EPA/
822/
B­
98/
005
July
1998
AMBIENT
WATER
QUALITY
CRITERIA
DERIVATION
METHODOLOGY
HUMAN
HEALTH
TECHNICAL
SUPPORT
DOCUMENT
FINAL
DRAFT
Office
of
Science
and
Technology
U.
S.
Environmental
Protection
Agency
Washington,
DC
20460
i
AMBIENT
WATER
QUALITY
CRITERIA
DERIVATION
METHODOLOGY
FOR
THE
PROTECTION
OF
HUMAN
HEALTH
­
TECHNICAL
SUPPORT
DOCUMENT
1.
INTRODUCTION
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1
1.1
Background
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1
1.2
Need
for
Revision
of
the
1980
AWQC
National
Guidelines
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2
l.
2.1
Scientific
Advances
Since
1980
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2
1.2.2
EPA
Risk
Assessment
Guidelines
Development
Since
1980
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3
1.2.3
Differing
Risk
Assessment
and
Risk
Management
Approaches
for
AWQC
and
MCLGs
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4
1.2.3.1
Group
C
Chemicals
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4
1.2.3.2
Consideration
of
Non­
Water
Sources
of
Exposure
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5
1.2.3.3
Cancer
Risk
Ranges
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6
1.3
Purpose
of
this
Document
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6
1.4
Criteria
Equations
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7
1.5
Glossary/
Acronyms
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9
List
of
Acronyms
Used
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9
2.
ELEMENTS
OF
METHODOLOGY
REVISIONS
AND
ISSUES
BY
TECHNICAL
AREA
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11
2.1
Cancer
Effects
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11
2.1.1
Background
on
EPA
Cancer
Assessment
Guidelines
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11
2.1.1.1
1980
AWQC
National
Guidelines
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11
2.1.1.2
1986
EPA
Guidelines
for
Carcinogenic
Risk
Assessment
14
2.1.1.3
Scientific
Issues
Associated
with
the
Current
Cancer
Risk
Assessment
Methodology
for
the
Development
of
AWQC16
2.1.2
Proposed
Revisions
to
EPA's
Carcinogen
Risk
Assessment
Guidelines
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17
2.1.3
Revised
Carcinogen
Risk
Assessment
Methodology
for
Deriving
AWQC
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19
2.1.3.1
Weight­
of­
Evidence
Narrative
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19
2.1.3.2
Dose
Estimation
(
by
the
Oral
Route)
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20
2.1.3.3
Dose­
Response
Analysis
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22
2.1.3.4
AWQC
Calculation
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29
2.1.3.5
Risk
Characterization
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31
2.1.3.6
Use
of
Toxicity
Equivalence
Factors
(
TEF)
and
Relative
Potency
Estimates
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31
2.1.4
Case
Study
(
Compound
Y,
a
Rodent
Bladder
Carcinogen)
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32
2.1.4.1
Background
and
Evaluation
for
Compound
Y
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32
2.1.4.2
Conclusion
and
Use
of
the
MOE
Approach
for
Compound
Y
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33
2.1.4.3
Use
of
the
Default
Linear
Approach
for
Compound
Y
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36
2.1.4.4
Use
of
the
LMS
Approach
for
Compound
Y
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38
ii
2.1.4.5
Comparison
of
Approaches
and
Results
for
Compound
Y
38
2.1.5
References
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39
2.2
Noncancer
Effects
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40
2.2.1
Introduction
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40
2.2.2
Hazard
Identification
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41
2.2.3
Dose­
Response
Assessment
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42
2.2.4
Selection
of
Critical
Data
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43
2.2.4.1
Critical
Study
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43
2.2.4.2
Critical
Data
and
Endpoint
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44
2.2.5
Deriving
RfD
Using
the
NOAEL/
LOAEL
Approach
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44
2.2.5.1
Selection
of
Uncertainty
Factors
and
Modifying
Factors
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45
2.2.5.2
Confidence
in
NOAEL/
LOAEL­
Based
RfD
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48
2.2.5.3
Presenting
the
RfD
as
a
Single
Point
or
as
a
Range
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49
2.2.6
Deriving
an
RfD
Using
a
Benchmark
Dose
Approach
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52
2.2.6.1
Overview
of
the
Benchmark
Dose
Approach
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53
2.2.6.2
Calculation
of
the
RfD
Using
the
Benchmark
Dose
Method54
2.2.6.3
Limitations
of
the
BMD
Approach
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61
2.2.6.4
Example
of
the
Application
of
the
BMD
Approach
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61
2.2.7
Categorical
Regression
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66
2.2.7.1
Summary
of
the
Method
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66
2.2.7.2
Steps
in
Applying
Categorical
Regression
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66
2.2.8
Chronic,
Practical
Nonthreshold
Effects
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67
2.2.9
Acute,
Short­
Term
Effects
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68
2.2.10
Mixtures
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68
2.2.11
References
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70
2.3
Exposure
Analyses
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73
2.3.1
Role
of
Exposure
Data
in
Setting
AWQC
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73
2.3.2
Exposure
Factors
in
AWQC
Algorithms
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74
2.3.2.1
Body
Weight
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77
Chronic
Exposure
Scenarios
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77
Developmental
Effects
Exposure
Scenarios
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78
2.3.2.2
Drinking
Water
Intake
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81
Chronic
Exposure
Scenarios
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82
Developmental
Effects
Exposure
Scenarios
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84
Inhalation
and
Dermal
Exposure
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85
2.3.2.3
Fish
Intake
Rates
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85
Preference
#
1:
Use
of
Local
Information
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87
Preference
#
2:
Use
of
Surveys
from
Similar
Geographic
Areas
and
Population
Groups
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89
Preference
#
3:
Use
of
Distributional
Data
from
National
Food
Consumption
Surveys
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103
Preference
#
4:
Use
of
Default
Intake
Rates
from
the
CSFII
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122
2.3.2.4
Incidental
Ingestion
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123
2.3.3
Quantification
of
Exposure
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125
iii
2.3.4
Consideration
of
Non­
Water
Sources
of
Exposure
When
Setting
AWQC
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127
2.3.4.1
Exposure
Decision
Tree
Approach
.
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129
Problem
Formulation
.
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130
Data
Adequacy
.
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132
Regulatory
Actions
.
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134
Allocation
Decisions
.
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134
2.3.4.2
Notes
on
Use
of
the
Exposure
Decision
Tree
Approach
for
Setting
AWQC
.
.
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136
2.3.4.3
Setting
AWQC
for
Chemical
X
Using
the
Decision
Tree
Approach
.
.
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140
Sources
and
Uses
of
Chemical
X
.
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140
Population
of
Concern
.
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140
Data
Used
to
Assess
Exposure
to
Chemical
X
.
.
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.
141
Adequacy
of
Exposure
Data
.
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150
Setting
AWQC
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153
2.3.4.4
Bioavailability
of
Substances
from
Different
Routes
of
Exposure
.
.
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157
2.3.5
References
.
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158
2.4.
Use
of
BAFs
in
the
Derivation
of
AWQC
.
.
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165
2.4.1
Introduction
.
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165
2.4.1.1
Bioaccumulation
and
Bioconcentration
Concepts
.
.
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.
.
166
2.4.2
Definitions
.
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.
167
2.4.3
Determining
BAFs
for
Nonpolar
Organics
.
.
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172
2.4.4
Estimating
Baseline
BAFs
.
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173
2.4.4.1
Field­
Measured
Baseline
BAF
.
.
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.
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.
173
2.4.4.2
Baseline
BAF
Derived
from
Biota­
Sediment
Accumulation
Factors
(
BSAFs)
.
.
.
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.
186
2.4.4.3
Baseline
BAF
Derived
from
a
Laboratory­
Measured
BCF
and
Food­
Chain
Multiplier
.
.
.
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219
2.4.4.4
Baseline
BAF
from
Predicted
BCF
and
Food­
Chain
Multiplier
.
.
.
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235
2.4.4.5
Metabolism
.
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237
2.4.4.6
Mixtures
.
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237
2.4.5
BAFs
Used
in
Deriving
AWQC
.
.
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238
2.4.5.1
General
Equation
for
an
AWQC
BAF
.
.
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.
238
2.4.5.2
Baseline
BAF
.
.
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.
238
2.4.5.3
Lipid
Content
of
Aquatic
Species
Eaten
by
Humans
.
.
.
239
2.4.6
Determining
BAFs
for
Inorganic
Substances
.
.
.
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.
258
2.4.7
Example
Calculations
.
.
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.
259
2.4.7.1
Example
1:
Field­
Measured
BAF
for
Chemical
M
.
.
.
.
259
2.4.7.2
Example
2:
Laboratory­
Measured
BCF
for
Chemical
R
263
2.4.8
Trophic
Level­
Specific
Fish
Consumption
Rates
.
.
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.
267
2.4.9
References
.
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.
270
iv
3.
MINIMUM
DATA
CONSIDERATIONS
.
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.
276
3.1
Background
.
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.
276
3.1.1
Threshold
Effects
Guidelines
.
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.
276
3.1.2
Non­
Threshold
Effects
.
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.
277
3.1.2.1
Animal
Studies
.
.
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.
277
3.1.3
Exposure
Assumptions
.
.
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.
278
3.2
Minimum
Data
Considerations
in
the
Federal
Register
Notice
.
.
.
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.
.
.
278
3.2.1
Noncancer
­
Data
Suggestions
.
.
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.
278
3.2.1.1
RfD
Development
(
Minimal
Data)
.
.
.
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.
278
3.2.1.2
RfD
Development
(
Ideal
Situation)
.
.
.
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.
279
3.2.2
Cancer
­
Data
Suggestions
.
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.
279
3.2.2.1
Minimum
Data
.
.
.
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.
279
3.2.2.2
Ideal
Situation
.
.
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.
280
3.2.3
Exposure
­
Data
Suggestions
.
.
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.
280
3.3
Site­
Specific
Criterion
Calculation
.
.
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.
280
3.4
Organoleptic
Criteria
.
.
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.
281
3.5
Criteria
for
Chemical
Classes
.
.
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.
281
3.6
Criteria
for
Essential
Elements
.
.
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.
282
Appendix
A
Average
Fish
Consumption
.
.
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.
A­
1
Appendix
B
Evaluation
of
the
Quality
of
Data
Set(
s)
for
Use
in
Deriving
an
RfD
.
.
.
.
.
.
.
.
B­
1
Appendix
C
Derivation
of
Basic
Equations
Concerning
Bioconcentration
and
Bioaccumulation
of
Organic
Chemicals
.
.
.
.
.
.
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.
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.
C­
1
Appendix
D
Derivation
of
the
Equation
Defining
f
fd
.
.
.
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.
.
.
.
.
.
.
.
.
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.
.
D­
1
Appendix
E
Derivation
of
the
Equation
to
Predict
BAF
from
the
BSAF
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
E­
1
Appendix
F
EPA
New
Draft
Protocol
for
Determining
Octanol­
water
Partition
Coefficients
(
K
ow)
for
Compounds
with
Log
K
ow
Values
>
5
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
F­
1
Appendix
G
Amount
of
Commercial
Food
Items
Consumed
and
Intake
of
Chemical
X
from
Commercial
Food
Items
.
.
.
.
.
.
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.
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.
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.
.
.
.
.
.
G­
1
Appendix
H
Summary
of
Criteria
Documents
for
Acrylonitrile,
1,3­
Dichloropropene,
and
Hexachlorobutadiene
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
H­
1
1
1.
INTRODUCTION
1.1
Background
EPA
published
the
availability
of
ambient
water
quality
criteria
(
AWQC)
documents
for
64
toxic
pollutants
and
pollutant
categories
identified
in
Section
307(
a)
of
the
Clean
Water
Act
(
CWA
or
the
Act)
in
the
Federal
Register
on
November
28,
1980
(
45
FR
79318).
The
November
1980
Federal
Register
notice
also
summarized
the
criteria
documents
and
discussed
in
detail
the
methods
used
to
derive
the
AWQC
for
those
pollutants.
The
AWQC
for
those
64
pollutants
and
pollutant
categories
were
published
pursuant
to
Section
304(
a)(
1)
of
the
CWA:

"
The
Administrator,
.
.
.
shall
develop
and
publish,
.
.
.
,
(
and
from
time
to
time
thereafter
revise)
criteria
for
water
quality
accurately
reflecting
the
latest
scientific
knowledge
(
A)
on
the
kind
and
extent
of
all
identifiable
effects
on
health
and
welfare
including,
but
not
limited
to,
plankton,
fish,
shellfish,
wildlife,
plant
life,
shorelines,
beaches,
esthetics,
and
recreation
which
may
be
expected
from
the
presence
of
pollutants
in
any
body
of
water,
including
ground
water;
(
B)
on
the
concentration
and
dispersal
of
pollutants,
or
their
byproducts,
through
biological,
physical,
and
chemical
processes;
and
(
C)
on
the
effects
of
pollutants
on
the
biological
community
diversity,
productivity,
and
stability,
including
information
on
the
factors
affecting
rates
of
eutrophication
and
rates
of
organic
and
inorganic
sedimentation
for
varying
types
of
receiving
waters."

The
AWQC
published
in
November
1980
provided
two
essential
types
of
information:
(
1)
discussions
of
available
scientific
data
on
the
effects
of
the
pollutants
on
public
health
and
welfare,
aquatic
life,
and
recreation;
and
(
2)
quantitative
concentrations
or
qualitative
assessments
of
the
levels
of
pollutants
in
water
which,
if
not
exceeded,
will
generally
ensure
adequate
water
quality
for
a
specified
water
use.
Water
quality
criteria
developed
under
Section
304(
a)
are
based
solely
on
data
and
scientific
judgments
on
the
relationship
between
pollutant
concentrations
and
environmental
and
human
health
effects.
The
304(
a)
criteria
do
not
reflect
consideration
of
economic
impacts
or
the
technological
feasibility
of
meeting
the
chemical
concentrations
in
ambient
water.
As
discussed
below,
304(
a)
criteria
are
used
by
States
and
Tribes
to
establish
water
quality
standards,
and
ultimately
provide
a
basis
for
controlling
discharges
or
releases
of
pollutants.

The
1980
AWQC
were
derived
using
guidelines
and
methodologies
developed
by
the
Agency
for
calculating
the
impact
of
waterborne
pollutants
on
aquatic
organisms
and
on
human
health.
Those
guidelines
and
methodologies
consisted
of
systematic
procedures
for
assessing
valid
and
appropriate
data
concerning
a
pollutant's
acute
and
chronic
adverse
effects
on
aquatic
organisms,
nonhuman
mammals,
and
humans.
The
guidelines
and
methodologies
were
fully
described
in
Appendix
B
(
for
protection
of
aquatic
life
and
its
uses)
and
Appendix
C
(
for
protection
of
human
health)
of
the
November
1980
Federal
Register
notice.

The
focus
of
the
current
Federal
Register
notice,
which
this
document
accompanies,
is
the
draft
revisions
to
the
methodology
for
the
development
of
AWQC
to
protect
human
health;
a
similar
2
process
to
revise
the
methodology
for
deriving
AWQC
for
the
protection
of
aquatic
life
is
currently
underway
at
the
Agency.
Once
the
draft
revisions
are
finalized,
the
Agency
will
use
the
revised
AWQC
methodology
to
both
develop
new
AWQC
for
additional
chemicals
and
to
revise
existing
AWQC.
The
notice
includes
summaries
of
three
criteria
developed
using
the
draft
revised
methodology
which
are
also
included
in
this
document
(
Appendix
H).
The
full
criteria
documents
for
these
three
chemicals
are
available
through
the
National
Technical
Information
Service
(
NTIS)
or
on
EPA's
Internet
web
site.
These
AWQC
were
developed
to
demonstrate
the
different
risk
assessment
and
exposure
approaches
presented
in
the
Federal
Register
notice.
In
addition,
EPA
intends
to
derive
AWQC
for
the
protection
of
human
health
for
several
chemicals
of
high
priority,
including
but
not
limited
to,
PCBs,
lead,
mercury,
arsenic,
and
dioxin,
within
the
next
several
years.
EPA
anticipates
that
the
focus
of
304(
a)
criteria
development
will
be
criteria
for
bioaccumulative
chemicals
and
chemicals
considered
highest
priority
by
the
Agency.
EPA's
prioritization
process
for
developing
and
revising
AWQC
is
discussed
in
Appendix
II
of
the
Federal
Register
notice.
It
is
important
to
emphasize
that
the
Draft
AWQC
Methodology
Revisions
presented
here
are
also
intended
to
provide
States
and
Tribes
flexibility
in
setting
water
quality
standards
by
providing
scientifically
valid
options
for
developing
their
own
water
quality
criteria
that
consider
local
conditions.
States
and
Tribes
are
encouraged
to
use
the
methodology
once
it
is
finalized
to
derive
their
own
AWQC.
However,
the
draft
methodology
in
the
Federal
Register
also
defines
the
default
factors
EPA
will
use
in
evaluating
and
determining
consistency
of
State
water
quality
standards
with
the
requirements
of
the
CWA.
These
default
factors
will
also
be
used
by
the
Agency
to
calculate
304(
a)
criteria
values
when
promulgating
water
quality
standards
for
a
State
or
Tribe
under
Section
303(
c)
of
the
Act.

1.2
Need
for
Revision
of
the
1980
AWQC
National
Guidelines
l.
2.1
Scientific
Advances
Since
1980
Since
1980,
EPA
risk
assessment
practices
have
evolved
significantly,
particularly
in
the
areas
of
cancer
and
noncancer
risk
assessments,
exposure
assessments,
and
bioaccumulation.

In
cancer
risk
assessment,
there
have
been
advances
with
respect
to
the
use
of
mode
of
action
information
to
support
both
the
identification
of
carcinogens
and
the
selection
of
procedures
to
characterize
risk
at
low,
environmentally
relevant
exposure
levels.
Related
to
this
is
the
development
of
new
procedures
for
quantifying
cancer
risk
at
low
doses
to
replace
the
current
default
linear
multistage
model
(
LMS).

In
noncancer
risk
assessment,
the
Agency
is
moving
toward
the
use
of
statistical
models,
such
as
the
benchmark
dose
approach
and
categorical
regression,
to
derive
reference
doses
(
RfDs)
in
place
of
the
traditional
NOAEL­
(
no
observed
adverse
effect
level)
based
method.

In
exposure
analysis,
several
new
studies
have
addressed
water
consumption
and
fish­
tissue
consumption.
These
exposure
studies
provide
a
more
current
and
comprehensive
description
of
national,
regional,
and
special­
population
consumption
patterns;
these
are
reflected
in
the
Draft
AWQC
Methodology
Revisions
presented
in
the
Federal
Register
notice
accompanying
this
technical
3
support
document.
In
addition,
more
formalized
procedures
are
now
available
to
account
for
human
exposure
from
multiple
sources
when
setting
health
goals
that
address
only
one
exposure
source.

With
respect
to
bioaccumulation,
the
Agency
has
moved
toward
the
use
of
a
bioaccumulation
factor
(
BAF)
to
reflect
the
uptake
of
a
contaminant
by
fish
from
all
sources
rather
than
just
from
the
water
column
as
reflected
by
the
use
of
a
bioconcentration
factor
(
BCF)
in
the
1980
methodology.
The
Agency
has
also
developed
detailed
procedures
and
guidelines
for
estimating
BAF
values.

1.2.2
EPA
Risk
Assessment
Guidelines
Development
Since
1980
When
the
1980
AWQC
National
Guidelines
were
developed,
EPA
had
not
yet
developed
formal
cancer
or
noncancer
risk
assessment
guidelines.
Since
then
EPA
has
published
several
risk
assessment
guidelines
documents.
In
1996,
the
Agency
published
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
61
FR
17960),
which,
when
finalized,
will
supersede
the
carcinogenic
risk
assessment
guidelines
published
in
1986
(
51
FR
33992).
In
addition,
guidelines
for
mutagenicity
assessment
were
also
published
in
1986
(
51
FR
34006).
With
respect
to
noncancer
risk
assessment,
the
Agency
published
guidelines
in
1988
for
assessing
male
and
female
reproductive
risk
(
53
FR
24834)
and
in
1991
for
assessing
developmental
toxicity
(
56
FR
63798).
In
1991,
the
Agency
also
developed
an
external
review
draft
of
revised
risk
assessment
guidelines
for
noncancer
health
effects.

In
addition
to
these
risk
assessment
guidelines,
EPA
also
published
the
Exposure
Factors
Handbook
in
1990,
which
presents
commonly
used
Agency
exposure
assumptions
and
the
surveys
from
which
they
are
derived.
In
1992
EPA
published
the
Guidelines
for
Exposure
Assessment
(
57
FR
22888),
which
describes
general
concepts
of
exposure
assessment,
including
definitions
and
associated
units,
and
provides
guidance
on
planning
and
conducting
an
exposure
assessment.
Also,
in
the
1980'
s
the
Agency
published
the
Total
Exposure
Assessment
Methodology
(
TEAM),
which
presents
a
process
for
conducting
comprehensive
evaluation
of
human
exposures.
Finally,
the
Agency
has
recently
developed
the
Relative
Source
Contribution
Policy,
which
is
currently
undergoing
Agency
review,
for
assessing
total
human
exposure
to
a
contaminant
and
allocating
the
RfD
among
the
media
of
concern.

Additionally,
since
1980
work
groups
have
been
established
at
EPA,
specifically,
CRAVE
and
the
RfD/
RfC
Work
Group,
to
support
the
consistent
evaluation
of
the
carcinogenic
and
noncarcinogenic
effects
of
chemicals.

1.2.3
Differing
Risk
Assessment
and
Risk
Management
Approaches
for
AWQC
and
MCLGs
There
are
some
differences
that
have
arisen
in
the
risk
assessment
and
risk
management
approaches
used
by
EPA's
Office
of
Water
for
the
derivation
of
AWQC
under
the
authority
of
the
Clean
Water
Act
and
MCLGs
(
Maximum
Contaminant
Level
Goals)
under
the
Safe
Drinking
Water
Act.
Two
notable
differences
are
with
respect
to
the
treatment
of
chemicals
designated
as
Group
C
carcinogens
and
the
consideration
of
non­
water
sources
of
exposure
when
setting
an
AWQC
or
MCLG
for
a
noncarcinogen.
4
1.2.3.1
Group
C
Chemicals
Chemicals
are
typically
classified
as
Group
C
 
i.
e.,
possible
human
carcinogens
 
under
the
existing
EPA
cancer
classification
scheme
for
any
of
the
following
reasons:

C
Carcinogenicity
has
been
documented
in
only
one
test
species
and/
or
only
one
cancer
bioassay,
and
the
results
do
not
meet
the
requirements
of
"
sufficient
evidence."

C
Tumor
response
is
of
marginal
significance
due
to
inadequate
design
or
reporting.

C
Benign,
but
not
malignant,
tumors
occur
with
an
agent
showing
no
response
in
a
variety
of
short­
term
tests
for
mutagenicity.

C
There
are
responses
of
marginal
statistical
significance
in
a
tissue
known
to
have
a
high
or
variable
background
rate.

The
1986
Guidelines
for
Carcinogenic
Risk
Assessment
specifically
recognized
the
need
for
flexibility
with
respect
to
quantifying
the
risk
of
Group
C
carcinogens.
The
guidelines
noted
that
agents
judged
to
be
in
Group
C
may
generally
be
regarded
as
suitable
for
quantitative
risk
assessment,
but
that
case­
by­
case
judgments
may
be
made
in
this
regard.

The
EPA
Office
of
Water
has
historically
treated
Group
C
chemicals
differently
under
the
CWA
and
the
SDWA.
It
is
important
to
note
that
the
1980
AWQC
National
Guidelines
for
setting
AWQC
under
the
CWA
predated
EPA's
carcinogen
classification
system,
which
was
proposed
in
1984
(
49
FR
46294)
and
finalized
in
1986
(
51
FR
33992).
The
1980
AWQC
National
Guidelines
did
not
explicitly
differentiate
among
carcinogens
with
respect
to
the
weight­
of­
evidence
for
characterizing
them.
For
all
pollutants
judged
as
having
adequate
data
for
quantifying
carcinogenic
risk
 
including
those
now
classified
as
Group
C
 
AWQC
were
derived
based
on
carcinogenic
risk
data.
In
the
November
1980
Federal
Register
notice,
EPA
emphasized
that
the
AWQC
for
carcinogens
should
state
that
the
recommended
concentration
for
maximum
protection
of
human
health
is
zero.
At
the
same
time,
the
criteria
published
for
specific
carcinogens
presented
water
concentrations
for
these
pollutants
corresponding
to
individual
lifetime
cancer
risk
levels
in
the
range
of
10­
7
to
10­
5.

In
the
development
of
national
primary
drinking­
water
regulations
under
the
SDWA,
EPA
is
required
to
promulgate
a
health­
based
MCLG
for
each
contaminant.
The
Agency
policy
has
been
to
set
the
MCLG
at
zero
for
chemicals
with
strong
evidence
of
carcinogenicity
associated
with
exposure
from
water.
For
chemicals
with
limited
evidence
of
carcinogenicity,
including
many
Group
C
carcinogens,
the
MCLG
is
usually
obtained
using
the
RfD
for
that
chemical
based
on
its
noncancer
effects
with
the
application
of
an
additional
uncertainty
factor
(
UF)
of
1
to
10
to
account
for
its
possible
carcinogenicity.
If
valid
noncancer
data
for
a
Group
C
carcinogen
are
not
available
to
establish
an
RfD
but
adequate
data
are
available
to
quantify
the
cancer
risk,
then
the
MCLG
is
based
upon
a
nominal
lifetime
excess
cancer
risk
calculation
in
the
range
of
10­
5
to
10­
6
(
ranging
from
one
case
in
a
population
of
100,000
to
one
case
in
a
population
of
one
million).
Even
in
those
cases
5
where
the
RfD
approach
has
been
used
for
the
derivation
of
the
MCLG
for
a
Group
C
carcinogen,
the
drinking
water
concentrations
associated
with
excess
cancer
risks
in
the
range
of
10­
5
to
10­
6
are
also
provided
for
comparison.

It
should
also
be
noted
that
EPA's
pesticides
program
has
applied
both
of
the
previously
described
methods
for
addressing
Group
C
chemicals
in
actions
taken
under
the
Federal
Insecticide,
Fungicide,
and
Rodenticide
Act
(
FIFRA)
and
finds
both
methods
applicable
on
a
case­
by­
case
basis.
Unlike
the
drinking
water
program,
however,
the
pesticides
program
does
not
add
an
extra
UF
to
account
for
potential
carcinogenicity
when
using
the
RfD
approach.

1.2.3.2
Consideration
of
Non­
Water
Sources
of
Exposure
The
1980
AWQC
National
Guidelines
for
setting
AWQC
recommended
the
use
of
the
following
equation
to
derive
the
criterion:

C
=
[
ADI
­
(
DT
+
IN)]
÷
[
2
+
0.0065R]

(
Equation
1.2.1)

where
C
is
the
criterion
value;
ADI
is
the
acceptable
daily
intake
(
mg/
kg­
day);
DT
is
the
non­
fish
dietary
intake
(
mg/
kg­
day);
IN
is
the
inhalation
intake
(
mg/
kg­
day);
2
is
the
assumed
daily
water
intake
(
L/
day);
0.0065
is
the
assumed
daily
fish
consumption
(
kg);
and
R
is
the
bioconcentration
factor
(
L/
kg).
As
implied
by
this
equation,
the
contributions
from
non­
water
sources,
namely
air
and
non­
fish
dietary
intake,
were
to
be
subtracted
from
the
ADI,
thus
reducing
the
amount
of
the
ADI
"
available"
for
water­
related
sources
of
intake.
In
practice,
however,
when
calculating
human
health
criteria,
these
other
exposures
were
generally
not
considered
because
reliable
data
on
these
exposure
pathways
were
not
available.
Consequently,
the
AWQC
were
usually
derived
such
that
drinking
water
and
fish
ingestion
accounted
for
the
entire
ADI
(
now
called
RfD).

In
the
drinking
water
program,
a
similar
"
subtraction"
method
was
typically
used
in
the
derivation
of
MCLGs
proposed
and
promulgated
in
drinking
water
regulations
through
the
mid­
1980s.
More
recently,
the
drinking
water
program
has
consistently
used
a
"
percentage"
method
in
the
derivation
of
MCLGs
for
noncarcinogens.
In
this
approach,
the
percentage
of
total
exposure
typically
accounted
for
by
drinking
water,
referred
to
as
the
relative
source
contribution
(
RSC),
is
applied
to
the
RfD
to
determine
the
maximum
amount
of
the
RfD
"
allocated"
to
drinking
water.
In
using
this
percentage
procedure,
the
drinking
water
program
also
applies
a
ceiling
level
of
80
percent
of
the
RfD
and
a
floor
level
of
20
percent
of
the
RfD.
That
is,
the
MCLG
cannot
account
for
more
than
80
percent
of
the
RfD,
nor
less
than
20
percent
of
the
RfD.

The
drinking
water
program
usually
takes
a
conservative
approach
of
applying
an
RSC
factor
of
20
percent
to
the
RfD
when
adequate
exposure
data
do
not
exist,
assuming
that
the
major
portion
(
80
percent)
of
the
total
exposure
comes
from
other
sources,
such
as
diet.

1.2.3.3
Cancer
Risk
Ranges
1
Throughout
this
document,
the
term
"
risk
level"
regarding
a
cancer
assessment
endpoint
specifically
refers
to
an
upper
bound
estimate
of
excess
lifetime
cancer
risk.

