
1 1 1
CO C£
8 8
o c
ci c
• Female, Age 15-45
DMale, Age 15-45
• Child, Age 6
--.
h Fl _n _rn 1
iirlnrftTn_
CO CM CD CO O
i- CM CM CM CO
O O O O O
O O O O O
ci ci ci ci ci
Absorbed MCA Dose: Dermal, mg
Figure 27. Histogram for Absorbed Dermal MCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A^
n A
o
*•' n ^R
jjU.-io
r> n ^
o
"S0'25
g
*t\ n 1 ^
OJ
LL n 1
n OR
0.0000000
-
0.0000006
l¥|
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
fl f 1
1 111 1 III |~l d~| _ J-,
1 ill 1 ill 1 ill 1 1 i . . 1
T — |"^ CN 00 ^~ O) LO O CD
T-T-CMCMCOCO^LO-*-
888888888
ciciciciciciciciP
5?
Absorbed MCA Dose: Inhalation, mg
Figure 28. Histogram for Absorbed Inhalation MCA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 93
-------
.2 o.7-
(0
3 DR
o
Q- 05
o
s
"0 ,-,0
ul
n
f
1
1
• Female, Age 15-45
DMale, Age 15-45
I f
!!! 1 mn 1 L J~l _n =
Absorbed MCA Dose: Ingestion, mg
•° 07
JS
O
°~ 05 -
O
.2
ul
n -
p
1
• Female, Aqe 15-45
DMale, Age 15-45
DChild, Aye 6
1
Absorbed MCA Dose: Direct Ingestion, mg
| 0.7-
_ro
o
°- 05 -
O
.2
'o n ,
ul
n -
r
1
1
• Female, Age 15-45
DMale, Age 15-45
•
ill
III 1 • 1 _ J~L _TL n rn „ n
8 8
8 8
8 8
Absorbed MCA Dose: Indirect Ingestion, mg
Figure 29. Histograms for the Absorbed MCA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 94
-------
n Q
n P
c
o
•— n 7
IS °-'
3 n R
o
°- 05-
O
o °-4 ~
^03
k.
LJ_
n 9
n 1
n -
• Female, Age 15-45
DMale, Age 15-45
• Child, Age 6
Ml
1 1 -i tfl -TI n __
o
o
q
ci
CO
CO
q
ci
CD
CD
q
ci
CO
CO
8
ci
CD
O O O
Total Absorbed MCA Dose, mg
CM
CO
-fr
q
ci
A
Figure 30. Histogram for the Total Absorbed MCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 95
-------
4.2.2.6 Uptake Results for DCA
The following Table 60 presents the resultant absorbed dose of DCA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 31 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of DCA and Figures 32, 33, and 34 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 35 presents the total absorbed DCA dose.
Table 60. DCA Absorbed Dose Results
Percentile
Total b
Dermal
Total
DCA Absc
Ingestion
Direct
>rbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
8.96E-03
1.18E-02
1.43E-02
1.91E-02
2.73E-02
3.91E-02
5.80E-02
7.03E-02
1.43E-01
Oa
Oa
7.42E-07
1.77E-06
1.05E-05
1.76E-05
2.58E-05
3.04E-05
4.04E-05
1.60E-03
2.83E-03
3.80E-03
6.49E-03
1.20E-02
2.34E-02
4.26E-02
5.41E-02
1.31E-01
3.96E-03
5.57E-03
6.47E-03
9.05E-03
1.25E-02
1.73E-02
2.33E-02
2.61E-02
3.67E-02
8.92E-03
1.18E-02
1.42E-02
1.91E-02
2.72E-02
3.91E-02
5.80E-02
7.03E-02
1.43E-01
8.71E-09
1.63E-07
5.87E-07
2.24E-06
5.46E-06
1.10E-05
1.83E-05
2.51E-05
1.58E-04
Male, Age 15-45
1
5
10
25
50
75
90
95
99
7.09E-03
1.08E-02
1.37E-02
2.01E-02
3.14E-02
4.91E-02
7.95E-02
1.03E-01
1.46E-01
Oa
Oa
Oa
1.48E-06
1.16E-05
1.96E-05
2.72E-05
3.34E-05
4.41E-05
1.18E-03
2.39E-03
3.30E-03
6.27E-03
1.23E-02
2.40E-02
4.50E-02
6.67E-02
1.10E-01
2.00E-03
3.68E-03
4.77E-03
7.65E-03
1.35E-02
2.43E-02
4.16E-02
5.75E-02
9.47E-02
7.08E-03
1.08E-02
1.37E-02
2.00E-02
3.14E-02
4.91E-02
7.94E-02
1.03E-01
1.46E-01
9.72E-09
1.72E-07
4.66E-07
2.03E-06
6.20E-06
1.13E-05
1.89E-05
2.85E-05
1.32E-04
Child, Age 6
1
5
10
25
50
75
90
95
99
2.15E-03
3.61E-03
4.40E-03
6.70E-03
1.12E-02
1.84E-02
2.73E-02
3.38E-02
5.49E-02
Oa
Oa
Oa
4.39E-07
1.26E-06
8.15E-06
1.23E-05
1.51E-05
2.00E-05
7.20E-04
1.34E-03
1.80E-03
3.20E-03
6.22E-03
1.19E-02
2.04E-02
2.71E-02
5.02E-02
3.18E-04
6.60E-04
9.24E-04
1.75E-03
3.08E-03
6.26E-03
1.09E-02
1.55E-02
2.32E-02
2.14E-03
3.61E-03
4.39E-03
6.70E-03
1.12E-02
1.84E-02
2.73E-02
3.38E-02
5.49E-02
4.89E-09
8.42E-08
2.29E-07
8.44E-07
3.01E-06
7.05E-06
1.23E-05
1.63E-05
2.51E-05
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 96
-------
100
90
80
70
60
8 50
40
30
20
10
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0 J '—
0 0.02 0.04 0.06 0.08 0.1
Absorbed DCA Dose, mg
0.12 0.14
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.02 0.04 0.06 0.08
Absorbed DCA Dose, mg
0.1
0.12 0.14
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.01 0.02 0.03
Absorbed DCA Dose, mg
0.04
0.05
Figure 31. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed DCA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 97
-------
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
I
8 8
q
ci
q
ci
q
ci
q
ci
q
ci
q
ci
CO
CM
q
ci
q
ci
05
8
8
q
ci
A
Absorbed DCA Dose: Dermal, mg
Figure 32. Histogram for Absorbed Dermal DCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A^
n A
c °'4~
•— n fv=\
ro
5 n ^
o
°-n9^
o
c n 9
°
"o n -m
gu. 10
"- 01 -
n nc;
n
0.000000
-
0.000002
ran
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
1
• 111
1 1 1 • |~L *-\
1 111 1 111 1 111 1 1 • • ^ 1
§r^OCMLOr^OCMCM
o o o o o o o o
888888888
ooooooooo
cicicicicicicicici
Absorbed DCA Dose: Inhalation, mg
Figure 33. Histogram for Absorbed Inhalation DCA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 98
-------
0.6
0.5 -
c
.o
JS0.4
Q.
O
c
.2
t>0.2
co
0.1
I
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
I IT. ti ji n
IlllLLHiTm 1
Absorbed DCA Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
O
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL IT m_ H
Absorbed DCA Dose: Direct Ingestion, mg
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
Absorbed DCA Dose: Indirect Ingestion, mg
Figure 34. Histograms for the Absorbed DCA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 99
-------
o o
Total Absorbed DCA Dose, mg
Figure 35. Histogram for the Total Absorbed DCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 100
-------
4.2.2.7 Uptake Results for TCA
The following Table 61 presents the resultant absorbed dose of TCA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 36 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of TCA and Figures 37, 38, and 39 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 40 presents the total absorbed TCA dose.
Table 61. TCA Absorbed Dose Results
Percentile
Total b
Dermal
Total
TCA Abse
Ingestion
Direct
>rbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
9.65E-03
1.25E-02
1.52E-02
2.03E-02
2.90E-02
4.15E-02
6.16E-02
7.48E-02
1.51E-01
Oa
Oa
1.21E-06
2.88E-06
1.71E-05
2.87E-05
4.20E-05
4.96E-05
6.58E-05
1.70E-03
3.01E-03
4.03E-03
6.89E-03
1.27E-02
2.48E-02
4.53E-02
5.75E-02
1.39E-01
4.21E-03
5.92E-03
6.88E-03
9.61E-03
1.32E-02
1.84E-02
2.48E-02
2.77E-02
3.90E-02
9.48E-03
1.25E-02
1.51E-02
2.03E-02
2.89E-02
4.15E-02
6.16E-02
7.47E-02
1.51E-01
1.52E-08
2.77E-07
1.03E-06
3.90E-06
9.47E-06
1.93E-05
3.24E-05
4.44E-05
2.76E-04
Male, Age 15-45
1
5
10
25
50
75
90
95
99
7.55E-03
1.15E-02
1.46E-02
2.14E-02
3.34E-02
5.22E-02
8.45E-02
1.10E-01
1.55E-01
Oa
Oa
Oa
2.41E-06
1.88E-05
3.19E-05
4.43E-05
5.44E-05
7.19E-05
1.26E-03
2.54E-03
3.51E-03
6.67E-03
1.31E-02
2.55E-02
4.78E-02
7.08E-02
1.17E-01
2.13E-03
3.91E-03
5.07E-03
8.12E-03
1.43E-02
2.58E-02
4.42E-02
6.11E-02
1.01E-01
7.52E-03
1.15E-02
1.45E-02
2.13E-02
3.33E-02
5.21E-02
8.44E-02
1.10E-01
1.55E-01
1.67E-08
3.02E-07
8.17E-07
3.57E-06
1.09E-05
1.99E-05
3.33E-05
5.05E-05
2.34E-04
Child, Age 6
1
5
10
25
50
75
90
95
99
2.30E-03
3.84E-03
4.68E-03
7.12E-03
1.19E-02
1.96E-02
2.90E-02
3.60E-02
5.83E-02
Oa
Oa
Oa
7.15E-07
2.06E-06
1.33E-05
2.01E-05
2.46E-05
3.26E-05
7.65E-04
1.42E-03
1.91E-03
3.40E-03
6.61E-03
1.26E-02
2.16E-02
2.88E-02
5.34E-02
3.38E-04
7.01E-04
9.82E-04
1.86E-03
3.28E-03
6.65E-03
1.16E-02
1.65E-02
2.46E-02
2.28E-03
3.84E-03
4.67E-03
7.12E-03
1.19E-02
1.96E-02
2.90E-02
3.59E-02
5.83E-02
8.58E-09
1.46E-07
4.01E-07
1.47E-06
5.22E-06
1.21E-05
2.16E-05
2.84E-05
4.35E-05
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 101
-------
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.02 0.04 0.06 0.08 0.1
Absorbed TCA Dose, mg
0.12
0.14
0.16
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.02 0.04 0.06 0.08 0.1
Absorbed TCA Dose, mg
0.12
0.14
0.16
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.01 0.02 0.03 0.04
Absorbed TCA Dose, mg
0.05
0.06
Figure 36. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed TCA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 102
-------
• Female, Age 15-45
DMale, Age 15-45
• Child, Age 6
O
d
O
d
O
d
O
d
O
d
O
d
O
d
^
O
d
O
d
Absorbed TCA Dose: Dermal, mg
Figure 37. Histogram for Absorbed Dermal TCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
n A^
n A
c u-4 "
O
3
o
0-025 -
O
c n 9
Q
1J015-
2u-n°
""01
n OR
0.0000000
0.0000040
• F
1
1
ll
0.0000080
h
1
1
0.0000120
1 J1
1
I 111
o
CD
O
8
o
d
fn_
1 1
o o
CM CM
8 8
o o
d d
1
0.0000280
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
ri
• rb n. th
o o o
CM CD CD
CO CO CO
O O O
888
o o o
odd
Absorbed TCA Dose: Inhalation, mg
Figure 38. Histogram for Absorbed Inhalation TCA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 103
-------
0.6
0.5 -
c
O
joO.4
3
Q.
o
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jO.2
0.1
H
• L in
1
Absorbed TCA Dose: Ingestion, mg
Fraction of Population
o o o o o
D -* Kj W 4^ O1
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
-
rk
1 III Hi li ru i-u ^ „ H
CD ^T
^T LO
Absorbed TCA Dose: Direct Ingestion, mg
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
Absorbed TCA Dose: Indirect Ingestion, mg
Figure 39. Histograms for the Absorbed TCA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 104
-------
n A^
OA
c
o
IS '
= no
o
^~ n 2c>
o
c 02
g U.Z
t> n HE
LJ_
n n^ -
n -
o c\
ci o
.-
1
1
CD
O
O
1
O
•
• Female, Age 15-45
DMale, Age 15-45
H Child, Age 6
l~l T
"I 1 J~ dl J
1 1 1 1 1 I 1
1^ (35 i- CM "3" CD T-
CM O O) 1"^ IO CO CD
STT -J U5 CD 1^ CO
O O O O O C\
ci ci ci ci ci ci o
Figure 40.
Total Absorbed TCA Dose, mg
Histogram for the Total Absorbed TCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 105
-------
4.2.2.8 Uptake Results for MBA
The following Table 62 presents the resultant absorbed dose of MBA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 41 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of MBA and Figures 42, 43, and 44 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 45 presents the total absorbed MBA dose.
Table 62. MBA Absorbed Dose Results
Percentile
Total b
Dermal
Total
MBA Abs<
Ingestion
Direct
jrbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
2.97E-03
3.91E-03
4.70E-03
6.30E-03
8.73E-03
1.24E-02
1.84E-02
2.22E-02
4.45E-02
Oa
Oa
1.59E-05
3.78E-05
2.32E-04
3.97E-04
6.31E-04
7.86E-04
1.17E-03
4.99E-04
8.85E-04
1.19E-03
2.03E-03
3.74E-03
7.30E-03
1.33E-02
1.69E-02
4.10E-02
1.24E-03
1.74E-03
2.02E-03
2.83E-03
3.89E-03
5.41E-03
7.28E-03
8.16E-03
1.15E-02
2.79E-03
3.68E-03
4.45E-03
5.98E-03
8.51E-03
1.22E-02
1.81E-02
2.20E-02
4.45E-02
2.90E-09
5.65E-08
2.09E-07
7.57E-07
1.79E-06
3.45E-06
5.60E-06
7.85E-06
4.72E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
2.50E-03
3.60E-03
4.45E-03
6.58E-03
9.97E-03
1.55E-02
2.55E-02
3.25E-02
4.57E-02
Oa
Oa
Oa
3.16E-05
2.50E-04
4.41E-04
6.28E-04
7.87E-04
1.17E-03
3.69E-04
7.48E-04
1.03E-03
1.96E-03
3.86E-03
7.49E-03
1.41E-02
2.08E-02
3.45E-02
6.26E-04
1.15E-03
1.49E-03
2.39E-03
4.20E-03
7.59E-03
1.30E-02
1.80E-02
2.96E-02
2.21E-03
3.38E-03
4.27E-03
6.26E-03
9.81E-03
1.53E-02
2.48E-02
3.22E-02
4.55E-02
3.17E-09
5.85E-08
1.59E-07
7.41E-07
1.99E-06
3.51E-06
5.75E-06
8.77E-06
4.04E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
8.28E-04
1.22E-03
1.51E-03
2.23E-03
3.61E-03
5.86E-03
8.61E-03
1.08E-02
1.72E-02
Oa
Oa
Oa
9.37E-06
2.70E-05
1.91E-04
2.99E-04
3.74E-04
5.15E-04
2.25E-04
4.19E-04
5.63E-04
l.OOE-03
1.95E-03
3.71E-03
6.36E-03
8.46E-03
1.57E-02
9.93E-05
2.06E-04
2.89E-04
5.46E-04
9.64E-04
1.96E-03
3.40E-03
4.84E-03
7.25E-03
6.69E-04
1.13E-03
1.37E-03
2.09E-03
3.50E-03
5.76E-03
8.53E-03
1.06E-02
1.72E-02
1.67E-09
2.82E-08
8.16E-08
3.09E-07
1.01E-06
2.24E-06
3.79E-06
5.04E-06
7.41E-06
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 106
-------
LJ-
Q en -
o bu
0 -
X
y*
V* Female, Age 15-45
/" Dermal
Inqestion
,-* Inhalation
- - Total
.i
•i
ft
:i
/i
;t
:*
i
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045
Absorbed MBA Dose, mg
LJ-
Q so -
Q ou
.*-**" '"
1 ..•*•**"
.-»
S'* Male, Age 15-45
y* Dermal
f' Ingestion
.•» Inhalation
- - Total
J
>
Jr
.1
i
;i
/i
A
.*'
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045
Absorbed MBA Dose, mg
100 -
LJ_
8 5°-
n
*-"•"*
,'•-"
S* Child, Age 6
ji Dermal
f Ingestion
m* - - Total
.c
-V
/•
;i
ft
,'g
.'*
.?
.'g
0.002 0.004 0.006 0.008 0.01
Absorbed MBA Dose, mg
0.012
0.014
Figure 41. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed MBA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 107
-------
n A^
04 -
^ n ^R -
CO
3 n Q
o
o
c n 9
.2
O n 1 c
(5 U. ID
LL.
0.1
0.05
1
c
c
o
111
-
• Female, Age 15-45
DMale, Age 15-45
01 Child, Age 6
I" n B-n
__• inn
It
in T- CD T- r^
§o o o o
o o o o
o o o o o
ci ci ci ci ci
0.00032
l~l Jl
1 h I L I"
1
n
1-^ CO 00 CO
CO ^ ^ CD
§O O CM
O O O
O O O O
ci ci ci ci
Absorbed MBA Dose: Dermal, mg
Figure 42. Histogram for Absorbed Dermal MBA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A^
n A
c °'4
~ n fv=\
OJ
3 03
O
°-no^
o
c n 9
°
"on 15
TOU- lo
"- 01
n n^ -
n
-
|¥|
•
F
1
1
I
IM -n
il— • mm-
11 | 11
1 1
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
1
888888888
8
ci ci ci ci ci ci ci
Absorbed MBA Dose: Inhalation, mg
Figure 43. Histogram for Absorbed Inhalation MBA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 108
-------
0.5 -
°
"5
Q.
O
0-n-j
tion of
D C
O C
s
u.
n
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 11 ti n
111 1 111 1 1 1 • r^ 1
Absorbed MBA Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL m_ m_ H
Absorbed MBA Dose: Direct Ingestion, mg
Fraction of Population
o o o o o
D -^ ho GJ 4^ O1
_[
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
n
i
J
LlL Jl -n .
i-i
8888
8 8
Absorbed MBA Dose: Indirect Ingestion, mg
Figure 44. Histograms for the Absorbed MBA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 109
-------
OA
o
_2
3 n Q
o
n
0 25 -
O
= D 9 -
.2
H n 1*5
LL
n 1 -
£
o -*•
O CO
o o
o o
ci o
-
oo
CD
o
o
o
CM
o
o
o
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 1
iiii _— -, n
III 1 1 1, H rfl J1 -n J 1
CD o ^ i^ i- in oo
CO r- O CO 1^ O "3"
T- T- CM CM CM CO 00
O O O O O O O
ci ci ci ci ci ci ci
Total Absorbed MBA Dose, mg
Figure 45. Histogram for the Total Absorbed MBA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 110
-------
4.2.2.9 Uptake Results for DBA
The following Table 63 presents the resultant absorbed dose of DBA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 46 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of DBA and Figures 47, 48, and 49 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 50 presents the total absorbed DBA dose.
Table 63. DBA Absorbed Dose Results
Percentile
Total b
Dermal
Total
DBA Abse
Ingestion
Direct
>rbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
1.28E-03
1.69E-03
2.02E-03
2.71E-03
3.76E-03
5.33E-03
7.91E-03
9.54E-03
1.92E-02
Oa
Oa
7.20E-06
1.72E-05
1.06E-04
1.82E-04
2.92E-04
3.66E-04
5.43E-04
2.15E-04
3.81E-04
5.10E-04
8.72E-04
1.61E-03
3.14E-03
5.73E-03
7.27E-03
1.76E-02
5.32E-04
7.49E-04
8.70E-04
1.22E-03
1.67E-03
2.33E-03
3.13E-03
3.51E-03
4.93E-03
1.20E-03
1.58E-03
1.91E-03
2.57E-03
3.66E-03
5.25E-03
7.79E-03
9.45E-03
1.91E-02
6.58E-10
1.26E-08
4.44E-08
1.77E-07
4.33E-07
8.74E-07
1.44E-06
1.97E-06
1.24E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
1.08E-03
1.55E-03
1.93E-03
2.84E-03
4.29E-03
6.69E-03
1.10E-02
1.40E-02
1.97E-02
Oa
Oa
Oa
1.44E-05
1.14E-04
2.03E-04
2.86E-04
3.63E-04
5.46E-04
1.59E-04
3.22E-04
4.44E-04
8.43E-04
1.66E-03
3.22E-03
6.05E-03
8.96E-03
1.48E-02
2.69E-04
4.95E-04
6.41E-04
1.03E-03
1.81E-03
3.26E-03
5.58E-03
7.73E-03
1.27E-02
9.51E-04
1.45E-03
1.84E-03
2.69E-03
4.22E-03
6.59E-03
1.07E-02
1.39E-02
1.96E-02
7.96E-10
1.29E-08
3.50E-08
1.65E-07
5.04E-07
9.07E-07
1.49E-06
2.23E-06
1.04E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
3.56E-04
5.23E-04
6.51E-04
9.60E-04
1.56E-03
2.52E-03
3.71E-03
4.65E-03
7.38E-03
Oa
Oa
Oa
4.26E-06
1.22E-05
8.75E-05
1.37E-04
1.72E-04
2.38E-04
9.68E-05
1.80E-04
2.42E-04
4.30E-04
8.36E-04
1.60E-03
2.74E-03
3.64E-03
6.75E-03
4.27E-05
8.87E-05
1.24E-04
2.35E-04
4.14E-04
8.41E-04
1.46E-03
2.08E-03
3.12E-03
2.88E-04
4.85E-04
5.90E-04
9.01E-04
1.51E-03
2.47E-03
3.67E-03
4.55E-03
7.38E-03
3.65E-10
6.26E-09
1.72E-08
6.56E-08
2.37E-07
5.59E-07
9.78E-07
1.28E-06
2.07E-06
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 111
-------
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01
Absorbed DBA Dose, mg
0.015
0.02
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01
Absorbed DBA Dose, mg
0.015
0.02
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.001 0.002 0.003 0.004 0.005
Absorbed DBA Dose, mg
0.006
0.007
Figure 46. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed DBA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 112
-------
0.5
0.45
• Female, Age 15-45
D Male, Age 15-45
I Child, Age 6
Absorbed DBA Dose: Dermal, mg
Figure 47. Histogram for Absorbed Dermal DBA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A
c °-4 "
O
s n ^
1SU-JO
o n ^
o
0-025 -
O
c n 9
°
"o n IR
OJ
u- n 1
n n^
0.0000000
0.0000002
run
1
f
1
1
0.0000003
m
I
1
1
0.0000005
J~|_
n
1 1
0.0000007
d~h tfi •
1 Tin
O5 O
§Q
Q
8 8
o o
ci ci
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
fl
[kXkjiJi]
CM -fr l£> l£>
8888
8888
o o o o
ci ci ci ci
Absorbed DBA Dose: Inhalation, mg
Figure 48. Histogram for Absorbed Inhalation DBA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 113
-------
0.5 -
°
"5
Q.
O
0-n-j
tion of
D C
O C
s
u.
n
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 11 ti n
111 1 111 1 1 1 • r^ 1
Absorbed DBA Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL m_ m_ H
Absorbed DBA Dose: Direct Ingestion, mg
0.5 -
O
"5
Q.
O
0-n-j -
tion of
D C
J C
2
u.
n
j-
PI
|
-
ti
1
1
1
• Female, Age 15-4E
DMale, Age 15-45
DChild, Age 6
11 H li *]_ i
.
"3" CO CM h-
8888888
oooooooo
Absorbed DBA Dose: Indirect Ingestion, mg
Figure 49. Histograms for the Absorbed DBA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 114
-------
n A^
OA
c
o
IS '
= no
o
^~ n 9c>
o
c n 9
.2
t> n HE
LJ_
0-1
n OR
n -
_r
O CM
§C
C
CD O
|-
f
1
|
-fr
o
rm\
-
CD
CO
8
o
=,
• Female, Age 15-45
DMale, Age 15-45
H Child, Age 6
-,
J]
n f\ i
Ilii ^ J
(3) T— OO IO l1^ OT IT
O O O O O O C
ci ci ci ci ci ci o
Figure 50.
Total Absorbed DBA Dose, mg
Histogram for the Total Absorbed DBA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 115
-------
4.2.2.10 Uptake Results for BCA
The following Table 64 presents the resultant absorbed dose of BCA from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 51 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of BCA and Figures 52, 53, and 54 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 55 presents the total absorbed BCA dose.
Table 64. BCA Absorbed Dose Results
Percentile
Total b
Dermal
Total
BCA Abse
Ingestion
Direct
>rbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
2.71E-03
3.56E-03
4.28E-03
5.74E-03
7.95E-03
1.13E-02
1.67E-02
2.02E-02
4.05E-02
Oa
Oa
1.49E-05
3.54E-05
2.18E-04
3.74E-04
5.97E-04
7.45E-04
1.11E-03
4.54E-04
8.06E-04
1.08E-03
1.84E-03
3.40E-03
6.65E-03
1.21E-02
1.54E-02
3.73E-02
1.13E-03
1.59E-03
1.84E-03
2.57E-03
3.54E-03
4.92E-03
6.62E-03
7.43E-03
1.04E-02
2.54E-03
3.35E-03
4.05E-03
5.44E-03
7.74E-03
1.11E-02
1.65E-02
2.00E-02
4.05E-02
3.93E-09
7.29E-08
2.68E-07
9.27E-07
2.09E-06
4.28E-06
7.00E-06
1.04E-05
6.61E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
2.28E-03
3.28E-03
4.07E-03
6.00E-03
9.08E-03
1.41E-02
2.32E-02
2.95E-02
4.16E-02
Oa
Oa
Oa
2.97E-05
2.35E-04
4.16E-04
5.89E-04
7.44E-04
1.11E-03
3.36E-04
6.81E-04
9.39E-04
1.78E-03
3.51E-03
6.82E-03
1.28E-02
1.90E-02
3.14E-02
5.69E-04
1.05E-03
1.36E-03
2.17E-03
3.82E-03
6.90E-03
1.18E-02
1.63E-02
2.69E-02
2.01E-03
3.08E-03
3.89E-03
5.70E-03
8.93E-03
1.39E-02
2.26E-02
2.93E-02
4.14E-02
4.31E-09
7.89E-08
2.04E-07
8.32E-07
2.35E-06
4.31E-06
7.42E-06
1.16E-05
5.20E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
7.54E-04
1.11E-03
1.38E-03
2.03E-03
3.29E-03
5.34E-03
7.84E-03
9.85E-03
1.56E-02
Oa
Oa
Oa
8.79E-06
2.53E-05
1.80E-04
2.82E-04
3.53E-04
4.88E-04
2.05E-04
3.81E-04
5.13E-04
9.10E-04
1.77E-03
3.38E-03
5.79E-03
7.70E-03
1.43E-02
9.04E-05
1.88E-04
2.63E-04
4.97E-04
8.77E-04
1.78E-03
3.09E-03
4.41E-03
6.59E-03
6.09E-04
1.03E-03
1.25E-03
1.91E-03
3.19E-03
5.24E-03
7.76E-03
9.62E-03
1.56E-02
2.26E-09
3.55E-08
1.03E-07
3.94E-07
1.26E-06
2.97E-06
5.27E-06
6.89E-06
1.04E-05
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 116
-------
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
100
90
80
70
60
u.
8 50
40
30
20
10
0
Female, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01 0.015 0.02 0.025
Absorbed BCA Dose, mg
0.03
0.035
0.04
Male, Age 15-45
Dermal
Ingestion
Inhalation
- - Total
0.005 0.01 0.015 0.02 0.025
Absorbed BCA Dose, mg
0.03
0.035
0.04
Child, Age 6
Dermal
Ingestion
Inhalation
- - Total
0.002 0.004 0.006 0.008 0.01
Absorbed BCA Dose, mg
0.012
0.014
Figure 51. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed BCA Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 117
-------
Absorbed BCA Dose: Dermal, mg
Figure 52. Histogram for Absorbed Dermal BCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A
c °-4 "
O
s n ^
1SU-JO
n n ^
O
0-025 -
O
c n 9
°
^5 n 1 R
OJ
u- n 1
0 05
0.0000000
0.0000009
n
1
1
1
0.0000018
n
1
Li
0.0000027
1 J~l
1
II
0.0000036
1 dl
l 1
0.0000045
1
0.0000053
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
r
11 rt d^ In
CM T- O O
Ills
O O O O
ci ci ci ci
Absorbed BCA Dose: Inhalation, mg
Figure 53. Histogram for Absorbed Inhalation BCA Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 118
-------
0.5 -
°
"5
Q.
O
0-n-j
tion of
D C
O C
s
u.
n
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 11 ti n
111 1 111 1 1 1 • r^ 1
Absorbed BCA Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL IT m_ H
Absorbed BCA Dose: Direct Ingestion, mg
88
88888888
oooooooo
Absorbed BCA Dose: Indirect Ingestion, mg
Figure 54. Histograms for the Absorbed BCA Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 119
-------
n A^
OA
c
o
_ro
2 n ^ -
o
^~ n 9R
o
c n 9
.2
"§ 0 15 -
it
04
n n^
n .
_T
|-
f
1
I
pi
-
™.
• Female, Age 15-45
D Male, Age 15-45
II Child, Age 6
-,
n
1 JH 1
llll^l"_iTJ1 •_
O
8
o
ci
CD
s
q
ci
T- 1^
§ &
q q
ci ci
CO
o
q
ci
O5
CM
O
ci
O
ci
O CD
00 O
T- CM
O O
ci ci
CM
CO
CM
O
ci
Total Absorbed BCA Dose, mg
q
ci
A
Figure 55.
Histogram for the Total Absorbed BCA Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution. )
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 120
-------
4.2.2.11 Uptake Results for DCAN
The following Table 65 presents the resultant absorbed dose of DCAN from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 56 presents the resultant cumulative distribution
function plots forthe analysis of absorbed dose of DCAN and Figures 57, 58, and 59 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 60 presents the total absorbed DCAN dose.