6
In
addition
to
the
different
risk
assessment
approaches
discussed
above
for
deriving
AWQC
and
MCLGs
for
Group
C
carcinogens,
different
risk
management
approaches
have
arisen
between
the
drinking
water
and
ambient
surface
water
programs
for
using
upper
bound
lifetime
excess
risk
values
when
setting
health­
based
criteria
for
carcinogens.
1
As
indicated
previously,
the
surface
water
program
derives
AWQC
for
carcinogens
that
generally
correspond
to
lifetime
excess
cancer
risk
levels
of
10­
7
to
10­
5.
The
drinking
water
program
has
set
MCLGs
for
Group
C
carcinogens
based
on
a
slightly
less
stringent
risk
range
of
10­
6
to
10­
5,
while
MCLGs
for
chemicals
with
strong
evidence
of
carcinogenicity
are
set
at
zero.

It
is
also
important
to
note
that
under
the
drinking
water
program,
for
those
substances
having
an
MCLG
of
zero,
enforceable
Maximum
Contaminant
Levels
(
MCLs)
have
generally
been
promulgated
to
correspond
with
cancer
risk
levels
ranging
from
10­
6
to
10­
4.
Unlike
AWQC
and
MCLGs,
which
are
strictly
health­
based
criteria,
MCLs
are
developed
with
consideration
given
to
the
costs
and
technological
feasibility
of
reducing
contaminant
levels
in
water
to
meet
those
standards.

1.3
Purpose
of
this
Document
This
document
is
meant
to
add
technical
detail
to
the
principles
and
recommendations
presented
in
the
Federal
Register
notice
for
the
Proposed
Revisions
to
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
This
document
includes
detailed
examples
of
many
of
the
ideas
presented
in
the
Federal
Register
in
an
effort
to
explain
the
thought
process
behind
many
of
the
new
risk
assessment
directions
being
taken
by
the
Agency.
For
instance,
there
is
an
example
of
how
to
apply
the
new
cancer
guidelines
to
a
chemical
which
causes
cancer
but
may
not
be
genotoxic
or
mutagenic.
In
addition,
three
sample
criteria
have
been
derived
applying
the
new
cancer
guidelines;
these
are
included
in
Appendix
H
and
should
be
read
together
with
this
document
and
the
Federal
Register
notice.
On
the
noncancer
side,
an
example
is
included
on
how
to
use
the
benchmark
dose
approach.
To
supplement
the
discussion
in
the
Federal
Register
on
exposure,
many
datasets
on
fish
consumption
rates
(
both
nationally
and
regionally)
have
been
incorporated
into
this
document.
In
addition,
a
detailed
discussion
on
deriving
relative
source
contributions
is
presented.
To
support
the
understanding
of
bioaccumulation,
the
data
used
to
calculate
the
percent
lipid
by
fish
species
has
been
added.

As
noted
above,
three
sample
criteria
(
actual
307(
a)
list
toxic
chemicals)
have
been
updated
using
the
revised
methodology
to
(
1)
illustrate
the
changes
that
can
be
expected
(
numerically)
when
applying
the
revised
methodology;
and
(
2)
to
demonstrate
the
logic
behind
the
revised
methodology
and
the
judgments
required
to
fulfill
the
recommendations
of
the
guidance.
As
noted
on
the
criteria
documents
themselves,
the
Agency
is
proposing
to
develop
streamlined
criteria
with
a
focus
on
critical
toxicological
and
exposure
studies
only.
Due
to
limited
resources
and
a
need
to
update
criteria
as
quickly
as
possible,
EPA
has
decided
to
develop
more
abbreviated
versions
of
criteria
documents
with
an
emphasis
on
existing
risk
assessments
(
IRIS
or
other
EPA
health
assessment
documents)
where
available
and
still
relevant,
focusing
to
a
greater
degree
on
pertinent
exposure
and
7
AWQC
'
RfD
@
RSC
@
BW
DI
%
(
FI
@
BAF)

(
Equation
1.4.1)

AWQC
'
Pdp
SF
@
RSC
@
BW
DI
%
(
FI
@
BAF)

(
Equation
1.4.2
)
toxicological
studies
which
may
influence
the
development
of
a
criterion.
EPA
will
continue
to
conduct
comprehensive
reviews
of
the
literature
for
the
latest
studies
but
will
not
provide
a
summary
or
evaluation
of
those
studies
which
are
deemed
less
significant
in
the
criterion
development
process.

1.4
Criteria
Equations
The
following
equations
for
deriving
AWQC
include
toxicological
and
exposure
assessment
parameters
which
are
derived
from
scientific
analysis,
science
policy,
and
risk
management
decisions.
For
example,
parameters
such
as
a
field­
measured
BAF
or
a
point
of
departure
from
an
animal
study
(
in
the
form
of
a
LOAEL/
NOAEL/
LED
10)
are
scientific
values
which
are
empirically
measured,
whereas
the
decision
to
use
animal
effects
as
a
surrogate
for
human
effects
involves
judgment
on
the
part
of
the
EPA
(
and
other
agencies)
as
to
the
best
practice
to
follow
when
human
data
are
lacking.
Such
a
decision
is,
therefore,
a
matter
of
science
policy.
On
the
other
hand,
the
choice
of
default
fish
consumption
rates
for
protection
of
a
certain
percentage
(
in
this
case,
90
percent
and
95
percent
respectively)
of
the
general
population,
is
clearly
a
risk
management
decision.
In
many
cases,
the
Agency
has
selected
parameters
using
its
best
judgment
of
the
overall
protection
afforded
by
the
resulting
AWQC
when
all
parameters
are
combined.
For
a
longer
discussion
of
the
differences
between
science,
science
policy,
and
risk
management,
please
refer
to
Appendix
I,
Section
E
of
the
Federal
Register
notice.
Section
E
also
provides
further
details
with
regard
to
risk
characterization
as
related
to
this
methodology,
with
emphasis
placed
on
explaining
the
uncertainties
in
the
overall
risk
assessment.

The
generalized
equations
for
deriving
AWQC
based
on
noncancer
and
cancer
effects
are:

Noncancer
Effects
Nonlinear
Cancer
Effects
8
AWQC
'
RSD
@
BW
DI
%
(
FI
@
BAF)

(
Equation
1.4.3
)
Linear
Cancer
Effects
where:
AWQC
=
Ambient
Water
Quality
Criterion
(
mg/
L)
RfD
=
Reference
dose
for
noncancer
effects
(
mg/
kg­
day)
Pdp
=
Point
of
departure
for
nonlinear
carcinogens
(
mg/
kg­
day),
usually
a
LOAEL,
NOAEL,
or
LED
10
SF
=
Safety
Factor
for
nonlinear
carcinogens
(
unitless)
RSD
=
Risk­
specific
dose
for
linear
carcinogens
(
mg/
kg­
day)
(
Dose
associated
with
a
target
risk,
such
as
10­
5)
RSC
=
Relative
source
contribution
factor
to
account
for
non­
water
sources
of
exposure.
(
Not
used
for
linear
carcinogens.)
May
be
either
a
percentage
(
multiplied)
or
amount
subtracted,
depending
on
whether
multiple
criteria
are
relevant
to
the
chemical.
BW
=
Human
body
weight
(
proposed
default
=
70
kg
for
adults)
DI
=
Drinking
water
intake
(
proposed
default
=
2
L/
day
for
adults)
FI
=
Fish
intake
(
proposed
defaults
=
0.0178
kg/
day
for
general
population
and
sport
anglers,
and
0.039
kg/
day
for
subsistence
fishers)
BAF
=
Bioaccumulation
factor,
lipid
normalized
(
L/
kg)

1.5
Glossary/
Acronyms
List
of
Acronyms
Used
ADI
Acceptable
Daily
Intake
ASTM
American
Society
of
Testing
and
Materials
AWQC
Ambient
Water
Quality
Criteria
BAF
Bioaccumulation
Factor
BCF
Bioconcentration
Factor
BMD
Benchmark
Dose
BMR
Benchmark
Response
BSAF
Biota­
Sediment
Accumulation
Factors
BW
Body
Weight
C
18
Carbon­
18
CDC
U.
S.
Centers
for
Disease
Control
and
Prevention
CR
Consumption
Rate
CSFII
Continuing
Survey
of
Food
Intake
by
Individuals
9
CWA
Clean
Water
Act
DI
Drinking
Water
Intake
DNA
Deoxyribonucleic
Acid
DOC
Dissolved
Organic
Carbon
DT
Non­
Fish
Dietary
Intake
ED
10
Dose
Associated
with
a
10
Percent
Extra
Risk
EMAP
Environmental
Modeling
and
Assessment
Program
EPA
Environmental
Protection
Agency
FCM
Food
Chain
Multiplier
FDA
Food
and
Drug
Administration
FEL
Frank
Effect
Level
FI
Fish
Intake
FIFRA
Federal
Insecticide,
Fungicide,
and
Rodenticide
Act
FR
Federal
Register
FSTRAC
Federal
State
Toxicology
and
Risk
Analysis
Committee
GI
Gastrointestinal
GLI
Great
Lakes
Water
Quality
Initiative
IARC
International
Agency
for
Research
on
Cancer
II
Incidental
Intake
ILSI
International
Life
Sciences
Institute
IN
Inhalation
Intake
IRIS
Integration
Risk
Information
System
kg
kilogram
K
ow
Octanol­
Water
Partition
Coefficient
L
Liter
LED
10
The
Lower
95
Percent
Confidence
Limit
on
a
Dose
Associated
with
a
10
Percent
Extra
Risk
LMS
Linear
Multistage
Model
LOAEL
Lowest
Observed
Adverse
Effect
Level
LR
Lifetime
Risk
MCL
Maximum
Contaminant
Level
MCLG
Maximum
Contaminant
Level
Goal
MF
Modifying
Factor
mg
Milligrams
ml
Milliliters
MLE
Maximum
Likelihood
Estimate
MoA
Mode
of
Action
MoE
Margin
of
Exposure
MoS
Margin
of
Safety
NCHS
National
Center
for
Health
Statistics
NHANES
National
Health
and
Nutrition
Examination
Survey
NIEHS
National
Institute
of
Environmental
Health
Sciences
NOAEL
No
Observed
Adverse
Effect
Level
NOEL
No
Observed
Effect
Level
2See
also:
Notice
of
Draft
Revisions
to
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health,
in
the
Federal
Register.
Herein
after
referred
to
as
EPA,
1998).

10
NPDES
National
Pollutant
Discharge
Elimination
System
NTIS
National
Technical
Information
Service
NTR
National
Toxics
Rule
ODES
Ocean
Data
Evaluation
System
PAH
Polycyclic
Aromatic
Hydrocarbon
PBPK
Physiologically
Based
Pharmacokinetic
PCB
Polychlorinated
Biphenyl
PCS
Permits
Compliance
System
Pdp
Point
of
Departure
POC
Particulate
Organic
Carbon
q
1*
Cancer
Potency
Factors
RDA
Recommended
Daily
Allowance
RfC
Reference
Concentration
RfD
Reference
Dose
RPF
Relative
Potency
Factor
RSC
Relative
Source
Contribution
RSD
Risk
Specific
Dose
SAR
Structure­
Activity
Relationship
SAB
Science
Advisory
Board
SDWA
Safe
Drinking
Water
Act
SF
Safety
Factor
STORET
STOrage
and
RETrieval
U.
S.
Waterways
Parametric
Data
Base
TCDD­
dioxin
Tetrachlorodibenzo­
p­
dioxin
TEAM
Total
Exposure
Assessment
Methodology
TEF
Toxicity
Equivalency
Factor
TSD
Technical
Support
Document
USDA
United
States
Department
of
Agriculture
UF
Uncertainty
Factor
2.
ELEMENTS
OF
METHODOLOGY
REVISIONS
AND
ISSUES
BY
TECHNICAL
AREA
2.1
Cancer
Effects
This
section
provides
a
discussion
of
the
current
status
of
the
cancer
risk
assessment
methodology
employed
by
EPA
and
modifications
in
that
methodology,
which
are
based
on
recent
scientific
developments
and
the
Agency's
experience
in
this
field.
2
A
discussion
is
provided
of:

C
Background
information
on
the
origins
of
current
cancer
risk
assessment
methods
and
limitations
associated
with
those
methods.
3The
term
"
1980
AWQC
National
Guidelines"
refers
to
material
presented
in
Appendix
C
of
the
November
1980
FR
notice
describing
EPA's
method
for
deriving
AWQC
for
the
protection
of
human
health.

11
C
New
approaches
recommended
in
the
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
61
FR
17960,
April
23,
1996),
which
revises
the
1986
Cancer
Guidelines.

C
Modifications
in
the
AWQC
methodology
for
carcinogens
proposed
by
EPA's
Office
of
Water.

C
An
example
showing
the
application
of
the
new
methodology
to
an
organophosphonate
pesticide.

2.1.1
Background
on
EPA
Cancer
Assessment
Guidelines
2.1.1.1
1980
AWQC
National
Guidelines
When
EPA
published
the
1980
AWQC
National
Guidelines,
3
formal
Agency
guidelines
for
assessing
carcinogenic
risk
from
exposure
to
chemicals
had
not
yet
been
adopted.
The
methodology
for
assessing
carcinogenic
risk
used
by
EPA
in
the
1980
AWQC
National
Guidelines
is
based
primarily
on
the
Interim
Procedures
and
Guidelines
for
Health
Risks
and
Economic
Impact
Assessment
of
Suspected
Carcinogens
published
by
EPA
in
1976
(
41
FR
21402).
Although
the
1980
AWQC
National
Guidelines
recommended
the
use
of
both
human
epidemiological
and
animal
studies
to
identify
carcinogens,
potential
human
carcinogens
were
primarily
identified
as
those
substances
causing
a
statistically
significant
carcinogenic
response
in
animals.
It
was
also
assumed,
for
risk
assessment
purposes,
that
chemical
carcinogenesis
was
a
non­
threshold
phenomenon.

Two
types
of
data
are
used
for
quantitative
cancer
risk
estimates:

C
Lifetime
animal
studies.

C
Human
studies
where
excess
cancer
risk
is
associated
with
exposure
to
the
agent.
(
Human
data
with
sufficient
quantification
to
carry
out
risk
assessment
are
not
available
for
MoE
agents.)

The
scaling
of
doses
from
animals
to
humans
uses
a
conversion
factor
of
body
weight
raised
to
the
2/
3
power
(
BW2/
3).
The
specific
equation
for
converting
an
animal
dose
to
a
human
equivalent
dose
using
the
BW2/
3
scaling
factor
is:
12
Human
Equivalent
Dose
(
mg/
kg
&
day)

'
Animal
Dose
(
mg/
kg
&
day)
×
Animal
BW
Animal
BW2/
3
×
Human
BW2/
3
Human
BW
(
Equation
2.1.1)

This
approach
is
based
on
the
assumption
that
doses
between
species
are
related
to
surface
area.
Exposure
is
defined
in
mg
of
contaminant/(
body
weight)
2/
3/
day
(
Mantel
and
Schneiderman,
1975).
This
assumption
is
more
appropriate
at
low
concentrations,
where
sources
of
non­
linearity,
such
as
saturation
or
induction
of
enzyme
activity,
are
less
likely
to
occur.

The
estimation
of
cancer
responses
typically
uses
animal
bioassay
data
extrapolated
to
low
doses
approximating
human
exposure.
Extrapolation
is
usually
carried
out
using
the
linearized
multistage
model
(
LMS).
The
LMS
model
is
used
to
fit
the
tumor
data
with
computer
programs
(
e.
g.,
GLOBAL
86)
that
calculate
the
95th
percentile
upper
confidence
limit
on
the
linear
slope
in
the
low
dose
range.
The
slope
which
is
obtained
is
referred
to
as
the
q
1*,
or
cancer
potency.

When
animal
data
are
used
for
these
calculations,
the
body
weights
are
scaled
using
BW2/
3,
as
discussed
above.
The
q
1*
values
obtained
using
the
LMS
model
are
expressed
in
the
form
of
the
upper
bound
estimate
of
lifetime
risk
per
(
mg/
kg­
day).
These
values
are
often
used
to
estimate
the
upper
bound
of
the
lifetime
cancer
risk
for
long­
term
low
level
exposure
to
agents.

The
risk
assessments
carried
out
with
this
model
are
generally
considered
conservative,
representing
the
most
plausible
95th
percentile
upper
bound
for
risk.
The
"
true
risk"
is
considered
unlikely
to
exceed
the
risk
estimate
derived
by
this
procedure,
and
could
be
as
low
as
zero.
The
LMS
approach
was
endorsed
by
four
agencies
in
the
Interagency
Regulatory
Liaison
Group
and
was
characterized
as
less
likely
to
under­
estimate
risk
at
the
low
doses
typical
of
environmental
exposure
than
other
models
and
approaches
that
were
available.

Because
of
the
uncertainties
associated
with
dose­
response
evaluations,
EPA
believed
that
it
was
prudent
to
use
the
LMS
to
estimate
cancer
risk
for
the
AWQC.
These
uncertainties
include:

C
The
need
for
animal­
to­
human
extrapolation;

C
The
use
of
average
exposure
assumptions;
and
C
The
serious
public
health
consequences
that
could
result
if
risk
were
underestimated.

In
deriving
water
quality
criteria,
the
slope
factors
are
currently
estimated
using
the
LMS
model
under
most
circumstances.
When
human
(
epidemiological)
data
are
available,
other
approaches
have
been
used.
13
AWQC
(
mg/
L)
'
(
10
&
5)
(
70)

(
q
(
1
)(
2
%
0.0065R)

(
Equation
2.1.2)
Basic
assumptions
which
are
used
to
calculate
the
AWQC
include:

C
An
"
average"
daily
consumption
rate
of
2
liters
of
water
per
person
per
day
(
from
all
sources).

C
An
average
daily
fish
consumption
rate
of
6.5
grams
per
day.

C
An
average
body
weight
of
70
kilograms
(
kg)
(
154
pounds).

The
maximum
lifetime
cancer
risk
generated
by
waterborne
exposure
to
the
agent
is
targeted
in
the
range
of
one
in
one
hundred
thousand
to
one
in
ten
million
(
10­
5
to
10­
7).
The
formula
for
deriving
the
AWQC
in
milligrams
per
liter
(
mg/
L)
for
carcinogens
presented
in
the
1980
AWQC
National
Guidelines
is:

where:

10­
5
=
Target
cancer
risk
level;
the
1980
AWQC
National
Guidelines
recommended
risk
levels
in
the
range
of
10­
5
to
10­
7
70
=
Assumed
body
weight
of
an
adult
human
being
(
kg)

q
1*
=
Carcinogenic
potency
factor
for
humans
derived
from
LMS
model
(
mg/
kgday
1
2
=
Assumed
daily
water
consumption
of
an
adult
human
(
L/
day)

.0065
=
Assumed
daily
consumption
of
fish
(
kg)

R
=
Bioconcentration
factor
(
L/
kg)
from
water
to
food
(
e.
g.,
fish,
birds)

2.1.1.2
1986
EPA
Guidelines
for
Carcinogenic
Risk
Assessment
Since
1980,
EPA
risk
assessment
practices
have
evolved
significantly.
In
September
1986,
EPA
published
its
Cancer
Risk
Assessment
Guidelines
(
referred
to
subsequently
in
this
document
as
the
1986
Cancer
Guidelines)
in
the
Federal
Register
(
51
FR
33992,
EPA,
1986).
The
1986
Cancer
Guidelines
were
based
on
the
publication
by
the
Office
of
Science
and
Technology
Policy
(
OSTP,
1985)
that
provided
a
summary
of
the
state
of
knowledge
in
the
field
of
carcinogenesis
and
a
14
statement
of
broad
scientific
principles
of
carcinogen
risk
assessment
on
behalf
of
the
federal
government.

The
1986
Cancer
Guidelines
established
a
classification
scheme
to
describe
the
nature
of
the
cancer
data
base
and
evidence
supporting
the
carcinogenicity
of
an
agent.
This
classification
system
is
based
on
a
similar
scheme
developed
by
the
International
Agency
for
Research
on
Cancer
(
IARC).
This
scheme
is
described
briefly
below.
More
detailed
information
can
be
obtained
from
the
1986
Cancer
Guidelines
(
EPA,
1986).

The
classification
scheme
utilizes
several
alpha­
numerical
groups
for
classifying
chemicals
with
respect
to
the
evidence
available
regarding
their
carcinogenic
potential
for
humans:

Group
A:
Human
carcinogen;
sufficient
evidence
from
epidemiological
studies.

Group
B:
Probable
human
carcinogen;
sufficient
evidence
in
animals
or
limited
evidence
in
humans.

Group
C:
Possible
human
carcinogen;
limited
evidence
of
carcinogenicity
in
animals
in
the
absence
of
adequate
human
data.

Group
D:
Not
classifiable;
inadequate
data
or
no
data.

Group
E:
No
evidence
of
carcinogenicity
in
adequate
studies
in
at
least
two
species
or
in
both
epidemiological
and
animal
studies.

Within
Group
B
there
are
two
subgroups:
B1
and
B2.
According
to
the
1986
Cancer
Guidelines:
"
Usually
Group
B1
is
reserved
for
agents
for
which
there
is
limited
evidence
of
carcinogenicity
from
epidemiologic
studies.
It
is
reasonable,
for
practical
purposes,
to
regard
an
agent
for
which
there
is
`
sufficient'
evidence
of
carcinogenicity
in
animals
as
if
it
presented
a
carcinogenic
risk
to
humans.
Therefore,
agents
for
which
there
is
`
sufficient'
evidence
from
animal
studies
and
for
which
there
is
`
inadequate
evidence'
or
`
no
data'
from
epidemiologic
studies
would
usually
be
categorized
under
Group
B2."
(
USEPA,
1986)

The
1986
Cancer
Guidelines
also
include
guidance
on
the
definition
of
sufficient
or
limited
evidence.
The
weight­
of­
evidence
for
human
studies
is
evaluated
as
sufficient
when
a
causal
relationship
is
indicated
by
the
study.
When
animal
studies
are
used
in
the
evaluation
of
carcinogenicity,
sufficient
evidence
includes
agents
which
have
been
demonstrated
to
cause:

C
an
increased
incidence
of
malignant
tumors;
or
C
an
increased
incidence
of
combined
malignant
and
benign
tumors:

1)
in
multiple
species
or
strains;
or
15
2)
in
multiple
experiments
(
e.
g.,
with
different
routes
of
administration
or
using
different
dose
levels);
or
3)
to
an
unusual
degree
in
a
single
experiment
with
regard
to
high
incidence,
unusual
site
or
type
of
tumor.

C
an
early
age
at
onset.

Additional
evidence
may
be
provided
by
data
on
dose­
response,
from
short­
term
tests,
or
on
chemical
structure.

Evidence
is
considered
limited
when
a
causal
interpretation
is
credible
but
alternative
explanations
are
not
sufficiently
excluded.
Limited
evidence
indicates
that
the
data
base
for
an
agent
can
be
placed
into
one
of
three
categories:

C
Studies
involve
a
single
species,
strain,
or
experiment
and
do
not
meet
criteria
for
sufficient
evidence;

C
Experiments
are
restricted
by
inadequate
dosage
levels,
inadequate
duration
of
exposure,
inadequate
period
of
follow­
up,
poor
survival,
too
few
animals,
or
inadequate
reporting;
or
C
An
increase
in
benign
but
not
in
malignant
tumors.

Subsequent
refinements
of
this
designation
have
included
agents
which
do
not
demonstrate
positive
responses
in
a
variety
of
short­
term
tests
for
mutagenicity
and
those
with
responses
of
marginal
statistical
significance
in
a
tissue
known
to
have
a
high
or
variable
background
rate.

For
cancer
risk
quantification,
the
1986
Cancer
Guidelines
continued
the
recommended
use
of
the
linearized
multistage
model
(
LMS)
as
the
only
default
approach.
The
1986
Cancer
Guidelines
also
state
that
low­
dose
extrapolation
models
and
approaches
other
than
the
LMS
model
might
be
considered
more
appropriate
based
on
biological
grounds.
However,
no
guidance
was
given
in
choosing
other
approaches;
thus,
departures
from
the
LMS
procedure
have
been
rare
in
practice.
The
1986
Guidelines
continued
to
recommend
the
use
of
BW2/
3
as
a
dose
scaling
factor
between
species.

2.1.1.3
Scientific
Issues
Associated
with
the
Current
Cancer
Risk
Assessment
Methodology
for
the
Development
of
AWQC
In
reviewing
the
current
approach
for
the
development
of
Water
Quality
Criteria
for
Human
Health,
EPA
believes
that
there
is
not
sufficient
flexibility
in
the
1986
Cancer
Guidelines.
In
addition,
insufficient
attention
is
given
to
critical
information
including:

C
The
mode
of
action.
4They
are
referred
to
hereafter
as
the
Proposed
Cancer
Guidelines.

16
C
Relevance
of
animal
bioassay
data
to
humans.

C
Route
of
exposure.

C
The
duration
and
magnitude
of
exposure.

C
Additional
difficulties
are
associated
with
the
following:

C
Many
agents
fall
between
groups
(
e.
g.,
between
B2
and
C)
and
may
be
difficult
to
assign
to
a
specific
group;

C
Effects
may
be
greatly
modulated
by
the
conditions
of
exposure.

C
The
use
of
linear
extrapolation
may
not
be
appropriate
for
all
agents,
including
some
which
appear
to
induce
tumors
at
high
but
not
low
doses
and
which
do
not
interact
directly
with
DNA.

All
of
these
issues
have
been
considered
in
the
development
of
new
guidelines.

After
the
1992
National
Workshop
on
Revision
of
the
Methods
for
Deriving
National
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health,
EPA
requested
its
Scientific
Advisory
Board
(
SAB)
to
review
the
Workshop
report.
The
SAB
recommended
against
the
interim
adoption
of
the
1986
Guidelines
into
the
AWQC
methodology,
indicating
that
it
might
create
considerable
confusion
in
the
future,
once
new
Cancer
Guidelines
were
formally
proposed
and
implemented.
EPA
was
encouraged
by
both
groups
to
incorporate
new
approaches
into
the
AWQC
methodology.
As
recommended
by
these
two
groups,
EPA
is
proposing
revisions
to
the
cancer
risk
assessment
methodology
for
the
development
of
AWQC
by
incorporating
new
approaches
discussed
in
the
EPA
Proposed
Cancer
Risk
Assessment
Guidelines
dated
April
23,
1996
(
61
FR
17960).

2.1.2
Proposed
Revisions
to
EPA's
Carcinogen
Risk
Assessment
Guidelines
EPA
has
recently
published
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
EPA,
1996),
which
contain
proposed
revisions
to
the
1986
Cancer
Guidelines.
These
revisions
are
designed
to
ensure
that
the
Agency's
cancer
risk
assessment
methods
reflect
the
most
current
scientific
information.
4
Although
many
fundamental
aspects
of
the
1986
Cancer
Guidelines
have
been
retained,
there
are
a
number
of
key
changes
proposed,
some
of
which
address
the
specific
issues
mentioned
in
the
preceding
section.
Proposed
changes
to
the
Cancer
Guidelines
are
discussed
here
because
many
of
the
agency­
wide
principles
that
are
proposed
are
incorporated
into
the
proposed
revisions
to
the
AWQC
methodology.

The
key
changes
in
the
Proposed
Cancer
Guidelines
include:
17
a)
Hazard
assessment
promotes
the
analysis
of
all
biological
information
rather
than
just
tumor
findings.

b)
An
agent's
mode
of
action
in
causing
tumors
is
emphasized
to
reduce
the
uncertainty
in
describing
the
likelihood
of
harm
and
in
determining
the
dose­
response
approach(
es).

c)
Increased
emphasis
on
hazard
characterization
to
integrate
the
data
analysis
of
all
relevant
studies
into
a
weight­
of­
evidence
conclusion
of
hazard,
to
develop
a
working
conclusion
regarding
the
agent's
mode
of
action
in
leading
to
tumor
development,
and
to
describe
the
conditions
under
which
the
hazard
may
be
expressed
(
e.
g.,
route,
pattern,
duration
and
magnitude
of
exposure).

d)
A
weight­
of­
evidence
narrative
with
accompanying
descriptors
(
listed
in
Section
2.1.3.2
below)
replaces
the
current
alphanumeric
classification
system.
The
narrative
is
intended
for
the
risk
manager
and
lays
out
a
summary
of
the
key
evidence,
describes
the
agent's
mode
of
action,
characterizes
the
conditions
of
hazard
expression,
and
recommends
appropriate
dose­
response
approach(
es).
Significant
strengths,
weaknesses,
and
uncertainties
of
contributing
evidence
are
highlighted.
The
overall
conclusion
as
to
the
likelihood
of
human
carcinogenicity
is
given
for
each
route
of
exposure.

e)
Biologically­
based
extrapolation
models
are
the
preferred
approach
for
quantifying
risk.
It
is
anticipated,
however,
that
the
necessary
data
for
the
parameters
used
in
such
models
will
not
be
available
for
most
chemicals.
The
new
guidelines
allow
for
alternative
quantitative
methods,
including
several
default
approaches.

f)
Dose­
response
assessment
is
a
two­
step
process.
In
the
first
step,
response
data
are
modeled
in
the
range
of
observation,
and
in
the
second
step,
a
determination
of
the
point
of
departure
or
range
of
extrapolation
below
the
range
of
observation
is
made.
In
addition
to
modeling
tumor
data,
the
new
guidelines
call
for
the
use
and
modeling
of
other
kinds
of
responses
if
they
are
considered
to
be
more
informed
measures
of
carcinogenic
risk.

g)
Three
default
approaches
are
provided
 
linear,
nonlinear,
or
both.
Curve
fitting
in
the
observed
range
would
be
used
to
determine
a
point
of
departure.
A
standard
point
of
departure
is
proposed
as
the
effective
dose
corresponding
to
the
lower
95
percent
limit
on
a
dose
associated
with
10
percent
extra
risk
(
LED
10).
The
linear
default
is
a
straight
line
extrapolation
from
the
response
at
the
LED
10
to
the
origin
(
zero
dose,
zero
extra
risk).
The
nonlinear
default
begins
with
the
identified
point
of
departure
and
provides
an
MoE
analysis
rather
than
estimating
the
probability
of
effects
at
low
doses.
The
MoE
analysis
is
used
to
compare
the
point
of
departure
with
the
human
exposure
levels
of
interest
(
Pdp/
exposure).
The
key
objective
of
the
5
See
also
EPA,
1996.

18
MoE
analysis
is
to
describe
for
the
risk
manager
how
rapidly
responses
may
decline
with
dose.
Other
factors
are
also
considered
in
the
MoE
analysis
(
nature
of
the
response,
human
variation,
species
differences,
biopersistence).

h)
The
approach
used
to
calculate
oral
human
equivalent
dose
when
assessments
are
based
on
animal
bioassays
has
been
refined
to
include
a
change
in
the
default
assumption
for
interspecies
dose
scaling
(
using
body
weight
raised
to
the
3/
4
power).

With
recent
proposals
to
emphasize
mode
of
action
understanding
in
risk
assessment
and
to
model
response
data
in
the
observable
range
to
derive
points
of
departure
or
BMDs
for
both
cancer
and
noncancer
endpoints,
EPA
health
risk
assessment
practices
are
beginning
to
come
together.
The
modeling
of
observed
response
data
to
identify
points
of
departure
in
a
standard
way
will
help
to
harmonize
cancer
and
noncancer
dose­
response
approaches
and
permit
comparisons
of
cancer
and
noncancer
risk
estimates.

It
is
important
to
note
that
the
cancer
risk
assessment
process
outlined
in
the
Proposed
Cancer
Guidelines
is
not
limited
just
to
the
quantitative
aspects.
Extensive
guidance
is
provided
in
the
Proposed
Cancer
Guidelines
regarding
hazard
assessment
and
risk
characterization
(
EPA,
1996).

The
Proposed
Cancer
Guidelines
should
be
consulted
for
detailed
information
regarding
the
new
methodology
and
the
scientific
basis
for
the
proposed
changes.
All
of
the
above
listed
changes,
as
well
as
other
methodological
issues
discussed
in
the
Proposed
Cancer
Guidelines,
have
a
direct
bearing
on
the
proposed
methods
for
deriving
AWQC
discussed
in
this
TSD.
Rather
than
including
a
summary
in
this
document
that
would
provide
only
limited
detail,
the
reader
is
urged
to
review
the
guidelines,
as
provided
in
the
Federal
Register
notice
in
their
original
form
(
USEPA,
1996).

2.1.3
Revised
Carcinogen
Risk
Assessment
Methodology
for
Deriving
AWQC
The
revised
methodology
for
deriving
numerical
AWQC
for
carcinogens
is
consistent
with
the
principles
included
in
the
Proposed
Cancer
Guidelines.
This
discussion
of
the
Draft
AWQC
Methodology
Revisions
for
carcinogens
focuses
primarily
on
the
quantitative
aspects
of
deriving
numerical
AWQC
values.
However,
the
Proposed
Cancer
Guidelines
emphasize
the
importance
of
qualitative
information
as
critical
to
the
cancer
risk
evaluation
process.
Consequently,
the
proposed
guidelines
also
recommend
that
a
numerical
AWQC
value
derived
for
a
carcinogen
is
to
be
accompanied
by
appropriate
hazard
assessment
and
risk
characterization
information.
5
This
section
contains
a
discussion
of
the
weight­
of­
evidence
narrative,
describing
information
relevant
to
a
cancer
risk
evaluation.
This
is
followed
by
a
discussion
of
the
quantitative
aspects
of
deriving
numerical
AWQC
values
for
carcinogens.
It
is
assumed
that
data
from
an
appropriately
conducted
animal
bioassay
provide
the
underlying
basis
for
deriving
the
AWQC
value.
The
discussion
focuses
on
the
following
topics:
19
C
Dose
estimation.

C
Characterizing
dose­
response
relationships
in
the
range
of
observation
and
at
low,
environmentally
relevant
doses.

C
Calculating
the
AWQC
value.

C
Risk
characterization.

C
Use
of
Toxicity
Equivalent
Factors
(
TEF)
and
Relative
Potency
Estimates.

The
first
three
listed
topics
encompass
the
quantitative
aspects
of
deriving
AWQC
for
carcinogens.

2.1.3.1
Weight­
of­
Evidence
Narrative
As
stated
in
the
EPA
Proposed
Cancer
Guidelines,
the
new
method
for
cancer
risk
assessment
includes
a
weight­
of­
evidence
narrative
which
is
based
on
an
overall
weight­
of­
evidence
of
biological,
chemical,
and
physical
considerations.
The
weight­
of­
evidence
narrative
lays
out
key
evidence
and
includes
a
discussion
of
tumor
data,
information
on
the
mode
of
action,
and
its
implications
for
human
hazard
and
dose­
response
evaluation.
Emphasis
will
also
be
focused
on
the
route
and
level
of
exposure
and
relevance
to
humans.
In
addition,
a
discussion
of
the
strengths
and
weaknesses
of
the
data
base
is
included.
The
hazard
assessment
emphasizes
analysis
of
all
biological
information
rather
than
just
tumor
findings.

The
weight­
of­
evidence
narrative
is
written
for
the
risk
manager,
and
thus
explains
in
nontechnical
language
the
key
data
and
conclusions,
as
well
as
the
conditions
for
hazard
expression.
Conclusions
about
potential
human
carcinogenicity
are
presented
by
route
of
exposure.
Contained
within
this
narrative
are
simple
likelihood
descriptors
that
essentially
distinguish
whether
there
is
enough
evidence
to
make
a
projection
about
human
hazard
(
i.
e.,
known
human
carcinogen,
should
be
treated
as
if
known,
likely
to
be
a
human
carcinogen,
or
not
likely
to
be
a
human
carcinogen)
or
whether
there
is
insufficient
evidence
to
make
a
projection
(
i.
e.,
the
cancer
potential
cannot
be
determined
because
evidence
is
lacking,
conflicting,
inadequate,
or
because
there
is
some
evidence
but
it
is
not
sufficient
to
make
a
projection
to
humans).
Because
one
encounters
a
variety
of
data
sets
on
agents,
these
descriptors
are
not
meant
to
stand
alone;
rather,
the
context
of
the
weight­
ofevidence
narrative
is
intended
to
provide
a
transparent
explanation
of
the
biological
evidence
and
how
the
conclusions
were
derived.
Moreover,
these
descriptors
should
not
be
viewed
as
classification
categories
(
like
the
alphameric
system),
which
often
obscure
key
scientific
differences
among
chemicals.
The
new
weight­
of­
evidence
narrative
also
presents
conclusions
about
how
the
agent
induces
tumors
and
the
relevance
of
the
mode
of
action
to
humans,
and
recommends
a
dose­
response
approach
based
on
the
mode­
of­
action
understanding.

2.1.3.2
Dose
Estimation
(
by
the
Oral
Route)

Determining
the
Human
Equivalent
Dose
20
An
important
objective
in
the
dose­
response
assessment
is
to
use
a
measure
of
internal
or
delivered
dose
at
the
target
site
when
sufficient
data
are
available.
This
is
particularly
important
in
those
cases
where
the
carcinogenic
response
information
is
being
extrapolated
to
humans
from
animal
studies.
Generally,
the
measure
of
dose
provided
in
the
underlying
human
studies
and
animal
bioassays
is
the
applied
dose,
typically
given
in
terms
of
the
unit
mass
per
unit
body
weight
per
unit
of
time,
(
e.
g.,
mg/
kg­
day).
When
animal
bioassay
data
are
used,
it
is
necessary
to
make
adjustments
to
the
applied
oral
dose
values
to
account
for
differences
in
pharmacokinetics
between
animals
and
humans
that
affect
the
relationship
between
applied
dose
and
delivered
dose
at
the
target
organ.

In
the
estimation
of
a
human
equivalent
dose,
the
Proposed
Cancer
Guidelines
recommend
that
when
toxicokinetic
data
are
available,
they
are
used
to
convert
the
doses
used
in
animal
studies
to
equivalent
human
doses.
However,
in
most
cases,
there
are
insufficient
data
available
to
compare
dose
between
species.
In
these
cases,
the
estimate
of
a
human
equivalent
dose
is
based
on
science
policy
default
assumptions.
In
the
past,
body
weight
raised
to
the
2/
3
power
was
used
(
as
discussed
in
Section
2.1.1.1).
To
derive
an
equivalent
human
dose
from
animal
data,
the
new
default
procedure
is
to
scale
daily
applied
oral
doses
experienced
over
a
lifetime
in
proportion
to
body
weight
raised
to
the
3/
4
power.

The
3/
4
adjustment
factor
is
used
because
metabolic
rates,
as
well
as
most
rates
of
physiological
processes
that
determine
the
disposition
of
a
dose,
scale
this
way.
Thus,
the
rationale
for
this
factor
rests
on
the
empirical
observation
that
rates
of
physiological
processes
consistently
tend
to
maintain
proportionality
with
body
weight
raised
to
3/
4
power.
Based
on
this
assumption,
the
"
human
equivalent"
of
the
applied
oral
dose
in
an
animal
study
is
obtained
from
the
following
algorithm
where
the
doses
are
in
mg/
kg­
day:

Human
Equivalent
Dose
'
Animal
Dose
x
Animal
BW
Animal
BW
3/
4
x
Human
BW
3/
4
Human
BW
(
Equation
2.1.3)

This
equation
can
be
simplified
to:

Human
Equivalent
Dose
=
(
Animal
Dose)[(
Animal
BW)/(
Human
BW)]
1/
4
(
Equation
2.1.4)

This
procedure
does
not
calculate
the
delivered
dose,
but
rather
adjusts
the
applied
dose
(
e.
g.,
exposure)
to
account
for
interspecies
differences
in
delivered
doses.
21
This
change
in
approach
yields
an
estimate
of
delivered
dose
which
is
larger
than
that
obtained
using
body
weight
raised
to
the
2/
3
power
in
cases
where
the
animals
used
in
the
study
have
a
lower
body
weight
than
humans
(
e.
g.
rodents,
dogs,
rabbits,
and
most
animals
used
for
toxicological
testing).
Since
a
larger
dose
is
estimated
using
this
approach,
the
cancer
potency
which
is
estimated
using
the
3/
4
scaling
approach
is
slightly
lower
than
the
potency
which
is
calculated
using
body
weight
raised
to
the
2/
3
power.

A
more
extensive
discussion
of
the
rationale
and
data
supporting
the
Agency's
change
in
scaling
factors
from
2/
3
to
3/
4
is
in
USEPA
(
1992b)
and
the
Proposed
Cancer
Guidelines.

Dose
Adjustments
for
Less­
than­
Lifetime
Exposure
Periods
In
the
1980
AWQC
National
Guidelines,
two
other
dose­
related
adjustments
were
discussed.
The
first
addressed
situations
where
the
experimental
dosing
period
(
l
e)
is
less
than
the
duration
of
the
experiment
(
L
e).
In
these
cases,
the
average
daily
dose
is
adjusted
downward
by
multiplying
by
the
ratio
(
l
e/
L
e)
to
obtain
an
equivalent
average
daily
dose
for
the
full
experimental
period.
This
adjustment
would
also
be
used
in
situations
where
animals
are
dosed
fewer
than
seven
days
per
week.
If,
for
example,
"
daily"
dosing
is
done
only
five
days
each
week,
the
lifetime
daily
dose
would
be
calculated
as
5/
7
of
the
actual
dose
given
on
each
of
the
five
days.

The
second
dose
adjustment
addresses
situations
where
the
experimental
duration
(
L
e)
is
substantially
less
than
the
natural
lifespan
(
L)
of
the
test
animal.
For
example,
for
mice
and
rats
the
natural
lifespans
are
defined
as
90
weeks
and
104
weeks
respectively.
If
the
study
duration
is
less
than
78
weeks
for
mice,
or
less
than
90
weeks
for
rats,
applied
doses
are
adjusted
by
dividing
by
a
factor
of
(
L/
L
e)
3.
(
Alternatively,
the
cancer
potency
factor
obtained
from
the
study
could
be
adjusted
upward
by
multiplying
by
the
factor
of
(
L/
L
e)
3.)

This
adjustment
is
considered
necessary
because
a
shortened
experimental
duration
does
not
permit
the
full
expression
of
cancer
incidence
that
would
be
expressed
during
a
lifetime
study.
In
addition,
most
carcinogenic
responses
are
manifest
in
humans
and
animals
at
higher
rates
later
in
life.
Age­
specific
rates
of
cancer
increase
as
a
constant
function
of
the
background
cancer
rate
(
Anderson,
1983)
by
the
2nd
or
higher
power
of
age
(
Doll,
1971).
In
the
adjustment
recommended
here,
it
is
assumed
that
the
cumulative
tumor
rate
will
increase
by
at
least
the
3rd
power
of
age.
It
is
important
to
note
that
although
both
dose
adjustments
discussed
in
this
section
were
included
in
the
1980
AWQC
National
Guidelines,
the
second
adjustment
has
not
been
commonly
used
in
practice.

2.1.3.3
Dose­
Response
Analysis
Dose­
response
analysis
addresses
the
relationship
of
dose
to
the
degree
of
response
observed
in
an
animal
or
human
study.
Extrapolations
are
necessary
when
environmental
exposures
are
outside
of
the
range
of
study
observations.
Past
observations
of
response
have
focused
on
the
observation
of
tumors.
The
Proposed
Cancer
Guidelines
suggest
that
responses
may
include
tumors
or
other
effects
related
to
carcinogenicity.
Non­
tumor
effects
may
include
changes
in
DNA,
chromosomes,
or
other
key
macromolecules;
effects
on
growth
signal
transduction,
induction
of
physiological
or
6An
example
of
a
biologically­
based
model
is
applied
in
the
case
of
diesel
exhaust
emission
(
See
Chen,
CW.
and
G.
Oberdorster.
1996.
Selection
of
Models
for
Assessing
Dose­
Response
Relationship
for
Particle­
Induced
Lung
Cancer.
Inhalation
Toxicol.,
8:
259­
278).

22
hormonal
changes,
effects
on
cell
proliferation,
or
other
effects
that
play
a
role
in
the
carcinogenic
process.
Non­
tumor
effects
are
referred
to
as
"
precursor
data"
in
the
following
discussion.

Specific
guidance
regarding
the
use
of
animal
data,
presentation
of
study
results,
and
selection
of
the
optimal
data
for
use
in
a
dose­
response
analysis
is
discussed
in
detail
in
the
Proposed
Cancer
Guidelines.
It
includes
recommendations
that
multiple
data
arrays
be
presented
including:
combined
data
from
different
experiments,
ranges
of
results
from
more
than
one
data
set,
tumors
generated
by
different
modes
of
action,
and
combined
tumors
at
more
than
one
site
within
a
single
experiment.

Characterizing
Dose­
Response
Relationships
in
the
Range
of
Observation
The
first
quantitative
component
in
the
derivation
of
AWQC
for
carcinogens
is
the
doseresponse
assessment
in
the
range
of
observation.
Two
options
are
available
for
the
assessment
in
the
observed
range:

C
Development
of
a
biologically­
based
or
case­
specific
model.

C
Curve­
fitting
of
the
tumor
or
precursor
data.

A
biologically­
based
model
is
one
whose
parameters
are
calculated
independently
of
curvefitting
of
tumor
data.
6
If
data
on
the
agent
are
sufficient
to
support
the
parameters
of
a
biologicallybased
or
case
specific
model
and
the
purpose
of
the
assessment
is
to
justify
investing
resources
supporting
its
use,
this
type
of
model
is
the
first
choice
for
both
the
observed
tumor
and
related
response
data
and
for
extrapolation
below
the
range
of
observed
data
in
either
animal
or
human
studies.
Extensive
data
are
required
to
both
build
the
model
and
to
estimate
how
well
it
conforms
with
observed
tumor
development
specific
to
the
agent.
Case­
specific
models
are
based
on
general
concepts
of
mode
of
action
and
data
on
the
agent.
The
Proposed
Cancer
Guidelines
contain
more
detail
on
these
approaches.
There
is
not
sufficient
data
to
utilize
these
types
of
models
for
most
agents.

In
the
absence
of
adequate
data
to
generate
a
biologically­
based
model
or
case­
specific
model,
dose­
response
relationships
in
the
observed
range
can
be
addressed
through
curve­
fitting
procedures
for
tumor
or
precursor
data.
The
models
should
be
appropriate
to
the
type
of
response
data
in
the
observed
range.

The
Proposed
Cancer
Guidelines
recommend
employing
the
lower
95
percent
confidence
limit
on
a
dose
associated
with
an
estimated
10
percent
extra
risk
of
tumor
or
relevant
nontumor
response
(
LED
10).
The
LED
10
(
the
lower
95
percent
confidence
limit
on
a
dose
associated
with
10
percent
7
Use
of
the
LED
10
as
the
point
of
departure
is
recommended
with
this
methodology,
as
it
is
with
the
Proposed
Cancer
Guidelines.
Public
comments
were
requested
on
the
use
of
the
LED
10,
ED
10,
or
other
points.
EPA
is
currently
evaluating
these
comments,
and
any
changes
in
the
Cancer
Guidelines
will
be
reflected
in
the
final
AWQC
methodology.

23
extra
risk)
is
a
standard
point
of
departure,
7
adopted
as
a
matter
of
science
policy
to
remain
as
consistent
and
comparable
from
case
to
case
as
possible.
It
is
also
a
comparison
point
for
noncancer
endpoints.

The
rationale
supporting
its
use
is
that
a
10
percent
response
is
at
or
just
below
the
limit
of
sensitivity
for
discerning
a
significant
difference
in
most
long­
term
rodent
studies.
The
lower
confidence
limit
on
dose
is
used
to
appropriately
account
for
experimental
uncertainty
(
Barnes
et
al.,
1995);
it
does
not
provide
information
about
human
variability.
Uncertainties
include
such
factors
as
number
and
spacing
of
doses,
sample
sizes,
the
precision
and
accuracy
of
dose
measurements,
the
accuracy
of
pathological
findings,
and
the
selection
of
low
dose
extrapolation
(
discussed
below).

For
some
data
sets,
a
choice
of
the
point
of
departure
other
than
the
LED
10
may
be
appropriate.
The
objective
is
to
determine
the
lowest
reliable
part
of
the
dose­
response
curve
for
the
beginning
of
the
second
step
of
the
dose­
response
assessment­­
determining
the
extrapolation
range.
Therefore,
if
the
observed
response
is
below
the
LED
10,
then
a
lower
data
point
may
be
a
better
choice.
Moreover,
some
forms
of
data
may
not
be
amenable
to
curve­
fitting
estimation,
but
can
be
evaluated
using
an
estimation
of
a
LOAEL
or
NOAEL,
e.
g.,
certain
continuous
data.

Analysis
of
human
studies
in
the
observed
range
is
designed
on
a
case
by
case
basis
depending
on
the
type
of
study
and
how
dose
and
response
are
measured
in
the
study.
In
some
cases
the
analysis
may
incorporate
consideration
of
an
agent's
interactive
effects
with
other
agents.
The
use
of
population
risk
rather
than
individual
risk
may
be
appropriate
in
some
cases,
depending
on
the
nature
of
the
data
set
(
e.
g.,
human
epidemiological
data).

Extrapolation
to
Low,
Environmentally
Relevant
Doses
In
most
cases,
the
derivation
of
an
AWQC
will
require
an
evaluation
of
carcinogenic
risk
at
environmental
exposure
levels
substantially
lower
than
those
used
in
the
underlying
bioassay.
Various
approaches
are
used
to
extrapolate
risk
outside
the
range
of
observed
experimental
data.
In
the
Proposed
Cancer
Guidelines,
the
choice
of
extrapolation
method
is
largely
dependent
on
the
mode
of
action.
The
Proposed
Guidelines
also
indicate
that
the
choice
of
extrapolation
procedure
follows
the
conclusions
developed
in
the
hazard
assessment
about
the
agent's
carcinogenic
mode
of
action,
and
it
is
this
mode
of
action
understanding
that
guides
the
selection
of
the
most
appropriate
doseresponse
extrapolation
procedure.
It
should
be
noted
that
the
term
"
mode
of
action"
is
deliberately
chosen
in
the
new
guidelines
in
lieu
of
the
term
"
mechanism"
to
indicate
the
use
of
knowledge
that
is
sufficient
to
draw
a
reasonable
working
conclusion
without
having
to
know
the
processes
in
detail
as
the
term
mechanism
might
imply.
The
proposed
guidelines
preferred
the
choice
of
a
biologicallybased
model,
if
the
parameters
of
such
models
can
be
calculated
from
data
sources
independent
of
tumor
data.
It
is
anticipated
that
the
necessary
data
for
such
parameters
will
not
be
available
for
most
24
y
'
mx
(
Equation
2.1.5)
chemicals.
Thus,
the
new
guidelines
allow
for
several
default
extrapolation
approaches
(
low­
dose
linear,
nonlinear,
or
both).

Biologically­
Based
Modeling
Approaches.
If
a
biologically­
based
or
case­
specific
model
has
been
used
to
characterize
the
dose­
response
relationships
in
the
observed
range,
and
the
confidence
in
the
model
is
high,
it
may
be
used
to
extrapolate
the
dose­
response
relationship
outside
the
observed
data
range.
Although
biologically­
based
and
case­
specific
approaches
are
appropriate
both
for
characterizing
observed
dose­
response
relationships
and
extrapolating
to
environmentally
relevant
doses,
it
is
not
expected
that
adequate
data
will
be
available
to
support
such
approaches
for
most
substances.
In
the
absence
of
such
data,
the
default
linear
approach,
the
non­
linear
(
margin
of
exposure)
approach,
or
both
linear
and
non­
linear
approaches
are
used.

Default
Linear
Extrapolation
Approach.
The
default
linear
approach
proposed
here
is
essentially
a
replacement
of
the
linearized
multistage
(
LMS)
approach
that
has
served
as
the
default
approach
for
EPA
cancer
risk
assessments.
This
new
approach
is
used
in
the
derivation
of
AWQC
for:

C
Agents
with
a
mode
of
action
of
gene
mutation
resulting
from
reactivity
with
DNA;

C
Agents,
with
evidence
that
supports
a
mode
of
action
other
than
DNA
reactivity,
that
are
better
supported
by
the
assumption
of
low
dose
linearity;
and
C
Carcinogenic
agents
lacking
information
on
the
mode
of
action.

As
this
suggests,
the
linear
default
is
used
for
carcinogens
which
lack
information
supporting
the
use
of
a
non­
linear
approach.
The
proposed
default
linear
approach
is
considered
generally
health­
conservative.
Evidence
of
effects
on
cell
growth
control
via
direct
interaction
with
DNA
constitutes
an
expectation
of
a
linear
dose­
response
relationship
in
the
low
dose
range,
unless
there
is
information
to
the
contrary.

The
procedures
for
implementing
the
default
linear
approach
begin
with
the
estimation
of
a
point
of
departure
(
LED
10).
The
point
of
departure
value
incorporates
the
interspecies
conversion
to
the
human
equivalent
dose
and
the
other
adjustments
for
less­
than­
lifetime
experimental
duration.
In
most
cases,
the
extrapolation
for
estimating
response
rates
at
low,
environmentally
relevant
exposures
is
accomplished
by
drawing
a
straight
line
between
the
response
at
the
"
point
of
departure"
(
LED
10)
and
the
origin
(
i.
e.,
zero
dose,
zero
response).
This
is
mathematically
represented
as:

where:

y
=
Response
or
incidence
25
m
'
y
2
&
y
1
x
2
&
x
1
(
Equation
2.1.7)

y
2
x
2
(
Equation
2.1.8)
m
=
Slope
of
the
line
(
cancer
potency
factor)
x
=
Dose
The
slope
of
the
line,
"
m"
(
i.
e.,
ª
y/
ª
x,
the
estimated
cancer
potency
factor
at
low
doses),
is
computed
as:

m
'
0.10
LED
10
(
Equation
2.1.6)

When
an
LED
10
isn't
used,
the
standard
equation
for
the
slope
of
a
line
may
be
used:

where:

y
2
=
Response
at
the
point
of
departure
y
1
=
Response
at
the
origin
(
zero)
x
2
=
Dose
at
the
point
of
departure
x
1
=
Dose
at
the
origin
(
zero)

Due
to
the
use
of
the
origin
for
y
1
and
x
1,
the
equation
simplifies
to:

The
risk­
specific
dose
(
RSD)
is
then
calculated
for
a
specific
incremental
targeted
lifetime
cancer
risk
(
in
the
range
of
10­
4
to
10­
6)
as:
8
In
1980,
the
target
lifetime
cancer
risk
range
was
set
at
10­
7
to
10­
5.
However,
both
the
expert
panel
for
the
AWQC
workshop
(
1992)
and
SAB
recommended
that
EPA
change
the
risk
range
to
10­
6
to
10­
4,
to
be
consistent
with
drinking
water.

26
RSD
'
Target
Incremental
Cancer
Risk
m
(
Equation
2.1.9)

where:

RSD
=
Risk­
specific
dose
(
mg/
kg­
day)
Target
Risk8
=
Value
typically
in
the
range
of
10­
4
to
10­
6
m
=
Cancer
potency
factor
(
mg/
kg­
day)­
1
The
use
of
the
RSD
to
compute
the
AWQC
is
described
below
in
the
section
titled
"
AWQC
Calculation."

Default
Non­
Linear
Approach.
As
discussed
in
the
Proposed
Cancer
Guidelines,
the
use
of
a
non­
linear
approach
for
risk
assessment
is
recommended
where
there
is
no
evidence
for
linearity
and
there
is
sufficient
evidence
to
support
an
assumption
of
non­
linearity.
As
noted
above,
this
would
NOT
be
used
for
agents
with:

C
A
mode
of
action
of
gene
mutation
resulting
from
reactivity
with
DNA;

C
Evidence
that
supports
another
mode
of
action
that
is
anticipated
to
be
linear;
or
C
Carcinogenic
agents
lacking
information
on
the
mode
of
action.

A
definitive
determination
regarding
an
agent's
mutagenicity
may
not
be
possible,
since
many
agents
yield
mixed
results
in
mutagenicity
assays.
Mode
of
action
data
are
used
in
a
case
study
provided
in
Section
2.1.4
of
this
document.
The
chemical
discussed
is
not
mutagenic
but
causes
stone
formation
in
male
rat
bladders,
leading
to
tumor
formation
at
high
doses.
Stone
and
subsequent
tumor
formation
are
not
expected
to
occur
at
doses
lower
than
those
that
induced
the
physiologic
change
that
leads
to
stones,
based
on
the
mode
of
action
data.

The
non­
linear
approach
is
indicated
for
agents
having
a
mode
of
action
that
may
lead
to
a
dose­
response
relationship
that
is
non­
linear,
with
response
falling
much
more
quickly
than
linearly
with
dose,
or
those
being
most
influenced
by
individual
differences
in
sensitivity.
Alternatively,
the
mode
of
action
may
theoretically
have
a
threshold
(
e.
g.,
the
carcinogenic
response
may
be
a
secondary
effect
of
toxicity
or
of
an
induced
physiological
change
(
that
is
itself
a
threshold
phenomenon).
EPA
does
not
generally
try
to
distinguish
between
modes
of
action
that
might
imply
27
a
"
true
threshold"
from
others
with
a
non­
linear
dose­
response
relationship,
because
there
is
usually
not
sufficient
information
to
determine
empirically.

The
Proposed
Cancer
Guidelines
recommend
that
non­
linear
probability
functions
NOT
be
fitted
to
the
response
data
to
extrapolate
quantitative
low­
dose
risk
estimates.
Different
models
can
lead
to
a
very
wide
range
of
results.
Also,
there
is
currently
no
basis
to
choose
among
the
different
models.
If
there
is
sufficient
information
to
choose
a
model,
a
biologically­
based
or
case­
specific
model
should
be
used.

The
Proposed
Cancer
Guidelines
recommend
use
of
a
margin
of
exposure
(
MoE)
approach
to
evaluate
concern
for
various
levels
of
exposure.
This
entails
the
comparison
of
a
minimum
effect
dose
level
such
as
the
LED
10,
NOAEL,
or
LOAEL
environmental
exposures
of
interest.
In
the
context
of
deriving
AWQC,
the
environmentally
relevant
exposures
are
targets
rather
than
actual
exposures.
A
Safety
Factor
(
SF)
is
then
applied
to
account
for
various
types
of
uncertainty.
This
approach
is
similar
to
the
benchmark
dose
approach
described
in
the
noncancer
section
of
this
TSD.