Table 65. DCAN Absorbed Dose Results
Percentile
Total b
Dermal
Total
DCAN Abs
Ingestion
Direct
orbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
6.56E-04
8.52E-04
l.OOE-03
1.34E-03
1.83E-03
2.59E-03
3.76E-03
4.53E-03
8.91E-03
Oa
Oa
2.85E-06
6.78E-06
4.08E-05
7.07E-05
1.10E-04
1.37E-04
1.99E-04
9.98E-05
1.77E-04
2.37E-04
4.05E-04
7.48E-04
1.46E-03
2.66E-03
3.38E-03
8.20E-03
2.47E-04
3.48E-04
4.05E-04
5.65E-04
7.79E-04
1.08E-03
1.46E-03
1.63E-03
2.29E-03
5.58E-04
7.36E-04
8.91E-04
1.20E-03
1.70E-03
2.44E-03
3.62E-03
4.40E-03
8.91E-03
1.07E-07
2.32E-06
6.86E-06
1.81E-05
4.39E-05
9.00E-05
1.58E-04
2.48E-04
1.32E-03
Male, Age 15-45
1
5
10
25
50
75
90
95
99
5.31E-04
7.61E-04
9.47E-04
1.38E-03
2.09E-03
3.26E-03
5.20E-03
6.51E-03
9.15E-03
Oa
Oa
oa
5.68E-06
4.46E-05
7.75E-05
1.11E-04
1.38E-04
2.02E-04
7.38E-05
1.50E-04
2.06E-04
3.92E-04
7.72E-04
1.50E-03
2.81E-03
4.17E-03
6.90E-03
1.25E-04
2.30E-04
2.98E-04
4.78E-04
8.41E-04
1.52E-03
2.60E-03
3.59E-03
5.92E-03
4.42E-04
6.76E-04
8.54E-04
1.25E-03
1.96E-03
3.07E-03
4.97E-03
6.45E-03
9.10E-03
9.34E-08
2.26E-06
5.22E-06
1.51E-05
4.26E-05
9.25E-05
1.62E-04
2.49E-04
1.09E-03
Child, Age 6
1
5
10
25
50
75
90
95
99
1.93E-04
2.75E-04
3.38E-04
4.91E-04
7.72E-04
1.22E-03
1.77E-03
2.26E-03
3.52E-03
Oa
Oa
Oa
1.68E-06
4.84E-06
3.36E-05
5.18E-05
6.47E-05
8.88E-05
4.50E-05
8.37E-05
1.13E-04
2.00E-04
3.89E-04
7.42E-04
1.27E-03
1.69E-03
3.14E-03
1.99E-05
4.13E-05
5.78E-05
1.09E-04
1.93E-04
3.91E-04
6.80E-04
9.69E-04
1.45E-03
1.34E-04
2.26E-04
2.74E-04
4.19E-04
7.01E-04
1.15E-03
1.71E-03
2.11E-03
3.43E-03
6.30E-08
9.64E-07
2.40E-06
9.20E-06
2.57E-05
6.42E-05
1.25E-04
1.63E-04
2.55E-04
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 121
-------
j-
>s>
,- ^
/»' Female, Age 15-45
[ :• Dermal
Inqestion
•'* Inhalation
0 50 -•'-' ' " Total
o bu
0 -
//
; f
/ f
: i
/ f
/ f
: t
.'i
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009
Absorbed DC AN Dose, mg
LJ-
Q so -
Q ou
f-"
-^
..•;'' ' Male, Age 15-45
.•' *
/,' Dermal
Ingestion
/'« Inhalation
- - Total
,'f
• 1
,' *
: r
•' i
.' i
,' i
,'i
/«
ft
.'f
.•t
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009
Absorbed DCAN Dose, mg
100
LJ_
'f
/
/ ...;'' Child, Age 6
/m' Dermal
Ingestion
- - Total
8 50 I
n -
/.'
.' 1
: i
: i
,•;•'
.' t
0.0005 0.001 0.0015 0.002 0.0025
Absorbed DCAN Dose, mg
0.003
0.0035
Figure 56. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed DCAN Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 122
-------
Absorbed DCAN Dose: Dermal, mg
Figure 57. Histogram for Absorbed Dermal DCAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A^
n A
c °'4~
™ n fv=\
ro
5 n ^
o
°-no^
O
c no
.1
"o n -m
gu. 10
"- ni
n nc;
n
0.000000
"
0.000019
r
1
1
1
0.000038
1r
0.000057
11 B-I-, .
irlTn
CD in
h- (5)
o o
8 8
o o
ci ci
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
I
•*t CO CM i- T-
T- co in t~~ t-
88888
o o o o o
ci ci ci ci ci
Absorbed DCAN Dose: Inhalation, mg
Figure 58. Histogram for Absorbed Inhalation DCAN Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 123
-------
0.5 -
°
"5
Q.
O
0-n-j
tion of
D C
O C
s
u.
n
i
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
1 11 ti n
111 1 111 1 1 1 • r^ 1
Absorbed DCAN Dose: Ingestion, mg
0.5 -
O
"5
Q.
O
°~ 03 -
O
c
o
1
i±!
n -
-
m
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
jn
I 111 ft IL m_ m_ H
Absorbed DCAN Dose: Direct Ingestion, mg
Fraction of Population
o o o o o
D -^ ho GJ 4^ O1
_[
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
n
i
,r
LlL Jl -n .
i-i
8888888888S
ooooooooooo
A
Absorbed DCAN Dose: Indirect Ingestion, mg
Figure 59. Histograms for the Absorbed DCAN Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 124
-------
n A^
n A
c °'4
CO
3 0-3
o
°- 025
O
c n 9
0 U'Z
"o n 1=;
ro u. ID
"- 01
n n^ -
n
|
1
=
-
-
• Female, Age 15--1
DMale, Age 15-45
H Child, Age 6
In
1 1 dl
ill 1 1 1 1 r
\5
\
J
|
8
O5
o
o
o
o
o
o
o oo
•* oo
CO CO
8 8
ci ci
O O O O O
Total Absorbed DCAN Dose, mg
q
ci
A
Figure 60. Histogram for the Total Absorbed DCAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 125
-------
4.2.2.12 Uptake Results for TCAN
The following Table 66 presents the resultant absorbed dose of TCAN from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 61 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of TCAN and Figures 62, 63, and 64 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 65 presents the total absorbed TCAN dose.
Table 66. TCAN Absorbed Dose Results
Percentile
Total b
Dermal
Total
TCAN Abs
Ingestion
Direct
orbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
4.43E-05
5.71E-05
6.89E-05
9.19E-05
1.26E-04
1.76E-04
2.60E-04
3.13E-04
6.24E-04
Oa
Oa
2.80E-07
6.66E-07
4.18E-06
7.30E-06
1.18E-05
1.50E-05
2.28E-05
6.99E-06
1.24E-05
1.66E-05
2.84E-05
5.23E-05
1.02E-04
1.86E-04
2.37E-04
5.74E-04
1.73E-05
2.44E-05
2.83E-05
3.96E-05
5.45E-05
7.57E-05
1.02E-04
1.14E-04
1.60E-04
3.90E-05
5.15E-05
6.23E-05
8.37E-05
1.19E-04
1.71E-04
2.54E-04
3.08E-04
6.23E-04
2.50E-09
4.73E-08
1.48E-07
4.01E-07
9.73E-07
2.00E-06
3.65E-06
5.49E-06
3.17E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
3.63E-05
5.27E-05
6.48E-05
9.54E-05
1.45E-04
2.23E-04
3.59E-04
4.55E-04
6.41E-04
Oa
Oa
Oa
5.58E-07
4.47E-06
8.17E-06
1.16E-05
1.49E-05
2.27E-05
5.17E-06
1.05E-05
1.45E-05
2.74E-05
5.40E-05
1.05E-04
1.97E-04
2.92E-04
4.83E-04
8.76E-06
1.61E-05
2.09E-05
3.35E-05
5.88E-05
1.06E-04
1.82E-04
2.52E-04
4.14E-04
3.10E-05
4.73E-05
5.98E-05
8.76E-05
1.37E-04
2.15E-04
3.48E-04
4.51E-04
6.37E-04
2.09E-09
4.67E-08
1.13E-07
3.34E-07
l.OOE-06
2.16E-06
3.74E-06
5.69E-06
2.54E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
1.28E-05
1.82E-05
2.26E-05
3.25E-05
5.20E-05
8.39E-05
1.22E-04
1.54E-04
2.42E-04
Oa
Oa
Oa
1.65E-07
4.76E-07
3.55E-06
5.68E-06
7.13E-06
9.87E-06
3.15E-06
5.86E-06
7.88E-06
1.40E-05
2.72E-05
5.20E-05
8.91E-05
1.18E-04
2.20E-04
1.39E-06
2.89E-06
4.04E-06
7.65E-06
1.35E-05
2.74E-05
4.76E-05
6.78E-05
1.01E-04
9.37E-06
1.58E-05
1.92E-05
2.93E-05
4.91E-05
8.06E-05
1.19E-04
1.48E-04
2.40E-04
1.51E-09
1.97E-08
5.51E-08
1.91E-07
5.57E-07
1.41E-06
2.76E-06
3.55E-06
5.53E-06
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 126
-------
LJ-
Q en -
o bu
c
..<•>•*'
._*'•
/•' Female, Age 15-45
-V
/• Dermal
Inqestion
/' Inhalation
- - Total
• 1
; i
; 1
: i
: i
/•
; t
.'*
j
••i
0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006
Absorbed TCAN Dose, mg
LJ-
Q so -
Q ou
((
..V"
..;'*' '
..'««
..;•*'* Male, Age 15-45
/,' Dermal
Inqestion
/' Inhalation
- - Total
/*
/•
/ 1
-'*
.' 1
/t
•'f
/
/,'
0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006
Absorbed TCAN Dose, mg
100
LJ_
8 5°-
n -
1 ... :
-•""
_.>.>'' Child, Age 6
>f Dermal
/. Ingestion
..-;'* - - Total
/f
•1
; i
•t
.' g
/i
: t
//
.'f
0 0.00005 0.0001 0.00015 0.0002
Absorbed TCAN Dose, mg
0.00025
Figure 61. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed TCAN Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 127
-------
n A^
g
o_ n ^
o
0-025
.5 0.25
c n 9
°
"o n 1 ^
gU.1t)
"- 01 -
n n^
n
j
I
0.0000000
_
0.0000016
0.0000031
• Female, Age 15-45
D Male, Age 15-45
H Child Age 6
1 •_! In
ri 1 1 |~ Ji •-,
1 III 1 1 1 1 r . H
r^CMOOCr>a5"3-Ocr>
OOOOOOOO
cicicicicicicici
Absorbed TCAN Dose: Dermal, mg
Figure 62. Histogram for Absorbed Dermal TCAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
n A^
n A
c u-4 "
O
s n ^
1SU-JO
n n ^
o
0-025 -
O
c n 9
°
"o n IR
OJ
u- n 1
n n^
0.0000000
0.0000004
run
ii
r
I
i
0.0000009
IT
1
1
0.0000013
j
1
0.0000017
a-r
0.0000022
Ibl
0.0000026
• Female, Age 15-45
r
D Male, Age 15-45
D Child, Age 6
1
o in co co
CO CO CO CO
8888
8888
o o o o
ci ci ci ci
Absorbed TCAN Dose: Inhalation, mg
Figure 63. Histogram for Absorbed Inhalation TCAN Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 128
-------
0.5 -
.2
"5
Q.
O
0-n-i
tion of
D C
O C
s
u.
n
1
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
\ 11 ii ji n
111 LlJLlUULr] t
8 S
Absorbed TCAN Dose: Ingestion, mg
0.5 -
c
o
"5
Q.
O
°~ 03 -
O
c
o
fj°"
i±!
n -
-
1
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
Ii
1 1 Hi Ii r^ m_ H
Absorbed TCAN Dose: Direct Ingestion, mg
Fraction of Population
o o o o o
D -^ k> GJ 4^. O1
_[
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
n
]
,r
. lL Jl ji .
PI
8888
8 8
Absorbed TCAN Dose: Indirect Ingestion, mg
Figure 64. Histograms for the Absorbed TCAN Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 129
-------
n A^
n A
C °'4
o
TO °'35
o n ^
O
n
n 9E;
o
c n 9
.2
o n IR
TO u- lo
"- 01
n nc;
n -
j-
•
_
PI
• Female, Age 15-45
DMale, Age 15-45
H Child, Age 6
--,
1 1 j-i
iiii 1 1 j-i n
111 1 1 1 I . J Jl
o
8
oo
s
8
oo
CO
CM
CD
oo
CM
CO
CO
o o o o o o o
Total Absorbed TCAN Dose, mg
o
q
ci
A
Figure 65. Histogram for the Total Absorbed TCAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 130
-------
4.2.2.13 Uptake Results for DBAN
The following Table 67 presents the resultant absorbed dose of DBAN from the analysis of the
dermal, ingestion and inhalation exposure routes for each of the population groups: female age
15-45, male age 15-45, and child age 6. Figure 66 presents the resultant cumulative distribution
function plots for the analysis of absorbed dose of DBAN and Figures 67, 68, and 69 present the
histograms for absorbed dermal dose, inhalation dose, and ingestion dose, respectively, for the
female, male and child populations. Figure 70 presents the total absorbed DBAN dose.
Table 67. DBAN Absorbed Dose Results
Percentile
Total b
Dermal
Total
DBAN Abs
Ingestion
Direct
orbed Dose,
Ingestion
Indirect
mg
Ingestion
Total
Inhalation
Total
Female, Age 15-45
1
5
10
25
50
75
90
95
99
2.45E-04
3.18E-04
3.82E-04
5.11E-04
7.09E-04
l.OOE-03
1.49E-03
1.80E-03
3.61E-03
Oa
Oa
1.23E-06
2.94E-06
1.79E-05
3.07E-05
4.86E-05
6.06E-05
8.90E-05
4.04E-05
7.17E-05
9.61E-05
1.64E-04
3.03E-04
5.92E-04
1.08E-03
1.37E-03
3.32E-03
l.OOE-04
1.41E-04
1.64E-04
2.29E-04
3.15E-04
4.38E-04
5.90E-04
6.61E-04
9.28E-04
2.26E-04
2.98E-04
3.61E-04
4.84E-04
6.89E-04
9.89E-04
1.47E-03
1.78E-03
3.61E-03
4.37E-09
8.41E-08
2.73E-07
7.88E-07
1.88E-06
3.92E-06
6.72E-06
1.01E-05
6.20E-05
Male, Age 15-45
1
5
10
25
50
75
90
95
99
2.05E-04
2.92E-04
3.65E-04
5.36E-04
8.13E-04
1.26E-03
2.06E-03
2.63E-03
3.70E-03
Oa
Oa
Oa
2.46E-06
1.94E-05
3.40E-05
4.86E-05
6.07E-05
8.99E-05
2.99E-05
6.06E-05
8.36E-05
1.59E-04
3.12E-04
6.07E-04
1.14E-03
1.69E-03
2.80E-03
5.07E-05
9.32E-05
1.21E-04
1.94E-04
3.40E-04
6.14E-04
1.05E-03
1.46E-03
2.40E-03
1.79E-04
2.74E-04
3.46E-04
5.07E-04
7.94E-04
1.24E-03
2.01E-03
2.61E-03
3.69E-03
4.03E-09
8.56E-08
2.15E-07
6.75E-07
1.99E-06
4.07E-06
7.09E-06
1.10E-05
4.95E-05
Child, Age 6
1
5
10
25
50
75
90
95
99
6.89E-05
9.94E-05
1.23E-04
1.82E-04
2.94E-04
4.76E-04
6.97E-04
8.76E-04
1.39E-03
Oa
Oa
Oa
7.29E-07
2.10E-06
1.47E-05
2.30E-05
2.88E-05
3.94E-05
1.82E-05
3.39E-05
4.56E-05
8.10E-05
1.58E-04
3.01E-04
5.15E-04
6.85E-04
1.27E-03
8.05E-06
1.67E-05
2.34E-05
4.43E-05
7.81E-05
1.58E-04
2.75E-04
3.92E-04
5.87E-04
5.42E-05
9.14E-05
1.11E-04
1.70E-04
2.84E-04
4.66E-04
6.91E-04
8.56E-04
1.39E-03
2.63E-09
3.50E-08
1.06E-07
3.62E-07
1.07E-06
2.64E-06
5.09E-06
6.66E-06
1.02E-05
b.
The zeroes entered in the dermal category represent the portion of the population that has no dermal contact with
the water supply during the simulated day. For the female (age 15-45) population group, 6.9% had no dermal
contact. For the male (age 15-45) population group, 6.9% had no dermal contact. For the child (age 6) population
group, 11.2% had no dermal contact.
The "Total" column gives the absorbed dose for the given percentile of the population for the sum of the three
routes. It is not the sum of the totals for the three routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 131
-------
LJ-
Q en -
o bu
' ^ — — :
_,>••"
,-«'*
>•*
* Female, Age 15-45
f Dermal
Inqestion
* Inhalation
- - Total
/I
•t
a
:i
;i
;t
.'I
/
t
0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035
Absorbed DBAN Dose, mg
LJ-
Q so -
Q ou
"\ „..
,."--
./ Male, Age 15-45
jw Dermal
Inqestion
/» Inhalation
- - Total
.1
/i
:i
.•i
•i
.'i
,'*
.;
,i
A
-»*
0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035
Absorbed DBAN Dose, mg
100-r
LJ_
8 5°-
n -
.,•'""
_„•*' Child, Age 6
( 4-» Dermal
.i Ingestion
/? - - Total
,'g
:i
it
•t
't
/i
:*
./
0.0002 0.0004 0.0006 0.0008 0.001
Absorbed DBAN Dose, mg
0.0012 0.0014
Figure 66. Cumulative Distribution Function for the Dermal, Ingestion, Inhalation, and
Total Absorbed DBAN Dose for Females, Males and Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 132
-------
Absorbed DBAN Dose: Dermal, mg
Figure 67. Histogram for Absorbed Dermal DBAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
C
o
3
o
CL025 -
O
c n 9
Q
1*015-
rou-1D
LJ~ 0 1
n OR
0.0000000
-
0.0000008
ran
k
1 4V=n-
1 11 tti
iUtm
CD •* CM
i- CM CO
888
888
o o o
ci ci ci
• Female, Age 15-45
D Male, Age 15-45
D Child, Age 6
1
O 00 CD -fr CM CM
888888
o o o o o o
ci ci ci ci ci ci
Absorbed DBAN Dose: Inhalation, mg
Figure 68. Histogram for Absorbed Inhalation DBAN Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 133
-------
0.5 -
.2
"5
Q.
O
0-n-i
tion of
D C
O C
s
u.
n
1
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
\ 11 ii ji n
111 LlJLlUULr] t
Absorbed DBAN Dose: Ingestion, mg
0.5 -
c
o
"5
Q.
O
°~ 03 -
O
c
o
fj°"
i±!
n -
• Female, Age 15-45
DMale, Age 15-45
D Child, Age 6
-
Ii
1 1 Hi Ii r^ m_ H
Absorbed DBAN Dose: Direct Ingestion, mg
Fraction of Population
o o o o o
D -^ k> GJ 4^. O1
_[
• Female, Age 15-45
DMale, Age 15-45
DChild, Age 6
n
]
,r
. lL Jl ji .
PI
8 8
8 8
8 8
Absorbed DBAN Dose: Indirect Ingestion, mg
Figure 69. Histograms for the Absorbed DBAN Ingestion Dose for Females, Males and
Children.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 134
-------
n A^
OA
O
•— n ^
CO
5 n ^ -
o
°- 025
O
c n 9
0 U'^
"o n -IR
nj u. io
"- 01
n nc;
n -
J"
-
"
• Female, Age 15-45
D Male, Age 15-45
II Child, Age 6
rn
1 1
!!!! 1 1 ^ -, n
in inoLiLZ -n J i
8
8
q
ci
8
q
ci
8
q
ci
8
q
ci
o
q
ci
o
q
ci
in
CD
o
q
ci
o
q
ci
8
S
q
ci
Total Absorbed DBAN Dose, mg
S 8
q q
ci ci
A
Figure 70.
Histogram for the Total Absorbed DBAN Dose for Females, Males and
Children.
(The skin permeability coefficient to the dermal model for this compound is very uncertain. The possible range for the skin
permeability coefficient crosses an order of magnitude. Therefore these results are uncertain and should be used with caution.)
4.2.3 Analysis of the Impact of the Uncertainty in the Dermal Parameters
The skin permeability rates, given in Table 46, are generally poorly quantified. The values
presented in the table are estimated based on correlation with other chemical properties, and there
are few measured values for this parameter to serve as a validation. As a result, the uncertainty in
this parameter is quite large. The range presented in the table represents an educated guess based
on both the estimates and the general lack of measured values. The impact of this uncertainty is
examined by calculating the dermal uptake at the minimum and maximum values of the identified
range. Figures 71, 72, and 73 compare and contrast the uptake for the three population groups for
three of the chemicals: Chloroform, DCA, and TCA. As shown in the figures, the resulting
calculated dermal uptake is significantly different at the extremes of the range. The mean
estimated dermal uptake for each of population groups and chemicals is between approximately 6
and 75 times higher at the upper end of the range as compared to the lower end of the range. This
large range of uncertainty makes it difficult to compare the dermal route to the inhalation and
ingestion routes.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 135
-------
0.5
0.4 -
£0.3
o
Q.
0.2
0.1
DMale, Age 15-45 (Minimum Condition)
• Male, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 0.13cm/h, Skin Partition Coefficient = 1.62
Maximum Condition: Psc = 0.0156cm/h. Skin Partition Coefficient = 1.62
Mean Dose (Minimum Condition) = 0.0060 mg
Mean Dose (Maximum Condition) = 0.037 mg
0 o
O
S8S88S88
doc
) O O O O O
o
o
A
Absorbed Chloroform Dose: Dermal, mg
n c
u.o
0.4 -
c
.0
2
Q. 0.3
o
Q.
14-
o
1 °-2
'u
£
u.
0.1 -
]
D Female, Age 15-45 (Minimum Condition)
• Female, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 0.13cm/h, Skin Partition Coefficient = 1.62
t
Maximum Condition: P
sc = 0.0156cm/h. Skin Partition Coefficient = 1
6?
Mean Dose (Minimum Condition) = 0.0059 mg
Mean Dose (Maximum Condition) = 0.037 mg
,1
I 1 T
0 o
o
injJJ hi 1
S8S88S88
00000000
o
o
Absorbed Chloroform Dose: Dermal, mg
3
Q.
O
Q.
0.4 -
n 1 -
o -I
Minimum Condition: Ps
Maximum Condition: Ps
D Child, Age 6 (Minimum Condition)
• Child, Age 6 (Maximum Condition)
; = 0.13cm/h, Skin Partition Coefficient = 1.62
c = 0.0156cm/h, Skin Partition Coefficient = 1.62
Mean Dose (Minimum Condition) = 0.0060 mg
Mean Dose (Maximum Condition) = 0.037 mg
n 1
i Lrirl j_.
I
8
o
q
o
q
o
q
o
q
o
q
o
q
o
q
o
s
o
s
o
Absorbed Chloroform Dose: Dermal, mg
Figure 71. Comparison of Estimated Dermal Absorbed Chloroform Dose Across the
Range of Uncertainty in the Permeability Coefficient
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 136
-------
3
Q.
O
Q.
0.7 -
n -
DMale, Age 15-45 (Minimum Condition)
• Male, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 1.8 E-6 cm/h, Skin Partition Coefficient = 0.43
Mean Dose (Minimum Condition) = 1.2 E-5 mg
ent - U.4J
Mean Dose (Maximum Condition) = 7.8 E-4 mg
ri
. PL ,-•••••- • .____ I
in
LLJ
8
in
LLJ
8
in
LLJ
8
CO
in
LLJ
8
CO
LLJ
8
LLJ
O
LLJ
8
Absorbed DCADose: Dermal, mg
3
Q.
O
Q.
'S
c
.S
0.7 -
n -
D Female, Age 15-45 (Minimum Condition)
• Female, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 1.8 E-6 cm/h, Skin Partition Coefficient = 0.43
Maximum Condition: Psc = 0.00412 cm/h, Skin Partition Coefficient = 0.43
Mean Dose (Minimum Condition) = 1.2 E-5 mg
n
Mean Dose (Maximum Condition) = 7.5 E-4 mg
n
. ILlkjj --
oinininin^^^^^^
LULULULULULULULULULU
qqqqqcN-^cpcqo)
CN^CDCO-^-^-^-^-^-^
A
Absorbed DCADose: Dermal, mg
c
.3
Is
3
Q.
O
Q.
14-
o
c
.S
0.7-
0.6
0.1 -
n
T
D Child, Age 6 (Minimum Condition)
• Child, Age 6 (Maximum Condition)
Maximum Condition: Psc = 0.00412 cm/h, Skin Partition Coefficient = 0.43
Mean Dose (Minimum condition) - 4. / E-6 mg
Mean Dose (Maximum Condition) = 3.1 E-4 mg
II
in in in in -^ -^ ^-
LLI LLI LLI LLI LLI LLI LLI I
q q q q q CN -^ !
CN ^ CD cd -^ -^ -^
Absorbed DCADose: Dermal, mg
LLI LLI
8 8
Figure 72. Comparison of Estimated Dermal Absorbed DCA Dose Across the Range of
Uncertainty in the Permeability Coefficient
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 137
-------
3
Q.
O
Q.
0.7 -
n -
DMale, Age 15-45 (Minimum Condition)
• Male, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 3.6 E-6 cm/h, Skin Partition Coefficient = 0.52
Mean Dose (Minimum Condition) = 2.0 E-5 mg
Mean Dose (Maximum Condition) = 1.3 E-3 mg
n
i 1 J 1 • . _ _ . _
in
LLJ
q
CO
LLJ LLJ
CD -^
•^ CN
LLJ
O
LLJ
CO
LLJ
CD
in
LLJ
CD
Absorbed TCADose: Dermal, mg
C
.3
2
3
Q.
O
Q.
14-
o
c
.S
0.7 -
n -
n
D Female, Age 15-45 (Minimum Condition)
• Female, Age 15-45 (Maximum Condition)
Minimum Condition: Psc = 3.6 E-6 cm/h, Skin Partition Coefficient = 0.52
Maximum Condition: Psc = 0.00814 cm/h, Skin Partition Coefficient = 0.52
Mean Dose (Minimum Condition) = 1.9 E-5 mg
Mean Dose (Maximum Condition) = 1.3 E-3 mg
n
1 II J J 1 I • ,
8
in
LU
8
LU
$
LLJ
8
LLI
$
LLI
8
LLJ
$
T
LLJ
8
Absorbed TCADose: Dermal, mg
Q.
O
Q.
'S
c
.S
0.7 -
n -
D Child, Age 6 (Minimum Condition)
• Child, Age 6 (Maximum Condition)
1
Minimum Condtion: Psc = 3.6 E-6 cm/h, Skin Partition Coefficient = 0.52
Maximum Condition: Psc = 0.00814 cm/h, Skin Partition Coefficient = 0.52
M n - fM' ' P H't' ^ - 7 R F R
Mean Dose (Maximum Condition) = 5.4 E-4 mg
n
.Lruii..
LLJ
q
CD
LLJ
q
CO
LLJ
O
LLJ
CN
LLJ
CD
LLJ
CO
LLJ
O)
Absorbed TCADose: Dermal, mg
Figure 73. Comparison of Estimated Dermal Absorbed TCA Dose Across the Range of
Uncertainty in the Permeability Coefficient
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 138
-------
4.2.4 Discussion of Uptake Modeling Results
The results of the uptake modeling provides a massive amount of information for comparing and
contrasting the uptake as a function of chemical, the population group and behavior, and the route
of exposure. These results are summarized in the figures and tables in Section 4.2.2. General
conclusions about the importance of each route can be made by comparing the histograms of
uptake for each route. However, specific conclusions can be problematic due to large
uncertainties in some of the model parameters, most notably the dermal permeability coefficient
as described in the above section.
The route-specific values presented in the absorbed dose results tables for each chemical provide
a general understanding of the relative contribution of each route. However, this comparison can
be misleading because, as discussed earlier, for a given percentile, the member of the population
is likely to be different for each route (e.g., the person who has the 50th percentile absorbed dose
by the inhalation route is not the same person as has the 50th percentile dermal absorbed dose).
Many factors influence the uptake by each route. In addition to volatility, the inhalation is
influenced by the blood:air partition coefficient, which is inversely related to the Henry's law
constant. For example, while chloroform is more volatile that BDCM, the blood:air partition
coefficient is significantly higher for BDCM (6.11) than for chloroform. These values indicate
that for equal concentrations in the inspired air, the blood will absorb approximately 55% more
BDCM than chloroform. Similar relationships exist for the dermal route, where uptake is
influenced by the dermal permeability and partition coefficients.
The THMs are the most volatile class of chemicals in this study, and the inhalation route clearly
dominates the absorbed dose. The contribution of the ingestion and dermal routes are similar,
and given the uncertainty of the parameters, it is unclear which route provides the larger dose.
The HAAs and HANs are much less volatile, and therefore the inhalation route has the least
contribution to the absorbed dose. Given the large uncertainty in the dermal parameters, it is
unclear whether ingestion or dermal is the largest contributor to the total absorbed dose. The
results shown in plots are based on calculations using a midpoint estimate for the skin
permeability coefficient, as shown in Table 46. However as shown in section 4.2.3, if the value is
actually at the high end of the uncertainty range, in some cases, the dermal component becomes a
significant contributor to the total absorbed dose. In general for lower volatility compounds,
dermal absorption is less than ingestion, but is within an order of magnitude.
The contribution of the dose by route of exposure/uptake is presented for each chemical for the
50th and 95th percentiles of each population group. This summary further illustrates the role of the
route as a function of the chemical, particularly with respect to the chemical's volatility. In
addition, this summary further underscores the importance of understanding the uncertainties
associated with the dominant route. In the case of the dermal route, the summary also shows the
importance of understanding this uncertainty to identify the importance of the dermal route.
Given the large uncertainty in the dermal parameters, the dermal route cannot be dismissed as not
important even though the results indicate it is of lesser importance.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 139
-------
th
Table 68. Summary of Absorbed Dose by Route for the 50 Percentile of the Population
Chemical
Con
Dermal
tribution to Total by R
Ingestion
jute
Inhalation
Female, Age 15-45
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
9%
4%
5%
7%
3%
0%
0%
3%
3%
3%
2%
3%
3%
9%
13%
15%
27%
97%
100%
100%
97%
97%
97%
95%
96%
97%
81%
83%
80%
67%
0%
0%
0%
0%
0%
0%
2%
1%
0%
Male, Age 15-45
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
10%
4%
5%
7%
2%
0%
0%
2%
3%
3%
2%
3%
2%
11%
15%
17%
29%
98%
100%
100%
97%
97%
97%
96%
96%
97%
80%
81%
78%
64%
0%
0%
0%
0%
0%
0%
2%
1%
0%
Child, Age 6
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
1%
1%
1%
1%
1%
0%
0%
1%
1%
1%
1%
1%
1%
9%
15%
16%
23%
99%
100%
100%
99%
99%
99%
96%
98%
99%
89%
84%
83%
75%
0%
0%
0%
0%
0%
0%
4%
1%
0%
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 140
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th
Table 69. Summary of Absorbed Dose by Route for the 95 Percentile of the Population
Chemical
Con
Dermal
tribution to Total by R<
Ingestion
jute
Inhalation
Female, Age 15-45
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
8%
3%
4%
5%
3%
0%
0%
3%
4%
4%
3%
5%
3%
6%
8%
10%
14%
97%
100%
100%
97%
96%
96%
92%
94%
96%
86%
89%
87%
81%
0%
0%
0%
0%
0%
0%
5%
2%
1%
Male, Age 15-45
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
7%
2%
3%
4%
2%
0%
0%
2%
3%
2%
2%
3%
2%
7%
10%
11%
18%
98%
100%
100%
98%
97%
97%
94%
96%
97%
86%
88%
85%
78%
0%
0%
0%
0%
0%
0%
4%
1%
0%
Child, Age 6
Chloroform
BDCM
DBCM
Bromoform
MCA
DCA
TCA
MBA
DBA
BCA
DCAN
TCAN
DBAN
7%
2%
3%
4%
3%
0%
0%
3%
4%
4%
3%
4%
3%
5%
8%
8%
12%
97%
100%
100%
97%
96%
96%
90%
93%
96%
88%
90%
89%
84%
0%
0%
0%
0%
0%
0%
7%
2%
1%
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 141
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Other analyses not conducted as a part of this study could have benefits. A very intensive
evaluation of the results would allow an understanding of the impact of each activity and the
range of behavior across a population. An analysis of the relationship between water-use
behavior and resultant exposure and dose would be useful in identifying and potentially
modifying exposure related behaviors. In addition, the impact of a multitude of other factors,
such as air exchange rates, water use rates, and water temperature, could be evaluated.
As is clearly described in Section 3.0 Model Parameters, a large number of parameters are
required to properly represent exposure to water-borne contaminants. Each of these parameters
has an associated uncertainty. The overall uncertainty of the estimated absorbed dose is unclear,
and is examined in Section 5 of this report.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 142
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4.3 Pharmacokinetic Modeling Results
The TEM model was run to generate 250 time histories for use in PBPK model simulations.
They were generated for four chemicals and three demographic groups. The chemicals modeled
are a subset of the original 15 proposed for this study. Chloroform (CHC13),
bromodichloromethane (BDCM), dichloroacetic acid (DCA), and trichloroacetic acid (TCA)
were modeled for the Adult male and female aged 15-45, and the male child age 6-10. The PBPK
parameters for the child were chosen for age 6.
The exposure time histories from TEM were generated for a 24-hour period. They were then
repeated for another 24 hours. They were run through the ERDEM model and percentile curves
were generated for Liver, Kidney, Venous Blood, and Ovaries or Testes. In addition, percentile
curves were generated for exhaled air, chemical in the urine, and the total absorbed dose
(Appendix A). Tables of the percentiles at the end of the 48-hour period simulated were
generated for AUC and absorbed dose (Tables 70 - 73). Some analysis of the results are
presented in Table 74.
4.3.1 Meaning of Exposure Time Histories
Exposure time histories represent measurements of environmental conditions at a particular
location. This location could be around a person or a measuring device located on a pole. The
chemical in air or in water results in different forms of exposure. A person might be
hypothesized to be at the site of emission, or calculations could be performed to determine
probable exposure at a more likely location for an exposure. Chemical in air can be absorbed
through the skin, but depending on the chemical the greatest exposure would be through
inhalation. On the other hand, chemical in water could volatilize into the air, and be inhaled,
could be absorbed through the skin, or even absorbed through drinking the water. A person could
be exposed to many chemicals through many exposure routes at the same time. The ERDEM
model is designed to be able to have multiple exposure time histories, each for a different
chemical.
The time histories for inhalation, oral, and dermal exposures input to ERDEM have specific input
formats, but special subroutines can be written to convert from other formats as long as all
required information is available. The inhalation exposure time history consists of time and the
concentration of chemical in the inhaled air. For the oral exposure time and the amount of
chemical per unit time (concentration of the chemical in the food times the flow rate) being
ingested are required. The dermal exposure requires the time and the concentration of the
chemical in the fluid on the skin. In this case the area of the affected skin and the permeation
coefficient for the chemical are needed.
The 250 exposure time histories from TEM for each of the four chemicals and three demographic
groups were run through ERDEM and the resulting 250 time histories for a given dose metric
variable (see Section 4.3.2) were combined to determine the 10th, 50th and 90th percentiles at each
time step. This results in a set of three curves for each percentile. This was performed for the
concentration and AUC for the Kidney, Liver, Venous Blood, and the Ovaries or Testes.
Similarly, curves were determined for the total absorbed dose, the total amount in the urine (for
DCA and TCA), and the concentration in exhaled air for BDCM and Chloroform). These plots
are given in Appendix A.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 143
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4.3.2 Choosing Dose Metrics
Dose metrics may be any measure of chemical at a location and usually time in the body. The
dose metric is associated with a site where experimental measurements are available, or a site
where there is potential risk to the subject. Total amount of a chemical in the urine at given
times, concentration of chemical in exhaled air, peak concentration of chemical in the blood,
liver, kidneys, etc., AUC (Area Under the Concentration curve) at a given time are all examples
of dose metrics. When a time history for a dose metric is determined, it is called a dose metric
variable.
4.3.3 Variability of PBPK Model Results Due to Variability of Exposure Time Histories
The time histories from TEM are determined from random choice of subjects who perform
various activities. These activities have stochastic components. The simulation results in Tables
70 - 73 show, for each chemical and demographic group, the percentiles at 48 hours for the total
absorbed dose and the AUC in the Kidney, Liver, Venous Blood, and Ovaries or Testes. Table
74 includes notes on each chemical and demographic group. Appendix A has the time histories
described above.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 144
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Table 70. Analysis of PBPK Model Results for Bromodichloromethane for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00230
0.00450
0.455
0.00043
0.00176
0.00681
0.0134
1.31
0.00119
0.00517
9.98
9.98
10.0
9.95
9.96
0.0919
0.180
17.7
0.0161
0.0698
8.56E-06
1.68E-05
0.00730
1.11E-05
9.04E-06
6.72E-05
0.000132
0.0201
2.73E-05
5.52E-05
9.58E-05
0.000188
0.0340
4.26E-05
8.11E-05
0.000884
0.00173
0.184
0.000188
0.000682
0.00386
0.00757
0.732
0.000714
0.00294
0.00643
0.0126
1.25
0.00114
0.00490
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00269
0.00372
0.457
0.000525
0.00203
0.00721
0.00995
1.20
0.00133
0.00540
6.23
6.22
6.24
6.23
6.22
0.0640
0.0883
10.6
0.0118
0.0479
1.02E-05
1.4E-05
0.00793
1.51E-05
1.11E-05
5.36E-05
7.39E-05
0.0206
3.33E-05
4.85E-05
0.00013
0.00018
0.0328
4.41E-05
0.000107
0.00103
0.00142
0.177
0.000217
0.000778
0.00424
0.00584
0.703
0.000794
0.00319
0.00723
0.00994
1.22
0.00135
0.00539
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00132
0.00258
0.175
0.000377
0.00104
0.00149
0.00291
0.190
0.000392
0.00117
2.18
2.18
2.19
2.20
2.19
0.00899
0.0176
1.16
0.00244
0.00710
3.86E-06
7.57E-06
0.00174
6.51E-06
4.38E-06
4.85E-05
9.52E-05
0.0126
4.3E-05
4.54E-05
0.000142
0.000279
0.0232
5.94E-05
0.000119
0.000815
0.00160
0.113
0.000251
0.000653
0.00342
0.00670
0.437
0.000921
0.00268
0.00440
0.00864
0.567
0.00118
0.00345
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 71. Statistics for Chloroform Simulations for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.01118
0.0141
1.57
0.00120
0.00576
0.0319
0.0407
4.43
0.00331
0.0163
9.70
9.70
9.74
9.71
9.67
0.426
0.544
59.2
0.0443
0.218
1.68E-05
2.14E-05
0.0175
2.24E-05
1.07E-05
0.000251
0.000321
0.0650
6.32E-05
0.000142
0.000552
0.000704
0.1070
0.000106
0.000325
0.00445
0.00568
0.658
0.000522
0.00234
0.0187
0.0239
2.560
0.00194
0.00960
0.0316
0.0403
4.61
0.00339
0.0164
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0117
0.0114
1.58
0.001428
0.00629
0.0311
0.0302
4.10
0.00362
0.0164
6.16
6.16
6.18
6.17
6.15
0.275
0.267
36.4
0.0321
0.145
1.39E-05
1.35E-05
0.0214
3.17E-05
1.119E-05
0.000194
0.000189
0.0622
7.74E-05
0.000117
0.000532
0.000519
0.104
0.000116
0.000311
0.00448
0.00436
0.609
0.000585
0.00241
0.0183
0.0178
2.45
0.00219
0.00980
0.0314
0.0305
4.24
0.00366
0.0166
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00586
0.00772
0.601
0.0010
0.00319
0.00666
0.00878
0.657
0.00117
0.00358
2.23
2.23
2.24
2.26
2.24
0.0422
0.0556
4.20
0.00758
0.0229
5.19E-06
6.84E-06
0.00466
1.43E-05
4.19E-06
0.000137
0.000180
0.0354
9.92E-05
9.23E-05
0.000622
0.000821
0.0796
0.000165
0.000347
0.00364
0.00480
0.391
0.000738
0.00198
0.0152
0.0201
1.5
0.00272
0.00823
0.0201
0.0265
1.99
0.00354
0.0108
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 72. Statistics for Dichloroacetic Acid Simulations for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00399
0.00493
0.0693
0.00404
0.00498
0.00318
0.00393
0.0542
0.00322
0.00397
3.54
3.54
3.26
3.52
3.54
0.0310
0.0384
0.509
0.0313
0.0388
0.000392
0.000485
0.00633
0.000396
0.00049
0.00123
0.00152
0.0217
0.001248
0.00154
0.00141
0.00174
0.0242
0.001426
0.00176
0.00317
0.00392
0.0544
0.00321
0.00396
0.00733
0.00907
0.127
0.00743
0.00917
0.00983
0.0122
0.175
0.00997
0.0123
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00221
0.00262
0.0339
0.00224
0.00272
0.00227
0.00270
0.0347
0.00232
0.00281
4.30
4.30
4.22
4.31
4.30
0.0215
0.0255
0.325
0.0218
0.0265
0.000284
0.000337
0.00422
0.000288
0.000351
0.00048
0.000569
0.00726
0.000486
0.000592
0.000612
0.000726
0.00934
0.000620
0.000755
0.00159
0.00189
0.0243
0.00162
0.00196
0.00449
0.00533
0.0675
0.00456
0.00555
0.00547
0.00649
0.0848
0.00557
0.00675
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.00248
0.00307
0.0161
0.00249
0.00310
0.00208
0.00257
0.0133
0.00208
0.00259
2.46
2.46
2.46
2.46
2.46
0.0168
0.0208
0.109
0.0169
0.0211
0.000195
0.000241
0.00133
0.000196
0.000243
0.000559
0.000692
0.0036
0.000562
0.000699
0.000664
0.000821
0.00433
0.000667
0.000830
0.00185
0.00229
0.0121
0.00186
0.00232
0.00507
0.00627
0.0333
0.00510
0.00633
0.00617
0.00763
0.0389
0.00619
0.00771
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 73. Statistics for Trichloroacetic Acid Simulations for the Adult Male, Adult Female, and Male Child
Demographic Group
Average
Standard
Deviation
Skewness
Max
Min
5th
Percentile
10th
Percentile
50th
Percentile
90th
Percentile
95th
Percentile
Adult Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0201
0.0317
0.0737
0.0205
0.0305
0.0165
0.0260
0.0576
0.0167
0.0250
3.86
3.86
3.26
3.85
3.86
0.166
0.263
0.541
0.169
0.253
0.00216
0.00341
0.00673
0.00219
0.00328
0.00585
0.00923
0.0231
0.00597
0.00888
0.00729
0.0115
0.0257
0.00746
0.0111
0.0160
0.0252
0.0578
0.0163
0.0242
0.0375
0.0592
0.135
0.0382
0.0570
0.0488
0.0770
0.186
0.0497
0.0740
Adult Female
AUC Kidney (mg/L*hr)
AUC Ovaries (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0118
0.0176
0.0360
0.0120
0.0177
0.0123
0.0183
0.0369
0.0126
0.0185
4.43
4.43
4.22
4.45
4.43
0.117
0.174
0.346
0.119
0.175
0.00151
0.00224
0.00449
0.00153
0.00226
0.00251
0.00373
0.00772
0.00255
0.00376
0.00330
0.00489
0.00992
0.00335
0.00494
0.00848
0.0126
0.0258
0.00861
0.0127
0.0237
0.0352
0.0718
0.0242
0.0356
0.0293
0.0435
0.0901
0.0298
0.0439
Child Male
AUC Kidney (mg/L*hr)
AUC Testes (mg/L*hr)
Absorbed Dose (mg)
AUC Liver (mg/L*hr)
AUC Venous Blood
(mg/L*hr)
0.0154
0.0243
0.0171
0.0155
0.0234
0.0131
0.0206
0.0141
0.0131
0.0198
2.47
2.47
2.46
2.47
2.47
0.106
0.166
0.115
0.106
0.160
0.0011
0.00174
0.00142
0.00111
0.00167
0.00344
0.00543
0.00382
0.00347
0.00522
0.00405
0.00638
0.00459
0.00408
0.00614
0.0114
0.0180
0.0129
0.0115
0.0173
0.0308
0.0485
0.0354
0.0310
0.0466
0.0413
0.0652
0.0414
0.0416
0.0627
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 74. Analysis of ERDEM Model Simulations for Four Chemicals and Three Demographic Groups
Demographic
Group
Adult Male
Adult Female
Male Child
Chemical
CHC13
0.070mg/L
BDCM
0.023mg/L
DCA
0.032mg.L
TCA
0.034mg/L
CHC13
0.070mg/L
BDCM
0.023mg/L
DCA
0.032mg/L
TCA
0.034mg/L
CHC13
0.070mg/L
BDCM
0.023mg/L
DCA
0.032mg/L
TCA
0.034mg/L
Comments on AUC at 90th Percentile
AUC in the Kidney is about 50% higher than would be
expected from the BDCM/CHC13 concentration ratios.
AUC in Testes almost twice as much as that in the
Kidney. AUC in Liver is 1710th of AUC in the Testes.
The Kidney AUC is almost twice that for BDCM. The
AUC for the Liver is about the same as for the Kidney. .
The AUC in Venous Blood is close in value to that for
the Testes
The AUC in the Liver ratio to that for BDCM is 53.5.
The AUC in Venous Blood ratio to that for BDCM is
1 9.4. The AUC for the TESTES is about 8 times that for
BDCM This is due to the slow clearance of TCA from
the system.
Very small differences with the Adult Male. The AUC
in the Ovaries is about 25% less than for the Adult Male
Testes.
AUC in the Kidney is about 30% less than AUC in the
Ovaries. Liver AUC is slightly higher than for Adult
Male.
The AUC for the Liver is about the same as for the
Kidney. . The AUC in Venous Blood is close in value to
that for the Ovaries.
The AUC in the Liver ratio to that for BDCM is 30.4.
The AUC in Venous Blood ratio to that for BDCM is
1 1 .2. The AUC in the Ovaries is about 6 times that for
BDCM
The Male Child AUCs are with in 20% of the values for
the Adult Male and Female.
Kidney and Testes AUCs are a little less than for the
Adult Male. The Liver AUC is a little higher than for
the Adult Male
The AUC for the Liver is about the same as for the
Kidney. The AUC in Venous Blood is close in value to
that for the Testes.
The AUC in the Liver ratio to that for BDCM is 33.7.
The AUC in Venous Blood ratio to that for BDCM is
17.3. The AUC in the Testes is 7.2 times that for BDCM
Comments on Absorbed Dose at 90th Percentile after 48
Hours
Absorbed dose is 2.56 mg. Based on the ratio of concentrations in the
water for CHC13 to BDCM the absorbed dose should be 2.23. There is
more volatility of the CHC13, and greater skin permeation coefficient.
0.732 mg after 48 hours.
DCA has 176th of the absorbed dose of BDCM. There is not enough DCA
volatilized to model inhalation. The permeation coefficient is very low,
1 .84E-6 so much of the absorbed dose is due to chemical in drinking
water.
The absorbed dose is 0. 135277 mg. This is less than 175th of the absorbed
dose for BDCM.
Absorbed Dose is 2.45 mg. Very close to that of the Adult Male.
Absorbed dose is 0.703, a little less than for the Adult Male.
The absorbed dose is less than 1710th of that for BDCM. It is
approximately half of that for the Adult Male. They were very close in
value for CHC13 and BDCM.
The absorbed dose is very close to 1710th of that for BDCM. It is
approximately half of that for the Adult Male.
The absorbed dose is 1 .5 1 mg. Based on the water concentration ratios
the absorbed dose would be 1.33 mg. See discussion for the Adult Male.
The absorbed dose is 60% of that for the Adult Male - 0.437 mg.
Absorbed dose is 1713th of that for BDCM
The absorbed dose is about 1/12 of that for BDCM. It is approximately
l/4th of that for the Adult Male.
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Table 75 shows the largest values for the 90th percentile of the concentration (nig/Liter) in the
Liver, and the Testicles or Ovaries for chloroform and bromodichloromethane. They are taken
from the sets of Figures in Appendix A, A-2, A-6, A-10 for BDCM, and Figures A-14, A-18, and
A-22 for chloroform. The values show that the concentrations for the BDCM are a little more
than half of the corresponding values for chloroform for the Adult Male. For the Adult Female,
the BDCM values are about two-Thirds of the chloroform values. The BDCM concentrations for
the Male Child .are a little less than half of those for chloroform (similar to the Adult Male). The
activities for each subject are different and the peaks occur at different times. These
concentrations are not the peaks, but represent the 90th percentile of the frequency distribution at
each time step and the table values are the largest values over the full 48 hours of the exposure.
Table 75. Largest 90* Percentile Concentrations for Chloroform and
Bromodichloromethane for Three Demographic Groups
Demographic
Group
Adult Male
Adult Female
Male Child
Largest 90l
Chloroform
(mi
Liver
0.00011
0.000112
0.000105
h Percentiles of
Concentrations
'/Liter)
Testes/Ovaries
0.0012
0.00105
0.00098
Largest 90*
Bromodic
Concentral
Liver
0.00006
0.00008
0.00005
h Percentiles of
hloromethane
ions (nig/Liter)
Testes/Ovaries
0.0007
0.0006
0.0004
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5.0 Sensitivity Analysis
5.1 Overview
The values of the parameters that define the modeling problem ultimately determine the predicted
exposures and doses. The uncertainty in the estimated parameter values provided in this report
varies depending upon the parameter. For example, many estimated parameter values, such as
water flowrate, water volume, house and room volumes, etc. are known within a reasonable and
somewhat definable range of uncertainty. Other parameter estimates, such as those for skin
permeability coefficients and various behavioral parameters may have uncertainties of an order of
magnitude or higher.
When conducting this analysis, both sensitivity and uncertainty analyses were considered.
Uncertainty may be evaluated by framing a stochastic model simulation (i.e., Monte Carlo type
simulation of model inputs) and evaluating the impact of the uncertainty in each parameter on the
selected model outputs. However, due to the difficulty of separating uncertainty and variability in
many of the behavioral parameters, it was concluded that it would be more meaningful to conduct
a screening-level sensitivity analysis to identify the parameters having the most significant impact
(EPA, 1997). Therefore, neither Monte Carlo simulation nor uncertainty analyses are provided in
this section, however the results of sensitivity analysis are invaluable as a means of characterizing
the importance of each parameter, and allow a qualitative judgment of the importance of a
parameter's uncertainty.
The overall purpose of this section is to evaluate the model sensitivity to a selected set of the
parameters. In conducting this sensitivity analysis, it is recognized that due to the sheer number
of model parameters and the large uncertainty in some of the parameter values, the results of this
analysis may be used as guidance in selecting the set of important parameters, but a more refined
study may be necessary as the parameter estimates are refined. In addition, the sensitivity of the
various parameters is expected to be similar for each of the three modeled subjects. For this
reason, the analysis will focus on the adult male. Some results will also be presented for the adult
female and the child to demonstrate this similarity.
This study combines exposure and uptake modeling with pharmacokinetic modeling (PBPK) to
yield an estimate of population-based exposure and absorbed dose to 15 DBFs. The exposure and
uptake model is the Total Exposure Model (TEM), developed by Wilkes Technologies. The
PBPK model is the Exposure Related Dose Estimating Model (ERDEM, formerly DEEM)
developed by Anteon Corporation in collaboration with the Human Exposure Research Branch of
the National Environmental Research Laboratory of the USEPA in Las Vegas, Nevada. The
detailed discussions on TEM and ERDEM are presented in Section 2 of this report.
This combination provides both benefits and challenges. One significant benefit is the ability to
evaluate target tissue dose as a function of a variety of behaviors, environmental factors, and
other exposure related parameters. However, due to logistical constraints and the large number of
parameters affecting the outcome, it is not reasonable to attempt a comprehensive sensitivity
analysis. Therefore, the sensitivity analysis has been limited to a subset of the available
conditions. This sensitivity analysis is performed for two disinfection byproducts, chloroform
(CHC13) and dichloroacetic acid (DCA). The sensitivity analysis will evaluate the two modeling
components separately: (1) the exposure and uptake model components, and (2) the physiological
model components.
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The impact of the parameters affecting each of the three exposure routes (inhalation, dermal
exposure, and ingestion) are evaluated. The sensitivity analysis is conducted for a 24-hour
scenario during which the male individual is exposed to a single disinfection byproduct via
household water usage. The two chemicals of interest are chloroform and DCA (separately). The
sensitivity analysis is performed to determine the impact of the various parameters on the
resultant uptake and dose. It was expected that the uptake and dose of these two chemicals would
be impacted by a different set of parameters, due to the fact that chloroform is highly volatile and
DCA is not. The following sections describe the methods and results of the sensitivity analysis.
5.2 Methods
The sensitivity analysis is conducted by first establishing a base-case scenario, consisting of a
base-case set of activities and model parameters. To evaluate the sensitivity of a particular
parameter, the value of that parameter is varied by 10% (±10%) from its base-case value. The
impact of this change is then evaluated by comparing the relative change in the chosen dose
metrics.
In analyzing the exposure and uptake parameter sensitivities, the base-case scenario consists of
the same three-person household as in the exposure modeling study in Sections 1 through 4 (male
age 15-45, female age 15-45, and child age 6) and was chosen from the set of simulations derived
under the modeling study. The chosen simulation was selected primarily because it contained
most of the "typical" water uses. This "base-case" scenario is chosen because it represents
plausible activity pattern, and is not necessarily representative the mean behavior and exposure of
the population. The water uses were modified slightly from those simulated by inserting
additional water uses to provide a set of water uses consistent with the average behavior of each
population group as defined in Section 3.0. The activities and locations for the base-case scenario
are presented in Tables 76 - 78, with the resultant water uses summarized in Table 79 and the
base-case consumption behavior given in Table 80. The residence assumed for the base-case
scenario is shown in Figure 74. The values of zone volumes, interzonal air flows, and whole
house air exchange rate are those sampled for the chosen simulation, and are well within the
range of "typical" values for US housing. The water concentrations for chloroform and DCA
were assumed to be 0.07 mg/L and 0.032 mg/L, respectively, consistent with the above exposure
and uptake modeling calculations.
In analyzing the physiological parameter sensitivities, only the adult male (age 15-45) is
analyzed. He is assumed to have a mean alveolar ventilation rate of 540 liters/hour (L/h) at rest
and 600 L/h during sedentary activity, and a mean body volume of 77.6 kg (1 kg ~ 1 L).
Each model run is a simulation of activities and processes that occur over a 24-hour period. Each
24-hour simulation includes three sequential activity level periods: a period of rest in the morning
from midnight to 7:05 am, a sedentary period from 7:05 am to 8:30 pm, and another rest period in
the evening from 8:30 pm to midnight.
Chloroform was modeled with metabolism to phosgene and carbon dioxide. The metabolisms of
DCA were modeled as elimination in the liver. Inhalation was not modeled for DCA because the
volatility of DCA is very low.
5.2.1 Sensitivity Analysis Framework
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To study the impact that the various water-use parameters have on the resultant exposure to the
individual, a base-case value for each parameter was increased and decreased by 10% to derive
upper and lower end values for parameter inputs. The model was run using the upper end value of
a single parameter while maintaining all other parameters constant, and then the model was run
using the lower end value of the parameter. The difference in the resultant modeled absorbed
doses between the case using the upper end value and the case using the lower end value were
then evaluated to determine the sensitivity of that parameter on the dose metric.
The sensitivity analysis is conducted by altering each parameter or parameter set while holding
the remaining parameters at their baseline value, and executing the model or combined models as
required. To observe how changes of model parameters impacted dose metric outputs, we used a
measure of relative sensitivity defined by:
Relative Sensitivity = y~y°>'y° . i Q0% (5.1)
(x-x0)/x0
where y = the modeled output of dose (given the altered value of the model parameter)
jo = the modeled output of dose (given the base values of all model parameters)
x = the altered value of the chosen model parameter
x0 = the base value of the chosen model parameter
The resulting relative sensitivity is interpreted as the percent change in the output relative to the
input. A value of 100% indicates an identical relative change. A negative value indicates the
parameter and the output are inversely related.
5.2.2 Model Parameters
The combination of TEM and ERDEM require a large set of input parameters. A subset of model
parameters were selected from the available set for inclusion in this sensitivity analysis. The
basis and chosen parameters are described in the following sections.
5.2.2.1 Exposure and Uptake Model Parameters
The exposure and model parameters for inclusion in the sensitivity analysis are given in Tables
81-82. These parameters include the majority of parameter values required by the model.
However, other less straightforward model issues potentially have a large impact on the estimated
dose. These include:
• Impact of occupant location behavior: Occupant behavior is sampled from the NHAPS
database. However from other analyses, it is obvious that these reported activity patterns
are not always representative, and frequently lack the necessary detail to represent all
relevant activities.
• Impact of family size and demographics: This study assumes a three-person size with an
adult male, adult female and a child. The impact of other family sizes and makeup is not
evaluated. (Refer to Section 3.2 for a description of the demographic population groups.)
• Impact of changing household conditions: Conditions such as opening and closing of
doors and windows, operation of fans and mechanical equipment (e.g., heating and
cooling systems), etc. indirectly have an impact on air concentrations by changing the
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ventilation characteristics. Many of these impacts have been studied elsewhere, and are
not included in this study.
• Model appropriateness: This sensitivity analysis assumes the applied emission, fate and
transport, and exposure models appropriately represent the relevant processes.
Although these issues may have uncertainty, they are difficult to evaluate or are generally outside
the scope of this study.
When evaluating the sensitivity of a parameter, that parameter is changed while all other
parameters are held constant. However, in the case of household volumes and airflow rates, these
parameters are changed as a unit, such that as the household volume increases, all the individual
zones in the household increase by the same percentage. A similar approach is used in evaluating
the effect of air exchange rate, such that when the rate is increased the interzonal airflows are
increased by a proportional amount.
When evaluating parameters that are affected by the activity level (rest or sedentary), the
simulations were run such that the resultant breathing rate was increased or decreased by 10% for
all activity levels in a given simulation.
5.2.2.2 PBPK Model Parameters
The sensitivity analysis on the PBPK model parameters was conducted on the adult male. There
were 39 model input parameters for the chloroform, and 34 for the DCA sensitivity analysis. The
upper and lower perturbations were plus 10% and minus 10% of the baseline values. The
physiological model parameters evaluated in the sensitivity analysis are the various parameters
defining body compartment volumes and blood flows (by activity), alveolar ventilation rates, skin
permeability coefficients, gastro-intestinal absorption rates, partition coefficients, metabolism rate
constants, and elimination rate constants. The specific parameters are presented in Tables 83-84.
Each sensitivity analysis run spans 24 hours and contains time periods for both the "at rest" and
the "sedentary" activities. A compartment blood flow is perturbed for one activity at a time.
The impacts of the activity levels on the alveolar ventilation rate (breathing rate) were adjusted in
a manner similar to that described in Section 5.2.2.1. When analyzing the sensitivity of the
alveolar ventilation rates, blood flows or cardiac output, these three parameters were changed as a
unit due to their interdependency. The cardiac output is calculated as 85.43% of the alveolar
ventilation rate, and the blood flow value is determined from the cardiac output. When analyzing
the sensitivity of the blood flows, only the blood flow for the current compartment is altered.
Cardiac output is the sum of the blood flows to the individual compartments. The percentages of
cardiac output at rest or sedentary are 4.8% (for Dermis), 4.8% (Fat), 19.4% (Kidney), 23.7%
(Liver), 27% (Rapidly Perfused Tissue), 19% (Slowly Perfused Tissue), and 1.3% (Testes). For
example, when the blood flow in the liver decreases, the cardiac output is re-calculated as the new
sum of blood flow. The alveolar ventilation rate remains the same. This computational process is
applied on each of the activities, because the activity determines the value of alveolar ventilation
rate.
The sensitivity analysis for the blood flow in the liver for the adult male is as the follows:
1. The baseline values of the alveolar ventilation rate and cardiac output are defined for
rest and sedentary activity levels. The baseline alveolar ventilation rates are as 540
liters/hour for resting and 600 liters/hour for sedentary activity; and the baseline
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cardiac outputs are calculated as 461.34 liters/hour for resting, and 512.64
liters/hours for sedentary activity.
2. For the sensitivity analysis, the plus/minus 10% perturbations in blood flow for the
liver are calculated from the cardiac output for a given activity. Then the blood flows
for all compartments are added to get a new value for the cardiac output (based on
the adjusted blood flow to the liver). The blood flow percentages for each
compartment are then recalculated and used as input parameters for the particular
model run. These calculations are performed for both the resting and sedentary
activity levels.
A similar procedure is used for the volume of other compartments in the body, but they do not
change with the activity
5.2.3 Dose Metrics in Sensitivity Analysis
The term Dose Metric is used to describe a dose endpoint of interest. It typically describes the
amount of a chemical at a location within the body, either at a given time or integrated over a
specific time period. Ideally, the dose metric will be highly correlated with the risk associated
with an outcome, such as an undesirable health outcome (e.g., cancer risk). Several relevant dose
metrics are available as outputs from the exposure model and the PBPK model. From these, a set
of dose metrics is chosen for use in evaluating the sensitivity of the selected model parameters for
this sensitivity analysis.
5.2.3.1 Exposure and Uptake Model Dose Metrics
The exposure model provides an estimate of exposure, potential dose and absorbed dose as a
result of the modeled activities. The exposure and potential dose are generally not good dose
metrics because their route-specific values do not account for the processes that occur when the
chemical crosses a boundary (i.e., skin, lungs or stomach). Therefore, the absorbed dose (the
mass of chemical entering the person's bloodstream) represents the most meaningful metric.
Therefore, the absorbed dose, both total and route specific, is used as the primary dose metric for
the exposure and uptake models.
5.2.3.2 PBPK Model Dose Metrics
Six dose metrics were selected to study the impact of the various modeling parameters on
exposure to the disinfection byproducts of chloroform and DCA. The dose metrics evaluated
were: (1) total absorbed dose, (2) area under the concentration-time curve (AUC) at 24 hours in
the liver, (3) AUC at 24 hours in the testes, (4) total amount metabolized in the liver at 24 hours,
(5) peak concentration in the liver and (6) peak concentration in the testes. The total absorbed
dose indicates how much chemical enters the person's body. The AUC provides information on
the length of time at various chemical concentration levels in a particular organ or compartment.
A high AUC may mean either a very high concentration for a short time, or a low concentration
for a very long time. The amount metabolized is an indication of (1) the probable amount of the
parent chemical that remained available for clearance, and (2) the amount of metabolites that are
available for clearance. Analyzing peak concentrations is valuable when a short-term peak value
can cause damage to the tissue in question. The time of the peak helps determine how long a
person might have to get a chemical out of their system before damage might occur.
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5.3 Results
5.3.1 Results of Sensitivity Analysis Using TEM
The TEM model predicted air concentrations for the base-case are presented in Figures 75 and 76
for chloroform and DCA, respectively. At the top of each figure, a line graph indicates when
each water appliance is active. Combining these predicted air concentrations with each
occupant's breathing rate and location, the model estimates the potential inhalation dose. Using
the uptake models, TEM further calculates the absorbed inhalation dose. The absorbed dermal
dose is calculated by the uptake model considering the water use and the skin area in contact with
the water. The ingestion dose is calculated by estimating the amount of contaminant remaining in
the water at the time of consumption, and assumes 100% absorption. The base-case values for
potential and absorbed dose are presented in Tables 85-86 as a function of route and population
group. Tables 87 and 88 present the relative sensitivity for the TEM dose metrics, absorbed dose
and potential dose. Potential dose is shown only for route specific exposure and not for total
dose, since potential dose cannot be meaningfully compared across routes of exposure.