The
MoE
approach
used
here
is
similar
to
the
analysis
carried
out
by
EPA
to
accompany
estimates
of
RfD
or
concentrations
for
noncancer
endpoints.
However,
a
threshold
of
carcinogenic
response
is
not
necessarily
assumed.
If
the
evidence
for
an
agent
indicates
a
threshold,
(
e.
g.,
when
carcinogenicity
is
secondary
to
another
toxicity
that
has
a
threshold)
the
MoE
analysis
is
similar
to
what
has
been
done
for
a
noncancer
endpoint,
and
an
RfD
for
that
toxicity
may
also
be
estimated
and
considered
in
the
cancer
assessment.

To
support
the
use
of
the
MoE
approach,
information
is
provided
in
the
risk
assessment
about
the
current
understanding
of
the
phenomena
that
may
be
occurring
as
dose
(
exposure)
decreases
substantially
below
the
observed
data.
This
provides
information
about
the
risk
reduction
that
is
expected
to
accompany
a
lowering
of
exposure.
Information
regarding
the
various
factors
which
influence
the
selection
of
a
SF
are
also
included
in
the
discussion
below.

There
are
two
main
steps
in
the
MoE
approach:

C
The
first
step
is
the
selection
of
a
point
of
departure
(
Pdp)
that
is
a
"
minimum
effect
dose
level."
As
noted
above,
the
Pdp
may
be
the
LED
10
for
tumor
incidence,
or
in
some
cases,
it
may
also
be
appropriate
to
use
a
NOAEL
or
LOAEL
value
from
a
precursor,
such
as
a
response
that
is
a
precursor
to
tumors.
When
animal
data
are
used,
the
Pdp
is
a
human
equivalent
dose
or
concentration
arrived
at
by
interspecies
dose
adjustment
(
as
discussed
above)
or
toxicokinetic
analysis.

C
The
second
step
in
using
MoE
analysis
to
establish
an
AWQC
is
to
conduct
an
analysis
to
derive
a
SF
to
apply
to
the
Pdp.
(
This
is
supported
by
analysis
in
the
MoE
discussion
provided
in
the
risk
assessment).
The
following
issues
are
to
be
considered
when
establishing
the
SF
for
the
derivation
of
AWQC
using
the
MoE
approach
(
others
may
be
found
appropriate
in
specific
cases):
28
­
The
slope
of
the
observed
dose­
response
relationship
at
the
point
of
departure
and
its
uncertainties
and
implications
for
risk
reduction
associated
with
exposure
reduction
(
e.
g.,
a
steep
slope
implies
an
apparent
greater
reduction
in
risk
as
exposure
decreases
that
may
support
a
smaller
margin).

­
Variation
in
sensitivity
to
the
phenomenon
involved,
among
members
of
the
human
population.

­
Variation
in
sensitivity
between
humans
and
the
animal
study
population.

­
The
nature
of
the
response
used
for
the
dose­
response
assessment,
for
instance,
a
precursor
effect,
or
tumor
response.
The
latter
may
support
a
greater
margin.

­
Persistence
of
the
agent
in
the
body.
Greater
persistence
argues
for
a
greater
MoE.
This
persistence
issue
is
particularly
relevant
when
precursor
data
from
less
than
lifetime
studies
are
the
response
data
being
assessed.

As
a
default
assumption
for
two
of
the
factors
listed
above,
the
Proposed
Cancer
Guidelines
recommend
that
a
factor
of
no
less
than
10­
fold
each
be
employed
to
account
for
human
variability
and
for
interspecies
differences
in
sensitivity
when
humans
may
be
more
sensitive
than
animals.
When
data
indicate
that
humans
are
less
sensitive
than
animals,
a
default
factor
of
no
smaller
than
1/
10
may
be
employed
to
account
for
this.
If
information
about
human
variability
or
interspecies
differences
is
available,
it
is
used.

The
size
of
the
overall
SF
is
a
matter
of
policy.
The
rationale
for
selection
of
the
SF
should
be
fully
explained
and
related
to
the
toxicity
and
other
data
presented
in
the
weight­
of­
evidence
narrative
discussed
previously.

The
SF
is
used
to
modify
the
Pdp
in
the
final
equation.
This
is
shown
below
in
the
Section
2.1.3.4
on
AWQC
calculation.

Both
Linear
and
Non­
Linear
Approaches.
In
some
cases
both
linear
and
non­
linear
procedures
may
be
used.
When
data
indicate
that
there
may
be
more
than
one
operant
mode
of
action
for
cancer
induction
at
different
tumor
sites,
an
appropriate
procedure
is
used
for
each
site.
The
use
of
both
the
default
linear
approach
and
the
non­
linear
approach
may
be
appropriate
to
discuss
implications
of
complex
dose­
response
relationships,
and
may
be
decoupling
analysis
of
regions
of
the
overall
dose
response
that
reflect
differing
modes
of
action.

2.1.3.4
AWQC
Calculation
Linear
Approach
29
AWQC
'
Pdp
SF
x
BW
DI
%
(
FI
x
BAF)
x
RSC%

(
Equation
2.1.11)
The
following
equation
is
used
for
the
calculation
of
the
AWQC
for
carcinogens
where
a
RSD
is
obtained
from
the
default
linear
approach:

AWQC
'
RSD
x
BW
DI
%
(
FI
x
BAF)

(
Equation
2.1.10)

The
AWQC
calculation
shown
above
is
appropriate
for
water
bodies
that
are
used
as
sources
of
drinking
water
(
and
for
other
uses).
If
the
water
bodies
are
not
used
as
drinking
water
sources
the
approach
is
modified.
The
drinking
water
value
(
DI
in
the
equation
shown
above)
is
substituted
with
an
incidental
ingestion
value
(
II)
of
0.01
L/
day.
The
incidental
intake
is
assumed
to
occur
from
swimming
and
other
activities.
The
fish
intake
value
is
assumed
to
remain
the
same.

Non­
Linear
Approach
In
those
cases
where
the
non­
linear,
MoE
approach
is
used,
a
similar
equation
is
used
to
calculate
the
AWQC:

where:

AWQC
=
Ambient
water
quality
criterion
(
mg/
L)
RSD
=
Risk­
specific
dose
(
mg/
kg­
day)
Pdp
=
Point
of
departure
(
mg/
kg­
day)
SF
=
Safety
factor
(
unitless)
BW
=
Human
body
weight
(
kg)
DI
=
Drinking
water
intake
(
L/
day)
FI
=
Fish
intake
(
kg/
day)
BAF
=
Bioaccumulation
factor
(
L/
kg)
RSC%
=
Relative
source
contribution
(%)

As
noted
above
for
the
linear
approach,
the
AWQC
calculation
shown
above
is
appropriate
for
water
bodies
that
are
used
as
sources
of
drinking
water
(
and
for
other
uses).
If
the
water
bodies
are
not
used
as
drinking
water
sources
DI
is
substituted
with
an
incidental
ingestion
value
(
II)
of
0.01
L/
day.
30
A
difference
between
the
AWQC
values
obtained
using
the
linear
and
non­
linear
approaches
is
that
the
AWQC
value
obtained
using
the
default
linear
approach
corresponds
to
a
specific
estimated
incremental
lifetime
cancer
risk
level
in
the
range
of
10­
4
to
10­
6.
In
contrast,
the
AWQC
value
obtained
using
the
non­
linear
approach
does
not
describe
or
imply
a
specific
cancer
risk.

The
actual
AWQC
chosen
is
based
on
a
review
of
all
relevant
information,
including
cancer,
noncancer,
ecological,
and
other
critical
data.
The
AWQC
may,
or
may
not,
utilize
the
value
obtained
from
the
cancer
analysis,
if
it
is
less
protective
than
that
derived
from
the
noncancer
endpoint.

2.1.3.5
Risk
Characterization
Risk
characterization
information
is
included
with
the
numerical
AWQC
value
and
addresses
the
major
strengths
and
weaknesses
of
the
assessment
arising
from
the
availability
of
data
and
the
current
limits
of
understanding
of
the
process
of
cancer
causation.
Key
issues
relating
to
the
confidence
in
the
hazard
assessment
and
the
dose­
response
analysis
(
including
the
low
dose
extrapolation
procedure
used)
are
discussed.

Whenever
more
than
one
interpretation
of
the
weight­
of­
evidence
for
carcinogenicity
or
the
dose­
response
characterization
can
be
supported,
and
when
choosing
among
them
is
difficult,
the
alternative
views
are
provided
along
with
the
rationale
for
the
interpretation
chosen
in
the
derivation
of
the
AWQC
value.
Where
possible,
quantitative
uncertainty
analyses
of
the
data
are
provided;
at
a
minimum,
a
qualitative
discussion
of
the
important
uncertainties
is
presented.

Important
features
of
the
risk
characterization
include
significant
scientific
issues,
significant
science
and
science
policy
choices
that
were
made
when
alternative
interpretations
of
data
exist,
and
the
constraints
of
the
data
and
the
state
of
knowledge.
The
assessments
of
hazard,
dose­
response,
and
exposure
are
summarized
to
generate
risk
estimates
for
the
exposure
scenarios
of
interest.

The
Proposed
Cancer
Guidelines
contain
more
detailed
guidance
regarding
the
development
of
risk
characterization
summaries
and
analyses.

2.1.3.6
Use
of
Toxicity
Equivalence
Factors
(
TEF)
and
Relative
Potency
Estimates
The
1996
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
1991;
1996)
state:
"
A
Toxicity
Equivalence
Factor
(
TEF)
procedure
is
one
used
to
derive
quantitative
dose­
response
estimates
for
agents
that
are
members
of
a
category
or
class
of
agents.
TEFs
are
based
on
shared
characteristics
that
can
be
used
to
order
the
class
members
by
carcinogenic
potency
when
cancer
bioassay
data
are
inadequate
for
this
purpose.
The
ordering
is
by
reference
to
the
characteristics
and
potency
of
a
well­
studied
member
or
members
of
the
class.
Other
class
members
are
indexed
to
the
reference
agent(
s)
by
one
or
more
shared
characteristics
to
generate
their
TEFs."
In
addition,
the
Proposed
Cancer
Guidelines
(
USEPA,
1996)
state
that
TEFs
are
generated
and
used
for
the
limited
purpose
of
assessment
of
agents
or
mixtures
of
agents
in
environmental
media
when
better
data
are
not
available.
When
better
data
become
available
for
an
agent,
its
TEF
should
be
replaced
or
revised.
31
To
date,
adequate
data
to
support
use
of
TEFs
has
been
found
in
only
one
class
of
compounds
(
dioxins)
(
USEPA,
1989).

The
uncertainties
associated
with
TEFs
are
explained
when
this
approach
is
used.
This
is
a
default
approach
to
be
used
when
tumor
data
are
not
available
for
individual
components
in
a
mixture.
Relative
potency
factors
(
RPFs)
can
be
similarly
derived
and
used
for
agents
with
carcinogenicity
or
other
supporting
data.
These
are
conceptually
similar
to
TEFs,
but
are
less
firmly
based
on
science
and
do
not
have
the
same
levels
of
data
to
support
them.
TEFs
and
relative
potency
factors
are
used
only
when
there
is
no
better
alternative.
When
they
are
used,
uncertainties
associated
with
them
are
discussed.
As
of
today,
there
are
only
three
classes
of
compounds
for
which
relative
potency
approaches
have
been
examined
by
EPA:
dioxins,
polychlorinated
biphenyls
(
PCBs),
and
polycyclic
aromatic
hydrocarbons
(
PAHs).

2.1.4
Case
Study
(
Compound
Y,
a
Rodent
Bladder
Carcinogen)

This
section
illustrates
an
application
of
the
non­
linear
method
(
MoE)
for
a
rodent
bladder
carcinogen
(
Compound
Y).
A
brief
summary
of
the
data
set
is
provided
below
with
conclusions
regarding
the
weight­
of­
evidence.
The
AWQC
obtained
using
the
default
linear
and
LMS
approaches
are
included
for
purposes
of
comparison
only
and
would
not
be
used
for
agents
with
the
characteristics
described
for
Compound
Y.
In
addition,
considerably
more
detail
would
be
provided
in
a
weight­
of­
evidence
narrative.

2.1.4.1
Background
and
Evaluation
for
Compound
Y
Compound
Y
is
an
organophosphonate
which
has
been
tested
in
subchronic,
chronic,
reproductive,
and
carcinogenic
assays
in
multiple
species.
Tumors
were
observed
only
in
rat
studies.
No
human
data
are
available.
Based
on
a
review
of
the
toxicity,
mechanistic,
metabolic,
and
other
data
summarized
below
for
this
agent,
it
was
concluded
that
a
non­
linear
approach
is
most
appropriate
for
establishing
AWQC
based
on
carcinogenicity.

Lifetime
cancer
bioassays
of
Compound
Y
identified
bladder
tumors
and
hyperplasia
in
male
and
female
rats
at
doses
of
1500
mg/
kg­
day
and
higher
in
the
diet.
These
effects
were
not
observed
at
100
and
400
mg/
kg­
day.
The
rates
of
bladder
cancer
observed
in
females
were
lower
than
those
observed
in
males.
In
a
90­
day
study
designed
to
evaluate
the
mechanisms
of
tumor
induction,
the
following
sequence
was
identified
as
critical
to
bladder
tumor
formation
in
rats:

1)
Large
doses
of
Compound
Y
produce
urinary
calcium/
potassium
imbalance
followed
by
2)
Diuresis,
a
sharp
drop
in
urine
pH,
formation
of
urinary
calculi,
and
3)
Appearance
of
transitional
cell
hyperplasia
in
the
renal
pelvis,
ureter,
and
urinary
bladder.
32
These
effects
occurred
within
two
weeks
of
exposure
onset,
persisted
to
the
end
of
exposure,
and
were
reversible
upon
cessation
of
the
90­
day
exposure.

The
pathological
events
caused
by
Compound
Y
are
believed
to
result
from
prolonged
mechanical
irritation
by
bladder
calculi
that
developed
in
response
to
the
exposure.
At
high
but
not
lower
subchronic
doses
in
the
male
rat,
Compound
Y
leads
to
elevated
blood
phosphorus
levels;
the
body
responds
by
releasing
excess
calcium
into
the
urine.
The
calcium
and
phosphorus
combine
in
the
urine
and
precipitate
into
multiple
stones
in
the
bladder.
The
stones
are
very
irritating
to
the
bladder;
the
bladder
lining
is
eroded,
and
cell
proliferation
occurs
to
compensate
for
the
loss
of
the
lining.
This
leads
to
development
of
hyperplasia,
with
subsequent
tumor
formation.
A
prolonged
increase
in
the
rate
of
proliferation
of
cells
of
the
urinary
bladder
has
been
proposed
to
be
an
important
step
in
the
induction
of
urinary
bladder
tumors
(
Cohen
and
Ellwein,
1989;
1990).
Thus,
the
association
of
cell
proliferation,
hyperplasia,
and
subsequent
cancer
induction
as
a
result
of
urinary
stone
formations
due
to
exposure
to
Compound
Y
is
proposed
as
one
mode
of
action
which
may
justify,
after
a
review
of
all
relevant
data,
the
use
of
a
non­
linear
approach,
such
as
the
MoE
approach.

Studies
of
the
components
of
this
agent
yield
no
evidence
of
carcinogenicity
in
the
bladder.
In
metabolic
studies
in
animals,
the
metallic
component
in
isolation
from
the
parent
molecule
was
not
absorbed
to
a
significant
extent
from
the
gastrointestinal
tract.

Compound
Y
has
been
assessed
via
a
battery
of
mutagenicity
assays
that
have
yielded
negative
results,
and
a
review
of
the
chemical
structure
does
not
suggest
potential
genotoxicity.
The
metabolites
of
Compound
Y
have
also
yielded
negative
results
in
mutagenicity
assays
and
yielded
no
evidence
of
carcinogenicity.
The
negative
genotoxicity
results
for
Compound
Y
and
structurally
related
agents
provide
further
support
for
the
use
of
a
non­
linear
approach,
such
as
the
MoE
approach,
to
establish
AWQC.

2.1.4.2
Conclusion
and
Use
of
the
MoE
Approach
for
Compound
Y
Compound
Y,
a
metal
aliphatic
phosphonate,
is
likely
to
be
carcinogenic
to
humans
only
under
high­
exposure
conditions
following
oral
and
inhalation
exposure
that
lead
to
bladder
stone
formation,
but
is
not
likely
to
be
carcinogenic
under
low­
exposure
conditions.
It
is
not
likely
to
be
a
human
carcinogen
via
the
dermal
route,
given
that
the
compound
is
a
metal
conjugate
that
is
readily
ionized
and
its
dermal
absorption
is
not
anticipated.
The
weight­
of­
evidence
is
based
on
(
1)
bladder
tumors
only
in
male
rats
at
high
exposure;
(
2)
the
absence
of
tumors
at
any
other
site
in
rats
or
mice;
(
3)
the
formation
of
calcium­
phosphorus­
containing
bladder
stones
in
male
rats
at
high,
but
not
low,
exposure.
The
bladder
stones
erode
bladder
epithelium
and
result
in
profound
increases
in
cell
proliferation
and
cancer;
and
(
4)
the
absence
of
carcinogenic
structural
analogues
or
mutagenic
activity.

There
is
a
strong
mode
of
action
basis
for
the
requirements
of
high
doses
of
Compound
Y,
which
leads
to
excess
calcium
and
increased
acidity
in
the
urine,
resulting
in
the
precipitation
of
bladder
stones
and
subsequent
increase
in
cell
proliferation
and
tumor
hazard
potential.
Lower
doses
33
fail
to
perturb
urinary
constituents,
lead
to
stones,
produce
toxicity,
or
give
rise
to
tumors.
Therefore,
dose­
response
assessment
should
assume
non­
linearity.

A
major
uncertainty
is
whether
the
profound
effects
of
Compound
Y
may
be
unique
to
the
rat.
Even
if
Compound
Y
produced
stones
in
humans,
there
is
only
limited
evidence
that
humans
with
bladder
stones
develop
cancer.

Based
on
the
progression
of
pathology
leading
to
tumors,
in
which
hyperplasia
is
an
early
critical
step,
hyperplasia
was
selected
as
the
sentinel
precursor
effect
which
was
used
as
the
basis
for
the
calculation
of
AWQC
using
the
MoE
approach.
Hyperplasia
incidence
data
in
a
lifetime
rat
study
are
available
for
Compound
Y.
Tumor
data
from
the
same
lifetime
rat
study
were
used
to
calculate
AWQC
using
the
default
linear
and
LMS
approaches
for
purposes
of
comparing
methods
and
results.
The
data
used
for
all
three
approaches
are
summarized
in
Table
2.1.1
below.

Table
2.1.1:
Study
Results
from
a
Lifetime
Exposure
of
Male
Rats
to
Compound
Y
Animal
Dose
in
mg/
kg­
day
(
scaled
human
equivalent
doses)
Number
in
Group
Number
Responding
tumors
(
combined
papilloma
&
carcinoma)
hyperplasia
0
73
3
5
400
(
BW3/
4
=
106.4)
a
(
BW2/
3
=
68.4)
b
78
2
5
1500
(
BW3/
4
=
398.9)
a
(
BW2/
3
=
256.5)
b
78
21*
29*

a.
The
3/
4
scaling
factor
is
the
new
proposed
method
and
is
used
with
the
new
linear
model
in
this
case
study
for
comparison
purposes.

b.
The
2/
3
scaling
factor
is
presently
in
use
and
is
used
with
the
LMS
method
later
in
this
section
for
comparative
purposes.

*
There
were
statistically
significant
(
p<
0.05)
increases
in
both
tumor
incidence
and
hyperplasia
in
the
treated
group
compared
to
the
control
group.

Identification
of
the
Point
of
Departure
(
Pdp)
for
Compound
Y
9This
is
based
on
a
dietary
conversion
factor
for
rats
from
ppm
to
mg/
kg­
day
of
.05.

34
The
point
of
departure
(
Pdp)
chosen
for
the
MoE
calculations
was
400
mg/
kg­
day,
which
is
the
maximum
animal
dose
yielding
no
observable
hyperplastic
effects
(
the
NOAEL
shown
in
Table
2.1.1).
9
The
study
found
males
to
be
more
sensitive
to
tumor
induction
than
females
and
the
hyperplasia
results
in
male
rats
were
used
for
AWQC
calculations.
The
human
equivalent
dose
for
the
NOAEL
of
106.4
mg/
kg­
day
was
calculated
using
the
new
scaling
factor
of
body
weight
raised
to
the
3/
4
power
(
as
shown
in
Equation
2.1.3).

Discussion
of
the
Points
Affecting
Selection
of
the
SF
for
Compound
Y
Intraspecies
Variability.
There
is
variability
within
the
human
population
in
responses
to
xenobiotic
agents
which
may
result
from
a
variety
of
factors
including
health
status,
diet,
age,
and
genetic
composition.
Research
on
Compound
Y
did
not
identify
a
common
health
or
genetic
condition
which
would
yield
a
subpopulation
who
are
particularly
susceptible
to
the
carcinogenic
effects
of
Compound
Y
nor
did
it
indicate
an
exceptionally
high
or
low
level
of
intraspecies
variability.

Interspecies
Variability.
Animals
and
humans
may
vary
widely
in
their
responses
to
agents
due
to
their
differing
physiologies
and
metabolism.
A
review
of
human
case
studies
and
epidemiological
studies
indicate
that
humans
may
be
significantly
less
susceptible
to
the
influence
of
bladder
irritation,
stone
formation,
and
subsequent
tumor
formation
than
male
rodents.
This
would
suggest
a
smaller
factor
for
interspecies
variability.

Confidence
in
the
Study
(
Dose
Selection).
There
is
a
wide
range
in
dose
levels
between
the
NOAEL
and
LOAEL
in
the
selected
study.
The
hyperplastic
response
rate
at
the
LOAEL
is
37
percent
(
i.
e.,
29/
78),
which
is
high
for
the
initial
response
measurement.
Additional
data
would
help
to
refine
the
NOAEL
and
better
describe
the
dose­
response
dynamic
in
the
low
response
range.

Exposure
Duration.
This
exposure
scenario
is
chronic,
so
there
is
no
need
to
apply
an
additional
safety
factor.

Persistence.
This
chemical
is
not
persistent
in
the
body,
so
there
is
no
need
to
apply
an
additional
safety
factor.

Shape
of
the
Dose­
Response
Curve.
The
data
available
indicate
a
steep
slope
at
the
point
of
departure
(
at
400
mg/
kg­
day
animal
dose).
This
would
suggest
a
rapid
reduction
in
risk
with
lower
doses,
or
a
smaller
SF.

In
summary,
an
overall
SF
of
30
is
used
in
the
MoE
calculation.
The
selection
of
the
SF
is
based
on
a
consideration
of
all
the
factors
discussed
above,
such
as
intraspecies
variability
(
10),
interspecies
variability
(
3
is
used
here
because
animal
dose
has
already
been
adjusted
to
a
human
equivalent
dose),
and
the
adequate
data
base
on
this
chemical.
This
factor
of
30
is
sufficient
for
35
human
health
protection.
The
risk
may
decline
considerably
with
doses
lower
than
the
point
of
departure;
the
male
rat
is
a
very
sensitive
model
(
mice
do
not
respond).
Physiological
phenomenon
is
likely
to
fall
off
sharply
with
dose
as
shown
by
the
dose­
response
curve.
Further,
bladder
stone
and
subsequent
tumor
formation
is
not
a
common
phenomenon
in
humans.

AWQC
Calculations
for
Compound
Y
Equation
2.1.11
shown
in
Section
2.1.3
was
used
to
calculate
the
AWQC
for
Compound
Y:

AWQC
'
Pdp
SF
x
BW
DI
%
(
FI
x
BAF)
x
RSC%

(
Equation
2.1.11)

The
following
input
parameters
were
used:

Pdp
=
Point
of
departure
(
106.4
mg/
kg­
day
(
NOAEL))
SF
=
Safety
factor
of
30
BW
=
Body
weight
for
adult
(
70
kg)
DI
=
Drinking
water
intake
(
2
L/
day)
FI
=
Fish
intake
(
0.01780
kg/
day)
BAF
=
Assumed
bioaccumulation
factor
(
BAF)
(
300
L/
kg)
RSC
=
20%
(
assumed)

This
calculation
yields
an
AWQC
of
6.7
mg/
L.
The
body
weight,
water
intake,
fish
intake,
and
RSC%
values
used
in
the
above
calculation
are
the
currently
proposed
default
values
for
adults
(
see
the
exposure
section
of
this
document).
The
BAF,
which
accounts
for
the
accumulation
of
Compound
Y
from
water
through
the
food
chain
and
into
fish
tissue,
has
been
arbitrarily
chosen
for
purposes
of
this
case
study.

The
AWQC
calculations
shown
above
is
appropriate
for
water
bodies
that
are
used
as
sources
of
drinking
water
(
and
for
other
uses).
See
Section
2.1.3.4
for
additional
information
on
modifications
for
non­
drinking
water
sources.

2.1.4.3
Use
of
the
Default
Linear
Approach
for
Compound
Y
This
section
is
provided
for
purposes
of
illustrating
the
use
of
the
default
linear
approach
for
deriving
AWQC
based
on
carcinogenicity
and
to
compare
the
resulting
AWQC
to
that
obtained
above
using
the
MoE
approach.
As
discussed
in
Section
2.1.4.1
above,
it
is
important
to
note
that
the
default
linear
method
would
most
likely
not,
in
practice,
be
recommended
as
an
approach
for
quantifying
the
risk
and
deriving
the
AWQC
for
Compound
Y
given
the
hazard
characteristics
described
for
this
substance.
36
Computing
the
Human
Equivalent
Dose
for
Compound
Y
The
doses
used
in
the
study
were
adjusted
to
obtain
a
human
equivalent
dose,
as
shown
in
Table
2.1.1.
In
the
absence
of
pharmacokinetic
data,
this
was
done
using
a
scaling
factor
of
BW3/
4,
with
a
male
rat
weight
of
0.35
kg
and
a
human
weight
of
70
kg
(
as
shown
in
Equation
2.1.3).

Calculation
of
AWQC
for
Compound
Y
To
describe
the
dose­
response
of
tumor
incidence
data
in
the
observed
range,
a
curve­
fitting
model
such
as
the
multistage
or
other
approach
appropriate
for
the
data
can
be
used.
In
the
case
of
Compound
Y,
three
data
points
(
at
doses
of
0,
400,
and
1500
mg/
kg­
day)
were
used
in
the
multistage
model
(
GLOBAL
86)
to
calculate
the
LED
10
(
the
95
percent
lower
confidence
limit
on
a
dose
associated
with
a
10
percent
increase
in
response).
The
value
obtained
for
the
LED
10
is
204
mg/
kgday

The
cancer
slope
factor
(
m)
is
calculated
by
dividing
0.1
by
the
LED
10
using
Equation
2.1.6:

m
'
0.10
LED
10
(
Equation
2.1.6)

This
yields
an
estimated
cancer
slope
factor
of
4.9
x
10­
4
per
mg/
kg­
day.
The
cancer
slope
factor
is
then
used
in
Equation
2.1.9
with
a
specified
risk
level
(
in
this
case
10­
6)
to
calculate
a
RSD:

RSD
'
Target
Incremental
Cancer
Risk
m
(
Equation
2.1.9)

This
yields
an
RSD
of
2.0
x
10­
3
mg/
kg­
day.

The
RSD
is
used
in
Equation
2.1.10
with
the
same
input
parameters
(
body
weight,
drinking
water
intake,
fish
intake,
and
BAF)
as
those
used
for
the
MoE
approach:

AWQC
'
RSD
x
BW
DI
%
(
FI
x
BAF)

(
Equation
2.1.10)

This
yields
an
AWQC
of
0.019
mg/
L
(
rounded
from
0.0189
mg/
L)
for
a
target
risk
of
10­
6.
(
As
noted
above,
this
approach
is
appropriate
for
water
bodies
used
as
drinking
water
sources.
See
Section
2.1.3.4
for
non­
drinking
water
sources).
37
2.1.4.4
Use
of
the
LMS
Approach
for
Compound
Y
This
section
is
provided
strictly
for
purposes
of
comparing
the
use
of
the
MoE
approach
with
the
traditional
linearized
multistage
(
LMS)
method
for
deriving
AWQC
for
carcinogens.
As
discussed
above,
the
LMS
approach
would
not
be
used
in
practice
to
quantify
risk
and
derive
the
AWQC
for
Compound
Y
given
the
hazard
characteristics
described
for
this
substance.

First,
the
LMS
approach
was
used
to
fit
the
male
rat
tumor
data
shown
in
Table
2.1.1
with
the
computer
program
GLOBAL86.
This
program
calculates
the
95th
percentile
upper
confidence
limit
on
the
linear
slope
(
i.
e.,
the
q
1*)
in
the
low
dose
range.
A
human
equivalent
dose
was
calculated
using
the
BW2/
3
interspecies
dose
scaling
factor
for
purposes
of
illustrating
the
results
obtained
applying
the
1980
AWQC
derivation
methodology.
The
human
equivalent
doses
obtained
using
this
scaling
factor
are
shown
in
Table
2.1.1
above.
(
The
same
data
set,
using
differently
scaled
doses,
was
employed
for
both
the
new
linear
and
LMS
approaches.)
The
q
1*
value
obtained
using
the
LMS
approach
is
6
x
10­
4
(
mg/
kg­
day)­
1.