5.3.2 Results of Sensitivity Analysis Using ERDEM
The ERDEM model predicted values for the base-case scenario for the six dose metrics are given
in Table 89 for chloroform and DCA. The dose and relative sensitivity are given for the results of
the ERDEM model in Tables 90-100 consisting of 6 tables for chloroform and 5 tables for DCA.
The results are reported for the six dose metrics described above: Absorbed Dose; AUC for the
Liver; AUC for the Testes; Amount Metabolized in the Liver; Peak Concentration in the Liver;
and Peak Concentration in the Testes. The Absorbed Dose results for DCA are not provided,
since all relative sensitivities are negligible. Figures 77 (a) through (f) for chloroform and Figures
78 (a) through (f) for DCA exhibit dose metric curves over a 24-hour time period for the baseline
values of the input model parameters.
5.4 Discussion
The purpose of conducting sensitivity and uncertainty analysis is to identify which assumptions,
parameters, and uncertainties significantly impact the conclusions. This sensitivity analysis,
referred to as a "screening level" sensitivity analysis is conducted without a reasonable
understanding of the uncertainty inherent in many of the parameters. Some parameters, for
example skin permeability coefficients and some behavioral characteristics, are uncertain across
several orders of magnitude. If the uncertainties were well understood across the set of model
parameters, a combined uncertainty and sensitivity analysis would provide the basis for
evaluating the relative importance of each parameter. Alternatively, this screening level
sensitivity analysis provides a basis for evaluating only the sensitivity of each parameter. If the
output is highly sensitive to a given parameter and the parameter value has a high degree of
uncertainty relative to the assumed sensitivity range (10% for this study), it is reasonable to
conclude that the value of the given parameter is important relative to parameters that are less
sensitive. However, if a parameter is both less sensitive and highly uncertain relative to the
assumed sensitivity range, the conclusion is less obvious.
The following subsections present discussion about the sensitivity analysis of both TEM and
ERDEM.
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5.4.1 Discussion on Sensitivity Analysis Using TEM
The exposure model sensitivity analysis identified a number of important results. From the
results, it is clear that the conclusions are not consistent across chemicals. Table 101 presents a
ranking of the parameters by their absolute value of relative sensitivity. The parameters are
ranked in descending order for the adult male with shaded cells for the parameters that are out of
order (of different order of ranking) for the female and child. The table provides a clear means for
identifying input parameters as well as contrasting changes across activity patterns. For volatile
chemicals as represented by chloroform, the parameters influencing the air concentrations have
the most significant impact. These parameters include the overall mass transfer coefficients, air
exchange rates, zone volumes, water flowrates, and duration of water uses. The air exchange rates
and zone volumes are inversely related to the absorbed dose because of their effect of lowering
airborne concentrations. The overall mass transfer coefficient is the most sensitive parameter for
chloroform, consistent with the inhalation route having the largest dose, causing approximately an
8% change in the absorbed dose for a 10% change in the overall mass transfer coefficient.
Although the mass transfer coefficients were examined as a group, it is intuitively obvious that
larger inhalation exposure events, such as showering, will be more sensitive to this parameter.
For the volatile chemical, chloroform, the model is relatively insensitive to the actual volume of
non-flowing type water appliances (e.g., bath volume, dishwasher volume, clothes washer
volume, toilet volume, etc.) with less than a 0.2% change in dose for a 10% change in the volume
parameter. In addition, the model is relatively insensitive to Henry's law constant (H), yielding a
relative change of less than 0.3% for a 10% change in H.
For low volatility chemicals, as represented by DCA, consumption and dermal contact play the
most significant roles. Consumption is by far the most sensitive parameter, changing the
absorbed dose approximately 10% for a 10% change in the consumption volume. The dermal
influence, though much less significant, is evident in the shower duration for the adults (who
showered) and in the bath duration for the child (who took a bath). Although the inhalation
route's contribution to the absorbed dose is small relative to the other routes, it is interesting to
note that, with the exception of Henry's law constant, the sensitivity of the inhalation parameters
are in the same sequential order as for chloroform. The increased relative influence of Henry's
law constant as compared to the mass transfer coefficient is due to the dynamics of the
equilibrium relationship as defined by Henry's law. The concentration in the air is limited to the
equilibrium condition, as defined by Henry's law, which is approached in the vicinity of the water
appliance during water uses of duration longer than a few minutes, thereby attenuating the mass
transfer rate. For this reason, Henry's law constant is the most sensitive parameter for the
inhalation route.
Although chloroform is a volatile chemical and DCA is a low volatility chemical, and as such
they are generally representative of chemicals with similar chemical properties, many other
factors affect the exposure and uptake of a chemical. Factors such as skin permeability are not
highly correlated with volatility, and therefore the fractional dermal uptake can be very different
for chemicals with similar volatility. Therefore, the conclusions reached based on the sensitivity
analysis for the two chemicals considered here would have to consider the effect of the other
chemical properties which impact uptake.
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5.4.2 Discussion on Sensitivity Analysis Using EDREM
The PBPK model (ERDEM) sensitivity analysis identified a number of highly sensitive
parameters, but also identified numerous insensitive parameters. In some cases, the change in the
dose metric variables, due to the perturbation of an input variable, was less than the relative error
in the integration process. For these cases, the results are not reported.
Tables 92 and 93 contain the relative sensitivities for Liver AUC and Testes AUC dose metrics
for chloroform respectively. These tables show that the AUC estimates for the Liver differ by a
factor of around 10 from the estimates for the Testes. But, for DCA the values of AUC are very
similar for Liver versus Testes. The volumes of the Body, Fat, and the Slowly Perfused Tissue
show a high relative sensitivity in the Liver but not in the Testes. Liver Metabolism is sensitive
in the Liver, but not in the Testes.
Tables 94 and 95 contain the peak concentration of Liver and Testes dose metrics, respectively,
for chloroform. The input parameters that exhibit high relatively sensitivity are: volume of the
Body, Alveolar Ventilation Rate, Cardiac Output, the blood flows to the Liver and Slowly
Perfused Tissue, and the partition coefficients for the Static Lung/Air and Static Lung/Blood.
However, the volumes of the Dermis, Fat, Rapidly Perfused Tissue, and Slowly Perfused Tissue,
and the partition coefficient of Rapidly Perfused Tissue/Blood are sensitive in the Liver but not in
the Testes. The partition coefficient of Testes/Blood is sensitive in the Testes only. In a similar
manner to the results shown for chloroform, Tables 96-100 present the relative sensitivities for
each dose metric for DCA (except absorbed dose as mentioned above).
The dose metric - absorbed dose - has negligible relative sensitivity for all 34 input parameters
for DCA, while for chloroform the absorbed dose is most sensitive to Alveolar Ventilation rate
(relative sensitivity of 89.38%). The absorbed dose for chloroform has negligible relative
sensitivity for the rest of the 39 input parameters. (See Table 90)
The relative sensitivity of the most sensitive input parameter for each dose metric is shown in
Table 102 for each chemical. Table 103 presents a summary of the highly sensitive input
parameters for chloroform. Table 104 presents a summary of the highly sensitive input
parameters for DCA. These tables identify the parameters that have the greatest impact on each
dose metric for chloroform and DCA. Comments provided in Tables 103 and 104 describe some
of the primary reasons these parameters are most sensitive.
5.4.3 Other Model Sensitivity Issues
Several model parameters were not explicitly examined as a part of this study, including the
following:
• Location behavior of exposed individual
• Impact of other occupants (family size, behavior of other occupants, etc.)
• Impact of mechanical systems (e.g., the heating/air conditioning system, other fans, etc.)
• Impact of changing physical conditions in the house (e.g., opening and closing of doors
and windows)
• Impact of weather
• Water temperature
• Other chemical properties
• Model appropriateness (mass balance model, uptake models, behavioral models, etc.)
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Although these parameters were not explicitly studied, the impacts of several of the parameters
were indirectly addressed. The impact of changing physical conditions and weather were
addressed indirectly by looking at the effect of increasing the whole house air exchange rate and
inter-zonal airflows. It is clear that any change that causes ventilation to increase will, in general,
lower peak concentrations at the source. However, while opening an interior door will decrease
the peak concentration at the source, it will also increase the concentrations at other locations in
the home, thereby potentially providing additional exposure to the occupants in those locations.
Similarly, the use of a mechanical system will encourage mixing in the house causing lower
exposures at the source but potentially higher exposures at other locations. The impact of a local
exhaust fan was demonstrated by Wilkes et al. (1992). Wilkes et al. showed that using a
bathroom fan to exhaust emissions during a showering event lowered the estimated exposure to
trichloroethylene (TCE) by between 23 and 36% for assumed scenarios for 2 adults and a child.
The impact of water temperature and other chemical properties are also indirectly examined by
looking at the effect of changing the overall mass transfer coefficient. Water temperature
increases the chemical's diffusivity in water, and for chemicals whose volatilization is limited by
liquid phase mass transfer, an increased water temperature will increase the overall mass transfer
coefficient. The liquid and gas phase diffusivities will have a similar effect subject to the phase
that provides the greatest resistance to mass transfer.
The impact of behavioral characteristics of the occupants clearly has the potential for causing the
greatest variation. Wilkes et al. (1992) showed that, for TCE, someone taking a second shower
immediately following another person's shower would be exposed to much greater air
concentrations, and receive a higher dose. For the scenario examined by Wilkes et al., the second
shower was estimated to provide approximately a 50% higher dose than the first shower of
identical length and conditions due to the elevated air concentrations. Wilkes et al. (1996)
showed, for TCE, a high degree of correlation between behavior and predicted dose, with the
most important predictors being shower duration, bath duration, time spent in the bathroom, and
total household water use. Wilkes et al. (1992) also compared the estimated exposures of single
occupant households to two occupant households. The two person households showed a mean
increase in the potential inhalation dose of 38% for the male population group and 11% for the
female population group.
5.4.4 Conclusions
This sensitivity analysis provides the basis for designing and implementing a more detailed
sensitivity and uncertainty analysis of the variables that have been shown to be sensitive.
Variables that are identified as being highly uncertain or highly sensitive require a more in-depth
analysis. It is clear that identifying the range of uncertainty for each variable and conducting a
combined sensitivity and uncertainty analysis will provide additional insight.
As a result of the sensitivity analysis presented in this report, it is clear that additional research
into the more sensitive parameters would be beneficial. In addition, characterizing the variability
and uncertainty of these parameters as probability distributions would provide the basis for
designing and implementing a meaningful uncertainty and sensitivity analysis.
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Rest of House (ROH)
V= 293.0m3
o
Os
Q = 105.2 m3/day
Q = 81.6m3/day
= 43.5m3/day
Q = 39.3 m3/day
SUMMARY
WHACH = 0.58
Whole House Volume = 352.8
Water-Using Zones
Laundry
V = 22.2 m3
Kitchen
V= 17.2m3
Hall Bath
V = 9.2m3
Master Bath
V = 8.3m3
Shower
V = 2.9 m3
Figure 74. Schematic Representation of House Interzonal Air Flows for the Base-Case
Sensitivity Analysis.
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Table 76. Base-Case Activity and Water Use Patterns for Adult Male (Ages 15 - 45) Used in Sensitivity Analysis
Activity
Location
Kitchen
Kitchen
Master Bath
Master Bath
Shower
Master Bath
Master Bath
ROH
Master Bath
Master Bath
ROH
Master Bath
Master Bath
ROH
Outdoors
ROH
Outdoors
ROH
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Master Bath
Master Bath
ROH
Start
Hour
7
7
7
7
7
7
7
7
10
10
10
13
13
13
14
15
17
19
20
20
20
20
20
20
20
20
Start
Min
5.00
5.00
12.00
12.00
13.25
20.40
20.40
30.00
22.00
22.00
24.00
22.00
22.00
24.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
24.00
24.00
30.00
End
Hr
7
7
7
7
7
7
7
10
10
10
13
13
13
14
15
17
19
20
20
20
20
20
20
20
20
24
End
Min
12.00
12.00
13.25
13.25
20.40
30.00
30.00
22.00
24.00
24.00
22.00
24.00
24.00
0.00
0.00
0.00
0.00
0.00
24.00
24.00
24.00
24.00
24.00
30.00
30.00
0.00
Breathing
Rate
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
14.4
12.96
Water Use
Faucet ~ Kitchen
Faucet ~ Kitchen
Toilet
Faucet ~ Bathroom
Shower
Toilet
Faucet ~ Bathroom
Toilet
Faucet ~ Bathroom
Toilet
Faucet ~ Bathroom
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Kitchen
Toilet
Faucet ~ Bathroom
Location
Kitchen
Kitchen
Master Bath
Master Bath
Shower
Master Bath
Master Bath
Master Bath
Master Bath
Master Bath
Master Bath
Kitchen
Kitchen
Kitchen
Kitchen
Kitchen
Master Bath
Master Bath
Start
Hr
7
7
7
7
7
7
7
10
10
13
13
20
20
20
20
20
20
20
Start
Min
5.68
6.52
12.00
12.20
13.25
20.50
20.70
22.00
22.10
22.00
22.10
0.61
5.07
8.22
10.91
19.99
27.00
28.00
End
Hr
7
7
7
7
7
7
7
10
10
13
13
20
20
20
20
20
20
20
End
Min
6.52
6.97
12.10
13.20
20.40
20.60
21.80
22.10
23.10
22.10
23.10
0.92
5.65
9.41
11.76
21.08
28.00
29.30
Skin Area
913.74
913.74
0.00
913.74
17460.00
0.00
913.74
0.00
913.74
0.00
913.74
913.74
913.74
913.74
913.74
913.74
0.00
913.74
Household Water Use Activities
Clothes Washer
Dishwasher
Laundry
Kitchen
18
20
0
11
0
20
0
51
0
0
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 162
-------
Table 77. Base-Case Activity and Water Use Patterns for Adult Female (Ages 15 - 45) Used in Sensitivity Analysis
Activity
Location
Master Bath
Master Bath
ROH
Kitchen
Kitchen
Kitchen
Outdoors
Master Bath
Master Bath
Master Bath
Master Bath
Master Bath
Shower
Master Bath
ROH
Kitchen
Kitchen
ROH
Master Bath
ROH
Start
Hour
5
5
5
5
5
5
5
18
18
18
18
18
18
19
19
20
20
20
23
23
Start
Min
0.00
0.00
5.00
10.00
10.00
10.00
30.00
30.00
30.00
30.00
30.00
30.00
49.90
0.00
0.00
0.00
0.00
10.00
5.00
15.00
End
Hr
5
5
5
5
5
5
18
18
18
18
18
18
19
19
20
20
20
23
23
24
End
Min
5.00
5.00
10.00
30.00
30.00
30.00
30.00
49.90
49.90
49.90
49.90
49.90
0.00
2.00
0.00
10.00
10.00
5.00
15.00
0
Breathing
Rate
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
11.52
10.32
Water Use
Faucet ~ Bathroom
Toilet
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Bathroom
Toilet
Faucet ~ Bathroom
Faucet ~ Bathroom
Toilet
Shower
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Bathroom
Location
Master Bath
Master Bath
Kitchen
Kitchen
Kitchen
Master Bath
Master Bath
Master Bath
Master Bath
Master Bath
Shower
Kitchen
Kitchen
Master Bath
Start
Hr
5
5
5
5
5
18
18
18
18
18
18
20
20
23
Start
Min
2.44
3.40
12.96
13.46
25.52
33.79
35.55
39.48
48.34
48.30
49.90
0.92
2.57
8.00
End
Hr
5
5
5
5
5
18
18
18
18
18
19
20
20
23
End
Min
4.80
5.52
13.46
15.00
27.79
34.91
37.26
40.80
49.90
48.80
0.00
1.83
3.99
11.00
Skin Area
796.93
0.00
796.93
796.93
796.93
796.93
0.00
796.93
796.93
0.00
15228.00
796.93
796.93
796.93
Household Water Use Activities
Clothes Washer
Dishwasher
Laundry
Kitchen
18
20
0
11
0
20
0
51
0
0
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 163
-------
Table 78. Base-Case Activity and Water Use Patterns for the Child (Age 6) Used in Sensitivity Analysis
Activity
Location
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Kitchen
Outdoors
ROH
Kitchen
Kitchen
ROH
Hall Bath
Hall Bath
ROH
Start
Hour
11
11
11
11
11
11
11
11
12
17
17
17
17
19
19
19
Start
Min
0.00
0.00
0.00
0.00
0.00
0.00
0.00
40.00
15.00
5.00
20.00
20.00
45.00
20.00
20.00
30.00
End
Hr
11
11
11
11
11
11
11
12
17
17
17
17
19
19
19
24
End
Min
40.00
40.00
40.00
40.00
40.00
40.00
40.00
15.00
5.00
20.00
45.00
45.00
20.00
30.00
30.00
0
Breathing
Rate
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
10.44
9.84
Water Use
Hall Bath
Hall Toilet
Hall Faucet
Hall Toilet
Hall Toilet
Hall Faucet
Hall Faucet
Faucet ~ Kitchen
Faucet ~ Kitchen
Faucet ~ Kitchen
Hall Toilet
Hall Faucet
Location
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Hall Bath
Kitchen
Kitchen
Kitchen
Hall Bath
Hall Bath
Start
Hr
11
11
11
11
11
11
11
11
17
17
19
19
Start
Min
3.00
2.50
2.70
32.26
32.62
35.49
38.60
43.00
21.00
40.00
24.00
26.00
End
Hr
11
11
11
11
11
11
11
11
17
17
19
19
End
Min
25.00
2.80
3.00
32.62
33.66
37.79
40.00
45.00
22.50
41.00
25.00
29.00
Skin Area
7137.00
0.00
373.50
0.00
0.00
373.50
373.50
373.50
373.50
373.50
0.00
373.50
Household Water Use Activities
Clothes Washer
Dishwasher
Laundry
Kitchen
18
20
0
11
0
20
0
51
0
0
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 164
-------
Table 79. Summary of Base-Case Water Uses Used in Sensitivity Analysis
Water Use Event
Number of Shower Events
Shower Mean Duration, minutes
Number of Bath Events
Bath Mean Duration, minutes
Bath Volume, gallons
Number of Toilet Events
Toilet Volume, gallons/flush
Number of Dishwasher Events
Dishwasher Mean Duration, minutes
Dishwasher Volume, gallons
Number of Clothes Washer Events
Clothes Washer Mean Duration, min
Clothes Washer Volume, gal
Number of Kitchen Faucet Events
Kitchen Faucet Mean Duration, min
Number of Bathroom Faucet Events
Bathroom Faucet Mean Duration, min
Consumption Volume, liters
Male
(15-45 Years)
1
7.15
0
N/A
N/A
5
3.50
Female
(15-45 Years)
1
10.10
0
N/A
N/A
4
3.50
Child
(6 years)
0
N/A
1
22.00
50.00
4
3.50
1 (Household Characteristic)
60.00 (Household Characteristic)
8.51 (Household Characteristic)
1 (Household Characteristic)
24.7 (Household Characteristic)
37.78 (Household Characteristic)
7
0.76
5
1.08
2.46
5
1.33
5
1.75
0.71
3
1.50
4
1.75
1.01
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 165
-------
Table 80 Base-Case Consumption Activity Patterns Used in Sensitivity Analysis
Type
Start Time
End Time
Consumption Vol (L)
ADULT MALE
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Indirect
Indirect
Indirect
Indirect
Indirect
Total Direct
Total Indirect
Total
5-35-71 AM
6-39-70 AM
8- 7.4- 73 AM
10-07-18 AM
10-30-57 AM
7-1 8-75 PM
3-1 7-77 PM
8-1 3-59 PM
8-36-07 PM
9-56- 16PM
7-74-77 AM
7-07-31 PM
7-35-30 PM
7-43-78 PM
8-56-49 PM
5-37-37 AM
6-48-70 AM
8-78-51 AM
10-03-57 AM
10-37-10 AM
7-1 8-56 PM
3-77- 15PM
8-1 5-70 PM
8-39-57 PM
9-58-76 PM
7-77-17 AM
7 -05 -70PM
7-41 -45PM
7-44-77 PM
9-00-03 PM
003
003
001
001
001
003
007
007
007
001
004
0 13
035
037
036
0 19
1 75
1 44
ADULT FEMALE
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Direct
Indirect
Indirect
Indirect
Indirect
Total Direct
Total Indirect
Total
5:28:41 AM
5:31:35 AM
7:54:43 AM
7:55:03 AM
1:23:48 PM
2:50:58 PM
4:34:08 PM
7:41:12PM
8:38:35 PM
9:03:31 PM
9:31:48PM
9:54:24 AM
10:00:18 AM
2:54: 18PM
5:09:48 PM
5:29:04 AM
5:32:37 AM
7:58:14 AM
7:58:48 AM
1:35:26 PM
2:52:08 PM
4:34:43 PM
7:53:14 PM
8:40:46 PM
9:06:25 PM
9:33:12 PM
9:54:33 AM
10:04:30 AM
2:54:33 PM
5:26:02 PM
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.03
0.03
0.10
0.20
0.11
0.12
0.26
0.53
0.79
CHILD
Direct
Direct
Direct
Indirect
Indirect
Indirect
Indirect
Indirect
Indirect
Indirect
Indirect
Indirect
Total Direct
Total Indirect
Total
1:41:39 PM
3:03:57 PM
7:43:03 PM
5:27:43 AM
5:36:43 AM
7:26:12 AM
8:52:10 AM
9:42:27 AM
10:38:35 AM
11:01:36 AM
6:39:31 PM
8:48:03 PM
1:42:20 PM
3:05:28 PM
7:53:00 PM
5:31:12 AM
5:37:39 AM
7:28:14 AM
8:55:16 AM
9:47:26 AM
10:39:26 AM
11:02:38 AM
6:40:57 PM
11:01:32 PM
0.09
0.30
0.17
0.09
0.04
0.03
0.04
0.04
0.02
0.01
0.08
0.04
0.56
0.39
0.95
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 166
-------
Table 81. Master List of Water Use Variables in TEM for Sensitivity Analysis
Parameter
Number of Shower Events
Shower Mean Duration, minutes
Shower Flowrate, gal/min
Number of Bath Events
Bath Mean Duration, minutes
Bath Volume, gallons
Bath Flowrate, gal/min
Number of Toilet Events
Toilet Volume, gallons/flush
Number of Dishwasher Events
Dishwasher Mean Duration, min
Dishwasher Volume, gallons
Number of Clothes Washer Events
Clothes Washer Mean Duration, min
Clothes Washer Volume, gal
Number of Kitchen Faucet Events
Kitchen Faucet Mean Duration, min
Kitchen Faucet Flowrate, gal/min
Number of Bathroom Faucet Events
Bathroom Faucet Mean Duration, min
Bathroom Faucet Flowrate, gal/min
Number of Laundry Faucet Events
Laundry Faucet Mean Duration, min
Laundry Faucet Flowrate, gal/min
Consumption Volume, Liters/day
Consumption Volume, Direct, L/day
Consumption Volume, Indirect, L/day
(A
Base-
case
1
7.15
2.40
0
N/A
N/A
N/A
5
3.50
1
60.00
8.51
1
24.7
37.78
7
0.76
1.20
5
1.08
1.20
0
N/A
N/A
2.46
0.15
2.31
Male
ge 15-45
- 10%
1
6.44
2.16
0
N/A
N/A
N/A
5
3.15
1
54.00
7.66
1
22.23
34.00
7
0.68
1.08
5
0.97
1.08
0
N/A
N/A
2.21
0.14
2.08
)
+ 10%
1
7.87
2.64
0
N/A
N/A
N/A
5
3.85
1
66.00
9.36
1
27.17
41.56
7
0.83
1.32
5
1.19
1.32
0
N/A
N/A
2.71
0.17
2.54
V
Base-
case
1
10.10
2.40
0
N/A
N/A
N/A
4
3.50
Fern ah
^ge 15-<
- 10%
1
9.09
2.16
0
N/A
N/A
N/A
4
3.15
i
15)
+ 10%
1
11.11
2.64
0
N/A
N/A
N/A
4
3.85
I
Base-
case
0
N/A
N/A
1
22.00
50.00
2.27
4
3.50
Child
Age 6
- 10%
0
N/A
N/A
1
19.80
45.00
2.04
4
3.15
4- 10%
0
N/A
N/A
1
24.20
55.00
2.50
4
3.85
(Household Characteristic)
(Household Characteristic)
(Household Characteristic)
(Household Characteristic)
(Household Characteristic)
(Household Characteristic)
5
1.33
1.20
5
1.75
1.20
0
N/A
N/A
0.71
0.17
0.54
5
1.19
1.08
5
1.58
1.08
0
N/A
N/A
0.64
0.15
0.49
5
1.46
1.32
5
1.93
1.32
0
N/A
N/A
0.78
0.19
0.59
3
1.50
1.20
4
1.75
1.20
0
N/A
N/A
1.01
0.59
0.42
3
1.35
1.08
4
1.58
1.08
0
N/A
N/A
0.91
0.53
0.38
3
1.65
1.32
4
1.93
1.32
0
N/A
N/A
1.11
0.65
0.46
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 167
-------
Table 82. Master List of Environmental and Chemical Parameters in TEM for Sensitivity
Analysis
Parameter
House and Zone Volumes (m3)
House Air Exchange Rate (hr"1)
Interzonal Air Flows (m3/hr)
Henry's Law Constant (Dimensionless)
Chloroform
Henry's Law Constant (Dimensionless)
DCA
Overall Mass Transfer Coefficient
(KOLA)
Chloroform
Overall Mass Transfer Coefficient
(KOLA)
DCA
Whole House
ROH
Kitchen
Master Bath
Hall Bath
Laundry
Shower
ACH
Kitchen to ROH
Master Bath to ROH
Hall Bath to ROH
Laundry to ROH
Shower to Master Bath
25°C
30°C
35°C
40°C
25°C
30°C
35°C
40°C
Shower
Bath, Fill
Bath, Pool
Clothes Washer, fill
Clothes Washer, wash
Clothes Washer, rinse
Toilet
Faucets (35°C)
Faucets (30°C)
Shower
Bath, Fill
Bath, Pool
Clothes Washer, fill
Clothes Washer, wash
Clothes Washer, rinse
Toilet
Faucets (35°C)
Faucets (30°C)
Mean
352.86
293.03
17.22
8.30
9.18
22.20
2.92
0.58
3.40
1.64
1.81
4.38
50.00
0.153
0.195
0.238
0.287
3.4E-7
5.2E-7
7.9E-7
1.2E-6
0.432
0.243
0.078
0.317
0.113
0.403
0.00468
0.128
0.117
4.37E-4
7.42E-6
3.27E-6
3.69E-6
3.67E-7
1.52E-6
1.63E-7
3.58E-6
2.32E-6
Value
- 10%
317.58
263.73
15.50
7.47
8.26
19.98
2.63
0.52
3.06
1.48
1.63
3.94
45.00
0.138
0.176
0.214
0.258
3.1E-07
4.7E-07
7.1E-07
1.1E-06
0.389
0.219
0.070
0.285
0.102
0.363
0.00421
0.115
0.105
3.93E-04
6.678E-06
2.943E-06
3.321E-06
3.303E-07
1.368E-06
1.467E-07
3.222E-06
2.088E-06
+ 10%
388.15
322.34
18.94
9.14
10.10
24.42
3.22
0.64
3.74
1.80
1.99
4.82
55.00
0.168
0.215
0.262
0.316
3.7E-07
5.7E-07
8.7E-07
1.3E-06
0.475
0.267
0.086
0.349
0.124
0.443
0.00515
0.141
0.129
4.81E-04
8.16E-06
3.60E-06
4.06E-06
4.04E-07
1.67E-06
1.79E-07
3.94E-06
2.55E-06
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 168
-------
Table 83. List of Gender Specific Physiological Parameters in ERDEM for Sensitivity Analysis
Volume of the Body (Kg)
Arterial Blood (%)
Dermis (%)
Fat (%)
Kidney (%)
Liver (%)
Ovaries (%)
Rapidly Perfused Tissue (%)
Slowly Perfused Tissue (%)
Static Lung (%)
Testes (%)
Venous Blood (%)
Male
Mean
77.6
6
9
17
0.4
2.6
n/a
4.6
56.0
1.4
0.046
3
(Age 1
Lower
10%
69.8
5.4
8.1
15.3
0.36
2.34
4.14
50.4
1.26
0.041
2.7
5-45)
Upper
10%
85.5
6.6
9.9
18.7
0.44
2.86
5.06
61.5
1.54
0.051
3.3
Femak
Mean
63.8
6
9
23
0.4
2.6
0.0063
4.6
50.0
1.4
n/a
3
; (Age 15
Lower
10%
57.4
5.4
8.1
20.7
0.36
2.34
0.0057
4.14
45.0
1.26
2.7
-45)
Upper
10%
70.2
6.6
9.9
25.3
0.44
2.86
0.0069
5.06
55.0
1.54
3.3
Cl
Mean
22.5
6
9
17
0.4
2.6
n/a
4.6
56.0
1.4
0.0074
3
lild (Age
Lower
10%
20.3
5.4
8.1
15.3
0.36
2.34
4.14
50.4
1.26
0.0067
2.7
6)
Upper
10%
24.8
6.6
9.9
18.7
0.44
2.86
5.06
61.6
1.54
0.0081
3.3
Alveolar Ventilation Rates - (L/hr)
At Rest Activity
Sedentary Activity
540
600
486
540
594
660
430
480
387
432
473
528
410
435
369
391.5
451
478.5
Blood Flows - (L/hr)
Cardiac Output -
"At Rest" (L/hr)
Cardiac Output -
"Sedentary" (L/hr)
Dermis (%)
Fat (%)
Kidney (%)
Liver (%)
Ovaries (%)
Rapidly Perfused Tissue (%)
Slowly Perfused Tissue (%)
Testes (%)
461.3
512.6
4.8
4.8
19.4
23.7
n/a
27
19
1.3
415.2
461.3
4.32
4.32
17.5
21.3
24.3
17.1
1.17
507.5
563.9
5.28
5.28
21.3
26.1
29.7
20.9
1.43
423.6
472.8
4.8
4.8
19.6
24
0.02
27.6
19.2
n/a
381.2
425.5
4.32
4.32
17.6
21.6
0.018
24.8
17.28
465.9
520.1
5.28
5.28
21.6
26.4
0.022
30.3
21.12
350.3
371.6
4.8
4.8
19.6
24
n/a
27.4
19.2
0.21
315.3
334.5
4.3
4.3
17.6
21.6
24.7
17.3
0.19
385.3
408.8
5.3
5.3
21.6
26.4
30.1
21.1
0.23
a These variables are scaled to the body weight to the -0.25 power.
b Vmax is scaled to the body weight to the +0.7 power.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 169
-------
Table 84. List of General Physiological Parameters in ERDEM for Sensitivity Analysis
Chloroform Skin Permeability Coefficient (cm/hr)
DCA Skin Permeability Coefficient (cm/hr)
Al
Mean
0.13
1.84E-06
Demographic C
Lower 10%
0.117
1.656E-06
Jroups
Upper 10%
0.143
2.024E-06
Chloroform Gastro-Intestinal Absorption Rate Constants
Stomach to Portal Blood Rate Constant (1/hr)
Stomach to Intestine Rate Constant (1/hr)
Intestine to Portal Blood Rate Constant (1/hr)
5
2
6
4.5
1.8
5.4
5.5
2.2
6.6
DCA Gastro-Intestinal Absorption Rates Constants
Stomach to Portal Blood Rate Constant (1/hr)
Stomach to Intestine Rate Constant (1/hr)
Intestine to Portal Blood Rate Constant (1/hr)
13.65
2.18
0.044
12.285
1.962
0.0396
15.015
2.398
0.0484
Chloroform Partition Coefficients Used by ERDEM
Dermis/Blood
Fat/Blood
Kidney/Blood
Liver/Blood
Ovaries/Blood
Rapidly Perfused Tissue/Blood
Slowly Perfused Tissue/Blood
Static Lung/Air
Static Lung/Blood
Testes/Blood
1.62
37.69
1.48
2.29
1.37
2.29
1.62
7.43
1
1.89
1.458
33.921
1.332
2.061
1.233
2.061
1.458
6.687
0.9
1.701
1.782
41.459
1.628
2.519
1.507
2.519
1.782
8.173
1.1
2.079
DCA Partition Coefficients Used by ERDEM
Dermis/Blood
Fat/Blood
Kidney/Blood
Liver/Blood
Ovaries/Blood
Rapidly Perfused Tissue/Blood
Slowly Perfused Tissue/Blood
Static Lung/Air
Static Lung/Blood
Testes/Blood
0.43
2.8
0.8
0.8
0.95
0.8
0.43
n/a
0.16
0.99
0.387
2.52
0.72
0.72
0.855
0.72
0.387
0.144
0.891
0.473
3.08
0.88
0.88
1.045
0.88
0.473
0.176
1.089
Chloroform Metabolism Parameters
Liver Linear Metabolism Rate Constant (1/hr/kg) a
Kidney Linear Metabolism Rate Constant (1/hr/kg) a
Liver Metabolism Vmax (mg/hr/kg) b
Kidney Metabolism Ratio of Kidney to Liver Vmax
Liver Metabolism Michaelis-Menten Constant (mg/L)
Kidney Metabolism Michaelis-Menten Constant (mg/L)
0.39917
0.001857
15.7
0.033
0.448
0.448
0.359253
0.0016713
14.13
0.0297
0.4032
0.4032
0.439087
0.0020427
17.27
0.0363
0.4928
0.4928
DCA Elimination Rate Constants
Urine Elimination Rate Constant (1/hr/kg) a
Liver Elimination Rate Constant (1/hr/kg) a
0.023
20.5
0.0207
18.45
0.0253
22.55
a These variables are scaled to the body weight to the -0.25 power.
b Vmax is scaled to the body weight to the +0.7 power.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 170
-------
0.0016
WATERUSES (Indicates when each appliance is in use)
0.0012
Clothes Washer
Dishwasher
Faucet-M Bathroom
Faucet-Kitchen
Bath
Faucet-H Bathroom
Shower-H Bathroom
Toilet-Hall Bathroom
Shower-M Bathroom
Toilet-M Bathroom
Shower Stall
Master Bath
Hall Bath
Kitchen
Laundry
10 12 14
Time, hours
Figure 75. Predicted Chloroform Air Concentrations for the Base-case Scenario
Note: The time when water uses occur are shown in the upper inset.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 171
-------
WATERUSES (Indicates when each appliance is in use)
Dishwasher
Faucet-M Bathroom
Faucet-Kitchen
Bath
Faucet-H Bathroom
Shower-H Bathroom
Toilet-Hall Bathroom
Shower-M Bathroom
Toilet-M BE
Shower Stall
Master Bath
Hall Bath
Kitchen
O Laundry
O.E+00
10 12 14
Time, hours
16
18
20
22
24
Figure 76. Predicted DCA Air Concentrations for the Base-case Scenario
Note: The time when water uses occur are shown in the upper inset.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 172
-------
Perturb Baseline Lower Upper
Perturb Baseline Lower Upper
Amt.
[mg]
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0
30
10 20
Time [Hours]
(a) Absorbed Dose Perturbing on Alveolar Ventilation Rate
Amt.
[mg]
0.175
0.150
0.125
0.100
0.075
0.050
0.025
n nnn
//
//
1 /
I/
,//
i
^J
^^
-/''"''
,-' /
! /
f"
1
10 20
Time [Hours]
30
(b) Amount Metabolized in the Liver Perturbing on Alveolar
Ventilation Rate
Figures 77a and b. Dose Metric Curves for Chloroform: Absorbed Dose and Amount Metabolized in the Liver
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 173
-------
Perturb Baseline Lower Upper
Perturb Baseline Lower Upper
AUC
mg*h/L
Onnnoc
.UUUZD
Onnnon
.uuuzu
Onnn-i c
.UUU 1 O
Onnn-i n
.UUU 1 U
Onnnn^
.uuuuo
n nnnnn
/
^J
x:^-' '
,--
i / ,--''
)/
0
30
10 20
Time [Hours]
(c) AUC in the Liver Perturbing on Liver Metabolism Vmax
AUC
mg*h/L
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
n nnnn
/
I
^
^^
^ _.,."""
f
';
10 20
Time [Hours]
30
(d) AUC in the Testes Perturbing on Partition Coefficient
Testes/Blood
Figures 77c and d. Dose Metric Curves for Chloroform: AUC in the Liver and AUC in the Testes
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 174
-------
Perturb Baseline Lower Upper
Perturb Baseline Lower Upper
Cone.
[mg/L]
Onnno^
.UUUZD
Onnnon
.UUUZU
OnnrH c
.UUU 1 O
Onnn-i n
.UUU 1 U
Onnnnc
.UUUUO
0.00000
fv A
V
J^A^_
W— ^__^
c
0 10 20 30
Time [Hours]
(e) Cone, in the Liver Perturbing on Liver Metabolism Vmax
Cone.
[mg/L]
0.0030
0.0025
0.0020
0.0015
0.0010
0.0005
0.0000
V
c
10 20
Time [Hours]
30
(f) Cone. In the Testes Perturbing on Partition Coefficient
Testes/Blood
Figures 77e and f. Dose Metric Curves for Chloroform: Concentration in the Liver and Concentration in the Testes
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 175
-------
Perturb Baseline Lower Upper
Perturb Baseline Lower — Upper
Amt.
[mg]
0.08
0.06
0.04
0.02
0.00
10 20
Time [Hours]
30
Amt.
[mg]
0.0625
0.0500
0.0375
0.0250
0.0125
n nnnn
J
J
/
/
/ ,
//-
//''''
/
1
10 20
Time [Hours]
30
(a) Absorbed Dose Perturbing on Blood Flow in the Kidney
(b) Amount Eliminated in the Liver Perturbing on Volume in
the Liver
Figures 78a and b. Dose Metric Curves for DCA: Absorbed Dose and Amount Eliminated in the Liver
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 176
-------
Perturb Baseline Lower Upper
Perturb Baseline Lower Upper
AUC
mg*h/L
Onn/in
.UU4U
Onnoc
.UUoO
Onnon
.UUoU
Onno^
.UUZD
Onnon
.uuzu
Onn-i^
.UU ID
On/"H n
.UU IU
Onnnc
.UUUO
n nnnn
J_
A
••''' ,/;
//•••''
/ / .-•'
//
¥
/ /
/ /
/ /
j / /
/ /
/ !
AUC
mg*h/L
Onnc
.UUO
Onn/i
.UU4
Onno
.UUo
Onno
.UUZ
Onn-i
.UU 1
n nnn
/
^
/' / ..--•
/ / ,..••
// /
///
/
^>
/'' /
/ /
/ /
/ / ''
'//
/ /
0 10 20
Time [Hours]
(c) AUC in the Liver Perturbing on Volume of the Body
30
10 20
Time [Hours]
30
(d) AUC in the Testes Perturbing on Partition Coefficient
Testes/Blood
Figures 78c and d. Dose Metric Curves for DCA: AUC in the Liver and AUC in the Testes
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 177
-------
Perturb Baseline Lower
Upper
Perturb Baseline Lower
Upper
Amt.
[mg]
Onrn A
.UU 14
Onn-1 o
.UU \£.
Onn-1 n
.UU 1 U
Onnno
.UUUO
Onnno
.UUUO
Onnn/i
.UUU4
Onnno
.UUUz
n nnnn
u>J^
l
\
\
*V
i
\
V
10 20
Time [Hours]
30
(e) Cone, in the Liver Perturbing on Stomach to Portal Blood
Absorption Rate Constant
Amt.
[mg]
Onnno.
.uuuy
Onnno
.UUUO
Onnn-7
.UUU/
OnnnR
.UUUD
Onnn^
.UUUO
Onnn/i
.UUU4
Onnm
.UUUo
Onnno
.UUUz
Onnn-i
.UUU 1
n nnnn
^>KJ
I
I
^
1
\\
\V\ „ ,s
\K\,\ 1
"A%JS
1
\ I'1'-
10 20
Time [Hours]
30
(f) Cone, in the Testes Perturbing on Partition Coefficient
Testes/Blood
Figures 78e and f. Dose Metric Curves for DCA: Concentration in the Liver and Concentration in the Testes
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 178
-------
Table 85. TEM Base-case Potential and Absorbed Dose for Chloroform
Metric*
Potential Inhalation Dose
Absorbed Inhalation Dose
Absorbed Dermal Dose
Absorbed Ingestion Dose
Total Absorbed Dose
Male
15-45 yrs.
0.266010735
0.23151009
0.02207942
0.014786
0.26837551
Dose , n
Female
15-45 yrs.
0.239679844
0.211023873
0.02661466
0.008045
0.245683534
ig
Child
6 yrs.
0.208615334
0.180522758
0.025053723
0.009516
0.215092481
A Potential Dose is defined as the mass available for uptake, or an exposure multiplied by a contact rate. For
inhalation, the potential dose is the mass of the compound entering the lungs (C(t) * time * breathing rate). The
Absorbed Dose is the mass that is absorbed into the blood stream. The methods for estimating absorbed dose are
described in Section 3.7.
Table 86. TEM Base-case Potential and Absorbed Dose for DCA
Metric A
Potential Inhalation Dose
Absorbed Inhalation Dose
Absorbed Dermal Dose
Absorbed Ingestion Dose
Total Absorbed Dose
Male
15-45 yrs.
8 25994E-06
8.25954E-06
9.36504E-06
0.045075
0.045092625
Dose , mg
Female
15-45 yrs.
641688E-06
6.4166E-06
9.5867E-06
0.024519
0.024535003
Child
6 yrs.
3 1475E-06
3.14734E-06
6.08603E-06
0.029001
0.029010233
A. Potential Dose is defined as the mass available for uptake, or an exposure multiplied by a contact rate. For
inhalation, the potential dose is the mass of the compound entering the lungs (C(t) * time * breathing rate). The
Absorbed Dose is the mass that is absorbed into the blood stream. The methods for estimating absorbed dose are
described in Section 3.7.
Table 87. Relative Sensitivity Analysis of Potential and Absorbed Dose to Changes in
Environmental and Chemical Parameters for Chloroform
Parameter *
House and Zone
Volumes (m3)
House Air Exchange
Rate (hr-1) and Inter-
zonal Air Flows (m3/hr)
Henry's Law Constant
(Dimensionless),
Chloroform
Overall Mass Transfer
Coefficient (KOLA),
Chloroform
Metric
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose
Total Absorbed Dose
Male
15-45 yrs.
-10%
-32.95
-32.89
-28.37
-71.98
-72.05
-62.15
3.38
3.38
2.92
92.99
92.97
80.20
10%
-28.92
-28.87
-24.90
-61.34
-61.40
-52.97
2.74
2.74
2.37
92.93
92.91
80.15
Female
15-45 yrs.
-10%
-14.37
-14.34
-12.32
-88.81
-88.84
-76.31
3.64
3.64
3.13
93.56
93.55
80.35
10%
-13.78
-13.77
-11.82
-75.76
-75.79
65.10
2.94
2.94
2.53
93.60
93.59
80.39
Child
6 yrs.
-10%
-30.82
-30.78
-25.83
-78.72
-78.76
-66.10
2.58
2.58
2.17
90.06
90.06
75.58
10%
-24.96
-24.93
20.93
-63.63
-63.67
53.43
2.14
2.14
1.80
85.05
85.05
71.38
* Doses reflect the impact of changing the parameter by 10%
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 179
-------
Table 88. Relative Sensitivity Analysis of Potential and Absorbed Dose for Water Use
Parameters for Chloroform
Parameter *
Shower Mean
Duration, minutes
Shower Flowrate,
gal/min
Bath Mean
Duration, minutes
Bath Volume,
gallons
Bath Flowrate,
gal/min
Toilet Volume,
gallons/flush
Dishwasher Mean
Duration, minutes
Dishwasher Volume,
gallons
Clothes Washer
Mean Duration,
minutes
Metric
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Potential Dermal Dose, mg
Absorbed Dermal Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Potential Dermal Dose, mg
Absorbed Dermal Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Potential Dermal Dose, mg
Absorbed Dermal Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Potential Dermal Dose, mg
Absorbed Dermal Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Potential Dermal Dose, mg
Absorbed Dermal Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
Potential Inhalation Dose, mg
Absorbed Inhalation Dose, mg
Total Absorbed Dose, mg
M
15--:
-10%
28.16
28.12
92.73
85.75
31.31
39.64
39.59
0.00
0.00
34.15
1.03
1.03
0.00
0.00
0.89
0.33
0.33
0.00
0.00
0.29
1.06
1.06
0.00
0.00
0.92
0.00
0.00
0.00
0.13
0.13
0.11
1.62
1.62
1.40
3.48
3.50
3.02
[ale
Syrs.
10%
32.16
32.11
92.73
85.75
34.76
39.48
39.43
0.00
0.00
34.02
0.97
0.97
0.00
0.00
0.84
0.29
0.29
0.00
0.00
0.25
1.03
1.03
0.00
0.00
0.89
0.00
0.00
0.00
0.11
0.11
0.09
1.60
1.60
1.38
3.95
3.99
3.44
Fern
15-45
-10%
22.61
22.59
92.35
87.65
28.90
37.25
37.23
0.00
0.00
31.98
0.34
0.34
0.00
0.00
0.29
0.10
0.10
0.00
0.00
0.09
0.33
0.33
0.00
0.00
0.28
0.00
0.00
0.00
0.15
0.15
0.13
1.44
1.44
1.24
2.78
2.79
2.40
ale
yrs.
10%
15.96
15.95
92.32
87.62
23.19
37.09
37.06
0.00
0.00
31.83
0.32
0.32
0.00
0.00
0.28
0.09
0.09
0.00
0.00
0.08
0.32
0.32
0.00
0.00
0.27
0.00
0.00
0.00
0.13
0.13
0.11
1.43
1.43
1.23
3.16
3.18
2.73
Ch
6y
-10%
3.73
3.75
0.00
0.00
3.15
5.53
5.55
0.00
0.00
4.66
10.99
10.97
97.34
95.05
20.28
5.48
5.47
0.00
0.00
4.59
20.82
20.79
0.00
0.00
17.45
0.00
0.00
0.00
0.14
0.14
0.12
1.40
1.41
1.18
3.45
3.47
2.91
ild
rs.
10%
3.16
3.18
0.00
0.00
2.67
5.50
5.53
0.00
0.00
4.64
9.25
9.23
97.34
95.05
18.82
4.72
4.71
0.00
0.00
3.96
20.24
20.21
0.00
0.00
16.96
0.00
0.00
0.00
0.12
0.12
0.10
1.39
1.40
1.17
3.93
3.95
3.32
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 180
-------
Table 88. Relative Sensitivity Analysis of Potential and Absorbed Dose for Water Use
Parameters for Chloroform (Continued)
Parameter *
Clothes Washer
Volume, gallons
Kitchen Faucet
Mean Duration,
minutes
Kitchen Faucet
Flowrate,
gallons/minutes
Bathroom Faucet
Mean Duration,
minutes
Bathroom Faucet
Flowrate,
gallons/minute
Consumption
Volume, liters/day
Metric
Potential Inhalation Dose
Absorbed Inhalation Dose
Total Absorbed Dose
Potential Inhalation Dose
Absorbed Inhalation Dose
Potential Dermal Dose
Absorbed Dermal Dose
Total Absorbed Dose
Potential Inhalation Dose
Absorbed Inhalation Dose
Potential Dermal Dose
Absorbed Dermal Dose
Total Absorbed Dose
Potential Inhalation Dose
Absorbed Inhalation Dose
Potential Dermal Dose
Absorbed Dermal Dose
Total Absorbed Dose
Potential Inhalation Dose
Absorbed Inhalation Dose
Potential Dermal Dose
Absorbed Dermal Dose
Total Absorbed Dose
Absorbed Ingestion Dose
Total Absorbed Dose
Ma
15-45
-10%
1.64
1.65
1.42
7.96
7.95
3.04
2.84
7.09
9.50
9.50
0.00
0.00
8.20
7.93
7.92
3.67
3.39
7.11
7.50
7.50
0.00
0.00
6.47
100.03
5.51
tie
yrs.
10%
1.42
1.43
1.24
6.88
6.88
3.04
2.82
6.16
9.49
9.49
0.00
0.00
8.19
4.98
4.97
3.67
3.39
4.57
7.50
7.49
0.00
0.00
6.46
99.96
5.51
Fen
15-4f
-10%
1.31
1.32
1.13
3.15
3.15
2.94
2.79
3.01
5.70
5.71
0.00
0.00
4.90
12.16
12.15
7.02
6.66
11.15
10.20
10.19
0.00
0.00
8.76
99.94
3.27
lale
5 yrs.
10%
1.14
1.15
0.98
3.10
3.10
2.93
2.78
2.96
5.70
5.71
0.00
0.00
4.90
4.97
4.97
1.37
1.30
4.41
10.19
10.19
0.00
0.00
8.75
100.06
3.28
a
63
-10%
1.64
1.65
1.39
7.20
7.19
1.04
1.02
6.15
9.19
9.19
0.00
0.00
7.71
4.34
4.34
1.62
1.59
3.83
3.84
3.84
0.00
0.00
3.22
100.04
4.43
lild
frs.
10%
1.43
1.44
1.21
6.26
6.25
1.04
1.02
5.37
9.18
9.18
0.00
0.00
7.71
1.80
1.80
1.62
1.59
1.69
3.84
3.84
0.00
0.00
3.22
98.89
4.37
* Doses reflect the impact of changing the parameter by 10%. The exposure routes not shown do not impact the parameters,
e.g. dishwashers impact only via the inhalation route not via ingestion or dermal exposure.
Table 89. Six Dose Metric Outputs with All Parameters at Baseline Values
Dose Metrics
Absorbed Dose at 24 hr (mg)
Amount Metabolized in Liver at 24 hr (mg)
Amount Eliminated in Liver at 24 hr (mg)
AUC in Liver at 24 hr (mg*h /L)
AUC in Testes at 24 hr (mg*h /L)
Peak Cone, in Liver (mg/L)
Peak Cone, in Testes (mg/L)
Output Value for G
Chloroform
0.290179
0.159827
n/a
0.00021678
0.00256914
0.00018316 at 7.35 hr
0.00232539 at 7.35 hr
iven Dose Metric
DCA
0.079672
n/a
0.0490569
0.00352027
0.00417252
0.00127294 at 8.75 hr
0.00074819 at 19.8 hr
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 181
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Table 90. Chloroform - Relative Sensitivity of Absorbed Dose at 24 hrs.
Description
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Perturbation
Lower
Upper
Absorbed
Dose
0.264291
0.316116
Relative
Sensitivity*, %
89.21389901
89.3827603
*Note: In the adult male study of exposure to disinfection byproducts of chloroform (CHC13), with three multiple routes
dermis, inhalation, and ingestion in a 24 h time period, with the exposure and dose model TEM and the PBPK model ERDEM,
the dose metric Absorbed Dose is given with the dose estimate and relative sensitivity. The base-case value for the Absorbed
Dose is 0.290179. This table displays only the results for the parameters whose relative sensitivity is >10%. For a complete list
of all parameters see Tables 83 and 84.
Table 91. Chloroform - Relative Sensitivity of Amount Metabolized in the Liver at 24 hrs.
Description
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%)- Blood Flow
Static Lung/Air - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Blood - Partition Coef.
Static Lung/Blood - Partition Coef.
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Amount
Metabolized
in Liver
0.15136
0.167699
0.153462
0.165516
0.152928
0.166024
0.153769
0.16519
0.165754
0.154364
Relative
Sensitivity*, %
52.97603033
49.25325508
39.82431004
35.59473681
43.16542261
38.77317349
37.90348314
33.55503138
-37.08384691
-34.18070789
*Note: The base-case value for the amount metabolized in the liver is 0.159827. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 182
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Table 92. Chloroform - Relative Sensitivity of Area Under the Curve for Liver at 24 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Fat (%) - Volume
Fat (%)- Volume
Slowly Perfused Tissue (%) - Volume
Slowly Perfused Tissue (%) - Volume
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%) - Blood Flow
Static Lung/Air - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Blood - Partition Coef.
Static Lung/Blood - Partition Coef.
Liver Metabolism Vmax (mg/hr/kg)
Liver Metabolism Vmax (mg/hr/kg)
Liver Metabolism Michaelis-Menten
Constant (mg/L)
Liver Metabolism Michaelis-Menten
Constant (mg/L)
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Area Under the
Curve for Liver
0.0002341
0.00020217
0.00022006
0.00021376
0.0002258
0.00020857
0.00020529
0.00022746
0.00020815
0.0002245
0.00020742
0.00022519
0.00020856
0.00022406
0.00022482
0.00020937
0.00024005
0.00019764
0.00019571
0.00023775
Relative
Sensitivity*, %
-79.89666943
-67.39551619
-15.1305471
-13.93117446
-41.60900452
-37.87249746
53.00304456
49.2665375
39.80994557
35.61214134
43.17741489
38.7950918
37.91862718
33.5824338
-37.08829228
-34.18212012
-107.3438509
-88.29227789
97.19531322
96.73401605
*Note: The base-case value for the area under the curve for the liver is 0.00021678. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 183
-------
Table 93. Chloroform - Relative Sensitivity of Area Under the Curve for Testes at 24 hrs.
Description
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%) - Blood Flow
Static Lung/Air - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Blood - Partition Coef.
Static Lung/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Area Under the
Curve for Testes
0.00242395
0.00270414
0.00271439
0.00244102
0.00270471
0.00244852
0.00246523
0.0026612
0.00267083
0.00247527
0.00231218
0.0028266
Relative
Sensitivity*, %
56.51307441
52.54676662
-56.53642853
-49.8688277
-52.76863075
-46.94956289
40.44544089
35.83300248
-39.5813385
-36.53751839
100.0179048
100.2125225
*Note: The base-case value for the area under the curve for the testes is 0.00256914. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 184
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Table 94. Chloroform - Relative Sensitivity of Peak Concentration in Liver at 7.35 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Dermis (%) - Volume
Dermis (%) - Volume
Fat (%) - Volume
Fat (%) - Volume
Rapidly Perfused Tissue (%) - Volume
Rapidly Perfused Tissue (%) - Volume
Slowly Perfused Tissue (%) - Volume
Slowly Perfused Tissue (%) - Volume
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%) - Blood Flow
Slowly Perfused Tissue (%) - Blood Flow
Slowly Perfused Tissue (%) - Blood Flow
Rapidly Perfused Tissue/Blood - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Blood - Partition Coef.
Static Lung/Blood - Partition Coef.
Liver Metabolism Vmax (mg/hr/kg)
Liver Metabolism Vmax (mg/hr/kg)
Liver Metabolism Michaelis-Menten Constant
(mg/L)
Liver Metabolism Michaelis-Menten Constant
(mg/L)
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Peak
Concentration
in Liver
0.00020033
0.00016886
0.00018558
0.00018103
0.00018531
0.00018106
0.00018564
0.00018092
0.00019095
0.00017616
0.00016959
0.00019613
0.00017628
0.00018942
0.00017068
0.00019485
0.000187
0.00017963
0.00018506
0.00017901
0.00018677
0.00018681
0.00017965
0.00020312
0.00016685
0.00016514
0.00020113
Relative
Sensitivity*
%
-93.74317537
-78.07381524
-13.21249181
-11.62917668
-11.73837082
-11.46538546
-13.54007425
-12.22974449
-42.53112033
-38.21795152
74.08822887
70.81240446
37.56278664
34.17776807
68.13714785
63.82397904
-20.96527626
-19.27276698
-10.37344398
22.65778554
19.70954357
-19.92793186
-19.16357283
-108.9757589
-89.04782704
98.38392662
98.11094125
*Note: The base-case value for the peak concentration in the liver is 0.00018316. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 185
-------
Table 95. Chloroform - Relative Sensitivity of Peak Concentration in Testes at 7.35 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Alveolar Ventilation Rates (L/hr)
Alveolar Ventilation Rates (L/hr)
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%) - Blood Flow
Slowly Perfused Tissue (%) - Blood
Flow
Slowly Perfused Tissue (%) - Blood
Flow
Static Lung/Air - Partition Coef.
Static Lung/Air - Partition Coef.
Static Lung/Blood - Partition Coef.
Static Lung/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Peak Concentration
in Testes
0.00238841
0.00226551
0.00214884
0.00249441
0.00245547
0.00220859
0.0023853
0.00226804
0.00236983
0.0022822
0.00227382
0.00236856
0.0023725
0.0022795
0.00209401
0.00255444
Relative Sensitivity*
%
-27.1008304
-25.75051927
75.92274844
72.68458194
-55.93900378
-50.22813378
-25.76342033
-24.66252973
-19.11077282
-18.57322858
22.17692516
18.56462787
-20.25896731
-19.73432413
99.50158898
98.49960652
*Note: The base-case value for the peak concentration in the testes is 0.00232539. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 186
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Table 96. DCA - Relative Sensitivity of Amount Eliminated in the Liver at 24 hrs.
Description
Fat (%) - Volume
Fat (%) - Volume
Liver (%) - Volume
Liver (%) - Volume
Slowly Perfused Tissue (%) - Volume
Slowly Perfused Tissue (%) - Volume
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Stomach to Intestine Absorption Rate Constant
(1/hr)
Fat/Blood - Partition Coef.
Fat/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Slowly Perfused Tissue/Blood - Partition Coef.
Liver Elimination Rate Constant (1/hr/kg)
Liver Elimination Rate Constant (1/hr/kg)
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Lower
Upper
Amount Eliminated
in Liver
0.0497513
0.0481616
0.0468566
0.0507773
0.0498701
0.0480279
0.0485266
0.0485567
0.0499402
0.048244
0.0471381
0.0505815
0.0495909
0.0470954
0.0508403
Relative
Sensitivity*, %
-14.15499145
-18.25023595
44.85199839
35.06948054
-16.57666913
-20.97564257
10.80989626
-10.19632305
-18.00562204
-16.57055379
39.11376381
31.07819695
-10.88531888
39.98418163
36.35370356
*Note: The base-case value for the amount eliminated in the liver is 0.0490569. In the adult male study of exposure to
disinfection byproducts of dichloroacetic acid (DCA), with two multiple routes dermis and ingestion in a 24 hr time period, with
the exposure and dose model TEM and the PBPK model ERDEM, the dose metric Amount Eliminated in Liver is given with the
dose estimate and relative sensitivity. This table displays only the results for the parameters whose relative sensitivity is >10%.
For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 187
-------
Table 97. DCA - Relative Sensitivity of Area Under the Curve for Liver at 24 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Fat (%) - Volume
Liver (%) - Volume
Liver (%) - Volume
Stomach to Portal Blood Rate Absorption Constant
(1/hr)
Stomach to Intestine Absorption Rate Constant (1/hr)
Fat/Blood - Partition Coef.
Fat/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Slowly Perfused Tissue/Blood - Partition Coef.
Liver Elimination Rate Constant (1/hr/kg)
Liver Elimination Rate Constant (1/hr/kg)
Perturbation
Lower
Upper
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Lower
Upper
Area Under
Curve for Liver
0.00383383
0.00325814
0.00347062
0.00373356
0.00331463
0.00348222
0.00348438
0.00358366
0.00346194
0.00338258
0.00362968
0.00355859
0.00375502
0.00331659
Relative
Sensitivity*%
-89.07271317
-74.46303835
-14.10403179
-60.58910254
-58.41597377
10.80883001
-10.19524071
-18.0071415
-16.56975175
39.11347709
31.08000239
-10.88552867
-66.68522585
-57.8591983
*Note: The base-case value for the area under the curve for the liver is 0.00352027
parameters whose relative sensitivity is >10%. For a complete list of all parameters
This table displays only the results for the
see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 188
-------
Table 98. DCA - Relative Sensitivity of Area Under the Curve for Testes at 24 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Fat (%) - Volume
Fat (%) - Volume
Liver (%) - Volume
Liver (%) - Volume
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Stomach to Intestine Absorption Rate Constant
(1/hr)
Fat/Blood - Partition Coef.
Fat/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Slowly Perfused Tissue/Blood - Partition Coef.
Slowly Perfused Tissue/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Liver Elimination Rate Constant (1/hr/kg)
Liver Elimination Rate Constant (1/hr/kg)
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Area Under the
Curve for Testes
0.00456356
0.0038456
0.0042237
0.00410349
0.0044206
0.0039322
0.00412679
0.00412953
0.00425809
0.00409361
0.0044507
0.00391417
0.00422405
0.00412586
0.00375753
0.00459209
0.00444646
0.00393475
Relative
Sensitivity* %
-93.71794503
-78.35073289
-12.26596877
-16.54395905
-59.45567667
-57.59588929
10.95980367
-10.30312617
-20.50799038
-18.91183266
-66.66954263
-61.91701897
-12.34985093
-11.18269056
99.45788157
100.5555396
-65.65337015
-56.98474783
*Note: The base-case value for the area under the curve for the testes is 0.00417252. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 189
-------
Table 99. DCA - Relative Sensitivity of Peak Concentration in Liver at 8.75 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Liver (%) - Volume
Liver (%) - Volume
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Liver (%) - Blood Flow
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Liver/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Peak
Concentration
in Liver
0.00133066
0.00122181
0.00131732
0.00123098
0.0013474
0.00120687
0.00134695
0.00120887
0.00117726
0.00136351
0.00118527
0.00135408
Relative
Sensitivity*
%
-45.34384967
-40.16685783
-34.8641727
-32.96306189
-58.49450877
-51.90346756
-58.14099643
-50.3323016
75.16457964
71.1502506
68.87205996
63.74220309
*Note: The base-case value for the peak concentration in the liver is 0.00127294. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 190
-------
Table 100. DCA - Relative Sensitivity of Peak Concentration in Testes at 19.8 hrs.
Description
Volume of the Body (Kg)
Volume of the Body (Kg)
Arterial Blood (%) - Volume
Arterial Blood (%) - Volume
Liver (%) - Volume
Liver (%) - Volume
Slowly Perfused Tissue (%) - Volume
Slowly Perfused Tissue (%) - Volume
Cardiac Output (L/hr)
Cardiac Output (L/hr)
Liver (%) - Blood Flow
Slowly Perfused Tissue (%) - Blood Flow
Slowly Perfused Tissue (%) - Blood Flow
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Stomach to Portal Blood Absorption Rate Constant
(1/hr)
Liver/Blood - Partition Coef.
Liver/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Testes/Blood - Partition Coef.