Equation
2.1.9
was
used
with
a
reference
incremental
cancer
risk
of
10­
6
to
calculate
an
RSD
of
1.7
x
10­
3.
Equation
2.1.10
was
then
used
to
calculate
the
AWQC
with
the
same
input
parameters
(
body
weight,
drinking
water
intake,
fish
intake,
and
BAF)
as
those
used
for
the
MoE
approach.
(
As
noted
above,
this
approach
is
appropriate
for
water
bodies
used
as
drinking
water
sources.
See
Section
2.1.3.4
for
non­
drinking
water
sources.)
The
AWQC
was
calculated
to
be
0.016
mg/
L
and
was
rounded
from
0.0157
mg/
L.

2.1.4.5
Comparison
of
Approaches
and
Results
for
Compound
Y
The
results
of
the
three
approaches
used
for
Compound
Y
are
summarized
in
Table
2.1.2.
The
AWQC
calculated
using
the
MoE
approach
is
substantially
higher
than
that
obtained
using
the
default
linear
and
LMS
approaches.
If
larger
or
smaller
SFs
were
used
in
the
MoE
calculations,
the
AWQC
obtained
using
the
MoE
approach
would
decrease
or
increase
accordingly.
The
quantitative
relationship
between
AWQC
derived
using
different
methods
will
vary
depending
on
the
nature
of
the
data
set
and
the
SFs
and
Pdp
selected
for
use
in
the
MoE
approach.
38
Table
2.1.2:
Comparison
of
AWQC
Obtained
for
Compound
Y
Using
the
MoE,
Default
Linear,
and
LMS
Approaches
Method
AWQC
(
mg/
L)
MoE
Using
hyperplasia
as
a
precursor
for
determining
the
Point
of
Departure
(
Pdp)
and
a
SF
of
30.
6.7
Default
Linear
Using
linear
extrapolation
from
the
LED
10
with
a
10­
6
risk
level
and
an
interspecies
scaling
factor
based
on
BW3/
4.
0.019
LMS
Using
the
linearized
multistage
approach
with
a
10­
6
risk
level
and
an
interspecies
scaling
factor
based
on
BW2/
3.
0.016
2.1.5
References
Anderson,
E.
L.
1983.
Quantitative
Approaches
in
Use
to
Assess
Cancer
Risk.
Risk
Analysis.
3(
4):
227­
295.

Barnes,
D.
G.,
G.
P
Daston,
J.
S.
Evans,
A.
M.
Jarabek,
R.
J.
Kavlock,
C.
A.
Kimmel,
C.
Park,
and
H.
L.
Spitzer.
1995.
Benchmark
Dose
Workshop:
Criteria
for
Use
of
a
Benchmark
Dose
to
Estimate
a
Reference
Dose.
Regul.
Toxicol.
Pharmacol.
21:
296­
306.

Doll,
R.
1971.
Weibull
Distribution
of
Cancer:
Implications
for
Models
of
Carcinogenesis.
J.
Roy.
Stat.
Soc.
A.
13:
133­
166.

Mantel,
N.
and
M.
A.
Schneiderman.
1975.
Estimating
"
Safe
Levels,"
a
Hazardous
Undertaking.
Cancer
Res.
35:
1379.

Office
of
Science
and
Technology
Policy
(
OSTP).
1985.
Chemical
Carcinogens:
Review
of
the
Science
and
its
Associated
Principles.
Federal
Register
50:
10372­
10442.

USEPA.
1976.
Interim
Procedures
and
Guidelines
for
Health
Risks
and
Economic
Impact
Assessment
of
Suspected
Carcinogens.
Federal
Register
41:
21402­
21405.
39
USEPA.
1986.
Guidelines
for
Carcinogen
Risk
Assessment.
Federal
Register
51:
33992­
34003.

USEPA.
1989.
Interim
Procedures
for
Estimating
Risks
Associated
with
Exposures
to
Mixtures
of
Chlorinated
Dibenzo­
p­
dioxins
and
Dibenzofurans
(
CDDs
and
CDFs)
and
1989
Update.
Washington,
DC:
Risk
Assessment
Forum.
EPA/
625/
3­
89/
016.

USEPA.
1991.
Workshop
Report
on
Toxicity
Equivalency
Factors
for
Polychlorinated
Biphenyl
Congeners.
Washington,
DC:
Risk
Assessment
Forum.
EPA/
625/
3­
91/
020.

USEPA.
1992a.
Report
of
the
National
Workshop
on
Revision
of
the
Methods
for
Deriving
National
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Washington,
DC.

USEPA.
1992b.
Draft
Report:
A
Cross­
Species
Scaling
Factor
for
Carcinogen
Risk
Assessment
Based
on
Equivalence
of
Mg/
Kg3/
4/
Day.
Federal
Register
57:
24152­
24173.

USEPA.
1996.
Proposed
Guidelines
for
Carcinogen
Risk
Assessment.
Federal
Register
61:
17960.
April
23.

USEPA.
1998.
Notice
of
Draft
Revisions
to
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Federal
Register
Notice.

2.2
Noncancer
Effects
2.2.1
Introduction
The
evaluation
of
risks
from
noncarcinogenic
chemicals
traditionally
has
been
based
on
the
assumption
that
noncarcinogens
have
a
dose
or
level
below
which
no
adverse
effects
are
expected
to
occur.
The
risk
parameter
developed
by
EPA
for
noncarcinogens
is
called
the
Reference
Dose
(
RfD).
The
Integrated
Risk
Information
System
(
IRIS)
Background
Document
entitled
Reference
Dose
(
RfD):
Description
and
Use
in
Health
Risk
Assessments
(
USEPA,
1988)
defines
an
RfD
as
"
an
estimate
(
with
uncertainty
spanning
approximately
an
order
of
magnitude)
of
a
daily
exposure
to
the
human
population
(
including
sensitive
subgroups)
that
is
likely
to
be
without
appreciable
risk
of
deleterious
effects
over
a
lifetime."
The
RfD
is
acknowledgedly
an
estimate
and,
thus,
may
not
be
completely
protective
of
every
individual
within
a
highly
variable
population,
conversely,
neither
are
exposures
above
the
RfD
necessarily
unsafe.
Some
individuals
may
have
better
adaptive
or
protective
capacities
than
others
and
responses
may
vary
with
age
and
state
of
health;
thus,
individuals
respond
differently
to
toxicant
exposure
(
Barnes
and
Dourson,
1988).

The
key
step
in
deriving
water
quality
criteria
for
the
protection
of
human
health
from
noncancer
effects
is
the
determination
of
the
RfD.
As
described
in
Section
1.4,
the
RfD
is
used
in
concert
with
additional
information
regarding
exposure
and
the
bioaccumulation
potential
of
the
substance
to
derive
an
AWQC
for
noncancer
effects.
The
procedures
presented
in
USEPA
(
1988)
10The
Agency
has
also
developed
guidelines
that
explain
the
process
of
hazard
identification
for
developmental
(
USEPA,
1991a)
and
reproductive
(
USEPA,
1994b)
effects.
Please
refer
to
these
EPA
documents
for
guidance
in
these
areas.

40
for
deriving
the
RfD
using
an
experimentally
derived
No
Observed
Adverse
Effect
Level
(
NOAEL)/
Lowest
Observed
Adverse
Effect
Level
(
LOAEL)
approach
are
incorporated
into
this
chapter.
The
Agency
is
also
investigating
alternative
methods
for
estimating
the
RfD;
thus,
this
guidance
document
contains
information
on
two
alternative
methods,
the
Benchmark
Dose
(
BMD)
and
Categorical
Regression
approaches.
The
Agency
continues
to
conduct
research
on
the
utility
of
both
of
these
methods
in
the
noncancer
risk
assessment
process
and
recommends
their
application
in
circumstances
where
the
data
are
sufficient.
The
Agency
used
the
BMD
approach
to
derive
a
RfD
for
methyl
mercury
(
USEPA,
1994a).

This
section
begins
with
a
discussion
of
hazard
identification
and
dose­
response
characterization.
This
is
followed
by
a
description
of
factors
to
be
considered
in
the
selection
of
critical
data
sets
for
use
in
the
risk
assessment
evaluation.
The
procedures
for
deriving
an
RfD
for
a
substance
using
the
traditional
NOAEL/
LOAEL
approach
are
presented
as
the
accepted
current
risk
assessment
practice
used
by
USEPA.
Next,
the
BMD
method
for
deriving
an
RfD
is
discussed
and
an
example
of
its
application
is
provided
for
illustrative
purposes.
A
brief
discussion
of
Categorical
Regression
is
also
included,
with
references
to
the
relevant
literature.
The
chapter
concludes
with
specific
sections
on
several
issues
relevant
to
noncancer
risk
assessment,
including
practical
nonthreshold
effects
and
risks
from
short­
term
exposures
and
mixtures.

While
the
intent
of
this
guidance
is
to
provide
sufficient
information
to
apply
methods
for
deriving
RfDs,
this
document
does
not
detail
all
relevant
issues
and
underlying
theory
associated
with
these
methods.
For
further
information,
the
reader
is
referred
to
the
sources
cited
in
the
reference
list
(
in
particular,
USEPA,
1988;
Crump
et
al.,
1995;
and
Hertzberg
and
Miller,
1985).

2.2.2
Hazard
Identification
The
first
step
in
the
risk
assessment
involves
preparing
a
hazard
identification,
based
on
a
review
of
data
available
to
characterize
the
health
effects
associated
with
chemical
exposure.
The
RfD
Background
Document
(
USEPA,
1988)
outlines
considerations
for
choosing
data
upon
which
to
base
a
hazard
identification
for
noncancer
health
effects.
10
Assessors
should
prepare
a
hazard
identification
document
that
describes
the
nature
of
exposure,
the
type
and
severity
of
effects
observed,
and
the
quality
and
relevance
of
data
to
humans.
Well­
conducted
human
studies
are
considered
the
best
for
establishing
a
link
between
exposure
to
an
agent
and
manifestation
of
an
adverse
effect.
In
the
absence
of
adequate
human
data,
the
Agency
relies
primarily
on
animal
studies.
In
such
cases,
the
principle
studies
are
drawn
from
experiments
conducted
on
laboratory
mammals,
most
often
rat,
mouse,
rabbit,
guinea
pig,
dog,
monkey,
or
hamster.
Well­
designed
animal
studies
offer
the
benefit
of
controlled
chemical
exposures
and
definitive
toxicological
analysis.
Supporting
evidence
provides
additional
information
for
dose­
response
assessment
and
may
come
from
a
wide
variety
of
sources,
such
as
metabolic
and
pharmacokinetic
studies.
In
vitro
studies
seldom
provide
41
definitive
hazard
identification
data,
but
they
can
often
provide
insight
into
the
compound's
potential
for
human
toxicity.

Important
to
the
hazard
identification
is
consideration
of
the
biological
and
statistical
significance
of
observed
effects.
The
determination
of
whether
an
effect
is
adverse
requires
professional
judgment.
For
guidance,
adverse
health
effects
are
those
deleterious
effects
which
are
or
may
become
debilitating,
harmful,
or
toxic
to
the
normal
functions
of
an
organism,
including
reproductive
and
developmental
effects.
Adverse
effects
do
not
include
such
effects
as
tissue
discoloration
without
histological
or
biochemical
effects,
or
the
induction
of
the
enzymes
involved
in
the
metabolism
of
the
substance.
Guidelines
for
defining
the
severity
of
adverse
effects
have
been
suggested
by
Hartung
and
Durkin
(
1986).
EPA
has
also
developed
guidelines
for
the
ranking
of
observed
effects
(
USEPA,
1995)
and
a
ranking
scheme
for
slight
to
severe
effects.
Distinguishing
slight
effects
such
as
reversible
enzyme
induction
and
reversible
subcellular
change
from
more
serious
effects
is
critical
in
distinguishing
between
a
NOAEL
and
LOAEL.

It
is
also
important
to
evaluate
the
reversibility
of
an
effect.
Reversibility
refers
to
whether
or
not
a
change
will
return
to
normal
or
within
normal
limits
either
during
the
course
of
or
following
exposure.
However,
even
a
reversible
effect
may
be
adverse
to
an
organism.
In
performing
a
hazard
identification,
irreversible
effects
should
be
distinguished
from
less
serious,
but
still
adverse,
reversible
changes.

The
exposure
conditions
for
toxicity
tests,
including
the
route
(
e.
g.,
inhaled
versus
ingested),
source
(
e.
g.,
water
versus
food),
and
duration,
should
be
discussed
in
the
hazard
identification.
The
hazard
identification
should
also
include
an
evaluation
of
the
quality
of
studies.
Elements
that
affect
the
quality
of
studies
include
the
soundness
of
the
study
protocol,
the
adequacy
of
data
analysis,
the
characterization
of
the
study
compound,
the
types
of
species
used,
the
number
of
individuals
per
study
group,
the
number
of
study
groups,
dose
spacing,
the
types
of
observations
recorded,
sex
and
age
of
animals,
and
the
route
and
duration
of
exposure
(
USEPA,
1988).

The
hazard
identification
should
conclude
with
a
weight­
of­
evidence
discussion.
In
general,
the
discussion
should
review
the
results
of
different
studies
and
develop
an
overall
picture
of
the
chemical's
toxicity.
Evidence
for
possible
toxicity
in
humans
is
supported
by
similar
results
across
species
and
across
investigators.
A
plausible
mechanism
of
action
for
the
effect,
as
well
as
similar
toxic
activity
in
chemicals
of
similar
structure,
also
add
to
the
weight­
of­
evidence.

2.2.3
Dose­
Response
Assessment
The
dose­
response
assessment
involves
the
evaluation
of
toxicity
data
to
identify
doses
at
which
statistically
and/
or
biologically
significant
effects
occur
and
identify
NOAEL
and/
or
LOAEL
values.
The
effects
data
are
also
evaluated
to
see
if
there
is
a
quantitative
relationship
between
dose
and
the
magnitude
of
the
effect.
Dose­
response
relationships
can
be
linear,
curvilinear
or
U­
shaped.
The
RfD
is
traditionally
estimated
by
identifying
the
most
appropriate
NOAEL
for
the
critical
effect.
The
LOAEL
may
be
used
to
estimate
the
RfD
if
no
appropriate
NOAELs
have
been
identified.
42
2.2.4
Selection
of
Critical
Data
2.2.4.1
Critical
Study
Ideally,
the
scientific
data
for
noncancer
effects
should
include
sufficient
information
to
characterize
quantitatively
the
incidence
and
severity
of
response
as
dose
increases.
However,
complete
data
are
frequently
lacking.
Instead,
the
Agency
bases
the
derivation
of
the
RfD
on
the
NOAEL
or
LOAEL
from
a
critical
study
or
collection
of
critical
studies.
The
choice
of
the
critical
study
or
studies
to
use
in
the
derivation
of
the
chronic
RfD
requires
professional
judgment
concerning
the
quality
of
the
studies,
the
definition
of
adverse
effects
and
their
level
of
occurrence.
As
part
of
the
hazard
identification,
all
relevant
toxicity
data
on
a
chemical
should
be
evaluated
to
support
the
establishment
of
the
RfD.
Those
studies
representing
the
best
quality
and
most
appropriate
data
should
be
considered
for
defining
adverse
effects
and
their
level
of
occurrence.

In
choosing
a
study
on
which
to
base
the
RfD,
the
Agency
recommends
a
hierarchy
of
acceptable
data.
Most
preferable
is
a
well­
conducted
epidemiologic
study
that
demonstrates
a
positive
association
between
a
quantifiable
exposure
to
a
chemical
and
human
disease.
Use
of
acceptable
human
studies
avoids
the
problems
of
interspecies
extrapolation,
and
thus,
confidence
in
the
estimate
is
often
greater.
At
present,
however,
human
data
adequate
to
serve
as
a
basis
for
quantitative
risk
assessment
are
available
for
only
a
few
chemicals.
Most
often,
inference
of
adverse
health
effects
for
humans
must
be
drawn
from
toxicity
information
gained
through
animal
experiments
with
human
data
serving
qualitatively
as
supporting
evidence.
Under
this
condition,
health
effects
data
must
be
available
from
well­
conducted
animal
studies
and
relevant
to
humans
based
on
a
defensible
biological
rationale,
e.
g.,
similar
metabolic
pathways.
In
the
absence
of
data
from
a
more
"
relevant"
species,
data
from
the
most
sensitive
animal
species
tested,
i.
e.,
the
species
demonstrating
an
adverse
health
effect
at
the
lowest
administered
dose
via
a
relevant
route
of
exposure,
shall
generally
be
used
as
the
critical
study.

The
route
of
administration
must
be
considered
when
choosing
the
critical
study
from
among
quality
toxicity
tests.
The
vehicle
in
which
the
chemical
is
administered
is
also
relevant.
For
example,
within
the
oral
route
of
exposure,
the
bioavailability
of
a
chemical
ingested
from
one
source
(
e.
g.,
food)
may
differ
from
when
it
is
ingested
from
another
source
(
e.
g.,
water).
Usually,
the
toxicity
data
base
does
not
provide
data
on
all
possible
routes,
sources,
and/
or
durations
of
administration.
In
general,
the
preferred
exposure
route
is
that
which
is
considered
most
relevant
to
environmental
exposure.
For
example,
when
developing
drinking
water
standards,
the
Agency
has
placed
greater
weight
on
oral
studies
in
experimental
animals,
especially
those
studies
in
which
the
contaminant
is
administered
via
water.
However,
in
the
absence
of
data
on
the
exposure
route
and/
or
source
of
concern,
it
is
the
Agency's
view
that
the
potential
for
the
toxicity
manifested
by
one
route
and/
or
source
of
exposure
may
be
relevant
to
other
exposure
routes
and/
or
sources.
EPA
guidelines
for
the
development
of
interim
inhalation
reference
concentrations
(
USEPA,
1989)
discuss
specific
issues
relevant
to
route­
to­
route
extrapolation.
These
include
issues
of
portal­
of­
entry
effects,
available
pharmacokinetic
data
for
the
routes
of
interest,
measurements
of
absorption
efficiency
by
each
route
of
interest,
comparative
excretion
data
when
the
associated
metabolic
pathways
are
equivalent
by
43
RfD
(
mg/
kg
&
day)
'
NOAEL
UF
(
MF
or
LOAEL
UF
(
MF
(
Equation
2.2.1)
each
route
of
interest,
and
comparative
systemic
toxicity
data
when
such
data
indicate
equivalent
effects
by
each
route
of
interest.

Preference
should
be
given
to
studies
involving
exposure
over
a
significant
portion
of
the
animal's
lifespan
since
this
is
anticipated
to
reflect
the
most
relevant
environmental
exposure.
Studies
with
shorter
time
frames
can
miss
important
effects.
In
selected
cases,
studies
of
less
than
90
days
can
be
used
for
quantification
but
the
study
must
be
of
exceptionally
high
quality.
In
general
shortterm
tests
should
not
be
used
for
anything
other
than
interim
RfDs
or
for
developmental
RfDs.
However,
developmental
effects
can
sometimes
be
the
critical
effect
and
serve
as
the
basis
of
an
RfD.
The
duration
of
a
developmental
study
is
generally
less
than
15
days.

2.2.4.2
Critical
Data
and
Endpoint
The
experimental
exposure
level
representing
the
highest
dosage
level
tested
at
which
no
adverse
effects
were
demonstrated
in
any
of
the
species
evaluated
should
be
used
for
criteria
development.
By
basing
criteria
on
the
critical
toxic
effect,
it
is
assumed
that
all
toxic
effects
are
prevented
(
USEPA,
1988).
In
the
absence
of
such
data,
the
lowest
LOAEL
dosage
may
be
used
for
criteria
development
and
an
additional
uncertainty
factor
for
LOAEL
to
NOAEL
extrapolation
is
applied.
When
two
or
more
studies
of
equal
quality
and
relevance
exist,
the
geometric
means
of
the
NOAELs
or
LOAELs
may
be
used.

Often
a
chemical
may
elicit
multiple
effects,
each
with
a
different
NOAEL
and
LOAEL.
From
among
these
effects,
the
Agency
selects
a
critical
endpoint.
The
critical
endpoint
is
the
effect
that
exhibits
the
lowest
LOAEL
(
USEPA,
1988).

2.2.5
Deriving
RfD
Using
the
NOAEL/
LOAEL
Approach
The
IRIS
background
document
(
USEPA,
1988)
describes
methods
used
to
derive
an
RfD
for
a
given
chemical
and
criteria
for
selection
of
the
critical
NOAEL
or
LOAEL.
Appropriate
uncertainty
factors
(
UF)
and
modifying
factors
(
MF)
are
then
applied
to
the
selected
endpoint
to
derive
the
RfD.

The
general
equation
for
deriving
the
RfD
is
(
USEPA,
1988):
44
where:

NOAEL
=
An
exposure
level
at
which
there
are
no
statistically
or
biologically
significant
increases
in
the
frequency
or
severity
of
observed
adverse
effects
between
the
exposed
population
and
its
appropriate
control;
some
effects
may
be
produced
at
this
level,
but
they
are
not
considered
as
adverse,
nor
precursors
to
specific
adverse
effects.

LOAEL
=
The
lowest
experimental
exposure
level
at
which
there
are
statistically
or
biologically
significant
increases
in
frequency
or
severity
of
observed
adverse
effects
between
the
exposed
population
and
its
appropriate
control
group.
The
LOAEL
may
be
used
if
the
NOAEL
cannot
be
determined.

UF
=
An
uncertainty
factor
which
reduces
the
dose
to
account
for
several
areas
of
scientific
uncertainty
inherent
in
most
toxicity
data
bases.
Standard
UFs
are
used
to
account
for
variation
in
sensitivity
among
humans,
extrapolation
from
animal
studies
to
humans,
and
extrapolation
from
less
than
chronic
NOAELs
to
chronic
NOAELs.
An
additional
UF
may
be
employed
if
a
LOAEL
is
used
to
define
the
RfD.

MF
=
A
modifying
factor,
to
be
determined
using
professional
judgment.
The
MF
provides
for
additional
uncertainty
not
explicitly
included
in
UF,
such
as
completeness
of
the
overall
data
base
and
the
number
of
species
tested.
(
The
value
for
MF
must
be
greater
than
zero
and
less
than
or
equal
to
10;
the
default
value
for
the
MF
is
1).

The
RfD
is
generally
expressed
in
units
of
milligrams
per
kilogram
of
body
weight
per
day
(
mg/
kg­
day).

2.2.5.1
Selection
of
Uncertainty
Factors
and
Modifying
Factors
The
choice
of
appropriate
UFs
and
MFs
must
be
a
case­
by­
case
judgment
by
experts
and
should
account
for
each
of
the
applicable
areas
for
uncertainty
and
nuances
in
the
available
data
that
impact
uncertainty.
Several
reports
describe
the
underlying
basis
of
UFs
(
Zielhuis
and
van
der
Kreek,
1979;
Dourson
and
Stara,
1983)
and
research
into
this
area
(
Calabrese,
1985;
Hattis
et
al.,
1987;
Hartley
and
Ohanian,
1988;
Lewis
et
al.,
1990;
Dourson
et
al.,
1992).

The
uncertainty
factors
(
UFs)
summarized
in
Table
2.2.1
account
for
five
areas
of
scientific
uncertainty
inherent
in
most
toxicity
data
bases:
inter­
human
variability
(
H)
(
to
account
for
variation
in
sensitivity
among
the
members
of
the
human
population);
experimental
animal­
to­
human
extrapolation
(
A);
subchronic
to
chronic
extrapolation
(
S)
(
to
account
for
uncertainty
in
extrapolating
45
from
less­
than­
chronic
NOAELs
(
or
LOAELs)
to
chronic
NOAELs);
LOAEL
to
NOAEL
extrapolation
(
L);
and
data
base
completeness
(
D)
(
to
account
for
the
inability
of
any
single
study
to
adequately
address
all
possible
adverse
outcomes).
Each
of
these
five
areas
is
generally
addressed
by
the
Agency
with
a
factor
of
1,
3,
or
10.
The
default
value
is
10.

In
addition,
a
modifying
factor
(
MF)
may
be
used
to
account
for
areas
of
uncertainty
that
are
not
explicitly
considered
using
the
standard
UF.
This
value
of
the
MF
is
greater
than
zero
and
less
than
or
equal
to
10,
but
it
should
generally
be
used
on
a
log
10
basis
(
i.
e.,
0.3,
1,
3,
10)
as
are
the
standard
UFs.
The
default
value
for
this
factor
is
1.

Table
2.2.1:
Uncertainty
Factors
and
the
Modifying
Factor
Uncertainty
Factor
Definition
UFH
Use
a
1­,
3­,
or
10­
fold
factor
when
extrapolating
from
valid
data
in
studies
using
long­
term
exposure
to
average
healthy
humans.
This
factor
is
intended
to
account
for
the
variation
in
sensitivity
(
intraspecies
variation)
among
the
members
of
the
human
population.

UFA
Use
an
additional
1­,
3­,
or
10­
fold
factor
when
extrapolating
from
valid
results
of
long­
term
studies
on
experimental
animals
when
results
of
studies
of
human
exposure
are
not
available
or
are
inadequate.
This
factor
is
intended
to
account
for
the
uncertainty
involved
in
extrapolating
from
animal
data
to
humans
(
interspecies
variation).

UFS
Use
an
additional
1­,
3­,
or
10­
fold
factor
when
extrapolating
from
less­
than­
chronic
results
on
experimental
animals
when
there
are
no
useful
long­
term
human
data.
This
factor
is
intended
to
account
for
the
uncertainty
involved
in
extrapolating
from
less­
than­
chronic
NOAELs
to
chronic
NOAELs.

UFL
Use
an
additional
1­,
3­,
or
10­
fold
factor
when
deriving
an
RfD
from
a
LOAEL,
instead
of
a
NOAEL.
This
factor
is
intended
to
account
for
the
uncertainty
involved
in
extrapolating
from
LOAELs
to
NOAELs.

UFD
Use
an
additional
1­,
3­,
or
10­
fold
factor
when
deriving
an
RfD
from
an
"
incomplete"
data
base.
Missing
studies,
e.
g.,
reproductive,
are
often
encountered
with
chemicals.
This
factor
is
meant
to
account
for
the
inability
of
any
study
to
consider
all
toxic
endpoints.
The
intermediate
factor
of
3
(
½
log
unit)
is
often
used
when
there
is
a
single
data
gap
exclusive
of
chronic
data.
It
is
often
designated
as
UFD.

Modifying
Factor
Use
professional
judgment
to
determine
the
MF,
which
is
an
additional
uncertainty
factor
that
is
greater
than
zero
and
less
than
or
equal
to
10.
The
magnitude
of
the
MF
depends
upon
the
professional
assessment
of
scientific
uncertainties
of
the
study
and
data
base
not
explicitly
treated
above
(
e.
g.,
the
number
of
species
tested).
The
default
value
for
the
MF
is
1.

Note:
With
each
UF
or
MF
assignment,
it
is
recognized
that
professional
scientific
judgment
must
be
used.
46
The
Agency's
reasoning
in
its
use
of
the
MF
is
that
the
areas
of
scientific
uncertainty
labeled
H,
A,
S,
L,
or
D
do
not
represent
all
of
the
uncertainties
in
the
estimation
of
an
RfD.
For
example,
the
fewer
the
number
of
animals
used
in
a
dosing
group,
the
more
likely
it
is
that
no
adverse
effect
will
be
observed
at
a
dose
point
which
may
have
had
an
effect
in
a
larger
population.
Such
a
case
might
argue
for
modifying
the
usual
10­
fold
factors
 
a
100­
fold
UF
might
be
raised
to
250
if
too
few
animals
were
used
in
a
chronic
study.
While
this
increase
is
scientifically
reasonable,
it
introduces
two
difficulties:
the
adjustments
applied
could
differ
between
risk
assessors,
and
the
applied
precision
of
the
result
might
not
be
justified
by
the
data.
For
example,
a
UF
of
250
has
an
implied
precision
of
2
digits
and
is
not
appropriate
in
relation
to
the
variability
of
the
biological
response.
The
Agency
intends
to
avoid
these
difficulties
through
limiting
the
options
for
the
modifying
factor
(
1,
3,
10).

In
practice,
the
magnitude
of
the
overall
UF
is
dependent
on
professional
judgment
as
to
the
total
uncertainty
in
all
areas.
When
uncertainties
exist
in
one,
two
or
three
areas,
the
Agency
generally
uses
10­,
100­,
and
1,000­
fold
UF
respectively.
When
uncertainties
exist
in
four
areas,
the
Agency
generally
uses
an
UF
no
greater
than
3,000.
It
is
the
Agency's
opinion
that
toxicity
data
bases
that
are
weaker
and
would
result
in
UFs
in
excess
of
3,000
are
too
uncertain
as
a
basis
for
quantification.
In
such
cases,
the
Agency
does
not
estimate
an
RfD,
and
additional
toxicity
data
are
sought
or
awaited.
For
a
few
chemicals,
an
UF
of
10,000
was
applied.
However,
in
such
cases,
the
risk
assessment
was
completed
before
current
policies
for
the
maximum
UF
were
in
place.

The
Agency
occasionally
uses
a
factor
of
less
than
10
or
even
a
factor
of
1,
if
the
existing
data
reduce
or
obviate
the
need
to
account
for
a
particular
area
of
uncertainty.
For
example,
the
use
of
a
1­
year
rat
study
as
the
basis
of
an
RfD
may
suggest
the
use
of
a
3­
fold,
rather
than
10­
fold,
factor
to
account
for
subchronic
to
chronic
extrapolation,
since
it
can
be
empirically
demonstrated
that
1­
year
rat
NOAELs
are
generally
closer
in
magnitude
to
chronic
values
than
are
3­
month
NOAELs
(
Swartout,
1990).
Lewis
et
al.
(
1990)
more
fully
investigate
this
concept
of
variable
uncertainty
factors
through
an
analysis
of
expected
values.