Liver Elimination Rate Constant (1/hr/kg)
Liver Elimination Rate Constant (1/hr/kg)
Perturbation
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Peak
Concentration
in Testes
0.00080629
0.00069742
0.00075761
0.00073797
0.00077579
0.00072209
0.00075795
0.00073839
0.00076373
0.000733
0.0007399
0.00076176
0.00073481
0.00071976
0.00077154
0.00077707
0.00072114
0.00067383
0.00082257
0.00077102
0.00072792
Relative
Sensitivity*, %
-77.65407183
-67.85709512
-12.5903848
-13.65963191
-36.88902551
-34.88418717
-13.04481482
-13.09827718
-20.77012524
-20.30232962
11.08007324
-18.13710421
-17.88315802
37.9983694
31.20865021
-38.5998209
-36.15391812
99.38651947
99.41325065
-30.51363958
-27.09204881
*Note: The base-case value for the peak concentration in the testes is 0.00074819. This table displays only the results for the
parameters whose relative sensitivity is >10%. For a complete list of all parameters see Tables 83 and 84.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 191
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Table 101. Average Relative Sensitivity Analysis of Absorbed Total Dose for Water Use, Environmental and Chemical
Parameters for Chloroform and DCA, Ranked by Absolute Value
Parameter *
Overall Mass Transfer
Coefficient (KOLA)
Air Exchange Rate (hr-1) and
Interzonal Air Flows (m3/hr)
Shower Flowrate, gal/min
Shower Mean Duration, min
House and Zone Volumes
(m3)
Kitchen Faucet Flowrate,
gal/min
Kitchen Faucet Mean
Duration, min
Bathroom Faucet Flowrate,
gal/min
Bathroom Faucet Mean
Duration, min
Consumption Volume, L/day
Clothes Washer Mean
Duration, min
Henry's Law Constant
Dishwasher Volume, gal
Clothes Washer Volume, gal
Bath Flowrate, gal/min
Bath Mean Duration, min
Bath Volume, gal
Dishwasher Mean Duration,
Toilet Volume, gal/flush
Relativ
Male (15-45)
80.17 (1)
-57.56 (2)
34.08 (3)
33.03 (4)
-26.63 (5)
8.19 (6)
6.63 (7)
6.47 (8)
5.84 (9)
5.51 (10)
3.23 (11)
2.64 (12)
1.39 (13)
1.33 (14)
0.90 (15)
0.86 (16)
0.27 (17)
0.10 (18)
0.00 (19)
Chloroform
e Sensitivity, %
Female (15-45)
80.37 (1)
-70.70 (2)
31.91 (3)
26.04 (4)
-12.07 (5)
4.90 (8)
2.98 (10)
8.75 (6)
7.78 (7)
3.27 (9)
2.56 (12)
2.83 (11)
1.24 (13)
1.06 (14)
0.276 (16)
0.28 (15)
0.08 (18)
0.12 (17)
0.00 (19)
(Rank)
Child (6 yrs)
73.48 (1)
-59.77 (2)
4.65 (8)
2.91 (13)
-23.38 (3)
7.71 (6)
5.76 (7)
3.22 (11)
2.76 (14)
4.40 (9)
3.11 (12)
1.98 (15)
1.18 (17)
1.30 (16)
17.20 (5)
19.55 (4)
4.27 (10)
0.11 (18)
0.00 (19)
Parameter *
Consumption Volume, L/day
Shower Mean Duration, min
Henry's Law Constant
Overall Mass Transfer
Coefficient (KOLA)
Air Exchange Rate (hr-1) and
Interzonal Air Flows (m3/hr)
Shower Flowrate, gal/min
House and Zone Volumes
(m3)
Kitchen Faucet Mean
Duration, min
Bathroom Faucet Mean
Duration, min
Kitchen Faucet Flowrate,
Bathroom Faucet Flowrate,
gal/min
Clothes Washer Mean
Duration, min
Dishwasher Mean Duration,
Bath Mean Duration, min
Bath Flowrate, gal/min
Dishwasher Volume, gal
Bath Volume, gal
Clothes Washer Volume, gal
Toilet Volume, gal/flush
Relative
Male (15-45)
99.96 (1)
0.0135 (2)
0.0129 (3)
0.00299 (4)
-0.00177 (5)
0.00165 (6)
-0.00137 (7)
0.00134 (8)
0.00103 (9)
6.79E-4 (10)
1.64E-4 (11)
1.06E-4 (12)
9.36E-5 (13)
8.81E-5 (14)
7.57E-5 (15)
4.75E-9 (16)
4.71E-10(17)
1.76E-10 (18)
0.00 (19)
DCA
Sensitivity, %
Female (15-
99.94 (1)
0.0197 (2)
0.0188 (3)
0.00383 (6)
-0.00874 (4)
0.00208 (8)
0.00485 (5)
0.00165 (9)
0.00214 (7)
6.67E-4 (10)
4.62E-4 (11)
1.41E-4 (13)
1.84E-4 (12)
3.77E-5 (14)
2.94E-4 (15)
8.50E-9 (16)
1.86E-10 (18)
2.33E-10 (17)
0.00 (19)
(Rank)
Child (6 yrs)
99.96 (1)
5.54E-4 (12)
0.00385 (5)
0.00650 (3)
-0.00548 (4)
2.01E-4 (13)
-0.00133 (7)
9.87E-4 (8)
7.01E-4 (10)
8.01E-4 (9)
0.000191 (14)
1.30E-4 (16)
1.30E-4 (15)
0.0108 (2)
0.00211 (6)
6.03E-09 (17)
1.18E-08(16)
2.18E-10(18)
0.00 (19)
Note: Shaded cells indicate parameters that have a different rank for the adult female and child as compared to the adult male. Negative values indicate that parameter is
inversely related to absorbed dose.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 192
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Table 102. Summary of the Most Sensitive Model Parameters for Each Dose Metric
Dose Metrics
Absorbed Dose at 24 hr (mg)
Amount Metabolized in Liver at 24 hr
(mg)
Amount Eliminated in Liver at 24 hr
(mg)
AUC in Liver at 24 hr (mg*h /L)
AUC in Testes at 24 hr (mg*h /L)
Concentration in Liver (mg/L)
Concentration in Testes (mg/L)
Most Sensitive ,
(Relative
Chloroform
Alveolar Ventilation Rate
(89.83%)
Alveolar Ventilation Rate
(52.98%)
n/a
Liver Metabolism Vmax
(-107.34%)
Partition Coef. Testes/Blood
(100.21%)
Liver Metabolism Vmax
(-108.98%)
Partition Coef. Testes/Blood
(99.50%)
Model Parameters
Sensitivity)
DCA
Blood Flow in Kidney
(4.88%)
n/a
Volume in Liver
(44.85%)
Volume in Body
(-89.07%)
Partition Coef. Testes/Blood
(100.56%)
Stomach to Portal Blood Rate
(75.16%)
Partition Coef. Testes/Blood
(99.41%)
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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Table 103. Chloroform - Sensitive Input Model Parameters
Dose Metrics |Sensitivity Parameters |Comments
Absorbed Dose at 24 hr
|Alveolar Ventilation Rates [Affects inhalation
Amount Metabolized in the Liver
Alveolar Ventilation Rates
Blood Flow in the Liver
Cardiac Output
Affects inhalation
Metabolism occurs in the Liver, and chemical
passes through the Liver from oral exposure
Total blood flow to compartments
AUC for Liver at 24 hr
Liver Metabolism Vmax
Liver Metabolism
Michaelis-Menten Constant
Volume of the Body
Directly affect the amount of chemical in the
Liver
Determine the amount of metabolites in the
Liver (with Vmax)
The Liver metabolism Vmax is scaled to the
body volume
AUC for Testes at 24 hr
Testes/Blood - Partition
Coef.
Alveolar Ventilation Rates
Cardiac Output
Blood Flow in the Liver
Partly determines the amount of chemical in
the Testes
Affects inhalation
Total blood flow to compartments
Metabolism occurs in the Liver, and
chemical passes through the Liver from oral
exposure
Peak Concentration in Liver
Liver Metabolism Vmax
Liver Metabolism
Michaelis-Menten Constant
Volume of the Body
Blood Flow in the Liver
Directly affect the amount of chemical in the
Liver
Determine the amount of metabolites in the
Liver (with Vmax)
The Liver metabolism Vmax is scaled to the
body volume
Metabolism occurs in the Liver, and
chemical passes through the Liver from oral
exposure
Peak Concentration in Testes
Testes/Blood - Partition
Coef.
Alveolar Ventilation Rates
Cardiac Output
Blood Flow in the Liver
Partly determines the amount of chemical in
the Testes
Affects inhalation
Total blood flow to compartments
Metabolism occurs in the Liver, and
chemical passes through the Liver from oral
exposure
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 194
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Table 104. DCA - Sensitive Input Model Parameters
Dose Metrics
Sensitivity Parameters [Comments
Amount Eliminated in the Liver at 24 hr
Liver Elimination Rate
Constant
Liver/Blood - Partition Coef.
Volume of the Liver
Determines the amount eliminated
Determines the amount of chemical from
arterial blood
The amount eliminated increases with
volume of the Liver
AUC for Liver at 24 hr
Volume of the Body
Liver Elimination Rate
Constant
Volume of the Liver
Liver/Blood - Partition Coef.
Chemical remains in the body longer as the
body volume increases
Determines the amount eliminated
The amount eliminated increases with
volume of the Liver
Determines the amount of chemical from
arterial blood
AUC for Testes at 24 hr
Testes/Blood - Partition Coef.
Volume of the Body
Liver/Blood - Partition Coef.
Liver Elimination Rate
Constant
Volume of the Liver
Partly determines the amount of chemical
remaining in the Testes as blood passes
through
Chemical remains in the body longer as the
body volume increases
Determines the amount of chemical from
arterial blood
Determines the amount eliminated
The amount eliminated increases with
volume of the Liver
Peak Concentration in Liver
Stomach/Portal Blood Rate
Constant
Liver/Blood Partition Coef.
Cardiac Output
Blood Flow in the Liver
Volume of the Body
Chemical moves from stomach to liver via
portal blood
Partly determines the amount of chemical
remaining in the Liver
Total blood flow to compartments
Partly determines the amount of chemical
remaining in the Liver
Chemical remains in the body longer as the
body volume increases
Peak Concentration in Testes
Testes/Blood - Partition Coef.
Volume of the Body
Liver/Blood Partition Coef.
Partly determines the amount of chemical
remaining in the Testes as blood passes
through
Chemical remains in the body longer as the
body volume increases
Determines the amount of chemical from
arterial blood
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 195
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6.0 Quality Assurance
The purpose of this section is to identify the sources and quality of secondary data used in conducting
this modeling study. Secondary data are defined as the use of environmental, exposure, or health data
developed for another purpose. This modeling study used secondary data as inputs to model
algorithms for the purpose of estimating population-based exposure, absorbed dose, and tissue
concentrations. This is a demonstration project, and as such, best available data are identified and
utilized from a wide variety of sources. Consequently, the data vary in quality and documentation.
The data used in calculations, methods and models used to derive quantitative measures, including
those of internal exposure, tissue dosimetry, and risk were taken from publications and other sources
subjected to peer review where possible. These publications include peer reviewed journals and other
open literature. The sources of all data contained within this report have been documented by
reference or footnote describing the source of the data. In addition, a discussion of shortcomings of
data used in this study is included in the text of this report in the section where the data are
introduced.
Many diverse types of data are used in this study, including behavioral data, physical data, chemical
data, and physiological data. These data are taken from a variety of sources including databases,
peer-reviewed publications, and estimation techniques. In addition, numerous models are used to
develop the exposure, dose and tissue concentrations, including fate and transport models, mass-
transfer models, models to represent behavior, uptake and pharmacokinetic models. A general
summary of the models and data utilized in this study are presented in the following tables. The data
fall into 7 general categories, as described in Table 105. The sources of the major data utilized in this
study are categorized and described in Table 106. The models and model algorithms utilized in this
study are categorized and described in Tables 107 and 108.
Table 105. Categories of Data Sources and Models
Category
I
II
III
IV
V
VI
VII
Description
Taken from peer reviewed literature, used for the purpose
the measurement
Taken from peer reviewed literature, used for the purpose
intended by the measurement
intended by
other than
Taken from peer-reviewed database compiled for the purposes in
which it is being used.
Taken from non peer-reviewed database compiled for the
other than those for which it is being used.
purposes
Taken from other non peer-reviewed source
Estimated based on peer-reviewed method or data
Estimated based on non peer-reviewed method
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 196
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Table 106. Quality and Sources of Data Used in the Models
Variables
Mass-Transfer
Coefficient
Gas- and liquid-
phase diffusivities
Henry's Law
Constant
Exposure-Related
Behavior
Water Use
Behavior
Ingestion Behavior
House Volume
Water-Use Zones
Whole House Air
Exchange Rate
Interzonal
Airflows
Water
Concentrations
Ingestion
Concentrations
Breathing Rates
Body Weight
Category
VI
I, VI
I, II, VI
III
III, IV,
V
III
I, IV
VII
I
I
I
I, VII
I
I
Description
Predicted based on peer reviewed algorithms
Diffusivities are used in the prediction algorithm
for the mass transfer coefficient, as described in
Section 3.1. The sources of the diffusivities
vary. Several were obtained from the
Department of Energy, Risk Assessment
Information System (RAIS) database. The
values for many of the diffusivities were
estimated using peer reviewed prediction
algorithms as described in Section 3.1.3.
Reported in literature or in databases at specific
temperatures. A temperature adjustment was
applied based on a peer-reviewed method as
described in Section 3.1.3.
Activity patterns are sampled from the NHAPS
database
Compiled from a variety of databases including
REUWS, RECS, and NHAPS. NHAPS was
compiled for this purpose; REUWS and RECS
were compiled for other purposes.
Taken from the CSFII database
Household volumes are based on an analysis of
RECS data from 1993 and 1997. The 1993 data
are analyzed and presented in the Exposure
Factors Handbook.
Volumes are estimated based on architectural
design standards.
Sampled from the national distribution
recommended by the Exposure Factors
Handbook.
Interzonal airflows are based on several sources.
The interzonal airflows between the non-water
using zones and the kitchen and laundry room
are based on a correlation from Koontz and
Rector, 1995. The flows between the non-water
using zones and the bathrooms are based on
Giardino et al, 1996.
The water concentrations were characterized
based on published measurement data.
The ingestion concentrations were estimated for
a plausible set of activities based on published
results lab measurements.
Alveolar ventilation rates were assigned based on
two assumed activity levels: resting and
sedentary.
Calculated from the Exposure Factors Handbook,
Tables 7.2 and 7.3, adjusted for clothes
Citation
Corsi and Howard, 2000
Risk Assessment
Information System, Oak
Ridge National
Laboratory
Lymanetal., 1990.
Various, see Table 2 and
Section 3. 1 for a listing of
data sources and
temperature adjustment
algorithm
Described in Section 3.2
Described in Section 3.2
Jacobs et al., 2000
U.S. DOE, 1995
U.S. DOE, 1999
U.S. EPA, 1997b
Hoke, 1988
Hoke, 1994
U.S. EPA, 1997b
Koontz and Rector, 1995
Giardino et al., 1996
The Cadmus Group, Inc.,
2001
Howard and Corsi, 1996
Batterman et al., 2000
U.S. EPA, 1997b
U.S. EPA, 1999
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 197
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Table 106. Quality and Sources of Data Used in the Models (Continued)
Variables
Body
Compartmental
Blood Flow rates
Body
Compartmental
Volumes
Skin Permeability
Coefficients
Skin Partition
Coefficients
Gastro-Intestinal
Absorption Rate
Tissue Partition
Coefficients
Metabolism Rate
Constants
Elimination Rate
Constants
Category
I
VII
I
I
I
VII
VI
V
VI
VI
I
I
V
V
II
I
II
I
V
VII
v,i
V
I
II
II
Description
Taken from the Fisher paper with modifications
for the Ovaries and Testes. The flow to the
Ovaries and Testicles was determined from their
volume relative to their body weight.
Blood, estimated from Blancato
Dermis, Kidney:
Fat and Slowly Perfused estimated from
measurements from volunteers.
Liver, Rapidly Perfused Tissue, Static Lung
Ovaries and Testes volumes were estimated.
See Table 43, footnote "f '.
Taken from methods used by Krishnan -
Chloroform taken from Blancato, Personal
Communication .
DCA and TCA taken from McDougal, Personal
Communication
Krishnan, Personal Communication
Values for skin/Blood including Ovaries and
Testes
Chloroform -
BDCM
DCA, and TCA, estimated
Chloroform, Blancato, Personal Communication
BDCM, DCA, and TCA, from Abbas and
Fisher, 1997; but modified using Staata, et al,
1990
Chloroform partition coefficients for Corley
BDCM, estimated from ratios of tissue to air and
blood to air
TCA and DCA estimates from Fisher
Dermis, Fat and Rapidly Perfused were estimated
for TCA and DCA
Ovaries and Testes, from Personal
Communication, Krishnan, and Lipscomb
Chloroform metabolism parameters are from
Blancato, Personal Communication. The
Michaelis Menten constants are from Corley
BDCM Metabolism parameters are from
Lipscomb, Personal Communication
DCA Urine Rate Constant from Clewell
TCA Urine Rate Constant estimated from Fisher
DCA Liver Elimination Rate estimated from
mouse data of Abbas and Fisher
Citation
Fisher, et al, 1998
Corley, etal, 1990
Fisher, et al, 1998
Fisher, etal, 1998
ICRP-23, 1974
Krishnan Personal
Communication
Blancato, Personal
Communication
McDougal, Personal
Communication .
Krishnan, Personal
Communication
Corley, etal, 1990
Gargas, etal, 1989
Blancato, Personal
Communication
Abbas and Fisher, 1997,
Staats, etal, 1990
Corley, etal, 1990
Gargas, etal, 1989
Fisher, etal, 1998
Personal Communication
Krishnan and Lipscomb.
Blancato, Personal
Communication, and
Corley, etal, 1990
Lipscomb, Personal
Communication
Clewell, et al, 2000
Fisher, etal, 1998
Abbas and Fisher, 1997
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 198
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Table 107. Categories of Model Approaches and Algorithms
Category
A
B
C
Description
Widely accepted modeling approach
Approach similar to commonly used and accepted approaches, but
adapted to satisfy project specific requirements
Novel approach addressing specific requirements of estimating
exposure and uptake of water borne contaminants
Table 108. Quality of Modeling Approaches and Algorithms
Model
Representation of
the building
Fate and transport
modeling
Fate and Transport
Model Integration
Method
Behavior Models
Water Use Models
Exposure Models
Inhalation Uptake
Model
Dermal Uptake
Model
Category
B
A
A
C
C,A
A
A,C
A, C
Description
Building is represented as a collection of water using zones and a
lumped non- water using zones. Similar approaches are widely
used in the literature.
Commonly accepted approach based on mass balance. Method
assumes well mixed zones, each zone constrained by mass and
volumetric balance.
Model solves set of differential equations using the 4* order
Runge-Kutta method (Mathews, 1992). This method is widely
cited, is very stable, self starting, and accurate.
The behavior is sampled from the NHAPS database, but is
modified to address known deficiencies in the dataset and to
accommodate water-use related behavior not included in NHAPS.
Approach to simulating water uses incorporate techniques for
simulating water use occurrences as well as the duration of water
uses. The occurrences of water uses is simulated based on survey
data from NHAPS and REUWS using a Poisson process. The
durations of the water uses are simulated by sampling from
representative lognormal distributions. These techniques are used
for similar purposes in peer-reviewed literature, but the
implementation in this modeling effort is unique to exposure to
water borne contaminants. This work has been published in
several peer reviewed publications (Wilkes, 1999, Wilkes et al.,
1996)
The exposure model used in this study, TEM, has been published
in several journal articles. The basic model algorithms have been
validated (Wilkes, 1994).
The exposure model uptake algorithms are described in Section
3.7. These algorithms are taken from peer-reviewed literature
(Olin, 1999), but there integration into an exposure model
framework is unique to this exposure model.
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
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-------
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Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 206
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Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page 207
-------
Appendix A
Figures Presenting Results of Pharmacokinetic Modeling
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-1
-------
PLOT 10th percentile —-- 50th percentile,UC KD F
90th percentile K ' - -
A^K
PLOT 10th percentile —- 50th percentile,UC TS F
90th percentile K ' - -
,AV5
0.010000D
0.0010000
0.0001000
0.0000100
0.0000010
0.0000001
(a) AUC in
PLC
[mglL*T-fl
1.00E-04
1.00E-05
1.00E-06
1.00E-07
1.00E-08
(c) AUC in
PLOT—
/^
/,"
I /
j
^— -
_- '
"
_—
"
~
o.ai ooooo
n nnnnnm
X
7 ,-"
/•
/ 7
2
--— ^
:
—
—
D 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
-------
PLOT 10th percentile — - 50th percentile,C KD
90th percentile K
PLOT IQth Dercentile —- 50th percentile,C TS
90th percentile K
1 .OOE-03
1 OOE-08
^A
f^r v
..-- " v,.
/jiW
**x_X"
\r^~r^
' '
•J"~J" J
\
^
^X^v
.r
^^
1 .OOE-03
1 nnF-DR
,.
jvW^
i
~*~"''-""--'.-j-j
v^/Jlv
•^v ^
. ,-
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
^^
^—
0 40 5C
1 .OOE-04
1 OOE-09
AA
.,.-,.__
r
i
,,.,
V— 'V
•v^ J
l
-VM,
^^
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1 .OOE-03
1 nnF-ns
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r^1
^
?
--.,,.,.,,,-/
V-^-W^
~~V f
-^—-J
"^^
A/"^"""
-S
/^TV^V^
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
) Cone in Lver (d) Cone in Venous Blood
- 50th percentile,C_EXH_F
1 .OOE-04
1.00E-05
1.00E-06
1.00E-07
1.00E-08
1.00E-09
J^A
«v/w
I
^ J*
x-^v*'
V^vr1^
— 1 '
«-^t~~-~, '
-
•^-^
^v^v
./•"-—I
,.-'•'
1^^^^
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-2. Adult Male BDCM Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-3
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
O.CT1 00000
0 0000001
^_^i
(/
-~~~^
0 10 20 3
Time [Hours
(a)AUCin Kidney
0.0100000
n nnnnnm
/2_
/ x--
^ — —
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Testes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
Lma/lM
1TOOE-03
1 OOE-08
//^-
1 /
_— - — -
o.ai ooooo
n nnnnnm
^-
7Z
if
^~- — -
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00000
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
1.00000
0.00001
/^-
/
/
_— — — '
- -
_— — — -"
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-3. Adult Male BDCM Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-4
-------
PLOT mean, C_KD —- 50th percentile,C_KD
PLOT mean, C_TS —- 50th percentile,C_TS
1 .QOE-04
1 OOE-09
y-^w\
-^~
""-"."-,„',./""
— --'
"'"— .,.
^—^~~,
S~
1 .OOE-03
1 DDF-ns
y^-vy-
,,,,-,
^ ,,
""'.v'". .,•„'',/
, .-..,
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
•^J" "
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1 .OOE-04
1 .OOE-09
"-'VvV
~V.
V '" 1-"
1 .OOE-04
_^-
,..,,-.x-x,.
-^~
--.,,.,,/"'
— --
— ^
— —
20 30 40 50
Time [Hours]
(c) Cone in Liver
0 10 20 30 40 50
Time [Hours]
(d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Cone,
1 .OOE-04
1 .OOE-09
/vw\
^*v
"\ — ^V
--->Wy
„ j....,l
"A-JV\
V-^
--^
V
y'
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-4. Adult Male BDCM Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-5
-------
T - 10th Dercentile
- 90th percentile
— - 50th percentile,UC OV F
K ' - -
rrno/lW
0 0000001
X"
TX
' //
/
_
x_-~-
__^
, — — — — -
0 10 20 3
Time [Hours
(a)AUCin Kidney
AUC
[mg/L^Tff
n nnnnnm
*f
X
/.,,
~7
/
/
,
s^^^
— , '
_
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUC in Ovaries
[mg/lAW
1 OOE-08
^
X-
7 X
f
~7
^ —
^
_^
[mg/L^Vfi
0.0010000
n nnnnnm
^,,
\ z
7
i*- — — — =
^^^
—
^_ — • — • —
— - — — — • — •
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
1 .oooao
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
Ami
1.00000
0.00001
^
X-
/ ,x
/
/
1 /
~7
. -~-
^ - —
^^
^ — i
_^— • — -
_, — . — •
0 10 20 30 40
Time [Hours]
(f) Total Absorbed Dose
Figure A-5. Adult Female BDCM Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-6
-------
PLOT 10th percentile — - 50th percentile,C KD
90th percentile K
PLOT IQth Dercentile —- 50th percentile,C OV
90th percentile K
1 .OOE-03
1 OOE-08
a*
^
^
f
sy
"'•—••~^..j'
r^v^-x-
"'^-V
W
Z ^
^
"v-X
1 .OOE-03
1 DDF-ns
1*
'
/^
r3
'w I
^_
J'^"
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Ovaries
v^
ir^
_
0 40 5C
1 .OOE-04
1 .OOE-09
yw\
/••"\
jif^
[
N*- —
f ^
^ V
-v~--__y
A,,
"V*
Mv.
«—->,
y-.,;.-,.,.-
e±^V=
1 .OOE-03
(W
^
/
r^
xr
~^ — ^
-— _v
vv
v/
,
20 30 40 50
Time [Hours]
(c) Cone in Liver
0 10 20 30 40
Time [Hours]
(d) Cone in Venous Blood
50th percentile,C EXH F
~ ~
1.00E-05
1.00E-06
1.00E-07
1.OOE-08
1.OOE-09
/NA
f^—
^^
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-6. Adult Female BDCM Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-7
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_OV_F —- 50th percentile,UC_OV_F
O.CT1 00000
0 0000001
/-X\""
(/
^
0 10 20 3
Time [Hours
(a)AUCin Kidney
0.0100000
n nnnnnm
x^
/ ~-'
_ — — — •
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Ovaries
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
1TOOE-03
1 OOE-08
X^
/ ~7
^^^^
— — — —
o.ai ooooo
n nnnnnm
/-
Y /
1 /'
_- — —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00000
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
1.00000
0.00001
/s
(/
{
•
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-7. Adult Female BDCM Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-8
-------
PLOT mean, C_KD —- 50th percentile,C_KD
PLOT mean, C_OV —- 50th percentile,C_OV
1.00E-03
1 OOE-08
^j-
-~ — —
, — .
^i
'—,,„__
^— ,
1 .OOE-03
1 DDF-ns
: — ^
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Ovaries
-^
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1.00F-04
1 OOE-09
/-w
~r^
-
'"V~-^__ /
— s^
-\^
/"^-
1 .OOE-04
_^
^_^
-^_
-^_
^ — ,
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) Cone in Liver (d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Gone,
1 .OOE-04
1. OOE-09
_/~V
'"""—"-,../
r ""
^^
..„,_
^"^----.-1.
rt~"-f"
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-8. Adult Female BDCM Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-9
-------
T - 10th percentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
rrno/lW
0 0000001
^
P
1 /
)
, ' '
__^
'
/• — ' — ~
0 10 20 3
Time [Hours
(a)AUCin Kidney
AUC
[mg/L^Tff
n nnnnnm
/^ ~
I
/
/
/
, — : — :
_-—/
.^— - •
__
_J-— — -
— — •
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUCinTestes
1TOOE-03
1 OOE-08
- —
/^,
("'" /
/
/"
. — -— -
^^
^.
^-
_—
—
^ —
0.0100000
n nnnnnm
X"
P
1 /
2
. — -— ^
,£
y^
._ '
—
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
1 .oooao
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
Ami
1.00000
0.00001
/-^
p
' {
/
^— -^
. — '
_^ — • — • — - — ~~
-
0 10 20 30 40
Time [Hours]
(f) Total Absorbed Dose
Figure A-9. Child BDCM Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-10
-------
PLOT 10th percentile — - 50th percentile,C KD
90th percentile K
PLOT 10th Dercentile —- 50th percentile,C TS
90th percentile K
1 .OOE-03
1 OOE-08
^— -~~
fff\
"^
^^
•••-,,,/
-x^w
v
""""*— «_
•'-"
V
1 .OOE-03
1 DDF-ns
— j^
'•w**-.,,>"'
~>-w
-'---r-._.
A~^
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
-,
'"^X~I
S^
,,-"""
A~
^y
0 40 5C
1 .OOE-04
1 OOE-09
_^-«
......v-x
ada
^J
__^
SKJ**-'*'
y-
"V^vw"
"-x__
"•>-„,
A—-/
•~**v*-.
"X,
-X,
^^_,
,^-Jrt'-'--.
A
^v
1 .OOE-03
1 nnF-DR
, — <~»
r
^^-^
-«-,,,,-/'""
~^w
"^ —
7^^3
--x—
"^"Vxv^^
'""
_/'w
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
) Cone in Lver (d) Cone in Venous Blood
- 50th percentile,C_EXH_F
1 .OOE-04
1 .OOE-09
^^/^
Vr^^
--,x"
-x^vW
^
'"""I
W . j^
~ ^^~
x-^>
"^-^
s«^
^
M
Z7
10 20 30 40
Time [Hours]
(e) Cone in Exhaled Air
Figure A-10. Child BDCM Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-11
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
o.ai ooooo
0 0000001
^.
^
~^^-
0 10 20 3
Time [Hours
(a) AUC in Kidney
—- — • —
— — • — -
0.01 OOOOO
n nnnnnm
^
/
r^^
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUCinTestes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT - mean, UC_VB_F — - 50th percentile,UC_VB_F
[mg/L^
1TOOE-03
1 OOE-08
/•^
f
______
— — • — • — -
0.0100000
n nnnnnm
^'"
^^.
^^^
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00000
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
1.00000
0.00001
^<
/'"
^=^
_— • —
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-ll. Child BDCM Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-12
-------
PLOT mean, C_KD —- 50th percentile,C_KD
PLOT mean, C_TS —- 50th percentile,C_TS
1 .QOE-04
1 OOE-09
__^~
*"•
r^J
V-vv^
v—.,>/
_/-\
7-vv-
x- ,,•
y—V,
i/
1 .OOE-03
1 DDF-ns
rz^
\^f^^
^'"''•sX-'1'
SEE
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
E~v-
••--„-••
y"^
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1 .OOE-04
1 .OOE-09
r-'""--v._
"\rvw-7
"^X
'*'"-,„.
,,,"^Vvw
'"'''V
V
,^^\
uJ _^^
1 .OOE-04
1. OOE-09
^/^^\
V-NT~^
''—„..''"
^V
"^-v^
^r
20 30 40 50
Time [Hours]
(c) Cone in Livei
0 10 20 30 40
Time [Hours]
(d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Cone,
1 .OOE-04
1. OOE-09
nEE
V^A-^
'•-.,vy
r-E^
'~A~v,
'^—•~,,J.
^
J-"1
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-12. Child BDCM Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-13
-------
T - 10th percentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
(a) AUC in
PLC
Ima/lA^S
D.ai oooob
0.0010000
0.0001000
0.0000100
0.0000010
0.0000001
(c) AUC in
PLOT—
/s
fS
/ {_
/
•
/
,- —
^s~ '
AUC
[mg/L^Tff
0.010000
n nnnnm
X
7x
L (
/
- — - — "
— ---
— ~
s^
_>-""
— . —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours Time [Hours]
-------
PLOT 10th percentile — - 50th percentile,C KD
90th percentile K
PLOT IQth Dercentile —- 50th percentile,C TS
90th percentile K
0.01DOOOO
0 0000001
^/v
.--^ --""""%
A
/
J
^^
\
-"*v_j
-' - ------
,-—
^
"'X.
"jxx, — ,.
S~—
AW""^""
0.0100000
n nnnnnm
^
.--""""x
^
J
^^
,
X^A— -t
~*^—>
^~
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
W^
"•-,
-"X^~,
^~—
y^**^
0 40 5C
Cone,
[mq/LJ
1 OOE-08
^yv
,..../" "^
fvt^
,J
-^w^"
"-.A,,;"''"'
V-X^
"^V-J1
•~V-^-_J
Vv--.
"vwl.
•v^, i.
\~J\
*f~~^
f^~^
Cone,
[mq/LJ
^yv
A
?
r
s-v./^
^~"
— v i
^ ___,
^^
-N..,
-""--_
^—
ji^W^^~~"
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) Cone in Liver
(d) Cone in Venous Blood
1.00E-04
1.00E-05
1.00E-06
1.00E-07
1.OOE-08
(e) Cone in Exhaled Air
10 20 30 40 50
Time [Hours]
Figure A-14. Adult Male CHC13 Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-15
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
[mq/L^t
0 000001
^
//"'
^^
—^^_
0 10 20 3
Time [Hours
(a)AUCin Kidney
AUC
[mg/L^Tff
n nnnnm
^
/ /-'
-^^:.
__ — —
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUCinTestes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
Lms/lM
D. 0100000
0 0000001
^
//^
\f
——
— zz
0.0100000
n nnnnnm
///-
/ ~-
^^^
_— — — —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00000
0 00001
10.0000
1 .0000
n nnm
/-
/^ /•
i
-^IZ
_^^— —
— ^— — — =
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(e) Total in Urine (f) Total Absorbed Dose
Figure A-15. Adult Male CHC13 Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-16
-------
PLOT mean, C_KD —- 50th percentile,C_KD
.Cone,
PLOT mean, C_TS —- 50th percentile,C_TS
.Cone,
1.0DE-03
1 OOE-08
^_/—
^^
-,
— v_^
~V-/K-'V_
_,,_.'-""""•
1 .OOE-03
1 nnF-DR
/^v_^.
-— —""^
•^ „
^^_^r
/""'
— _^
''VVJ^X
^v_
''-„,
^_^_
J"
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(a) Cone in Kidney (b) Cone in Testes
PLOT - mean, C_LV — - 50th percentile,C_LV
Cone,
PLOT - mean, C_VB — - 50th percentile,C_VB
Cone,
1 .OOE-04
1 OOE-09
^_y—
„—'""'"""
-^~
x/.,,,,/"
— . /
""""''"" — -—.__,/
r^~^~~ \_
"''""•-,.
A/-— ^-^,
ff**~f~"
1 .OOE-03
1 nnF-nft
"^"^ fs~.
,
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) Cone in Liver (d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Cone,
1 .OOE-04
1. OOE-09
./^V/1-
_,.r-.^.,.j-*\
'V-\_/v'~
r
~--^-j
^^-k__^^
,,.- --J--.,-.- r-
"VWAA,
^^-A^
"X
s^— ^
^'"•J"' "1
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-16. Adult Male CHC13 Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-17
-------
PLOT
AUC
[mg/L * H]
0 000001
10th percenl
90th percenl
S
(/'
f d_
/
IS
,
~s^
—- 50th percentile,UC_KD_F
f — — - — —
0 10 20 3
Time [Hours
(a) AUC in Kidney
^— • —
PLOT
AUC
[mg/L * H]
n nnnnn-i
10th percenl
90th percenl
X
z>
1 /
/
!!
.
/
—- 50th percentile,UC_OV_F
,— -
0 40 50 0 10 20 3
Time [Hours
(b) AUC in Ovaries
" "
0 40 5C
PLOT 10th percent
90th percent
AUC
[mg/L * H]
0.0010000
/
(/
L /
J
le -— 50th percentile,UC LV PLOT 10th percent
le ~ 90th percent
[mg/LA*UHJ
_. — — -
^-^
0 10
) AUC in Liver
— =
20 3
Time [Hours
0.0010000
-^
/>
7 ~7
1 /
/
le — -- 50th percentile,UC_VB_F
„ •
/—^
___ •
,— - — • — ~
0 40 50 0 10 20 3
Time [Hours
(d) AUC in Venous Blood
_^— - — • —
. .— — — —
0 40 5C
PLOT 10thpercentile
90th percentile
Amt.
[mg]
— 50th percentile,A KD URN T PLOT 10th percentile
~ ~ 90th percentile
Amt.
[mg]
1.0000
) 10 20 30 40 50
Time [Hours]
X
//
/ /
j
•—
^^
-- 50th percentile, BDYM_1A_E>
_
—————— —
__ — — —
) 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
(f) Total Absorbed Dose
Figure A-17. Adult Female CHC13 Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-18
-------
— - 50th percentile,C KD
50th percentile, C OV
0 10
(a) Cone in Kidney
20 30
Time [Hours]
0 10
(b) Cone in Ovaries
20 30
Time [Hours]
PLOT - 10th percentile
- 10th percentile
- 90th percentile
------ 50th percentile, C LV
1 .OOE-03
1. OOE-04
1.00E-05
1 .OOE-06
1. OOE-07
1 .OOE-08
_£,
/
j^
V^-
"x--..y"
^H/-
~~v_>
•"'•-'—-_ ,'
-V-_7
'V^,
x^-^.
^— -S
^-W-V
3 10 20 30 40 5C
Time [Hours
(c) Cone in Liver
PLOT 10th percentile 50th percentile, C EXH F
90th percentile ~
[mg/L]
1. OOE-03
1. OOE-04
1 .OOE-05
1. OOE-06
1 .OOE-07
1. OOE-08
AA
^>' "
-" j '^
r^'
\y"
X /"
"""V^
""I
'-^"T^—w
\|A
V,
'--„..„
-s*—_.
^n
^—---^ i
10 20 30 40
Time [Hours]
(e) Cone in Exhaled Air
PLOT —
aci
1.00E-08
— 10th percent! e
— 90th percent! e
J-X
,"-'"--.
/
f
\ ^
•w
'•--,
r-^ s~
--— 50th percentile, C_VB
— N~-^
~~~_
**x
w
^—^
^—
^-^
0 10
(d) Cone in Venous Blood
20 30
Time [Hours]
40 50
Figure A-18. Adult Female CHC13 Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-19
-------
PLOT
AUC
[mg/L * H]
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
(a) AUC in Ki
PLOT -
AUC
[mg/L * H]
0.0100000
0.0010000
0.0001000
0.0000100
0.0000010
0.0000001
(c) AUC in Li
PLOT me
Ami.
[mg]
mean, UC_KD_F
^
/
" —
— - 50th percentile,UC_KD_F
— — ^
PLOT
AUC
[mg/L * H]
n nnnnn-i
mean, UC_OV_F
^
S/
I /
~~~ —
--— 50th percentile,UC_OV_F
— "~
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours Time [Hours
dney (b) AUC in Ovaries
— mean, UC_LV — - 50th percentile,UC_LV PLOT mean, UC_VB_F — -- 50th percentile,UC_VB_F
AUC
[mg/L * H]
/^~
f/
-^^
.__—- — —
//'
//-
^^^
—
7^^
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours Time [Hours
/er (d) AUC in Venous Blood
an, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_E>
Ami.
[mg]
0.0001
^
/ /
1 /"'
~^^~
10 20 30 40
Time [Hours]
10 20 30 40 50
Time [Hours]
(e) Total in Urine
(f) Total Absorbed Dose
Figure A-19. Adult Female CHC13 Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-20
-------
PLOT - mean, C_KD
Cone.
[mg/L]
— - 50th percentile,C_KD
1.00E-08
_^
,)"" ""•
""— ^^-^~
-~. '
— -_
'"-•,
, -,
^-- ,.-.'•"-
0 10
(a) Cone in Kidney
20 30
Time [Hours]
PLOT - mean, C_OV
Cone.
[mg/L]
-• 50th percent! le,C_OV
1 OOE-08
f=J^_
^_^-
'---.,
-X ,
-^_
, -X,
0 10
(b) Cone in Ovaries
20 30
Time [Hours]
PLOT mean, C_LV — - 50th percentile,C_LV PLOT mean, C_VB — -- 50th percentile,C_VB
a Cone.
[mg/L]
1 .OOE-09
^7^
"--~~ -^s^~~
'""•-.-.,. , f
~J~^__^
"™—J
r~yvv_
^ -V.
'•"•",-,„
S^-S*" ~>-_
1. OOE-08
r^
^^ r-^\
•*'""
,„.- — .
— ^
10 20 30 40
Time [Hours]
10 20 30 40
Time [Hours]
(c) Cone in Liver
(d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Cone.
[mg/L]
1.00E-09
r~j^
<-~\
^^~
/""
^^
r.-f-r'---—'
' \
r-~^-^
\
x-_
rt~s~ — "^
f '" I
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-20. Adult Female CHC13 Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-21
-------
T - 10th percentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
[mg/L^t
0 000001
/•"
^.x
f f_
/
. "
—^
^
r~
0 10 20 3
Time [Hours
(a) AUC in Kidney
_^- —
___^, — —
AUC
[mg/L^Tft
0.010000
n nnnnm
^
(\2_
i /
/
,_ — -
-""~~"
=— — '
/""
—
—
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUCinTestes
PLOT 10th percentile —- 50th percentile,UC LV
90th percentile K
PLOT - 10th percentile ------ 50th percentile, UC VB F
- 90th percentile K - -
[mo/lAW
0 0000001
^
i /
2
. — ^-^
^^
•
-^~
. — •
— •
[mg/L^Vfi
n nnnnnm
/^
/...•'
/
/"
-
^^
^-
_— •
— - —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
50th percentile,A KD URN T PLOT - IQth percentile
_ _ _ - gg-^n percenf||e
Amt,
- 50th percentile,BDYM_1A_EX
TmcQ
0 00001
Tmffi
1.0000
nnnm
^
2/
f /
j
, — - —
^
.,
^— —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(e) Total in Urine (f) Total Absorbed Dose
Figure A-21. Child CHC13 Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-22
-------
PLOT IQth percentile -—50th percentile,C KD
90th percentile K
PLOT IQth percentile —- 50th percentile,C TS
90th percentile K
1 .OOE-03
1 OOE-08
r^^.
- — -"\
^
—-/
^-^
s
•^ —
/''---.
A- •
*-x—
""''"',',
^-^
/^
„,-"' •
^x^
1 .OOE-03
1 nnF-nft
^ — ^»
- --" -'..^
^A
—^
v^^
'~v^~-/'
"^
A— •
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
~~x~~
-^— ^
S~~^
.'•'"
^"
0 40 5C
Cone,
[mg/LJ
1 OOE-08
r -"
r
W-^
•-~,,y'
-vw^
""^ '
V_ /
"~X^~
N^-""-,1»,."
Vm_
^^-~^
*S^^
A,
j,/" ^
Cone,
[mg/LJ
1 nnF-ns
_^~~.
.. '-s
./.
7
,
X
-^x^^^-
"V
'""'•-,
-'^—^
x^-
x....x--~..
^^
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
) Cone in Lver (d) Cone in Venous Blood
- 50th percentile,C_EXH_F
1.00E-05
1.00E-06
1.00E-07
1.OOE-08
1.00E-09
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-22. Child CHC13 Percentile Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-23
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
O.CT1 00000
0 0000001
^
/
^^^
^_^___
0 10 20 3
Time [Hours
(a)AUCin Kidney
0.0100000
n nnnnnm
^-"
I/
^
-^^
^^^
7^^7
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Testes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
Lma/lM
D. 0100000
0 0000001
^""
i;
-r^^
~^:::::::^r
0.0100000
n nnnnnm
^
I/
^^
_^_ — - - — —~~
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00000
0 00001
0 10 20 30 40 5C
Time [Hours]
(e) Total in Urine
1.00000
0.00001
^
If
^ ^
— - - — • — • —
. — • — —
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-23. Child CHC13 Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-24
-------
PLOT mean, C_KD —- 50th percentile,C_KD
PLOT mean, C_TS —- 50th percentile,C_TS
1 .QOE-03
1 OOE-08
r~^v\
-"'"\,
f^
-\j^j~~r-
~^
-•,---
""'V
^
1 .OOE-03
1 DDF-ns
/-"••v/v
-""\
J^
•-.
-v
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
~^
r-v
/"' "
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1 .OOE-04
1.00E-09
^^
. --•""
^^
•v,, y"""
>—
^-v^
**^--.:...>
^
"f
1 .OOE-03
1. OOE-08
c^C.,
^—J,,.
,.-.I^~~~ — ~
10 20 30 40
Time [Hours]
(c) Cone in Livei
0 10 20 30 40
Time [Hours]
(d) Cone in Venous Blood
PLOT mean, C_EXH_F — 50th percentile,C_EXH_F
Cone,
1 .OOE-04
1.00E-09
__^T^
Y- - -^
"V-
, -p^
•~^~,
'\
/rs
^ ]
/"
10 20 30 40 50
Time [Hours]
(e) Cone in Exhaled Air
Figure A-24. Child CHC13 Mean-Median Plot:
Concentration and Exhaled Air
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-25
-------
T - 10th Dercentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
rrno/lW
0 0000001
/
u
It
n
^~^
^ .--""'
/
_,
^^^^
0 10 20 3
Time [Hours
(a)AUCin Kidney
—
AUC
[mg/L^Tff
n nnnnnm
/
//
1 I
• i
^-
x-1" ,,.-"
._
-"~~~~~~^^
__ •
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b)AUCinTestes
[mg/lA^ [mq/lAVfi
0 0000001
/;
1 f
i
^-~~'
x^ .-'--'
_ — — — • — •
_— —
- —
n nnnnnm
/
V-
\
1
s- ...'-_.
/"
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
Amt
1.00E-04
1.00E-05
1 OOE-09
(/
0
(e) Total in Urine
if
^
__— — —
— ^^
— •
1.00000
n nnnm
^
(^
f
/
^^Z-
"^
_ — — —-
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(f) Total Absorbed Dose
Figure A-25. Adult Male DCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-26
-------
PLOT— 31 p
[Si
1 OOE-08
'
E
ercentile
erceniile
*— *~*"*" — •
^ —
— - 50th percentile,C_KD
^^
W ' '
"Z~
^
_ — ^
0 10 20 30 40 5C
Time [Hours]
1 .OOE-03
1. OOE-08
t
,/
-^
/
^~~~
^-•*"
_--
""
(a) Cone in Kidney
0 10
(b) Cone in Testes
20 30
Time [Hours]
40 50
Cone,
mq/L
1 OOE-08
aci
r
0
) Cone in Lver
/
^j^f*****"**'
-^~
-^
^— ~~*
'^~
-.---_j>^^
— -
*j*~
f '
/
^*~~ —
:^—
r^~*f~Mf~~'
^
^.
_
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) Cone in Venous Blood
Figure A-26. Adult Male DCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-27
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
O.CT1 00000
0 0000001
/
if
II
0
(a)AUCin Kidney
^"
10 20 3
Time [Hours
,===
0.0100000
n nnnnnm
/•
1
^
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Testes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
[mo/lA^S
0 0000001
[mq/lAVfi
/,.
77
//
0
(c) AUC in Liver
<^"
^_^___
-^===
^==-
n nnnnnm
/.-
If
/!
i
^
^7^=
^3—
,===
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00E-05
1.00E-10
/
//
I
|
^J
-====
0.100000
0.000001
/s'
L
I
^^
^=—
10 20 30 40 50
Time [Hours]
(e) Total in Urine
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-27. Adult Male DCA Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-28
-------
PLOT mean, C_KD —- 50th percent!Ie,C_KD
Cone,
PLOT mean, C_TS —- 50th percent!Ie,C_TS
Cone,
1 .OOE-03
1 OOE-08
ff^
,
^^,
1 .OOE-03
1 DDF-ns
f
I
0 10 20 30 40 50 0
Time [Hours]
(a) Cone in Kidney (b) Cone in Testes
"""--•
10 20 3
Time [Hours
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
rConc,
1. OOE-03
1 OOE-08
(?
\
:;;;^^
-~— "-
*7^T--. ••'
~~~— -~v_
0 10 20 30 40 5C
Time [Hours]
(c) Cone in Lver
PLOT mean, C_VB —- 50th percentile,C_VB
rConc,
1.0DE-03
1. OOE-08
f
0 10 20 30
Time [Hours]
(d) Cone in Venous Blood
40 50
Figure A-28. Adult Male DCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-29
-------
T - 10th percentile
- 90th percentile
— - 50th percentile,UC OV F
K ' - -
AUC AUC
Lma/L*>lJ [mq/L^Tf]
0 0000001
X
{''
U
n
^-^
"/^
— — — —
_, — —
0 10 20 3
Time [Hours
(a) AUC in Kidney
__— - — — ~
— —
— — ~~~~~
n nnnnnm
X
E
P
\
0 40 50 0
(b) AUC in Ovaries
i
— — •
^•^
/"
^,
_ — — —
•
_— — — -
— — — —
10 20 30 40 5C
Time [Hours]
[mo/lA^S [mg/lAVfi
0 0000001
/
E
U
0
^^
X"
_-^-
^—— —
_ —
__— - — — —
— — "~~~^
n nnnnnm
/
//
/
^— -
^^:
7"
.
_^ — — —
_-
__ — — —
— — — —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
1.00E-05
1.00E-10
Amt,
Imql
/
//
//
!i
^^"
/^^
/
.
_-— ~~
'
"
0.000001
r
^—~
__ —
--~^^~
.,
10 20 30 40
Time [Hours]
(e) Total in Urine
0 10 20 30 40
Time [Hours]
(f) Total Absorbed Dose
Figure A-29. Adult Female DCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-30
-------
PLOT— 31 p
[Si
1 OOE-08
r
[/
ercentile
ercentile
""
— - 50th percentile,C_KD
«w«^
--^^
— -
J— — X-^
1^—C
0 10 20 30 40 5C
Time [Hours]
1 .OOE-03
1. OOE-08
r.
LA
tj
f — -"—•"
^— -"
-^
— «-^— *•
^~^~ ~
— — >v_
. — •— ^^
(a) Cone in Kidney
0 10
(b) Cone in Ovaries
20 30
Time [Hours]
40 50
Cone,
mg/L
1 OOE-08
Cone,
.,, Wr
liV
0
) Cone in Lver
s-
*v>.
•--^TTJ!
^— — -^
G:
IY
_rj~~ ^— -
~^^
^-^— ^— *
jim^
— • -~^
rz-^
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) Cone in Venous Blood
Figure A-30. Adult Female DCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-31
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_OV_F —- 50th percentile,UC_OV_F
O.CT1 00000
0 0000001
2
0
(a)AUCin Kidney
//
^^
10 20 3
Time [Hours
0.0100000
n nnnnnm
//
^^-
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Ovaries
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
[mo/lA^S [mq/lAVfi
0 0000001
/
^^
n nnnnnm
//
II
^^
^_____
^Tr^rrrr
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00E-05
1.00E-10
/
t
•^
0.100000
0.000001
^
77
E3==±^
^-::"
10 20 30 40 50
Time [Hours]
(e) Total in Urine
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-31. Adult Female DCA Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-32
-------
PLOT mean, C_KD —- 50th percentile,C_KD
Cone,
PLOT mean, C_OV —- 50th percentile,C_OV
Cone,
1 .OOE-04
1 OOE-09
f^L
0
(a) Cone in Kidney
^~^-
-^c^.
1 .OOE-04
1 nnF-nq
^
10 20 30 40 50 0
Time [Hours]
(b) Cone in Ovaries
•^^
10 20 3
Time [Hours
^n^>-
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1 .OOE-04
1 OOE-09
r^
ci==
^rr^
0 10 20 30 40 5C
Time [Hours]
(c) Cone in Lver
1 .OOE-04
1. OOE-09
r^-
^^^^
-r^=^-
-~- -^
0 10 20 30
Time [Hours]
(d) Cone in Venous Blood
40 50
Figure A-32. Adult Female DCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-3 3
-------
T - 10th percentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
AUC AUC
Lma/L*>lJ [mq/L^Tf]
0 0000001
/
(L
if
ll
— —
•^-^
Y
--- —
0 10 20 3
Time [Hours
(a) AUC in Kidney
,
^—^-^
-~~~~ ~
n nnnnnm
/
(u
II
0 40 50 0
(b)AUCinTestes
^^
^ ..---'
/
.
_— — — —
.
_— — — —
10 20 30 40 5C
Time [Hours]
D.01 00000
0 0000001
X
ft
/
If]
— — •
/"
—
_^— - — —
- — - — •
_— — — —
__— - — —
0.0100000
n nnnnnm
/
ft
/
»
^±r
7"
_ •
^— - •— -—
_
__—- — -
.
_— - — •—
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
fmfl]
1.00E-10
/
/
x
f
f
1
-—
-^ ---""
s~" ^~-~~~'
y
_^— — —
, — > —
_— ——
_ — —
— — — —
___ — • —
AmL
Tmcfi
0.000001
/
/
!
^
f
1
'
^^~~
r _^^~- — '
^
__-
^___ .
^-^ —
10 20 30 40
Time [Hours]
(e) Total in Urine
0 10 20 30 40
Time [Hours]
(f) Total Absorbed Dose
Figure A-33. Child DCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-34
-------
T 10th percentile —- 50th percentile,C KD
90th percentile ~
1 .OOE-03
1. OOE-08
f /-""
I//
\ f
.rfW— *^——
^j^^.:*f*.S*
_^-~-~-
'•"— ^^
• - -\
0 10
(a) Cone in Kidney
20 30
Time [Hours]
PLOT— Jg
Cone
1 OOE-08
th percenti|e — - 50th percentile,C TS
in percentile ~
-j*
{,-
n
0
(b) Cone in Testes
/
*-*~r~' "
_~ — ~-
^^
"~\/
10 20 3
Time [Hours
^>-~ — •
'^— -
r- ^
— ^
0 40 5C
Cone,
mq/L
1 OOE-08
Cone,
.,, Wr
u_
P
0
) Cone in Lver
,-w— ~f*~—
5^
j~S****t~~*"
'— ^^
_^~c
1 nnF-ns
_/•
r ,_
lY
^-j-W^^^—
^iz;
-^
^— -~
''^-~
„ ^
~~— -C
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) Cone in Venous Blood
Figure A-34. Child DCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-3 5
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
^ffi
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
O.CT1 00000
0 0000001
/
ff
^^
0 10 20 3
Time [Hours
(a)AUCin Kidney
_____ — — —
0.0100000
n nnnnnm
/
(/
^'
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Testes
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
[mo/lA^S
0 0000001
[mq/lAVfi
/
/
0
(c) AUC in Liver
S^
^^^r
n nnnnnm
/
//
S^7
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
1.00E-05
1.00E-10
J
I!
/
I
^^
-
0.100000
0.000001
,
t
1
-^^
10 20 30 40 50
Time [Hours]
(e) Total in Urine
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-35. Child DCA Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-36
-------
PLOT mean, C_KD —- 50th percent!Ie,C_KD
Cone,
PLOT mean, C_TS —- 50th percent!Ie,C_TS
Cone,
1 .OOE-04
1 OOE-09
{?
i/
\
I
-^==:-
"^
^=^=-
-7^=^
1 .OOE-04
1 nnF-nq
(rL
I?
/
~^. —
"^
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
^^--••^
-^»
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
1 .OOE-04
1 OOE-09
f?
,r_
~^=^.
^^=
~=^,
0 10 20 30 40 5C
Time [Hours]
(c) Cone in Lver
1. OOE-09
/^
(j
'^^
rf-~-f^_..-.-
0 10 20 30
Time [Hours]
(d) Cone in Venous Blood
40 50
Figure A-36. Child DCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-37
-------
T - 10th Dercentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
AUC AUC
[mg/L*>ff [mq/L^Tf]
0 000001
/
U
I!
•''/•^
0 10 20 3
Time [Hours
(a) AUC in Kidney
__ — — •
~~—
—
-~~~~~~~~
n nnnnm
U-
I
0 40 50 0
(b)AUCinTestes
s*^z
/-^
— — • —
_ — - —
_-!• — —
__ — —
10 20 30 40 5C
Time [Hours]
on ooooo
0 000001
//
u
I
0
(c) AUC in Liver
^^
^/^
-~
^^
__
_ '
-~~~"
on ooooo
n nnnnm
//
V-
I
/^
""^
^-
— •
__ — —
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) AUC in Venous Blood
50th percentile,A KD URN T PLOT - Ipth percentile
_ _ _ - g^n ^ercenf||e
Ami
- 50th percentile,BDYM_1A_EX
Imq]
0 0000001
Imql
0
(e) Total in Urine
/ /
S^''^
/^
— ;
_ —
0.10000
n nnnm
E
|
i
-^^
'^
„—-——-
__ — — —~~
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(f) Total Absorbed Dose
Figure A-37. Adult Male TCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-3 8
-------
T 10th percentile —- 50th pereentile,C KD
90th percentile ~
T 10th percentile —- 50th percentile,C TS
90th percentile ~
0.01DOOOO
0 0000001
s
^
---•"""1^-
^
, — =
_^— — —
0.0100000
n nnnnnm
^
Q_
-^ -
^
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
- — • —
0 40 5C
&.\
0 0000001
Cone,
[mq/Ll
^
f f1
0
c) Cone in Lver
-^^— --
^
•,. •••— "
_— • — — — —
n nnnnnm
/7
^^
•^
_ — • — ^ —
10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(d) Cone in Venous Blood
Figure A-38. Adult Male TCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-3 9
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
[mg/L^t
0 000001
AUC
[mq/L^Tf]
^
0
(a) AUC in Kidney
//
^
S"'
^^"
10 20 3
Time [Hours
n nnnnm
/
I
0 40 50 0
(b)AUCinTestes
^
•^^^
^^^__
=====
10 20 30 40 5C
Time [Hours]
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT - mean, UC_VB_F — - 50th percentile,UC_VB_F
[mo/lA^S
0 000001
[mq/lAVfi
/
0
(c) AUC in Liver
j
^
-------
PLOT mean, C_KD —- 50th percent!Ie,C_KD
Cone,
PLOT mean, C_TS —- 50th percent!Ie,C_TS
Cone,
1 .OOE-03
1 OOE-08
f
^
^^
^^^
0.0100000
n nnnnnm
/?
ff
^^"
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
(a) Cone in Kidney (b) Cone in Testes
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
PLOT mean, C_VB —- 50th percentile,C_VB
Cone,
[mg/LJ
1 .OOE-08
r
(|
^^^
___ -rrrrrr
Cone,
[mg/LJ
0.0000001
/,
J
\
^^^
(c) Cone in Livei
20 30 40 50
Time [Hours]
0 10 20 30
Time [Hours]
(d) Cone in Venous Blood
40 50
Figure A-40. Adult Male TCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-41
-------
PLOT
AUC
[mg/L * H]
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
(a) AUC in Kidn
PLOT —
AUC
[mg/L * H]
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
(c) AUC in Liver
PLOT— 10
Amt.
[mg]
10thpercentile -— 50th percentile, UC KD F
90th percent le ~ ~
/
tf
JiL
^--
s ~-
/
-^'-
""
__
^^
_- — — •
— —
0 10 20 30 40 5
Time [Hours]
z*
— 10th percentile 50th percentile,UC LV
— 90th percentile
/
tl
Jl
^-^
'/^
/
^"~^~
~"^^
__ — •
- — —
„_
0 10 20 30 40 5
Time [Hours]
h percentile — 50th percentile,A KD URN
h percentile
z
u
G
s^-
//^
.
- — '-—
— -
PLOT
AUC
[mg/L * H]
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
3
(b) AUC in Ova
PLOT
AUC
[mg/L * H]
0.100000
0.010000
0.001000
0.000100
0.000010
0.000001
3
(d) AUC in Venc
T PLOT 1C
Amt.
[mg]
10th percentile -— 50th percentile, UC OV F
90th percentile
/
^
\(
x>^
/
, — ' — "
_—-— '
^
^^^^
• —
__ — — —
0 10 20 30 40 5C
Time [Hours
10th percentile ----- 50th percentile, UC VB F
90th percentile ~ ~
/
E
C
0
us Blood
h percen
h percen
^^
/>^
/
-'"^111
_ — — •
_— — -
.
_— - — -"•
10 20 30 40 5C
Time [Hours
tile -- 50th percentile,BDYM 1A E)
:ile
/ / '
F
0 10 20 30 40 50 0
Time [Hours]
(e) Total in Urine (f) Total Absorbed Dose
^~-
^^
_- ' —
- — — —
* —
10 20 30 40 5C
Time [Hours
Figure A-41. Adult Female TCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-42
-------
PLOT 10th percentile -—50th percentile,C KD PLOT 10th percentile -— 50th percentile,C OV
90th percentile ~ 90th percentile
Cone. Cone.
[mq/Ll [mq/Ll
0.0001000
(r
u
if
^-~—
0 10 20 3
Time [Hours
' —
^.M.II — -
0.0001000
X"
E
I
0 40 50 0
;
^-—^.
"/^
= =
j —
10 20 30 40 5C
Time [Hours]
(a) Cone in Kidney
(b) Cone in Ovarie;
PLOT 10th percentile -— 50th percentile, C LV PLOT 10th percentile -— 50th percentile, C VB
90th percentile ~ 90th percentile
Cone. Cone.
[mg/L] [mg/L]
^
{f.
u
I
^~-^~_
"^^
^ ' — '
, III
X"
r?
0 10 20 30 40 50 0
Time [Hours]
/
>^
__— - — ~~"~
j
10 20 30 40 5C
Time [Hours]
(d) Cone in Venous Blood
Figure A-42. Adult Female TCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-43
-------
PLOT mean,
AUC
[mg/L * H]
UC_KD_F — - 50th percentile,UC_KD_F PLOT mean, UC_OV_F — - 50th percentile,UC_OV_F
[mg/L * H]
/
//
//
0
II
^'""
^^
^^^
/
1
I
^
^'""
^^
10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
0 40 5C
(a) AUC in Kidney
PLOT —
AUC
[mg/L * H]
— mean, UC
/
if
1
1
_LV
<^""
-— 50th percentile,UC_LV
^^
PLOT
AUC
[mg/L * H]
mean, UC_VB_F
/
£
I
^
^
-— 50th percentile,UC_VB_F
^^
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
AUC in Liver (d) AUC in Venous Blood
0 40 5C
PLOT mean, A_KD_URN_T
-50th percentile,A_KD_URN_T
PLOT mean, BDYM_1A_EX
Amt
[mg]
- 50th percentile,BDYM_1A_EX
/
If
^
-x
^^
____—-
0.100000
^£
//
'^"
0 10 20 30 40 50 0 10 20 3
Time [Hours] Time [Hours
ie (f) Total Absorbed Dose
0 40 5C
Figure A-43. Adult Female TCA Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-44
-------
PLOT —
Cone.
[mg/L]
1 OOE-08
— mean, C_KD
/;
^^
'
-— 50th percentile,C_KD
0 10 20 3
Time [Hours
(a) Cone in Kidney
0 40 50
PLOT mean, C_OV
Cone.
-— 50th percentile,C_OV
1.00E-03
1. OOE-08
2
_— • -— T n
20 30
Time [Hours]
(b) Cone in Ovarie;
PLOT mean, C_LV — - 50th percentile,C_LV PLOT mean, C_VB -— 50th percentile,C_VB
Cone. Cone.