The
modification
of
UFs
from
their
standard
values
should
follow
the
general
guidelines
for
composite
UFs
and
the
overall
precision
of
one
digit
for
UFs.
The
composite
uncertainty
factor
to
use
with
a
given
data
base
is
again
strictly
a
case­
by­
case
judgment
by
experts.
It
should
be
flexible
enough
to
account
for
each
of
the
applicable
five
areas
of
uncertainty
and
any
nuances
in
the
available
data
that
might
change
the
magnitude
of
any
factor.
The
Agency
describes
its
choice
for
the
composite
UF
and
sub­
components
for
individual
RfDs
on
its
Integrated
Risk
Information
System
(
IRIS).
Because
of
the
high
degree
of
judgment
involved
in
the
selection
of
uncertainty
and
MFs,
the
risk
assessment
justification
should
include
a
detailed
discussion
of
the
selection
of
uncertainty
factors,
along
with
the
data
to
which
they
are
applied.

2.2.5.2
Confidence
in
NOAEL/
LOAEL­
Based
RfD
As
stated
previously,
when
available,
adequate
data
from
acceptable
human
studies
should
be
used
as
the
basis
for
the
RfD.
Use
of
good
epidemiology
studies
generally
give
the
highest
11A
RfD
for
developmental
toxicity
(
RfD
DT)
is
discussed
in
USEPA
(
1991a).

47
confidence
in
RfDs.
In
the
absence
of
such
data,
RfDs
are
estimated
from
studies
in
experimental
animals.

The
Agency
generally
considers
a
"
complete"
data
base
for
calculating
a
chronic
RfD
for
noncancer
health
effects
to
include
the
following:

C
Two
adequate
mammalian
chronic
toxicity
studies,
by
the
appropriate
route
in
different
species,
one
of
which
must
be
a
rodent.

C
One
adequate
mammalian
multi­
generation
reproductive
toxicity
study
by
an
appropriate
route.

C
Two
adequate
mammalian
developmental
toxicity
studies
by
an
appropriate
route
in
different
species.

For
a
"
complete"
data
base,
the
likelihood
that
additional
toxicity
data
may
change
the
RfD
is
low.
Thus,
the
Agency
usually
has
confidence
in
such
an
RfD
because
additional
toxicity
data
are
not
likely
to
change
the
value.

The
Agency
considers
a
NOAEL
from
a
well­
conducted,
mammalian
subchronic
(
90­
day)
study
by
the
appropriate
route
as
a
minimum
data
base
for
estimating
an
RfD.
However,
for
such
a
data
base,
additional
toxicity
data
may
change
the
RfD.
Thus
the
Agency
generally
has
less
confidence
in
such
an
RfD.

For
some
chemicals,
an
acute
health
hazard
is
the
critical
effect
of
concern.
These
could
include
neurotoxic
or
immunotoxic
effects
of
acute
exposures
at
environmental
levels
of
contaminant.
In
such
cases,
longer
term
studies
(
subchronic
or
chronic)
that
would
typically
be
included
in
a
review
of
the
toxicity
literature
may
not
capture
the
critical
endpoint.
Under
such
circumstances,
greater
emphasis
should
be
placed
on
characterizing
the
acute
threshold
as
opposed
to
the
potential
chronic
effects.

Developmental
toxicity
data,
if
they
constitute
the
sole
source
of
information,
are
not
considered
an
adequate
basis
for
chronic
RfD
estimation.
This
is
because
such
data
are
often
generated
from
short­
term
chemical
exposures,
and,
thus,
are
of
limited
relevance
in
predicting
possible
adverse
effects
from
chronic
exposures.
However,
if
a
developmental
toxicity
endpoint
is
the
critical
effect
established
from
a
"
complete"
data
base,
a
chronic
RfD
can
be
derived
from
such
data,
applying
the
uncertainty
and
MFs
normally
required.
Developmental
data
are
the
basis
for
developmental
reference
doses
(
RfD
DT).
11
The
term
RfD
DT
is
used
to
distinguish
the
developmental
value
from
the
chronic
RfD
which
refers
to
chronic
exposure
situations.
Uncertainty
factors
for
developmental
toxicity
include
a
10­
fold
factor
for
interspecies
variation
and
a
10­
fold
factor
for
48
intraspecies
variation;
in
general,
an
uncertainty
factor
is
not
applied
to
account
for
duration
of
exposure.
In
some
cases,
additional
factors
may
be
applied
due
to
a
variety
of
uncertainties
that
exist
in
the
data
base.
For
example,
the
standard
study
design
for
developmental
toxicity
study
calls
for
a
low
dose
that
demonstrates
a
NOAEL,
but
there
may
be
circumstances
where
a
risk
assessment
must
be
based
on
the
results
of
a
study
in
which
a
NOAEL
for
developmental
toxicity
was
not
identified.
For
details
regarding
risk
assessment
for
developmental
toxicants,
refer
to
EPA
risk
assessment
guidelines
(
USEPA,
1991b).

2.2.5.3
Presenting
the
RfD
as
a
Single
Point
or
as
a
Range
Although
the
RfD
has
traditionally
been
presented
and
used
as
a
single
point
estimate,
its
definition
contains
the
phrase
".
.
.
an
estimate
(
with
uncertainty
spanning
perhaps
an
order
of
magnitude)
.
.
."
(
USEPA,
1988).
Underlying
this
concept
is
the
reasoning
that
during
the
derivation
of
the
RfD,
the
selection
of
the
critical
effect
and
of
the
total
uncertainty
factor
is
based
on
the
"
best"
scientific
judgment
of
the
Agency
Work
Group
and
that
other
groups
of
competent
scientists
examining
the
same
database
would
reach
a
similar
conclusion,
within
an
order
of
magnitude.
For
example,
although
EPA
recently
verified
a
single
number
as
the
RfD
for
arsenic
(
0.3
µ
g/
kg­
day),
there
was
not
a
clear
consensus
on
the
oral
RfD.
Applying
the
Agency's
RfD
methodology,
"
strong
scientific
arguments
can
be
made
for
various
values
within
a
factor
of
2
or
3
of
the
currently
recommended
RfD
value,
i.
e.,
0.1
to
0.8
µ
g/
kg/
day"
(
USEPA,
1993).

Presenting
the
RfD
as
a
range
may
be
more
appropriate
than
expressing
it
as
a
point
estimate
because
rarely
are
sufficient
data
available
to
precisely
determine
a
lifetime
threshold
for
a
human.
Even
when
there
are
good,
reliable
data,
the
variability
of
response
in
the
human
population
argues
for
expressing
the
RfD
as
a
range.
However,
although
EPA
supports
the
use
of
a
range
that
spans
one
order
of
magnitude
for
most
RfDs,
there
are
a
number
of
potential
interpretations
of
the
term
"
order
of
magnitude"
as
described
below:

C
Range
=
x
to
10x.
(
where
point
estimate
of
RfD=
x).
This
view
is
supported
by
those
who
believe
that
the
risk
assessment
process
is
so
inherently
conservative
that
the
RfD
should
be
considered
to
be
the
lowest
estimate,
with
the
range
of
imprecision
all
resting
above
this
point
estimate.

C
Range
=
0.3x
to
3x.
This
view
is
held
by
many
EPA
scientists
who
have
developed
RfDs.
The
RfD
point
estimate,
x,
is
the
midpoint
of
a
range
that
spans
an
order
of
magnitude.

C
Range
=
0.1x
to
x.
This
is
the
view
held
by
many
risk
managers.
Regulatory
decisions
(
e.
g.,
setting
of
standards
or
cleanup
levels)
are
made
based
on
the
assumption
that
standards
or
cleanup
levels
are
protective
as
long
as
they
do
not
exceed
the
RfD.
49
Table
2.2.2:
Some
Scientific
Factors
to
Consider
When
Using
the
RfD
Range
Use
point
estimate
RfD
­
Default
position
­
Total
UF/
MF
product
is
100
or
less
­
Essential
nutrient
Use
lower
range
of
RfD
­
Increased
bioavailability
from
medium
­
The
seriousness
of
the
effect
and
whether
or
not
it
is
reversible
­
A
shallow
dose­
response
curve
in
the
range
of
observation
­
Exposed
group
contains
a
sensitive
population
(
e.
g.,
children
or
fetuses)

Use
upper
range
of
RfD
­
Decreased
bioavailability
with
humans
­
RfD
based
on
minimal
LOAEL
and
a
UF/
MF
of
1,000
or
greater
­
A
steep
dose­
response
curve
in
the
range
of
observation
­
No
sensitive
populations
identified
C
Range
=
0.1x
to
10x.
This
range
represents
the
assumption
that
the
order
of
magnitude
range
could
be
on
either
side
of
the
point
estimate
x.

The
Agency
is
considering
a
risk
management
approach
where
the
upper
and
lower
bounds
of
the
range
are
correlated
to
the
uncertainty.
Because
the
uncertainty
around
the
dose
response
relationship
increases
as
extrapolation
below
the
observed
data
increases,
the
use
of
a
range
for
the
RfD
may
be
more
appropriate
in
characterizing
risk
than
the
use
of
a
point
estimate.
Therefore,
as
a
matter
of
risk
management
policy,
it
is
proposed
that
if
the
product
of
the
UFs
and
MF
used
to
derive
the
RfD
is
100
or
less,
there
would
be
no
consideration
of
a
range.
When
greater
than
100
but
less
than
1,000,
the
maximum
range
that
could
be
considered
would
be
one
half
of
a
log
10
(
3­
fold)
or
a
number
ranging
from
the
point
estimate
divided
by
1.5
to
the
point
estimate
multiplied
by
1.5.
At
1,000
and
above
the
maximum
range
would
be
a
log
10
(
10­
fold)
or
a
number
ranging
from
the
point
estimate
divided
by
3
to
the
point
estimate
multiplied
by
3.

EPA
advocates
the
use
of
the
point
estimate
of
the
RfD
as
the
default
to
derive
the
AWQC.
The
use
of
another
number
within
the
range
defined
by
the
uncertainty
would
then
have
to
be
justified.
As
used
in
this
document,
justification
means
that
there
are
scientific
data
which
indicate
that
some
value
in
the
range
other
than
the
point
estimate
may
be
more
appropriate
than
the
point
estimate,
based
on
human
health
or
environmental
fate
considerations.
Table
2.2.2
gives
examples
of
some
factors
to
consider
when
determining
whether
to
use
the
point
estimate
of
the
RfD
or
values
higher
or
lower
than
the
point
estimate.
The
factors
presented
in
Table
2.2.2
should
be
considered
in
making
the
decision
as
to
whether
or
not
to
use
a
value
other
than
the
point
estimate
within
a
range;
the
uncertainty
will
influence
the
magnitude
of
the
range.
50
The
use
of
an
order
of
magnitude
may
not
be
appropriate
for
all
chemicals.
There
are
many
factors
that
can
affect
the
degree
of
"
precision"
of
the
RfD,
and
thereby
affect
the
magnitude
of
the
RfD
range.
The
completeness
of
the
data
base
plays
a
major
role.
Observing
the
same
effects
in
several
animal
species,
including
humans,
can
increase
confidence
in
the
RfD
point
estimate
and
thereby
narrow
the
range
of
uncertainty.
Other
factors
that
can
affect
the
precision
are
the
slope
of
the
dose­
response
curve,
seriousness
of
the
observed
effect,
spacing
of
doses,
and
the
route
of
exposure.
For
example,
a
steep
dose­
response
curve
indicates
that
relatively
large
differences
in
effect
occur
with
a
given
change
in
dose;
thus,
there
will
be
a
greater
chance
that
the
data
will
allow
scientists
to
distinguish
clearly
(
i.
e.,
statistically)
between
doses
that
produce
an
effect
and
those
that
do
not.
For
a
situation
where
the
RfD
is
derived
from
a
LOAEL
for
a
serious
effect,
an
additional
uncertainty
factor
is
often
used
in
the
RfD
derivation
to
protect
against
less
serious
effects
that
could
have
occurred
at
lower
doses
had
lower
doses
been
evaluated.
Dose
spacing
and
the
size
of
the
study
groups
used
in
the
experiment
can
also
affect
the
confidence
in
the
RfD.
The
"
true"
NOAEL
can
fall
anywhere
between
the
experimentally
determined
NOAEL
(
the
highest
dose
administered
without
an
adverse
effect)
and
the
LOAEL
(
the
lowest
dose
administered
causing
an
observable
adverse
effect).
The
wider
the
dose
spacing,
the
greater
the
margin
of
uncertainty
about
where
the
"
true"
NOAEL
may
fall.
Finally,
for
some
RfDs,
the
route
of
exposure
in
the
experiment
may
not
match
the
route
of
exposure
in
the
environment,
and
interroute
extrapolation
may
be
considered
using
assumptions
about
differences
in
absorption
rates
between
routes.

There
are
cases
when
a
range
should
not
be
used.
For
example,
the
RfD
for
zinc
(
USEPA,
1992)
is
based
on
consideration
of
nutritional
data,
a
minimal
LOAEL,
and
a
UF
of
3.
If
the
factor
of
3
were
used
to
bound
the
RfD
for
zinc,
then
the
upper­
bound
level
would
approach
the
minimal
LOAEL.
This
situation
must
be
avoided,
since
it
is
unacceptable
to
set
a
standard
at
levels
that
may
cause
an
adverse
effect.
The
risk
manager
must
be
informed
of
those
specific
cases
when
it
is
not
scientifically
correct
to
use
the
RfD
range.
Table
2.2.2
provides
managers
with
guidelines
on
the
scientific
basis
for
using
the
range.

2.2.6
Deriving
an
RfD
Using
a
Benchmark
Dose
Approach
A
number
of
issues
have
been
raised
regarding
the
development
of
the
RfD
based
on
the
traditional
NOAEL/
LOAEL
approach.
These
concerns
include
the
following:

C
The
traditional
approach
does
not
incorporate
information
on
the
shape
of
the
doseresponse
curve,
but
focuses
only
on
a
single
point
(
the
NOAEL
or
LOAEL).

C
The
value
of
the
NOAEL
depends
on
the
number
and
spacing
of
the
doses
in
the
experiment.
The
possible
NOAEL
values
are
limited
to
the
discrete
values
of
the
experimental
doses.
Theoretically,
the
experimental
no
adverse
effect
level
could
be
any
value
between
the
experimental
NOAEL
and
the
LOAEL,
and
typically
the
true
NOAEL
is
below
the
observed
NOAEL.
51
C
Data
variability
is
not
directly
taken
into
account.
For
example,
studies
based
on
a
larger
number
of
animals
may
detect
effects
at
lower
doses
than
studies
with
fewer
animals;
as
a
result,
the
NOAEL
from
a
small
study
may
be
higher
than
the
NOAEL
from
a
similar
but
larger
study
in
the
same
species.
The
traditional
approach
does
not
have
a
mechanism
to
account
for
such
data
variability.

C
The
determination
of
the
NOAEL
is
dependent
on
the
background
incidence
of
the
effect
in
control
animals;
therefore,
statistically
significant
differences
between
the
dose
groups
and
the
control
group
are
more
difficult
to
detect
if
background
incidence
is
relatively
high,
even
if
biologically
significant
effects
occur.

C
In
conjunction
with
exposure
data,
the
NOAEL­
based
RfD
can
be
used
to
estimate
the
size
of
the
population
at
risk,
but
not
the
magnitude
of
the
risk.

In
response
to
these
concerns,
alternative
approaches
have
been
developed
that
attempt
to
address
some
of
these
shortcomings.
One
such
alternative,
the
BMD
approach,
has
been
the
subject
of
extensive
research
over
the
past
decade
(
Crump
1984,
1995;
Gaylor,
1983,
1989;
Dourson
et
al.,
1985;
Brown
and
Erdreich,
1989;
Kimmel,
1990;
Faustman
et
al.,
1994;
Allen
et
al.,
1994a,
1994b).
The
following
discussion
presents
the
general
methods
for
calculation
of
a
RfD
using
the
BMD
approach;
for
more
extensive
discussion,
the
reader
is
referred
to
Crump
et
al.
(
1995).
To
date,
the
Agency
has
used
the
BMD
approach
for
deriving
the
RfD
for
methyl
mercury
(
USEPA,
1994a)
and
the
RfC
for
several
compounds.

2.2.6.1
Overview
of
the
Benchmark
Dose
Approach
A
benchmark
dose
(
BMD)
or
concentration
(
BMC)
is
defined
as
a
statistical
lower
confidence
limit
on
the
dose
producing
a
predetermined
level
of
change
in
response
(
the
benchmark
response­
BMR)
relative
to
controls
The
BMD/
BMC
is
intended
to
be
used
as
an
alternative
to
the
NOAEL
in
deriving
a
point
of
departure
for
low
dose
extrapolations.
The
BMD/
BMC
is
a
dose
corresponding
to
some
change
in
the
level
of
response
relative
to
background
and
is
not
dependent
on
the
doses
used
in
the
study.
The
BMR
is
based
on
a
biologically
significant
level
of
response
or
on
the
response
level
at
the
lower
detection
limit
of
the
observable
dose
range
for
a
particular
endpoint
in
a
standard
study
design.
The
BMD/
BMC
approach
does
not
reduce
uncertainty
inherent
in
extrapolating
from
animal
data
to
humans
(
except
for
that
in
the
LOAEL
to
NOAEL
extrapolation),
and
does
not
require
that
a
study
identify
a
NOAEL,
only
that
at
least
one
dose
be
near
the
range
of
the
response
level
for
the
BMD/
BMC.

The
central
step
in
deriving
a
BMD
is
to
calculate
lower
bounds
directly
on
the
dose
estimate.
This
modeling
process
is
limited
to
the
experimental
range
and
no
attempt
is
made
to
extrapolate
to
doses
far
below
the
experimental
range.
Generally,
the
models
used
in
the
BMD
approach
are
statistical
rather
than
biologically­
based
models;
thus,
they
cannot
be
reliably
used
to
extrapolate
to
low
doses
without
incorporating
detailed
information
on
the
mechanisms
through
which
the
toxic
agent
causes
the
particular
effect
being
modeled.
52
Once
a
mathematical
dose­
response
curve
and
its
corresponding
curve
of
confidence
limits
are
established,
the
assessor
selects
a
point
on
the
lower
confidence
dose
curve
corresponding
to
the
chosen
BMR
(
e.
g.,
1
percent,
5
percent,
or
10
percent
increase
in
the
incidence
of
an
effect).
This
point
on
the
lower
confidence
curve
is
the
lower
confidence
bound
of
the
effective
dose
for
that
BMR
(
denoted
as
the
BMD)
(
see
Exhibit
2.2.1).
A
BMD
may
be
calculated
for
each
agent
for
which
there
is
an
adequate
data
base.

The
BMD
approach
offers
a
number
of
advantages
over
the
traditional
approach
for
the
derivation
of
the
RfD
from
the
NOAEL/
LOAEL
divided
by
uncertainty
factors.
The
advantages
of
the
BMD
approach
are
that
it
considers
the
dose­
response
curve,
including
its
shape;
better
accounts
for
statistical
variability
in
the
data;
is
not
overly
sensitive
to
dose
spacing
and,
thus,
is
not
limited
to
experimental
doses
for
determining
the
effect
level.
In
addition,
studies
with
small
group
sizes
and
evaluation
of
a
limited
number
of
endpoints,
which
may
identify
artificially
high
NOAELs,
will
tend
to
yield
lower
BMD
values
because
the
confidence
bands
will
be
wider.
The
BMD
analyses
for
developmental
effects
shows
that
the
NOAELs
from
studies
are
actually
at
about
a
5
percent
response
level
(
Faustman
et
al.,
1994).
Therefore,
the
BMD
approach
provides
an
incentive
to
conduct
more
robust
studies,
since
better
studies
give
narrower
confidence
bands.

Derivation
of
RfD
Using
BMD
Approach
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%

0
50
100
150
200
250
300
Dose
Response
ED10
LED10
RfD
UFs
Dose
Response
Modeling
uses
animal
or
human
data
Exhibit
2.2.1
53
Table
2.2.3:
Steps
and
Decisions
Required
in
the
BMD
Approach
Step
Decisions
1.
Selection
Study/
Response
1.
Experiments
to
include
2.
Responses
to
model
2.
Model
dose­
response
1.
Format
of
data
2.
Mathematical
model(
s)
3.
Handling
model
fit
4.
Measure
of
altered
response
3.
Select
BMD(
s)
1.
Critical
BMR
2.
Confidence
limit
calculation
4.
Calculate
RfD
1.
Uncertainty
factors
Source:
Crump
et
al.,
1995
2.2.6.2
Calculation
of
the
RfD
Using
the
Benchmark
Dose
Method
The
determination
of
an
RfD
using
the
BMD
approach
involves
four
basic
steps.
The
first
step
involves
the
selection
of
the
experiments
and
responses
that
will
be
used
for
modeling
the
BMD.
Second,
BMDs
are
calculated
for
the
selected
responses;
BMD
values
should
be
calculated
for
all
endpoints
that
have
the
potential
for
yielding
the
critical
BMD.
Third,
a
single
BMD
is
selected
from
among
those
calculated.
Finally,
the
RfD
is
calculated
by
dividing
the
chosen
BMD
by
appropriate
uncertainty
factors.
The
decision
points
associated
with
these
steps
are
outlined
in
Table
2.2.3.
The
discussion
below
summarizes
the
critical
issues
unique
to
the
BMD
approach.
The
following
discussion
of
the
issues
largely
incorporates
the
information
from
Crump
et
al.
(
1995).

Selection
of
Response
Data
to
Model
The
selection
of
experiments
and
responses
suitable
for
BMD
modeling
involves
considerations
similar
to
those
for
identifying
the
appropriate
studies
upon
which
to
base
a
NOAEL.
There
may
be
several
appropriate
studies
and
relevant
health
effects
that
could
be
modeled
for
a
chemical.
Ideally,
BMD
calculations
would
be
performed
for
the
complete
set
of
relevant
effects.
However,
utilizing
all
relevant
responses
for
the
calculation
of
BMDs
may
be
resource­
intensive.
Further,
it
is
difficult
to
interpret
results
from
a
large
number
of
dose­
response
analyses.
When
selecting
the
data
to
model
it
is
often
considered
appropriate
to
limit
attention
to
those
responses
for
12This
is
due
to
the
fact
that
an
effect
seen
only
at
doses
above
the
LOAEL
but
having
a
shallow
dose­
response
could
produce
a
lower
BMD
than
an
effect
seen
at
the
LOAEL,
which
has
a
steeper
dose­
response.

54
which
there
is
evidence
of
a
dose­
response
relationship.
Statistically,
such
a
relationship
may
be
indicated
by
significant
trends
(
either
increasing
or
decreasing)
in
the
response
as
dose
level
increases.
Considerations
of
biological
significance
may
also
be
warranted.
Another
alternative
is
to
focus
efforts
on
modeling
the
most
critical
effects
as
seen
at
the
LOAEL.
However,
limiting
the
number
of
responses
modeled
may
potentially
misrepresent
the
minimum
BMD.
12
Use
of
Categorical
Versus
Continuous
Data
A
central
issue
in
the
selection
of
data
to
model
concerns
the
form
of
the
data
used.
Categorical
data,
particularly
quantal
data,
are
relatively
straightforward
to
use
in
the
BMD
approach,
since
the
data
are
expressed
as
the
number
(
or
percent)
of
subjects
exhibiting
a
defined
response
at
a
given
dose.
Data
may
also
be
of
the
continuous
form,
where
results
are
expressed
as
the
measure
of
a
continuous
biological
endpoint,
such
as
a
change
in
organ
weight
or
serum
enzyme
level.
With
continuous
data
the
results
are
generally
presented
in
terms
of
means
and
standard
deviation
for
dose
groups
but
are
most
valuable
when
data
for
individual
animals
are
available.
To
perform
doseresponse
modeling
of
such
data,
the
way
the
data
are
expressed
must
be
decided.
Continuous
data
can
be
modeled
by
looking
at
the
mean
response
for
each
dose
group
as
a
fraction
of
the
mean
response
of
the
control
group
or
as
the
percentage
of
animals
showing
an
adverse
response
at
each
dose
level.
(
Gaylor
and
Slikker,
1990;
Crump,
1995).
Such
approaches
take
advantage
of
the
continuous
nature
of
the
response
data,
but
express
the
results
in
terms
that
are
directly
comparable
to
those
derived
from
analysis
of
categorical
data,
i.
e.,
in
terms
of
additional
or
extra
risk,
rather
than
in
terms
of
changes
in
mean
response.
In
particular,
Crump
(
1995)
has
extended
those
considerations
so
that
the
model
used
for
analysis
of
a
continuous
endpoint
yields
the
same
model
type
as
used
for
analysis
of
any
quantal
endpoints.
Such
developments
have
enhanced
the
consistency
of
results
across
different
endpoints
for
any
particular
chemical.
In
any
case,
application
of
the
BMD
approach
to
continuous
data
requires
professional
judgment
in
order
to
determine
what
level
or
category
of
response
constitutes
an
abnormal
(
adverse)
effect.
The
BMD
approach
is
not
recommended
for
routine
use
but
may
be
used
when
data
are
available
and
justify
the
extensive
analyses
required.

Choice
of
Mathematical
Model
Various
mathematical
approaches
have
been
proposed
for
determining
the
BMD.
Table
2.2.4
shows
a
number
of
dose­
response
models
that
may
be
used
for
estimating
the
BMD
with
quantal
or
continuous
data.

Information
generally
required
for
application
of
dose­
response
models
for
categorical
(
including
quantal)
data
includes
the
experimental
doses,
the
total
number
of
animals
in
each
dose
group,
and
the
number
of
these
whose
responses
are
in
each
of
the
categories
of
response.
For
continuous
data,
the
experimental
doses,
number
of
animals
in
each
dose
group,
mean
response
in
each
group,
and
sample
variance
of
response
in
each
dose
group
are
needed.
55
The
BMD
approach
should
not
be
applied
to
data
sets
with
only
two
experimental
groups
(
a
control
and
one
positive
dose).
In
such
cases,
much
of
the
advantage
of
the
BMD
approach
with
respect
to
consideration
of
the
dose­
response
shape
will
be
lost;
such
data
supply
little
information
about
the
shape
of
the
dose­
response
curve.
The
more
doses
available,
especially
at
lower
doses,
the
greater
the
expected
benefit
of
the
BMD
approach
as
compared
to
the
NOAEL­
based
approach.
56
Table
2.2.4:
Dose­
Response
Models
Proposed
for
Estimating
BMDs
Model
Formula
Quantal
Data
Quantal
linear
regression
(
QLR)

Quantal
quadratic
regression
(
QQR)

Quantal
polynomial
regression
(
QPR)

Quantal
Weibull
(
QW)

Log­
normal
(
LN)
P(
d)
=
c
+
(
1­
c){
1­
exp[­
q
1(
d­
d
0)]}

P(
d)
=
c
+
(
1­
c){
1­
exp[­
q
1(
d­
d
o)
2]}

P(
d)
=
c
+
(
1­
c){
1­
exp[­
q
1
d
1­...
q
k
dk]}

P(
d)
=
c
+
(
1­
c){
1­
exp[­
q
1
dk]}

P(
d)
=
c
+
(
1­
c)
N(
a+
b
log
d)

Continuous
data
Continuous
linear
regression
(
CLR)

Continuous
quadratic
regression
(
CQR)

Continuous
linear­
quadratic
regression
(
CLQR)

Continuous
polynomial
regression
(
CPR)

Continuous
power
(
CP)
m(
d)
=
c
+
q
1(
d­
d
0)

m(
d)
=
c
+
q
1(
d­
d
0)
2
m(
d)
=
c
+
q
1
d+
q
2
d2
m(
d)
=
c
+
q
1
d+...+
q
k
dk
m(
d)
=
c
+
q
1(
d)
k
Notes:
P(
d)
is
the
probability
of
a
response
at
the
dose,
d;
m(
d)
is
the
mean
response
at
the
dose,
d.
In
all
models,
c,
q1,...
qk,
and
do
are
parameters
estimated
from
the
data.
For
the
quantal
models,
0
#
c
#
1
and
qi
$
0.
For
the
CPR
model
proposed
by
Crump
(
1984),
all
the
qi
have
the
same
sign.
In
the
CLQR
model
discussed
by
Gaylor
and
Slikker
(
1990),
q1
and
q2
were
not
constrained
to
have
the
same
sign.
For
all
models,
d0
$
0,
k
$
1.
N(
x)
denotes
the
normal
cumulative
distribution
function.

SOURCE:
Crump
et
al.,
1995.
Handling
Model
Fit
57
Fitting
the
models
to
experimental
data
gives
estimates
of
the
parameters
that
describe
each
model.
This
fitting,
usually
accomplished
through
maximum
likelihood
methods,
estimates
the
probability
of
response
(
for
quantal
data)
or
the
mean
response
(
for
continuous
data)
for
each
dose
level.
Goodness­
of­
fit
tests
can
be
used
to
determine
if
a
model
adequately
describes
the
doseresponse
data.

In
many
cases,
several
models
may
fit
the
data
well.
In
these
cases,
other
considerations
can
be
used
to
select
an
appropriate
model.
For
example,
the
statistical
assumptions
underlying
the
model
should
be
reasonable
for
the
given
data.
Quantal
results,
for
example,
are
assumed
to
follow
a
binomial
distribution
around
a
dose­
dependent
expected
value.
This
assumption
requires
that
each
subject
responds
independently
and
all
have
an
equal
probability
of
responding.
Continuous
responses
for
each
dose
level
are
assumed
to
follow
a
normal
distribution
and
are
also
assumed
to
be
independent.
When
biological
factors
may
be
important
(
e.
g.,
intralitter
correlation
for
developmental
toxicity
data)
they
may
also
be
used
to
select
appropriate
models.
Another
biological
consideration
may
be
whether
or
not
a
threshold
is
assumed
to
exist.
If
a
threshold
is
expected
for
the
given
effect,
then
a
model
that
allows
for
a
threshold
dose
may
be
chosen
for
modeling.
The
biological
plausibility
of
the
dose­
response
curve
shape
should
also
be
a
consideration
in
model
selection.

Even
with
these
considerations,
several
different
models
may
often
adequately
describe
the
data.
In
these
cases,
the
choice
of
the
model
may
not
be
critical,
especially
since
the
estimation
will
be
confined
to
the
observed
dose
range.
Thus,
any
model
that
suitably
fits
the
empirical
data
is
likely
to
provide
a
reasonable
estimate
of
a
BMD.

In
certain
data
sets,
none
of
the
standard
models
may
provide
a
reasonable
fit
to
the
data.
Fit
is
assessed
statistically
by
comparing
the
model
predictions
to
the
observations.
Goodness­
of­
fit
statistics
formalize
that
comparison
and
provide
p­
values,
ranging
between
0
and
1,
as
a
measure
of
fit.
When
using
a
P
2
statistic,
larger
p­
values
are
indicative
of
good
fit;
smaller
p­
values
of
poorer
fit.
Sufficiently
small
p­
values
(
e.
g.,
less
than
0.01
or
0.05)
are
typically
viewed
as
an
indication
that
the
model
was
not
adequate
for
describing
the
observed
dose­
response
pattern.

Poor
fit
is
often
due
to
reduced
responses
at
higher
doses
that
are
inconsistent
with
the
doseresponse
trend
for
lower
doses,
perhaps
due
to
competing
toxic
processes
or
saturation
of
metabolic
systems
related
to
the
toxic
response
of
interest.
Several
procedures
can
be
used
to
adjust
the
modeling
process
in
these
circumstances.
For
example,
responses
at
the
highest
doses
could
be
eliminated,
since
those
doses
are
usually
least
informative
of
responses
in
the
lower
dose
region
of
interest.
In
the
case
of
saturated
metabolic
pathways,
pharmacokinetic
data
can
be
used
to
estimate
delivered
dose
to
the
organ
of
interest.
The
BMD
modeling
can
then
be
conducted
on
the
delivered
dose.
(
Andersen
et
al.,
1987,
1993;
Gehring
et
al.,
1978).

A
particularly
valuable
exercise
with
respect
to
all
of
these
fit
issues
is
a
visual
(
graphical)
examination
of
the
model
predictions
in
relation
to
the
observations.
This
supplements
the
formal
58
statistical
assessment
of
fit
and
may,
in
fact,
be
equally
or
more
informative.
The
statistical
test
assesses
overall
fit.
For
the
purposes
of
BMD
estimation,
fit
is
most
crucial
in
and
around
the
response
level
used
to
define
the
BMD
(
i.
e.,
the
BMR).
Thus,
for
example,
models
that
have
similar
fits
to
the
entire
data
set
may
differ
with
respect
to
their
predictions
near
the
BMR,
and
it
may
be
possible
to
select
one
over
another
on
the
basis
of
that
more
local
behavior.

Measure
of
Altered
Response
Crump
(
1984)
proposed
two
measures
of
increased
response
for
quantal
data.
These
are
additional
risk
and
extra
risk.
Additional
risk
is
simply
the
probability
of
response
at
dose
d,
P(
d),
minus
the
probability
of
response
at
zero
dose
(
control
response),
P(
0).
It
describes
the
additional
proportion
of
animals
that
respond
in
the
presence
of
a
dose.
Extra
risk
is
additional
risk
divided
by
[
1­
P(
0)].
It
describes
the
additional
proportion
of
animals
that
respond
in
the
presence
of
a
dose,
divided
by
the
proportion
of
animals
that
would
not
respond
under
control
conditions.
These
measures
are
distinguished
in
the
way
they
account
for
control
responses.
For
example,
if
a
dose
increases
a
response
from
0
to
1
percent,
both
the
additional
risk
and
the
extra
risk
is
1
percent.
However,
if
a
dose
increases
risk
from
90
to
91
percent,
the
additional
risk
is
still
1
percent,
but
the
extra
risk
is
10
percent.
The
choice
of
extra
risk
versus
additional
risk
is
based
to
some
extent
on
assumptions
about
whether
an
agent
is
adding
to
the
background
risk.
Extra
risk
is
viewed
as
the
default
because
it
is
more
conservative.

Analogous
measures
of
risk
have
been
proposed
for
continuous
data
(
Crump,
1984).
First,
altered
response
can
be
expressed
as
the
difference
between
the
mean
response
to
dose
d
minus
the
mean
control
response.
The
second
measure
is
simply
the
difference
between
dose
and
control
means
divided
by
(
i.
e.,
normalized
by)
the
control
mean
response.
The
second
measure
expresses
change
as
a
fraction
of
the
control
response
rather
than
as
an
absolute
change.

More
recent
consideration
of
BMDs
for
continuous
endpoints
have
suggested
other
alternatives.
Allen
et
al.
(
1994a,
1994b)
and
Kavlock
et
al.
(
1995)
determined
that
normalizing
changes
in
mean
responses
by
a
multiple
of
the
background
standard
deviation
produced
BMDs
that
were
comparable,
on
average
to
NOAELs.
For
the
developmental
endpoints
that
those
investigators
studied,
the
preferred
multiple
for
the
standard
deviation
was
0.5.

It
is
not
clear
when
measures
of
risk
expressed
relative
to
the
background
(
e.
g.,
extra
risk)
are
preferable
to
measures
expressed
as
absolute
changes.
Additional
research
is
required
to
provide
guidance
regarding
the
measure
of
altered
response
that
is
most
appropriate
in
particular
circumstances.
59
Selection
of
the
BMR
A
critical
decision
for
deriving
the
BMD
is
the
selection
of
the
Benchmark
level
of
risk
(
BMR).
Since
the
BMD
is
used
like
a
NOAEL
in
the
derivation
of
the
RfD,
the
BMR
should
be
selected
near
the
low
end
of
the
range
of
increased
risks
that
can
be
detected
in
a
bioassay
of
typical
size.
The
ED
10
is
frequently
chosen
as
the
BMR.
The
ED
10
is
the
dose
predicted
to
cause
a
10
percent
increase
in
the
incidence
of
the
effect
in
the
test
population.
For
some
data,
it
may
be
possible
to
adequately
estimate
the
ED
05
or
ED
01,
which
are
closer
to
a
true
no­
effect
dose.
Levels
between
the
ED
01
and
the
ED
10
are
usually
the
lowest
levels
of
risk
that
can
be
estimated
adequately
for
binomial
endpoints
from
standard
toxicity
studies
(
Crump,
1984).
Another
consideration
is
the
goal
of
model
independence.
Different
dose­
response
models
may
fit
the
data
equally
well
yet
give
very
different
estimates
of
risk
far
below
the
observable
range
(
Crump,
1984).
This
argues
for
use
of
a
BMR
close
to
the
range
of
responses
that
can
be
reliably
measured
in
typical
studies.

During
a
BMD
Workshop,
sponsored
by
EPA,
participants
generally
agreed
that
the
appropriate
BMR
should
either
be
5
percent
or
10
percent,
but
acknowledged
that
future
research
might
demonstrate
the
advisability
of
selecting
one
value
over
another
(
ILSI,
1993).
Research
by
Allen
et
al.
(
1994a,
1994b)
and
Faustman
et
al.
(
1994)
indicates
that
BMDs
defined
in
terms
of
10
percent
increases
in
probability
of
response
tend
to
be,
on
average,
similar
to
corresponding
NOAELs
for
quantal
developmental
toxicity
studies.
For
the
purposes
of
water
quality
criteria
derivation,
EPA
recommends
the
use
of
the
ED
05
or
ED
10
when
deriving
a
BMD.

Calculating
the
Confidence
Interval
The
BMD
is
defined
to
be
the
lower
confidence
bound
on
the
dose
corresponding
to
the
selected
BMR.
A
statistical
lower
confidence
limit
is
used
rather
than
a
maximum
likelihood
estimate
(
MLE)
for
several
reasons.
The
use
of
confidence
limits
accounts
for
the
sample
size
of
the
experiment;
the
fact
that
NOAELs
do
not
account
for
sample
size
is
a
major
criticism
of
NOAELbased
derivation
of
the
RfD.
Furthermore,
a
lower
confidence
limit
is
more
stable
to
minor
changes
in
data
and,
rarely,
may
be
estimable
where
a
MLE
is
not.

To
calculate
the
upper
confidence
bound
on
response,
and
subsequently,
the
lower
bound
on
effective
dose,
decisions
must
be
made
regarding
the
selection
of
the
procedure
for
calculating
confidence
limits
and
the
size
of
the
confidence
limits.

The
recommended
method
used
to
calculate
the
confidence
bounds
on
the
curve
relies
on
maximum
likelihood
theory.
This
approach
is
the
same
one
used
by
EPA
in
the
computer
program
for
cancer
dose­
response
modeling.
The
approach
can
be
applied
to
BMD
modeling
using
commercially
available
software.
A
detailed
explanation
of
theory
supporting
this
approach
is
found
in
Crump
(
1984).

By
convention,
the
size
of
the
statistical
confidence
limits
can
range
from
90
to
99
percent.
The
methods
of
confidence
limit
calculation
and
choice
of
confidence
limits
are
critical.
The
Agency
60
recommends
the
use
of
one­
sided
95th
percentile
confidence
limits
for
BMD
modeling.
This
is
consistent
with
the
size
of
the
confidence
limits
used
in
cancer
dose­
response
modeling.

Selection
of
the
BMD
As
the
Basis
for
the
RfD
An
important
decision
is
the
choice
of
the
appropriate
BMD
to
use
in
the
RfD
calculation
when
multiple
BMDs
are
calculated.
Multiple
BMDs
can
be
calculated
when
different
models
fit
the
response
data
for
a
single
study,
when
more
than
one
response
is
modeled
in
a
single
study,
and
when
there
are
different
BMDs
from
different
studies.
When
multiple
BMDs
exist
because
several
models
fit
a
single
data
set,
the
analyst
may
select
the
smallest
BMD
or
combine
BMDs
by
using
the
geometric
average.
When
multiple
BMDs
are
calculated
due
to
different
responses
or
different
studies
that
examine
the
same
endpoint,
the
choice
among
BMDs
may
also
involve
the
selection
of
the
"
critical
effect"
and
the
most
appropriate
species,
sex,
or
other
relevant
feature
of
experimental
design.

Use
of
Uncertainty
Factors
with
BMD
Approach
Once
a
single
or
averaged
BMD
is
selected,
the
RfD
can
be
calculated
by
dividing
the
BMD
by
one
or
more
uncertainty
factors.
It
is
still
necessary
to
use
uncertainty
factors
with
a
BMD,
because
the
BMD
can
miss
sensitive
subpopulations
and
is
still
subject
to
interspecies
extrapolation
uncertainties.
As
a
default,
all
applicable
uncertainty
factors
used
in
the
traditional
NOAEL­
based
RfD
approach,
except
for
the
LOAEL­
NOAEL
extrapolation
factor,
should
be
retained.
Other
factors,
such
as
the
size
of
the
BMR
and
confidence
bounds,
biological
considerations
(
such
as
the
possibility
of
a
threshold),
severity
of
the
modeled
effect,
and
the
slope
of
the
dose­
response
curve,
may
affect
the
choice
and
magnitude
of
uncertainty
factors
(
see
Crump
et
al.,
1995,
for
more
detailed
discussion).

2.2.6.3
Limitations
of
the
BMD
Approach
The
BMD
approach
has
been
proposed
as
an
alternative
procedure
that
can
be
used
until
biologically
motivated
approaches
are
available
for
some
or
all
effects.
It
provides
specific
improvements
over
NOAEL­
based
approaches,
but
by
no
means
does
it
resolve
all
issues
or
difficulties
associated
with
noncancer
risk
assessment.
The
BMD
approach
allows
for
objective
extrapolation
of
animal
response
data
to
human
exposures
across
the
different
study
designs
encountered
in
noncancer
risk
assessment.

2.2.6.4
Example
of
the
Application
of
the
BMD
Approach
The
following
provides
a
simple
example
of
the
application
of
the
BMD
approach
to
quantal
toxicity
data.
The
example
given
is
taken
from
Crump
et
al.
(
1995)
for
acrylamide.
The
purpose
of
presenting
this
example
is
to
illustrate
the
method
only;
no
actual
risk
value
nor
AWQC
for
acrylamide
is
derived.
61
Selection
of
Data
to
Model
This
example
takes
the
approach
of
identifying
a
critical
study
rather
than
modeling
all
endpoints
seen
in
valid
studies.
For
this
example,
a
6­
month
dietary
study
of
neurological
effects
in
rats
is
used
as
the
critical
study
for
acrylamide
(
Johnson
et
al.,
1986,
as
cited
in
Crump
et
al.,
1995).
The
endpoint
examined
in
this
study
was
tibial
nerve
degeneration.
The
researchers
recorded
the
occurrence
of
nerve
degeneration
in
two
categories:
none
or
mild,
and
moderate
or
severe.
Since
mild
nerve
degeneration
occurs
spontaneously
in
older
rats,
and
because
mild
degeneration
showed
no
dose­
response
relationship,
only
moderate
and
severe
degeneration
were
recorded
as
responses.
The
data
are
presented
in
quantal
form,
with
no
or
mild
degeneration
considered
"
no
response,"
and
moderate
to
severe
degeneration
recorded
as
a
response.
The
dose
levels
and
number
of
animals
responding
in
each
dose
group
are
shown
in
Table
2.2.5.

Table
2.2.5:
Rats
Experiencing
Moderate
or
Severe
Nerve
Degeneration
in
Response
to
Acrylamide
Dose
Dose
(
mg/
kg­
day)
Number
affected
Number
tested
0
9
60
0.01
6
60
0.1
12
60
0.5
13
60
2.0
16
60
Choice
of
Mathematical
Model
From
Table
2.2.4,
we
can
select
from
among
the
various
models
available
for
quantal
data.
Fitting
is
accomplished
through
the
use
of
maximum
likelihood
estimation
to
estimate
the
probability
of
a
response
at
each
dose
level.
The
actual
fitting
exercise
is
done
through
the
use
of
computer
software.

Results
of
Information
Above
All
of
the
models
can
be
tried
to
see
which
achieves
the
best
fit.
The
following
Exhibits
illustrate
the
best­
fit
modeling
of
the
study
data
for
the
Weibull
model
(
Exhibit
2.2.2)
and
the
quadratic
model
(
Exhibit
2.2.3).
Table
2.2.6
provides
the
best­
fit
model
parameters
for
the
two
equations.
62
­
0.10
0.00
0.10
0.20
0.30
0.40
0.50
0
0.5
1
1.5
2
2.5
Dose
(
µ
g/
kg­
d)
P(
d)
P(
d)
Modeled
P(
d)
Observed
P(
d)
99th
P(
d)
95th
P(
d)
90th
LED10
Exhibit
2.2.2
Quantal
Weibull
Regression
­
Extra
Risk
­
0.10
0.00
0.10
0.20
0.30
0.40
0.50
0
0.5
1
1.5
2
2.5
Dose
(
µ
g/
kg­
d)
P(
d)
P(
d)
Modeled
P(
d)
Observed
P(
d)
99th
P(
d)
95th
P(
d)
90th
LED
10
Exhibit
2.2.3
Quantal
Quadratic
Regression
­
Extra
Risk
63
ER(
d)
'
[
P(
d)
&
(
P(
0)]/[
1
&
P(
0)]

(
Equation
2.2.2)
Table
2.2.6:
Best­
Fit
Model
Parameters
from
Modeling
of
the
Acrylamide
Data
Model
Background
rate
q1
k
chi­
square
p
value
Quantal
Weibull
0.15
0.08
1
0.48
Quantal
quadratic
0.16
0.034
­­
0.34
Note
that
in
example
given
here,
the
measure
of
altered
response
is
extra
risk,
which
is
defined
as:

Extra
risk
is
the
fraction
of
animals
that
respond
when
exposed
to
a
dose,
d,
among
animals
who
otherwise
would
not
respond.

Both
models
fit
the
data
adequately
as
shown
in
Table
2.2.6.
In
both
cases
the
chi­
squared
goodness
of
fit
yields
P­
values
greater
than
0.05.
Therefore,
either
model
can
be
used
for
derivation
of
BMD.
Neither
model,
as
fitted
to
this
data
set,
suggests
a
threshold
for
this
response.
However,
both
models
do
indicate
a
background
rate
in
the
absence
of
exposure
to
acrylamide.

Selection
of
the
BMR
For
the
data
set
discussed
above,
the
BMDs
were
calculated
using
the
quantal
Weibull
and
the
quantal
quadratic
models
for
1,
5,
and
10
percent
extra
risk
(
Table
2.2.7
estimates
are
in
units
of
mg/
kg­
day):
64
Table
2.2.7:
BMD
Values
Calculated
Using
Quantal
Weibull
and
Quadratic
Models
Model
BMR
BMD
(
mg/
kg­
day)
for
Confidence
Limit:

90th
percentile
95th
percentile
99th
percentile
Weibull
10
0.73
0.64
0.52
5
0.35
0.31
0.25
1
0.07
0.06
0.05
Quadratic
10
1.28
1.19
1.06
5
0.89
0.83
0.74
1
0.39
0.37
0.33
The
calculated
BMDs
are
about
a
factor
of
two
apart
for
the
BMD
10
values,
but
are
about
a
factor
of
six
apart
for
the
BMD
1.
This
demonstrates
the
model
dependence
of
the
BMD
values
when
low
BMR
levels
are
selected.

Calculating
the
Confidence
Interval
As
shown
in
Table
2.2.7,
the
BMDs
were
calculated
for
90th,
95th,
and
99th
percentile
confidence
limits.
The
effect
of
the
confidence
limit
on
the
estimated
BMD
was
slightly
less
for
the
quantal
quadratic
than
for
the
quantal
Weibull.
Model
results
were
most
comparable
for
the
90th
and
95th
percentile
confidence
limits
and
least
comparable
for
the
99th
percentile
confidence
limits.
These
results
demonstrate
that
the
BMD
tends
to
be
more
model­
dependent
for
wider
(
higher
percentile)
confidence
intervals.
For
the
remainder
of
the
example,
the
95th
percentile
confidence
limit
estimate
is
used.

Selection
of
the
BMD
as
the
Basis
for
the
RfD
The
example
above
yields
different
95th
percentile
BMD
10
values
based
on
the
two
models.
Since
there
is
no
basis
upon
which
to
eliminate
one
of
the
BMDs
(
i.
e.,
goodness
of
fit,
statistical
assumptions
and
biological
considerations),
both
must
be
considered.
Either
the
smaller
estimate
may
be
used,
or
a
geometric
average
may
be
used.
In
this
case,
the
selection
of
which
BMD
to
use
is
a
risk
management
decision.
In
the
example,
the
lower
of
the
two
BMDs
(
0.64)
was
chosen
for
the
RfD
calculation.
13Note
that
the
logistic
regression
could
be
used
to
estimate
the
response
to
exposures
greater
than
the
RfD.
BMD
models
could
be
used
similarly,
but
caution
is
warranted
when
doing
so
in
either
case.

65
Use
of
Uncertainty
Factors
with
BMD
Approach
Once
the
BMD
is
chosen,
the
RfD
is
derived
by
dividing
the
BMD
by
uncertainty
factors.
The
same
uncertainty
factors
applied
to
a
NOAEL
are
used.
In
this
case
a
factor
of
10
was
selected
for
interspecies
extrapolation
and
a
factor
of
10
for
human
interspecies
variability.
Using
an
total
UF
of
100
and
applying
it
to
the
95th
percentile
confidence
limit
BMD
for
10
percent
response
derived
with
the
quantal
Weibull
model
yields
an
RfD
of
0.006
mg/
kg­
day.

2.2.7
Categorical
Regression
2.2.7.1
Summary
of
the
Method
Categorical
regression
is
another
method
under
investigation
to
estimate
risks
associated
with
systemic
toxicity
(
Dourson
et
al.,
1997;
Guth
et
al.,
1997).
In
this
approach,
health
effects
are
grouped
into
ordered
severity
categories
(
ranging
from
no
effect
to
severe
effect).
This
simplification
allows
for
the
incorporation
into
the
analysis
of
both
quantal
and
continuous
data,
as
well
as
data
that
are
reported
qualitatively
rather
than
quantitatively.
Furthermore,
information
on
many
health
effects
can
be
considered
together.
Logistic
regression
analysis
techniques
are
then
applied
to
the
data:
the
cumulative
odds
of
falling
into
severity
categories
is
the
dependent
variable,
and
exposure
concentration,
exposure
duration,
and
other
parameters
are
the
independent
variables.
Using
the
regression
results,
the
RfD
is
then
specified
as
the
dose
at
which
the
probability
of
adverse
effects
is
sufficiently
small
at
some
level
of
confidence,
modified,
as
in
the
NOAEL
and
BMD
approaches,
by
appropriate
uncertainty
factors.
For
example,
the
dose
of
interest,
D,
might
be
defined
as
that
dose
for
which
one
could
conclude
with
95
percent
certainty
that
the
probability
of
an
adverse
effect
was
less
than
0.01.
The
value
D
would
then
be
adjusted
by
uncertainty
factors
to
derive
the
RfD.
13
2.2.7.2
Steps
in
Applying
Categorical
Regression
The
categorical
regression
approach
begins
with
a
review
of
the
toxicological
data
base
for
the
chemical.
For
each
valid
study,
the
toxic
responses
observed
are
assigned
to
one
of
several
ordered
severity
categories,
based
on
biological
and
statistical
considerations.
For
example,
responses
may
be
grouped
into
four
categories:
(
1)
no
effect;
(
2)
no
adverse
effect;
(
3)
mild­
tomoderate
adverse
effect;
and
(
4)
severe
or
lethal
effect.
These
correspond
to
the
dose
categories
used
in
setting
the
RfD,
namely
the
No
Observed
Effect
Level
(
NOEL),
NOAEL,
LOAEL,
and
Frank
Effect
Level
(
FEL),
respectively.

Since
all
response
data
are
used
in
categorical
regression
analysis,
there
is
no
need
to
specify
the
lowest
dose
showing
"
mild­
to­
moderate"
adverse
effects.
Accordingly,
a
more
general
term,
adverse­
effect
level
(
AEL),
is
generally
used
in
categorical
regression
in
place
of
the
term
LOAEL
to
describe
mild­
to­
moderate
effects.
66
The
probability
of
observing
a
response
in
a
category
at
a
given
dose
level
is
estimated
by
dividing
the
number
of
responses
observed
in
that
severity
category
divided
by
the
total
number
of
observations
recorded
for
that
dose
level.
Sufficient
numbers
of
dose
groups
in
each
of
several
categories
are
required
for
the
categorical
regression.
Judgment
is
required
to
define
the
types
of
effects
that
correspond
to
the
severity
categories.

The
log
odds
for
each
dose
and
severity
level
is
calculated,
and
then
regressed
against
dose.
The
resulting
regression
equation
can
be
used
to
calculate
the
probability
of
an
effect
of
given
severity
for
any
dose.

Several
model
structures
(
logistic,
Weibull,
or
others)
may
be
used
to
perform
the
categorical
regression.
Logistic
regression
on
the
ordered
categories
(
Harrell,
1986;
Hertzberg,
1989)
allows
the
dependent
variable
(
e.
g.,
severity
parameter)
to
be
categorical
and
the
independent
variables
to
be
either
categorical
or
continuous.

The
goodness
of
the
fit
of
the
model
to
the
data
can
be
judged
using
several
statistical
measures,
including
the
overall
P
2,
model
parameter
standard
errors
and
their
P
2
significance
levels,
concordance
statistics
and
correlation
coefficients
for
the
overall
model,
and
the
model
covariates
(
Hertzberg
and
Wymer,
1991).
A
variety
of
criteria
are
currently
being
investigated.

Some
advantages
of
using
the
categorical
regression
to
derive
the
RfD
include
the
following:
data
concerning
different
health
effects
can
be
incorporated;
it
allows
for
refinement
through
improved
data
and
statistical
methodology;
and
several
indicators
of
uncertainty
in
the
estimates
are
provided.
In
addition,
the
categorical
regression
approach
can
be
used
to
evaluate
likely
responses
to
exposures
above
the
RfD.

2.2.8
Chronic,
Practical
Nonthreshold
Effects
Noncarcinogens
are
generally
assumed
to
exhibit
a
threshold
below
which
adverse
effects
are
unlikely
to
occur.
There
are,
however,
exceptions
to
this
general
rule.
Of
particular
concern
are
teratogenic
and
reproductive
toxicants
that
may
act
through
a
genetic
mechanism.
EPA
has
recognized
the
potential
for
genotoxic
teratogens
and
germline
mutagens
and
discussed
this
issue
in
the
1991
Amendments
to
Agency
Guidelines
for
Health
Assessments
of
Suspect
Developmental
Toxicants
(
USEPA,
1991a)
and
in
the
1986
Guidelines
for
Mutagenicity
Risk
Assessment
(
USEPA,
1986a).
Various
statements
within
these
guidelines
raise
concern
for
the
potential
for
future
generations
inheriting
chemically
induced
germline
mutations
or
suffering
from
mutational
events
occurring
in
utero.
An
awareness
of
the
potential
for
such
teratogenic/
mutagenic
effects
should
be
established
in
order
to
deal
with
such
data.
At
this
time,
genotoxic
teratogens
and
germline
mutagens
should
be
considered
an
exception
and
the
traditional
uncertainty
factor
approach
the
rule
for
calculating
criteria
or
values
for
chemicals
demonstrating
developmental/
reproductive
effects.
In
the
absence
of
adequate
data
to
support
a
genetic
or
mutational
basis
for
developmental
or
reproductive
effects,
the
default
becomes
an
uncertainty
factor
approach.
For
such
chemicals,
this
guidance
recommends
the
procedures
described
above
for
noncarcinogens
assumed
to
have
a
threshold.
67
Where
evidence
for
a
genetic
or
mutational
basis
does
exist,
a
nonthreshold
mechanism
shall
be
assumed
for
genotoxic
teratogens
and
germline
mutagens.
Since
there
is
no
well
established
mechanism
for
calculating
criteria
protective
of
human
health
from
the
effects
of
these
agents,
criteria
will
be
established
on
a
case­
by­
case
basis.

2.2.9
Acute,
Short­
Term
Effects
States
may
choose
to
derive
criteria
that
correspond
to
acute
or
short­
term
exposures.
These
criteria
should
correspond
to
a
level
of
exposure
that
is
"
without
appreciable
risk
of
deleterious
effects
during
some
relatively
short
period
of
time"
(
USEPA,
1991c).
The
derivation
of
such
values
follows
the
same
general
approaches
described
above
for
criteria
based
on
chronic
effects.
The
primary
difference
lies
in
the
type
of
toxicity
data
used
as
the
basis
for
the
evaluation.
Generally,
studies
that
mimic
the
exposure
pattern
and
duration
of
interest
will
be
considered
more
relevant
to
the
development
of
acute
or
short­
term
criteria.
This
is
especially
important
where
acute
or
shortterm
effects
are
of
a
substantially
different
nature
than
low­
level
chronic
effects.
Where
toxicity
data
do
not
match
the
exposure
of
interest,
professional
judgment
is
required
to
evaluate
the
relevance
of
the
available
data.
Factors
such
as
the
pharmacokinetics,
potential
recovery
periods,
and
potential
for
bioaccumulation
should
be
considered
in
judging
the
relevance
of
the
data.

The
Office
of
Water
has
established
procedures
for
deriving
Health
Advisories
(
HAs)
for
one
day,
ten
days,
and
longer­
term.
In
general
HAs
are
developed
by
using
NOAELs
or
LOAELs
from
studies
with
similar
duration
to
the
exposure
period
of
concern,
though
there
is
some
flexibility
in
this
regard.
Studies
used
for
HAs
should
provide
information
on
the
critical
endpoint.
Studies
that
identify
only
frank
toxic
responses
should
not
be
used
since
these
levels
are
far
above
the
protective
level
targeted
by
HAs.
More
information
on
the
derivation
of
HAs
is
given
in
Ware
(
1988).

2.2.10
Mixtures
Exposures
to
multiple
noncarcinogens
may
occur
simultaneously.
Possible
interactions
among
chemicals
in
a
mixture
are
usually
placed
in
one
of
three
categories:

°
Antagonistic,
where
the
chemical
mixture
exhibits
less
toxicity
than
is
suggested
by
the
sum
of
the
toxic
effects
of
the
components.

°
Synergistic,
where
the
chemical
mixture
exhibits
greater
toxicity
than
is
suggested
by
the
sum
of
the
toxic
effects
of
the
components.