[mg/L] [mg/L]
f
^^
-==
{/'"
0 10 20 30 40 50 0
Time [Hours]
^^
10 20 30 40 5C
Time [Hours]
(d) Cone in Venous Blood
Figure A-44. Adult Female TCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-45
-------
T - 10th Dercentile
- 90th percentile
— - 50th percentile,UC TS F
K ' - -
[mfl/L*ffi
0 000001
/
ft
G
^-^
:<^
/
— "^_7_--
0 10 20 3
Time [Hours
(a)AUCin Kidney
__
^^
•
^—
AUC
[mg/L^Tft
n nnnnm
2
///
E
/^
-/^
—
^_ — • —
——
_ •
^^^
0 40 50 0 10 20 30 40 5C
Time [Hours]
(b) AUC in Testes
on ooooo
0 000001
/
ft
I/
^:
^^
'
^^— • — —-
^^—^~
— — — •—
on ooooo
n nnnnm
2
ft
fi
<^
__ —
"i^- —
__
__ — —
.
. — —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
- 50th percentile,BDYM_1A_EX
Amt Ami
Imq] Irnql
0.0010000
0 0000001
/
ft
G
/<^
/
_ — — •
^
.
^^^^
_ — ,
_— — •—
n nnnnm
/'/
i
|
/
~s^~
__.-.-
^
• —
^ —
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(e) Total in Urine (f) Total Absorbed Dose
Figure A-45. Child TCA Percentile Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-46
-------
T IQth percentile —- 50th percentile,C KD
90th percentile ~
0.01DOOOO
0 0000001
D
S
1
'
1
^-^ —
s^~
0 2
0 3
rime [Hours
— 77~
0 4
0 5C
PLOT
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0.0010000
0 0000001
IQth percenti|e — - 50th percentile,C TS
90th percentile r
/•
0
\u
If
,- ~
/•"""
10 20 3
Time [Hours
. •"
0 40 5C
(a) Cone in Kidney
(b) Cone in Testes
Cone,
[mgA.1
0 0000001
^
f
, — -—
"^^
^^
-.
Cone,
[mg/LJ
0.0010000
n nnnnnm
/-"
|
^-^:.
^
—
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
c) Cone in Lver (d) Cone in Venous Blood
Figure A-46. Child TCA Percentile Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-47
-------
PLOT mean, UC_KD_F —- 50th percentile,UC_KD_F
PLOT mean, UC_TS_F —- 50th percentile,UC_TS_F
[mg/L^t
0 000001
AUC
[mq/L^Tf]
/f
If
0
(a) AUC in Kidney
^
^
^^
10 20 3
Time [Hours
n nnnnm
/
0 40 50 0
(b)AUCinTestes
j
^^
''
^^
10 20 30 40 5C
Time [Hours]
PLOT mean, UC_LV —- 50th percentile,UC_LV
PLOT mean, UC_VB_F —- 50th percentile,UC_VB_F
[mo/lA^S [mq/lAVfi
0 000001
/
1!
i
^
^
n nnnnm
/L
1
^
X
-^^
0 10 20 30 40 50 0 10 20 30 40 5C
Time [Hours] Time [Hours]
(c) AUC in Liver (d) AUC in Venous Blood
PLOT mean, A_KD_URN_T — 50th percentile,A_KD_URN_T PLOT mean, BDYM_1A_EX — 50th percentile,BDYM_1A_EX
0.0100000
0.0000001
/
II
I
^,
^
^^
-^rr^^117^
0.100000
0.000001
/
(I
1
-^^
10 20 30 40 50
Time [Hours]
(e) Total in Urine
0 10 20 30 40 50
Time [Hours]
(f) Total Absorbed Dose
Figure A-47. Child TCA Mean-Median Plot:
AUC, Total Urine, Total Absorbed Dose
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-48
-------
PLOT
Cone,
fmq/L
1 OOE-08
mean, C_KD — - 50th percentile,C_KD PLOT
Cone,
fmq/Ll
2
0
(a) Cone in Kidney
^==
^^
n nnnnnm
mean, C_TS — - 50th percentile,C_TS
/
10 20 30 40 50 0
Time [Hours]
(b) Cone in Testes
If
10 20 3
Time [Hours
•
0 40 5C
PLOT mean, C_LV —- 50th percentile,C_LV
rConc,
1.00E-03
1 OOE-08
r
0
(c) Cone in Lver
^=
10 20 30 40 5C
Time [Hours]
PLOT mean, C_VB —- 50th percentile,C_VB
rConc,
1.00E-03
1 .OOE-08
x"
t
^^
^^^^
0 10
(d) Cone in Venous Blood
20 30
Time [Hours]
40 50
Figure A-48. Child TCA Mean-Median Plot:
Concentration
Developing Human Exposure Estimates for Individual DBFs, Draft Final Report
March 2002, Page A-49
-------
APPENDIX 2
A CONCEPTUAL MODEL FOR A CUMULATIVE RISK APPROACH
-------
The following text contains Chapter 4.0, excerpted in its entirety, from the U.S.
EPA (2000a) report entitled, Conducting a Risk Assessment of Mixtures of Disinfection
By-products (DBFs) for Drinking Water Treatment Systems (NCEA-C-0791). This
chapter details the Cumulative Relative Potency Factors (CRPF) risk assessment
approach that is discussed in the main report of this document and is provided here as
reference material. It has been edited slightly to remove cross references within the
EPA report (U.S. EPA, 2000a) that may cause confusion to the reader. Also note that
the equations in this Appendix use a value of Y to represent tap water consumption.
This is different from the equations found in Section 2.0 of the main document because
water consumption is included in the exposure modeling that produces estimates of
dose.
4. CONCEPTUAL MODEL FOR A CUMULATIVE RISK APPROACH
Several different risk characterization methods have been recommended for
estimating DBP mixtures risk: response-addition, relative potency factors and
proportional response addition. Although each of these approaches has its strengths,
neither of these examples accounts for 1) multiple routes of exposure, 2) any
toxicologic similarity among chemicals in the mixture (beyond target organ effects), and
3) temporal issues of exposure.
Section 4.1. presents a conceptual model that accounts for multiple routes of
exposure over time and toxicologic similarity of the components. This approach will be
expanded in an NCEA report on the feasibility of performing cumulative risk
assessments for non-cancer and cancer endpoints for mixtures of drinking water
disinfection by-products via inhalation, dermal, and oral exposures; the projected
completion date of this feasibility report is 2001.
-2-
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4.1. MODEL CONSIDERATIONS AND REQUIREMENTS
Currently, it is feasible to approach human health risks posed by DBFs as a
cumulative risk problem. The current effort to quantify human cancer risk from exposure
to DBP mixtures using animal data from the oral route alone produces risk estimates
several orders of magnitude lower than those projected using positive epidemiologic
data on chlorinated drinking water exposures in the study population (other
epidemiologic data indicate that risks posed may be negligible). If one assumes that
DBP exposures cause human cancers and that the positive epidemiologic results
provide unbiased quantitative estimates of the cancer risk posed by chlorinated water
exposures, then the discrepancy between risk estimates from the toxicological data and
the positive epidemiologic studies requires explanation. Several reasons for the
discrepancy are postulated, including failure to accurately extrapolate dosimetry
between animals and humans; failure to account for contribution to risk from inhalation
and dermal exposure routes; and failure to integrate the data according to the level of
organization at which the effects were observed (e.g., population, target organ, cellular).
The goals of a cumulative risk assessment for DBFs build upon those of the
current DBP mixture risk assessment. The goals of a new methodology would include:
To develop a mixtures approach that incorporates the flexibility to
integrate selected mixtures risk models based on an understanding of the
mode-of -action
To consider the temporal nature of DBP exposures and variability of
human activity patterns; address and appropriately integrate exposures
through the three routes of primary concern for environmental pollutants:
ingestion, dermal, and inhalation
-3-
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To address the main endpoints of concern in the epidemiologic literature:
developmental and reproductive effects and cancer
To identify the "risk-relevant" components of DBP mixtures, this may
include organic halides not measured individually as well as DBFs that are
not halogenated
To estimate risks for various drinking water treatment trains, reflecting
differences in those DBFs formed and their concentrations over time in the
distribution system
To generate central tendency risk estimates along with their associated
probability distributions; such distributions of risks are needed to
appropriately reflect both the uncertainty and variability found in these
data
To identify specific measures that could be incorporated into future
epidemiologic investigations to improve exposure classification
To develop mixtures risk characterization approaches that can be used in
the evaluation of causality.
4.2. CUMULATIVE RISK APPROACH
Three general approaches for addressing additivity associated with low doses
components of a chemical mixture exist. Dose addition assumes the mixture
components share an MOA; thus, doses of individual components can be added
together after being appropriately scaled for relative potency. Response addition
assumes component risks for a given target organ or tissue can be added given the
components' effects are toxicologically and statistically independent. Finally, effects
addition assumes health outcomes attributable to individual components can be added
-4-
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together, assuming that the toxicodynamics are similar across components. To
incorporate MOA data into the risk assessment, a dose-addition approach is
investigated here.
MOA refers to a continuum describing the key events and processes starting
from the point of toxicant-cell interaction and leading to the onset of a health endpoint
(see Figure 9). The MOA may involve several levels of toxicologic analysis and
influence based on the structural hierarchy of animal bodies: intracellular, intercellular,
tissue, organ, organ system, whole organism. Less is known about MOA as contrasted
with the term mechanism-of-action, which implies a detailed molecular description of the
induction of a health effect.
Both ILSI (1999) and Wilkinson et al. (2000) have documented the complexities
associated with assessing risks posed by chemical classes exhibiting a common MOA.
These reports describe a range of chemical mixture risk assessment methods that could
be applied to a set of pesticides that exhibit a shared MOA, the organophosphates
(OP). The potential utility of the hazard index approach (U.S. EPA, 2000b), a chemical
mixtures approach that requires dose response and exposure data for each component,
and a relative potency factors approach (detailed below) are presented in each.
Wilkinson and collaborators also detail a combined margin of exposure approach, which
is conceptually related and mathematically similar to the hazard index approach. The
ILSI report describes an exposure schematic that can combine exposure estimates for
inhalation, oral and dermal exposure routes; Olin
-5-
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Exposure
Effect
Susceptibility
FIGURE 9
Biological Marker Components in Sequential Progression from Exposure to Disease
Source: Schulte (1989)
-6-
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(1999) also describes conceptually similar approaches for assessing exposures to
drinking water contaminants and details additional exposure considerations for
combining estimates from multiple exposure routes. Wilkinson et al. (2000) and
Rhomberg (1999) elucidate the temporal considerations that impact an assessment of
risks posed by multiple chemicals. Specifically, they both conclude the internal dose of
the components matters more than the timing of the exposures.
Cumulative risk assessment, as used in this document, examines the potential
for increased risks by considering multiple chemical exposures through multiple routes
over multiple time frames. Cumulative risks are conjectured to occur under a number of
conditions:
When exposures (through multiple routes) to a group of chemicals that act
through a common mechanism of toxicity occur within a physiologically-
relevant time frame
When exposures occur (through multiple routes) to a group of chemicals
that impact different parts of a pharmacodynamic pathway that lead to a
toxic response given the temporal considerations of the impacts (e.g.,
repair processes)
When risks of a toxic effect estimated for each component using the
component's dose-response curve at the exposure concentration are
additive, given temporal considerations of the response
When there are synergistic interaction effects associated with exposures
to two different chemicals (or dose-additive chemical groups) that occur
over a physiologically-relevant time frame.
-7-
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The physiologic time frame can reflect the pharmacokinetics (PK) or the
pharmacodynamics (PD) associated with exposures to specific components of the
chemical mixture. PK is the study of the fate of chemicals in the body; it deals with
absorption, distribution, biotransformation, and elimination. PD is the study of
biochemical and physiological effects of chemicals and their mechanisms of action. The
PK depend on exposure routes and patterns (e.g., duration, magnitude, and frequency).
Although four conditions are listed previously in this section, only a cumulative risk
approach arising from exposures to groups of chemicals that act through a common
mechanism of toxicity within a physiologically-relevant time frame is described.
Figure 10 illustrates the decision processes that would be undertaken to apply
this approach. The decision diagram is presented from left to right, although some
steps may be interative. The initial step is to evaluate the MOA data for the components
of a chemical mixture. If the components share a common MOA, then it may be
possible to develop a cumulative relative potency factors approach. This assumes that
component data for individual exposure routes meet criteria established for
implementing an RPF approach; specifically, one component is well studied and has a
dose-response function available for the effect of interest, and it is reasonable to
conclude from available data on toxicity or chemical structure that all components share
a common MOA (U.S. EPA, 2000b). If the components do not meet the criteria, then
some other assessment approach should be considered.
The next step is to evaluate the exposure scenario. By which routes are
individuals exposed and over what time frames do these exposures occur? Three
-8-
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Exposure Scenario
Common
Mode of Time Pharmacokinetic
Action Route Frame Differences
Dermal Concurrent
Oral W
Inhalation /T\ Non-concurrent
\/pe>
• yc°
\ n°M Do Not
Develop
RPF
Absorption
/ Distribution
Assessrr
Pharmacodynamic charact*
lmPact of Index (
Interaction
Between
Subclasses
1 set of RPFsfor m
JCIass yes
\Subclasses of
RPF
\ no
y
\ Biotransformation
\ Elimination
Integrate w/ D-R
FIGURE 10
Schematic of CRPF Decision Process
-9-
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exposure routes are typically considered when assessing risks posed by environmental
mixtures: dermal, oral, and inhalation. DBP exposures occur through all three routes.
Similarly, the time frame of DBP exposures is thought to be intermittent throughout the
period of time spent indoors. Concentrations of volatile DBFs (e.g., THMs) increase
when activities such as showering, cooking, and clothes washing occur. Dermal
exposures occur through activities such as bathing and hand washing, and oral
exposures occur through drinking water and consuming water in or on foods.
The next step is to assess the impact of absorption, distribution,
biotransformation, and elimination on the DBP components as they are absorbed
through the various exposure routes. Specifically, are there differences in internal dose
arising from the multiple route exposures? For example, when environmental
concentrations of chloroform are absorbed through the intestines, they appear to be
rapidly biotransformed in the liver. Inhaled chloroform is not biotransformed by the liver
as rapidly because it is not subjected to first pass effects.
The next step is to assess the PD differences. Do the components of the mixture
share a common MOA at environmental doses in the biological moiety(ies) of interest?
Can the MOA be plausibly linked to adverse health outcomes? If the data are generated
in laboratory animals, is there a comparable human MOA? If the components are
consistent across routes, PK, and PD properties, then it may logical to develop a single
set of RPFs for the compound class under evaluation. If they vary, then it may be
logical to split the class into two or more subclasses and pose the question as to the
type of interactions that exist between the classes. The final step is to develop an
equivalent index chemical concentration. This exposure assessment is then integrated
with the dose-response function of the index chemical to quantitatively estimate risk.
-10-
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To implement this approach, it is critical to identify the assumptions made and
explain the basis for these assumptions. Typically, the data upon which to base many of
these decisions does not exist or may be difficult to interpret; expert judgement or
surrogate data may be used to facilitate decision making. In these cases, the
uncertainty introduced into the quantitative exposure assessment should be described.
The identification of uncertainty in mixtures risk assessments is critical (U.S. EPA, 1986,
2000b). The goal is to develop a transparent assessment, so that key assumptions can
be readily identified and evaluated.
The goal of the conceptual approach is the integration across routes of RPF-
based risk estimates that are route specific for toxicologically similar subclasses of
DBFs for an effect-specific period of duration. Once several RPF risk estimates are
generated, then the analyst can make some assumptions relative to the likely
relationships of the across-subclass risks and combine them (e.g., a response addition
assumption leads to summing these RPF risks) to yield the total risk estimate for the
mixture. This approach produces a transparent cumulative risk assessment because
assumptions about the toxicity and the interactions must be specifically identified.
4.2.1. Relative Potency Factors. The RPF approach has been proposed as an interim
approach for characterizing health risks associated with mixtures of chemical
compounds that have data indicating they are toxicologically similar (U.S.EPA, 2000b).
To develop an RPF-based risk estimate for a class of chemicals, toxicologic data are
needed for at least for one component of the mixture (referred to as the index chemical),
and scientific judgment is used to assess the relative toxicity of the other individual
components in the mixture as well as of the mixture as a whole. The RPF approach
assumes dose addition is appropriate for the related components that comprise the
-11-
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mixture. True dose addition assumes the components of the mixture act by the same
MOA. If they are reasonable, these assumptions predict the toxicity of the mixture by
using the dose-response curve of the index chemical.
The exposure level of each component in the mixture is scaled by its toxicity
relative to that of the index chemical resulting in an index chemical equivalent dose for
each component. This scaling factor (the RFP) is based on a comparison of relevant
dose-response information between the index chemical and the component, including
the results of toxicologic assays and analyses of structural similarity to other
compounds of known toxicologic potential. When data are available, the RPF can be
adjusted to account for intake and for dosimetry. For each component of the mixture,
the RPF approach predicts an equivalent exposure in terms of the index chemical;
these equivalent exposures are them summed to generate an index chemical equivalent
total mixture dose. The risk posed by the mixture is then estimated using the dose-
response curve of the index chemical. This estimate of risk developed through
equivalent index chemical exposure should be considered an interim and approximate
estimate of risk that should be revised as more complete and better data are generated.
The application of an RPF approach may be limited based on available data to
specific exposure routes, specific health endpoints, or specific members of a class of
compounds that have similar PD and possibly PK properties. Application of an RPF
approach when conducting a cumulative risk assessment allows the analyst to 1)
subdivide a class of chemicals that exhibit a common toxic endpoint but different PD
properties into toxicologically appropriate subclasses; 2) incorporate differences in
toxicity based on exposure route and exposure time frame into this subdivision; and 3)
appropriately limit the cumulative risk assessment to certain health endpoints based on
-12-
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available data. The RPF method requires that a quantitative uncertainty analysis or
qualitative description of uncertainty be included in the risk characterization. To apply
RPF to the DBP mixture problem for a single effect and route, the basic model would be
as follows:
- ( 1 >
(3)
where:
Rm(k) = mixture risk for a given endpoint (unitless) as a function of an index
chemical k
fk = dose response function of an index chemical k (a well-studied
chemical in the mixture), requiring the 1/1000 conversion factor of
mg to ug when dose units are mg/kg-day
Y = tap water intake rate (L/kg-day)
Cm(k) = concentration of the mixture in units of index chemical k (ug/L) [see
Equation 4 below for calculation of Cm(k).]
The RPF is based on dose addition, which carries with it the assumption of a
similar MOA for the mixture components, so each component can be considered a
dilution of the index chemical. To the extent that data are available, division of the
DBPs into subclasses could be performed by incorporating all relevant biological
information regarding toxicant-target interactions and response processes (e.g., it would
be important to distinguish between carcinogens that directly interact with and damage
DMA versus those that operate through epigenetic or nonmutagenic mechanisms such
as receptor-mediated pathways and hormonal or physiological disturbances).
-13-
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The index chemical is likely to be chosen because it is a well studied chemical for
which the endpoint of interest has been observed, and its dose-response curve for that
endpoint is available. The concentrations of the other DBFs in the group then are
expressed as the index chemical by developing a scaling factor, the RPF. Then, the
total mixture dose is estimated as:
(4)
i=l
where:
Cm(k) = mixture exposure concentration expressed as the index chemical k
(M9/L)
n = number of components in the mixture
RPF, = proportionality constant relative to the toxic potency of the index
chemical, k, for the ith mixture component
C, = measured concentration of the ith mixture component (ug/L).
Calculation of an RPF, involves making an estimate of relative potency for each
chemical compared with the index chemical. When data are available, dosimetric
adjustments, commensurate with level of effect observation and MOA, can be made
during this calculation to provide route-specific estimates of a cumulative internal dose
surrogate to adjust the RPF,.
Figure 11 presents a simple hypothetical RPF case for a single effect, route, and
duration. Chemical A1 is the index chemical. Equivalent concentrations of chemical
components A2 and A3 are developed and these are summed to estimate the index
chemical equivalent exposure for the simple mixture.
-14-
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Figure 12 presents a simple hypothetical RPF case for a single effect over a
consistent time frame of exposure for two exposure routes. The oral exposure of
chemical A1 again serves as the index chemical for both oral exposures to chemicals
A2 and A3 and for exposures to chemical A1 through the inhalation route. Equivalent
concentrations of chemicals A2 , A3, and inhaled A1 are developed and these are
summed to estimate the index chemical equivalent exposure for the simple mixture.
The equivalent exposure is compared to the dose-response function of the index
chemical to estimate a risk. The assumptions or dosimetry data supporting the route-to-
route conversion for inhaled and oral chemical A1 would need to be clearly identified.
Tables 12 and 13 provide example calculations for a hypothetical subclass of five
DBFs that are liver carcinogens acting by the same MOA after oral ingestion. Table 12
illustrates some of the considerations related to data set evaluations, including data
availability and quality and differences in species and study durations. ED01 values are
estimated from each chemical's critical study for use in the RPF approach; these should
be adjusted for dosimetry if enough data are available. The index chemical, k, exhibits
the best quality data set. For purposes of illustration, Table 13 shows a feasible set of
calculations that could be used to produce a risk estimate for this mixture using a RPF
approach. Ratios of the ED01 of the index chemical to the ith chemical's ED01 provide an
RPF: for that chemical. The measured concentration, C., of the ith chemical is then
-15-
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Concentration
Chemical Al
(Index Chemical)
Concentration
Chemical A2
Concentration
Chemical A3
X RPFA1
(RPFA1= 1)
X RPFA2 =
X RPF A3
Index
Chemical
Concentration
Al
Sum index chemical
equivalent concentration
to estimate total
mixtures exposure in
units of the
index chemical
Index
Chemical
Equivalent
Cone. Of
Chem A2
Index
Chemical
Equivalent
Cone. Of
Chem A3
FIGURE 11
RPF Approach for Three Hypothetical Chemicals, Single Effect, Route, and Duration
-16-
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•f-
f
= Mntin
Silk
Dos*
FIGURE 12
RPF Approach for Three Hypoih*Gea1 Chenvcals, TWD Exposure Routes
-------
TABLE 12
Hypothetical Characterization of the Toxicologic Properties of
Five DBFs that are Liver Carcinogens in Animal Studies
DBP
DBP^
(Index
Chemical)
DBP2
DBP3
DBP4
DBP5
Study
ED01
(ug/L)
5.6E+3
4.2E+3
1E+3
2.2E+1
7.7E+1
Test
Species
Rat
Mouse
Rat
Dog
Rat
Duration of
Critical
Study
2 years
2 years
90 days
2 years
90 days
Data Set Characteristics
Extensive. Many well conducted
and documented studies for a
broad spectrum of endpoints in
multiple species. Human
confirmation of relevance of
effects.
Good. Many well conducted and
documented studies for a broad
spectrum of endpoints in multiple
species.
Poor. Few poorly documented
studies.
Good. Many well conducted
and documented studies for a
broad spectrum of endpoints.
Limited. Few studies but well
conducted.
-18-
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TABLE 13
Hypothetical Example: Relative Potency Factors (RPF) and
Equivalent Exposures for Five Liver Carcinogens
DBP
DBP,
DBP2
DBP3
DBP4
DBP5
Total [CJ
Study*
ED01
(ug/L)
5.6E+3
4.2E+3
1.0E+3
2.2E+1
7.7E+1
Relative Potency
Factor (RPF,)
using Index
Chemical DBP.,
[EDm ! /EDm ,]
1.0
1.3
5.6
2.6E+2
7.2E+1
% of Equivalent Concentration from DBP.,
Cancer Risk [RJ from Exposure to DBP.,
Equivalent Concentration
(DBP! Unit Risk = 2.4 E-6 per ug/L)
Measured
Exposure
Concentration
(ug/L)
[CJ
24.4
10.2
0.001
0.003
0.01
DBP! Equivalent
Concentration
(ug/L)
[RPF, X CJ
24.4
13.6
0.006
0.76
0.72
39.5
62%
9.5E-5
f For purposes of illustration, these doses represent the actual experimental doses
converted to units of ug/L. In actual practice, this is where dosimetric adjustments
and interspecies scaling factors would be applied to provide more appropriate dose
surrogates to develop the RPF.
-19-
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multiplied by its RPF, to adjust it to an index chemical equivalent concentration. In this
example, the risk for the mixture, Rm(k), is then estimated by multiplying the sum of
these equivalent concentrations, Cm(k), by the unit risk of the index chemical. The index
chemical accounts for 62% of the risk; there is fairly good confidence in this risk
estimate (given the judgment of the dose-response data).
4.2.2. Cumulative Relative Potency Factors. The RPF approach described in
Section 4.2.1 . yields a single risk estimate for a subclass of toxicologically similar
chemicals for a specified endpoint and time frame. Combining risk information across
these chemical subclasses would require assumptions about the interrelationship of the
risk estimates. Given such assumptions, the total mixture risk for endpoint h (expressed
as RTh) could then be calculated as a function of the subclass risks (each risk expressed
as route-specific (w), chemical subclass (m) risk, Rmw). For example, if response
addition were assumed (i.e., that toxic effects for the subclasses are toxicologically
independent and events are statistically independent at low dose levels), then a simple
summation of the subclass risks would be:
<5)
mw
m=l w=l
where:
1 \ (6)
Yw Cm
-20-
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for the toxicologically similar chemical subclasses and exposure routes (oral, dermal,
inhalation) with a route-specific water intake rate Yw. The index chemical equivalent
concentrations for each subclass would be calculated as:
(7)
where:
w = route of exposure fixed as oral (w=o), dermal (w=d), or
inhalation (w=i)
Cmw(k) = mixture exposure concentration expressed as the index
chemical for route w
n = number of components in the s mixture group for route w
RPFiw = proportionality constant relative to the index chemical, k, for
the ith mixture component for route w
Ciw = exposure concentration of the ith mixture component for
route w
In the case of a simple summation of subclass risks shown above, response
addition is applied, carrying with it the assumption that the Rmw are biologically
independent, which may or may not be appropriate for the data. If other statistical or
biological behavior is more appropriate (e.g., if the effects and, hence, the risks are
correlated), then other functions of the Rmw, the multiple route RPFs, may be applied.
To illustrate the integration of dose addition and response addition, Figures 13
and 14 conceptualize the cumulative risk for two hypothetical mixtures. In Figure 13,
humans are exposed to the components of this mixture from a single route of exposure.
-21-
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Chemical Al
Index
Chemical
Equivalent
for set A
Risk 1
Rm(Al)
Risk 2
= Rm(Cl)
Total Risk =
Rm(Al)+Rm(Cl);
*Response
Addition
to estimate
Toxic Effect
Risk
^Assumes toxic effects for the
subclasses are lexicologically
independent and events are
statistically independent.
FIGURE 13
Integration of Dose Addition and Response Addition to Mixture Risk for a Single Exposure Route
-22-
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Chemical Al
Index
Chemical
Equivalent
for set A
Dose
Addition
to estimate
Riskl
Risk 1
Rm(Al)
Risk 2
= Rm(Cl)
Total Risk =
Rm(Al)+Rm(Cl)
*Response
Addition
to estimate
Toxic Effect
Risk
Assumes toxic effects for the
subclasses are lexicologically
independent and evenls are
statistically independent
FIGURE 14
Integration of Dose Addition and Response Addition to Estimate Mixture Risk for Two Exposure Routes
-23-
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In Figure 14, humans are exposed to the components of this mixture from two different
routes. For both cases, the logic for combining the RPF-based risk estimates is the
same. Based on limited data, the components are considered to have two different
MOA. Because of this, the components are subdivided into two sets for development of
RPFs. Toxicity data (measured in % responding) is available for chemicals in both sets.
An index chemical is determined and index chemical equivalent exposure
concentrations are developed for each set. The toxicological evidence from the two
index chemicals indicates that the same target organ is affected. The low
environmental concentrations lead to exposure assessments in the low dose region. In
this exposure region, component interactions are assumed not to be significant. The
MOA data indicate there is toxicologic independence of action. Based on these data,
response addition is selected as an appropriate method to estimate the risk associated
with the two index chemical equivalent concentrations. Risks are estimated for each
index chemical using its dose-response curve at the index chemical equivalent
exposure concentration. The component risks from each RPF set are added.
Table 14 continues the illustration (see Tables 12 and 13) by presenting a
hypothetical characterization of three RPF risk estimates that have been made for the
same DBP mixture, but for different exposure routes (Figure 14) and different cancer
sites. Ways to combine these risks depend on what is known about the independence
of the toxicologic mechanism of action for the groups of chemicals and their route- and
chemical-specific effects. If these three effects are considered functionally independent,
then the mixture risk is based on a response addition assumption, Equation 5. The total
mixture risk of any cancer is their sum (e.g., Rm(k) = 9.5E-5 +
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TABLE 14
Hypothetical Characterization of Several Relative Potency Factors
For the Same DBP Mixture; Different Routes, Different Effects
Index
Chemical
(DBP)
DBP!
DBPq
DBPr
Equivalent
Concentration /
Unit Risk
39.5 (ug/l_)
2.4 E-6 (ug/l_)-1
27.3 (ug/L)
1.8E-6(ug/l_)-1
1.7(ug/m3)
1.3E-5(ug/m3)-1
Attributable
to Index
Chemical
62%
69%
55%
Mixture
Risk
Estimate
9.5E-5
4.9E-5
2.2E-5
Route of
Exposure
Oral
Oral
Inhalation
Toxicologic
Effect of
Concern
Liver Cancer
Kidney
Cancer
Kidney
Cancer
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4.9E-5 + 2.2E-5 = 1.7E-4). If the assumptions of toxicologic or statistical independence
cannot be met, then other functions of the risks could be used or the maximum of the
three risks may serve as the mixture risk estimate.
4.2.3. Unidentified DBFs. The initial response addition assessment estimated an
additional amount of risk for the unidentified DBFs by determining a fraction of the
unidentified DBFs that can be associated with a given health endpoint and assuming
equal risk per concentration of organic halide material for both the measured and the
unidentified components of the mixture. A similar approach could be applied during
development of the RPF risk estimates, using information from either laboratory data or
from Quantitative Structure Toxicity Relationship models. The index chemical
equivalent concentration, Cm(k), could be adjusted to reflect the concentration of the
unidentified DBFs, Cu, that can be associated with the subclass being evaluated. A
relative potency factor, RPFU, for the unidentified DBFs in Cu could be estimated using
what is known about the likely chemical characterization of the unidentified DBFs. For
the same end point and route of exposure, Equation 4 could then be adjusted by using
Cu and RPFU to increase the value of Cm(k), reflecting the contribution of the unidentified
DBFs to that subclass of toxicologically similar chemicals.
4.2.4. Discussion. The development of RPF-based risk estimates and their integration
with response addition in a CRPF approach addresses many of the shortcomings of the
first response addition assessment in the Workshop Pre-meeting Report (U.S. EPA,
2000a), but not all issues are addressed. As shown above, the approach does not
directly address the differences in risks for sensitive subpopulations or the contribution
to the risk estimate that may be addressed by using what is known in the epidemiologic
literature. In addition, application of CRPF promises to be a resource-intensive exercise
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that may be more technically correct than the application of response addition, but, in
the end, may not produce risk estimates very different in magnitude. Furthermore, an
enormous problem lies in the fact that very little toxicity data are available for the dermal
and inhalation routes of exposure.
The CRPF approach described here is a conceptual model for development of a
cumulative risk assessment for DBP mixtures. As shown, it improves on the initial
response addition assessment by more carefully considering toxicologic similarities
among chemicals, routes of exposure, and dosimetry. It allows for treatment system-
specific exposures to be investigated and, although not specified in this discussion,
does not preclude the use of human activity patterns and distribution system effects
from incorporation into the analysis. A probabilistic analysis and full risk
characterization would be required with careful treatment of the variabilities and
uncertainties examined and explained.
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References
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Rhomberg, L. 1999. Report of the Toxicology Breakout Group (BOG). Appendix B: A
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