°
Additive,
where
the
toxicity
of
the
chemical
mixture
is
equal
to
the
sum
of
the
toxicities
of
the
components.

In
only
a
few
instances
have
the
interactive
effects
of
chemical
mixtures
been
specifically
studied.
Where
data
on
the
effects
of
chemical
mixtures
exist,
they
should
be
used
to
characterize
risk.
Using
the
available
data
is
especially
important
in
cases
where
the
resulting
toxic
effect
from
the
68
HI
mix
'
j
n
m
'
1
E
m
RfD
m
(
Equation
2.2.3)
mixture
has
been
demonstrated
to
be
greater
than
the
sum
of
the
individual
effects.
Where
specific
data
are
not
available
on
the
interactive
effects
of
particular
chemical
mixtures,
the
methods
described
below
can
be
used
by
states
to
characterize
risks
from
chemical
mixtures.
When
risks
from
multiple
chemicals
are
added,
the
quality
of
experimental
evidence
that
supports
the
assumption
of
dose
addition
should
be
stated
clearly
(
USEPA,
1986b).

In
cases
where
the
chemicals
in
the
mixture
induce
the
same
effect
by
similar
modes
of
action,
contaminants
may
be
assumed
to
contribute
additively
to
risk
(
USEPA,
1986b),
unless
specific
data
indicate
otherwise.
To
characterize
risks
from
multiple
chemical
exposure
to
noncarcinogens,
the
dose
for
each
chemical
with
a
similar
effect
first
is
expressed
as
a
fraction
of
its
RfD.
These
ratios
are
added
for
all
chemicals
to
obtain
the
chemical
mixture
hazard
index:

where
HI
mix
is
the
hazard
index
of
the
mixture
(
unitless),
E
m
is
the
exposure
to
chemical
m,
RfD
m
is
the
reference
dose
for
chemical
m,
and
n
is
the
number
of
chemicals
in
the
mixture.
A
hazard
index
greater
than
one
implies
that
the
individual
is
at
some
risk
of
the
non­
carcinogenic
effect,
and
the
concern
is
the
same
as
if
exposure
from
an
individual
chemical
exceeded
the
acceptable
level
by
the
same
amount
(
USEPA,
1986b).
However,
the
numerical
value
of
the
hazard
index
does
not
indicate
the
magnitude
and
severity
of
the
risk.

Some
chemical
mixtures
may
contain
chemicals
that
cause
dissimilar
health
effects.
Methods
currently
do
not
exist
for
combining
dissimilar
health
effects
to
characterize
overall
health
concerns
from
chemical
mixtures.
Instead,
States
should
characterize
and
present
the
risks
from
these
contaminants
separately.

2.2.11
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Information
System
(
IRIS).
Online.
(
Verification
date
2/
01/
93).
Cincinnati,
OH:
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.

USEPA.
1994a.
Reference
Dose
(
RfD)
for
Oral
Exposure
for
Methylmercury.
Integrated
Risk
Information
System
(
IRIS).
Online.
(
Verification
date
11/
23/
94).
Cincinnati,
OH:
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
72
USEPA.
1994b.
Guidelines
for
Reproductive
Toxicity
Risk
Assessment.
External
Review
Draft.
EPA/
600/
AP­
94/
001.
February.

USEPA.
1995.
RQ
Document
for
Solid
Waste.
Report
on
the
Benchmark
Dose
Peer
Consultation
Workshop:
Risk
Assessment
Forum.
Office
of
Research
and
Development.
EPA/
630/
R96/
011.
November.

USEPA.
1998.
Notice
of
Draft
Revisions
to
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Federal
Register
Notice.

Ware,
G.
W.
(
ed).
1988.
Reviews
of
Environmental
Contamination
and
Toxicology:
U.
S.
Environmental
Protection
Agency
Office
of
Drinking
Water
Health
Advisories.
Vol.
104.
New
York:
Springer­
Verlag,
Inc.

Zielhuis,
R.
L.
and
F.
W.
van
der
Kreek.
1979.
The
Use
of
a
Safety
Factor
in
Setting
Health
Based
Permissible
Levels
for
Occupational
Exposure.
Int.
Arch.
Occup.
Environ.
Health.
42:
191­
201.

2.3
Exposure
Analyses
2.3.1
Role
of
Exposure
Data
in
Setting
AWQC
The
AWQC
are
primarily
established
to
protect
individuals
from
adverse
health
effects
caused
by
pollutants
in
United
States'
inland
and
estuarine
waters.
To
achieve
this
goal,
exposure
factors
representative
of
the
population
to
be
protected
should
be
used
in
the
equations
to
derive
the
criteria
(
see
Section
2.3.4.2
for
the
equations
to
derive
criteria).
In
addition,
exposures
from
other
non­
water
sources
such
as
air
and
food
should
be
taken
into
account
so
that
the
criteria
are
protective
of
individuals
who
may
be
exposed
to
a
particular
pollutant
from
multiple
exposure
routes.

The
following
sections
describe
data
available
to
determine
exposure
factors
and
discuss
methods
for
incorporating
non­
water
sources
of
exposure,
including
EPA's
recommended
default
values
and
methods.
In
addition,
Table
2.3.1
presents
sources
of
exposure­
related
information.
While
not
intended
to
be
a
comprehensive
list,
Table
2.3.1
does
indicate
some
of
the
readily
available
sources
for
contaminant
concentration
data
and
exposure
intake
parameters.

2.3.2
Exposure
Factors
in
AWQC
Algorithms
Several
exposure
factors
are
included
in
the
equations
to
derive
AWQC.
These
factors
include
(
1)
body
weight
of
the
individuals
exposed;
(
2)
drinking
water
ingestion
rates;
(
3)
fish
consumption
rates;
(
4)
incidental
ingestion
of
water;
and
(
5)
the
relative
source
contribution
factor
to
account
for
other
exposures.
Body
weights
and
fish
intake
assumptions
are
used
in
each
criterion.
73
The
uses
of
and
values
for
these
factors
differ
based
on
several
considerations.
One
consideration
in
the
choice
of
values
for
a
specific
exposure
parameter
depends
on
whether
the
water
body
has
been
designated
as
a
drinking
water
supply
source
or
as
a
recreational,
non­
potable
source.
The
drinking
water
ingestion
rate
is
recommended
for
use
for
those
waters
designated
as
public
drinking
water
supply
sources.
This
rate
represents
the
amount
of
water
an
individual
drinks
per
day
(
see
Section
2.3.2.2
for
further
discussion
of
the
general
policy
to
include
a
drinking
water
ingestion
rate
when
setting
AWQC).
For
waters
that
are
used
for
recreational
purposes,
an
individual
may
incidentally
swallow
water
when
swimming
or
waterskiing.
Thus,
an
incidental
ingestion
rate
would
be
applied
in
these
circumstances.
Although
it
is
possible
that
individuals
would
incidentally
ingest
water
from
drinking
water
sources,
incidental
ingestion
is
not
included
for
these
sources
of
intake
because
the
incidental
ingestion
rate
is
negligible
when
the
assumed
daily
drinking
water
ingestion
rate
is
utilized.

Another
consideration
in
determining
the
exposure
factors
used
is
whether
health
effects
result
from
chronic
exposure
or
whether
developmental
health
effects
are
being
evaluated.
For
example,
if
a
chemical
causes
both
developmental
and
chronic
health
effects,
a
State
or
Tribe
may
wish
to
evaluate
the
chemical
using
relevant
chronic
or
developmental
exposure
factors
associated
with
those
health
effects,
respectively,
to
determine
whether
to
set
criteria
based
on
chronic
or
developmental
effects.
For
chronic
health
effects,
intake
rates
and
body
weights
of
adults
or
rates
relevant
to
lifetime
exposures
are
the
most
applicable
because
the
health
effects
are
associated
with
a
long
period
of
exposure.
However,
for
pollutants
that
may
cause
health
effects
after
shorter­
term
exposure
to
a
chemical,
exposure
factors
for
children
may
be
most
useful
when
setting
criteria
for
RfDs
based
on
health
effects
in
children,
because
children
often
have
a
higher
intake
per
body
weight
than
adults.
In
addition,
children
may
be
more
susceptible
to
certain
contaminants
than
adults,
and
may
have
less
capability
to
detoxify
contaminants
(
USEPA,
1994a).
Thus,
for
such
potential
situations,
EPA
default
values
include
intake
rates
for
children.

Table
2.3.1:
Sources
of
Contaminant
Concentration
and
Exposure
Intake
Information
Name
of
Source
Type
of
Information
Agency/
Author
Aerometric
Information
Retrieval
System
(
AIRS)
An
updated
air
data
base
of
many
different
sites
(
from
rural
to
urban/
industrial)
that
includes
Federally
required
information,
as
well
as
data
submitted
voluntarily
by
States.
Office
of
Air
Quality
Planning
and
Standards,
EPA
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII)
A
national
food
consumption
survey
conducted
approximately
annually.
U.
S.
Department
of
Agriculture
(
USDA)

Exposure
Factors
Handbook
Summarization
of
studies
and
data
bases
to
provide
statistical
data
on
factors
used
in
assessing
exposure.
National
Center
for
Environmental
Assessment,
EPA
Table
2.3.1:
Sources
of
Contaminant
Concentration
and
Exposure
Intake
Information
Name
of
Source
Type
of
Information
Agency/
Author
74
Inventory
of
Exposure­
Related
Data
Systems
Sponsored
by
Federal
Agencies
Compilation
of
information
on
Federally
managed
data
systems
that
contain
exposure
information.
Agency
for
Toxic
Substances
and
Disease
Registry;
Centers
for
Disease
Control;
EPA
National
Food
Consumption
Survey
(
NFCS)
A
national
food
consumption
survey
conducted
each
decade
by
USDA.
The
last
survey
was
conducted
in
1987­
88.
USDA
National
Health
and
Nutrition
Examination
Survey
(
NHANES)
A
national
health
and
nutrition
survey
conducted
each
decade.
Based
on
a
probability
sample
of
noninstitutionalized
people
residing
in
the
U.
S.
National
Center
for
Health
Statistics
National
Inorganics
and
Radionuclides
Survey
(
NIRS)/
National
Pesticides
Survey
(
NPS)
Most
recent
Federal
surveys
(
mid
to
late
1980s)
conducted
to
characterize
occurrence
of
a
series
of
inorganic
and
radionuclide
chemicals
(
NIRS)
and
pesticides
(
NPS)
in
public
drinking
water
supplies
from
ground
water
sources
(
and
rural
domestic
wells
with
the
NPS).
EPA
National
Sediment
Inventory
(
NSI)
Compilation
of
available
data
bases
on
sediment
contamination/
sediment
chemistry
data.
These
include
data
on
fish
tissue
residues
of
chemical
contaminants.
EPA
Safe
Drinking
Water
Information
System
(
SDWIS)
Compiled
data
that
includes
monitoring
required
and
provided
under
the
program
for
unregulated
contaminants
(
Section
1445
of
the
Safe
Drinking
Water
Act).
EPA
Total
Diet
Study
(
TDS)
­
also
known
as
Market
Basket
Survey
Contaminant
concentrations
in
foods
purchased
from
supermarkets
or
grocery
stores
throughout
the
U.
S.
four
to
five
times
a
year.
Food
items
in
the
TDS
are
of
similar
type
included
in
the
NFCS
and
the
second
NHANES
(
both
described
above).
Food
and
Drug
Administration
Total
Water
and
Tap
Water
Intake
in
the
United
States:
Population­
Based
Estimates
of
Quantities
and
Sources
Presents
estimates
of
total
water
(
includes
water
intrinsic
to
foods)
and
tap
water
intake
in
the
population
of
the
continental
U.
S.
Data
used
are
based
on
the
NFCS
(
described
above).
Ershow
and
Cantor
(
NCI/
NIH)

Shorter­
term
exposures
may
also
pose
risks
to
other
people
with
special
susceptibilities
due
to
illness
(
e.
g.,
persons
with
kidney,
liver,
or
other
diseases
may
be
especially
vulnerable
to
toxins
which
attack
those
systems).
When
States
and
Tribes
assess
intake
from
pollutants
that
cause
toxicity
resulting
from
such
exposures,
they
may
wish
to
investigate
intakes
for
these
other
population
groups
that
may
also
have
a
high
intake
per
body
weight,
and/
or
may
be
highly
subject
to
adverse
effects
75
from
these
toxicants.
It
may
be
appropriate
to
calculate
criteria
using
developmental
and
chronic
toxicity
and
exposure
assumptions
to
see
which
criterion
is
more
stringent.

Developmental
effects
resulting
from
prenatal
exposure
to
contaminants
have
become
an
area
of
significant
concern
(
USEPA,
1994a).
Thus,
in
addition
to
considering
use
of
exposure
factors
specific
to
adults
or
children,
States
and
Tribes
may
wish
to
use
exposure
factors
specific
to
women
of
childbearing
age
in
cases
where
developmental
health
effects
may
be
of
concern.
These
exposure
factors
are
described
below.

Fish
consumption
intake
rates
may
also
differ
based
on
the
target
population
to
be
protected.
Some
states
may
have
a
large
population
of
recreational
fishers
who
may
fish
a
few
times
a
year
or
during
a
fishing
vacation.
Other
States
may
have
populations
that
subsist
on
fish
for
a
large
portion
of
a
year.
Thus,
the
fish
intake
exposure
factor
may
differ
depending
on
the
population
that
is
to
be
protected.
Different
types
of
fishers
are
discussed
in
greater
detail,
below,
in
the
section
that
describes
fish
intake
rates.

When
setting
AWQC,
it
is
preferable
to
use
exposure
information
reflective
of
individuals
who
actually
use
the
water
body
for
which
AWQC
are
to
be
determined.
When
dealing
with
such
diverse
populations
as
those
throughout
the
United
States,
extreme
ranges
of
behaviors
and
activities
are
likely.
Therefore,
EPA
explicitly
recommends
that,
for
certain
exposure
factors
that
may
be
highly
variable
(
e.
g.,
fish
intake
rates),
States
use
available
local
data.
These
data
should
be
used
especially
in
cases
that
result
in
AWQC
that
are
more
stringent
than
criteria
derived
using
default
exposure
assumptions
suggested
by
EPA.
In
many
situations,
local
exposure
data
may
not
be
available.
Therefore,
EPA
also
recommends
default
values
for
each
of
the
exposure
values
discussed
below.

The
following
sections
discuss
available
data
and
describe
some
of
the
above
issues
in
greater
detail.
In
addition,
the
sections
discuss
EPA's
recommendations
for
use
of
the
exposure
factors
and
present
EPA's
suggested
default
values.
The
incorporation
of
these
exposure
factors
into
equations
to
derive
criteria
are
described
in
detail
in
Section
2.3.4.2.

2.3.2.1
Body
Weight
The
1980
AWQC
National
Guidelines
used
a
body
weight
of
70
kg
in
the
derivation
of
AWQC,
which
represents
EPA's
Agency­
wide
adult
body
weight
assumption
used
in
its
risk
assessments
and
approximates
the
average
adult
body
weight
of
71.8
kg
from
an
analysis
of
the
National
Health
and
Nutrition
Examination
Survey
(
NHANES
II),
as
reported
in
the
Exposure
Factors
Handbook
(
USEPA,
1997a).
In
the
current,
updated
guidance,
EPA
recommends
several
default
body
weight
values,
depending
on
whether
chronic
effects
or
acute
effects
are
being
evaluated.
The
use
of
these
data
in
equations
to
derive
AWQC
are
described
in
Section
2.3.4.2.
Chronic
Exposure
Scenarios
For
chemicals
that
cause
chronic
effects,
EPA
recommends
using
a
default
body
weight
of
70
kilograms.
This
value
approximates
the
mean
for
adults
from
two
sets
of
data.
The
first
set
of
data
comes
from
NHANES
II,
which
was
conducted
from
1976
through
1980
and
for
which
information
on
a
variety
of
health
and
nutritional
characteristics
of
individuals
were
collected
(
adapted
from
NCHS,
1987).
The
National
Center
for
Health
Statistics
compiled
body
weight
data
from
NHANES
II
for
over
20,000
individuals
aged
6
months
to
74
years.
Weighted
mean
body
weights
were
determined
from
this
data.
The
mean
body
weight
value
for
men
and
women
ages
18
to
74
years
old,
using
data
from
NHANES
II,
is
71.8
kg.
The
median
body
weights
for
men
and
women
from
this
study
are
76.9
and
62.4
kilograms,
respectively.
Table
2.3.2
includes
a
distribution
of
mean
and
median
adult
body
weights,
by
age
group,
based
on
NHANES
II
data.
Body
weights
are
presented
for
men,
women,
and
both
sexes
combined.

The
second
set
of
body
weight
data
come
from
Ershow
and
Cantor
(
1989).
These
authors
used
data
collected
during
the
1977­
1978
Nationwide
Food
Consumption
Survey
(
NFCS),
which
surveyed
30,770
individuals
who
constituted
a
stratified
random
sample
designed
to
represent
the
noninstitutionalized
U.
S.
population
living
in
households
(
USDA,
1988).
Body
weights
were
selfreported
by
participants.
The
mean
value
for
body
weight
for
adults
ages
20
­
64
years
old
is
70.5
kg.
Means
and
percentile
values
of
body
weight
from
Ershow
and
Cantor
(
1989)
listed
by
sex
and
age
are
presented
in
Table
2.3.3.
The
revised
EPA
Exposure
Factors
Handbook
(
USEPA,
1997a)
recommends
a
value
of
71.8
kg
for
adults,
based
on
the
NHANES
II
data.
However,
the
Handbook
also
acknowledges
the
70
kg
value
commonly
used
in
EPA
risk
assessments
and
cautions
assessors
on
the
use
of
values
other
than
70
kg.
Specifically,
the
point
is
made
that
the
70
kg
value
is
used
in
the
derivation
of
cancer
slope
factors
and
unit
risks
that
appear
on
IRIS.
Consistency
is
advocated
between
the
dose­
response
relationship
and
exposure
factors
assumed.
78
Table
2.3.2:
Body
Weight
(
in
kilograms)
of
Adults
from
NHANES
II
Age
Men
Women
Men
and
Women
Mean
Median
Mean
Median
Mean
18
­
24
years
73.7
72.0
60.6
58.0
67.2
25
­
34
years
78.7
77.5
64.2
60.9
71.5
35
­
44
years
80.9
79.9
67.1
63.4
74.0
45
­
54
years
80.9
79.0
68.0
65.5
74.5
55
­
64
years
78.8
77.7
67.9
65.2
73.4
65
74
years
74.8
74.2
66.6
64.8
70.7
Overall:
18
­
74
years
78.1
76.9
65.4
62.4
71.8
Source:
Adapted
from
NCHS
(
1987)

Developmental
Effects
Exposure
Scenarios
In
certain
cases,
pregnant
women
may
represent
a
more
appropriate
target
population
for
consideration
when
setting
water
quality
criteria
than
all
adults
in
cases
where
developmental
effects
may
be
of
concern
(
USEPA,
1994b).
In
these
types
of
cases,
body
weights
representative
of
women
of
childbearing
age
may
be
appropriate
to
adequately
protect
offspring
from
such
health
effects.
For
example,
in
the
Great
Lakes
Water
Quality
Initiative,
EPA
chose
women
of
childbearing
age
as
the
target
population
for
development
of
the
mercury
criterion
(
USEPA,
1995).
To
determine
a
mean
body
weight
value
appropriate
to
this
population,
separate
body
weight
values
for
women
in
individual
age
groups
within
the
range
of
15
to
44
years
old,
taken
from
NHANES
II
(
adapted
from
NCHS,
1987),
were
combined
and
weighted
by
current
population
percentages
(
U.
S.
Bureau
of
the
Census,
1996)
to
obtain
a
value
applicable
to
the
current
population.
The
resulting
mean
body
weight
value
for
this
age
group
is
63.8
kg.

Ershow
and
Cantor
(
1989)
also
present
data
on
mean
and
median
body
weights
for
pregnant
women,
of
65.8
and
64.4
kilograms,
respectively.
Based
on
these
data,
States
may
wish
to
use
a
value
of
65
kilograms
in
combination
with
relevant
developmental
toxicity
data
when
assessing
risks
for
pregnant
women
and
for
setting
AWQC.

Likewise,
for
some
contaminants,
RfDs
based
on
health
effects
in
children
may
be
of
primary
concern.
As
stated
in
the
Federal
Register
notice,
because
children
generally
eat
more
fish
and
drink
more
water
per
body
weight
than
adults,
higher
intake
rates
per
body
weight
may
be
more
appropriate
in
the
derivation
of
AWQC
to
provide
adequate
protection
for
these
individuals.
In
79
addition,
because
children
may
be
more
susceptible
to
the
effects
of
some
pollutants
than
adults
(
USEPA,
1994b),
they
should
be
especially
considered
when
assessing
adverse
effects
that
occur
following
such
exposures.
Information
on
children's
body
weights
(
from
NHANES
II)
are
included
in
Tables
2.3.3
and
2.3.4.

To
protect
children
against
health
effects
from
water
and
fish
intake
when
RfDs
are
based
on
health
effects
in
children,
EPA
recommends
a
default
body
weight
of
28
kilograms,
which
represents
a
mean
body
weight
for
children
0
to
14
years
old.
This
is
only
recommended
for
chemicals
for
which
adverse
effects
for
children
are
the
most
critical
endpoint
in
the
chemical's
toxicological
profile.
This
body
weight
can
be
used
with
fish
intake
rates
for
children
in
the
same
age
group
when
deriving
criteria
for
protection
against
such
health
effects
from
eating
fish.
The
default
recommendation
is
made,
in
part,
due
to
the
limitations
of
the
default
fish
consumption
data.
Specifically,
the
limited
sampling
base
prohibits
the
use
of
finer
age
group
divisions
due
to
unacceptable
confidence
intervals
with
such
finer
fish
intake
divisions.
However,
finer
age
divisions
are
provided
in
Tables
2.3.3
and
2.3.4
for
States
and
Tribes
to
consider
using
along
with
more
robust
fish
consumption
data.
As
with
other
recommended
body
weight
values,
the
default
estimate
is
based
on
information
from
analyses
of
NHANES
II
data
(
adapted
from
NCHS,
1987).
Current
population
estimates
(
U.
S.
Bureau
of
the
Census,
1996)
were
used
to
weight
information
on
body
weights
for
individuals
in
several
age
groups
up
to
age
14
years,
using
body
weight
information
from
NCHS
(
1987)
to
represent
values
applicable
to
the
current
population.
This
calculation
resulted
in
weighted
mean
body
weight
values
of
28
kg
for
this
age
group.
A
similar
analysis
using
body
weights
for
separate
age
groups
within
the
0­
14
year
range
from
Ershow
and
Cantor
(
1989),
and
weighting
by
current
population
estimates
also
resulted
in
a
mean
body
weight
of
28
kilograms.

If
States
wish
to
specifically
evaluate
infants
and
toddlers,
EPA
recommends
a
lower
default
body
weight
of
10
kilograms,
as
has
been
used
in
previous
water
program
guidance
and
regulations.
The
body
weight
is
representative
of
children
up
to
three
years
old.
EPA
recommends
using
data
from
this
particular
age
group
because
these
children
may
be
particularly
susceptible
to
acute
effects
from
water­
based
formula
intake
(
e.
g.,
nitrate).
Data
used
to
determine
this
body
weight
value
come
from
Ershow
and
Cantor
(
1989)
and
the
analysis
of
NHANES
II
data
(
adapted
from
NCHS,
1987).
The
analysis
of
NHANES
II
data
indicate
10th,
25th,
and
50th
percentile
values
for
children
less
than
three
years
old
as
8.5,
9.6,
and
11.3
kilograms
for
females,
and
9.1,
10.3,
and
11.8
kilograms
for
males,
respectively.
Mean
body
weights
from
NHANES
II
are
9.1
for
children
ages
6­
11
months,
11.3
for
1­
year­
olds,
and
13.3
for
2­
year­
olds
(
adapted
from
NCHS,
1987).
From
the
Ershow
and
Cantor
study,
the
10th,
25th,
and
50th
percentile
values
for
children
1­
3
years
old
are
10.4,
11.8,
and
13.6
kg,
respectively,
with
a
mean
value
of
14.1
kg
(
Ershow
and
Cantor
1989).

States
and
Tribes
may
instead
wish
to
consider
certain
general
developmental
ages
(
e.
g.,
preschool
pre­
adolescent,
adolescent,
etc.)
or
certain
specific
developmental
landmarks
(
e.
g.,
neurological
development
in
the
first
four
years,
etc.)
depending
on
the
chemical
of
concern.
EPA
encourages
States
and
Tribes
to
use
Tables
2.3.3
and
2.3.4
to
choose
a
body
weight
intake,
if
they
believe
a
particular
age
subgroup
is
more
appropriate
due
to
these
developmental
ages
or
landmarks.
80
Table
2.3.3:
Self­
Reported
Body
Weight
(
kilograms)
for
Both
Sexes
from
Ershow
and
Cantor
(
1989)
ab
Sex
Age
(
yr)
Mean
Standard
Deviation
Percentile
Distribution
1
5
10
25
50
Both
<
0.5
5.8
1.8
c
3.2
3.6
4.5
5.4
0.5­
0.9
9.2
2.0
c
6.8
7.3
8.2
9.1
1­
3
14.1
3.2
8.6
10.0
10.4
11.8
13.6
4­
6
20.3
4.6
12.7
13.6
15.4
17.2
20.0
7­
10
30.6
7.8
18.1
20.4
22.7
24.9
29.5
11­
19
55.2
13.4
28.6
34.0
38.6
45.4
54.4
20­
64
70.5
15.2
44.5
49.9
52.2
59.0
68.0
65+
68.6
13.1
40.8
48.5
52.2
59.0
68.0
All
59.3
22.6
9.1
15.9
22.7
48.5
61.2
Males
<
0.5
6.2
1.8
c
3.6
3.6
5.0
6.4
0.5­
0.9
9.6
2.1
c
6.8
7.3
8.2
9.1
1­
3
14.4
3.3
9.1
10.0
10.9
12.2
13.6
4­
6
20.5
4.5
13.6
14.5
15.9
18.1
20.4
7­
10
31.0
7.9
18.1
20.9
22.7
24.9
29.5
11­
19
58.3
14.9
29.0
34.0
38.6
47.2
59.0
20­
64
79.4
13.0
54.4
61.2
64.0
70.3
78.0
65+
74.4
11.5
49.9
56.7
61.2
67.1
73.9
All
63.8
25.3
9.1
15.4
20.9
49.9
70.3
Females
<
0.5
5.5
1.8
c
2.7
3.6
4.1
5.4
0.5­
0.9
8.8
1.7
c
6.8
6.8
7.7
8.6
1­
3
13.7
3.0
8.6
9.5
10.0
11.3
13.6
4­
6
20.0
4.6
12.7
13.6
15.0
17.2
19.1
7­
10
30.2
7.6
18.1
20.4
21.8
24.9
29.5
11­
19
52.1
11.0
28.1
34.0
38.6
45.4
52.2
20­
64
64.1
13.4
43.1
47.6
49.9
54.4
61.2
65+
64.5
12.6
39.9
45.4
49.9
55.8
63.5
All
55.7
19.5
9.1
15.9
24.9
48.1
56.7
a
Does
not
include
pregnant
women,
lactating
women,
or
breast­
fed
children.
b
Individual
values
may
not
add
to
totals
due
to
rounding.
c
Value
not
reported
due
to
insufficient
number
of
observations.
81
Table
2.3.4:
Mean
Body
Weights
(
kilograms)
of
Children
from
NHANES
II
Age
Boys
Girls
Boys
and
Girls
6­
11
months
9.4
8.8
9.1
1
year
11.8
10.8
11.3
2
years
13.6
13.0
13.3
3
years
15.7
14.9
15.3
4
years
17.8
17.0
17.4
5
years
19.8
19.6
19.7
6
years
23.0
22.1
22.6
7
years
25.1
24.7
24.9
8
years
28.2
27.9
28.1
9
years
31.1
31.9
31.5
10
years
36.4
36.1
36.3
11
years
40.3
41.8
41.1
12
years
44.2
46.4
45.3
13
years
49.9
50.9
50.4
14
years
57.1
54.8
56.0
Source:
Adapted
from
NCHS
(
1987)

2.3.2.2
Drinking
Water
Intake
The
1980
AWQC
National
Guidelines
used
a
value
of
2
liters/
day
to
represent
the
drinking
water
intake
of
an
individual.
In
these
updated
guidelines,
EPA
recommends
the
same
value
when
setting
chronic
criteria.
In
addition,
EPA
recommends
a
drinking
water
intake
specific
to
children
for
protecting
against
certain
health
effects
(
with
those
chemicals
for
which
the
critical
effect
of
concern
is
based
on
children)
because
young
children
may
intake
a
large
amount
of
water
per
body
weight.
Chronic
Exposure
Scenarios
To
protect
against
health
effects
due
to
chronic
exposure,
EPA
recommends
an
adult­
specific
drinking
water
intake
of
2
liters
per
day.
This
value
has
been
used
as
a
nationwide
estimate
of
adult
daily
water
consumption
in
the
drinking
water
program
for
setting
Maximum
Contaminant
Level
Goals
(
MCLGs)
and
Maximum
Contaminant
Levels,
to
be
protective
of
a
majority
of
the
population
over
the
course
of
a
lifetime.
The
value
is
also
suggested
by
the
Exposure
Factors
Handbook
to
be
supported
by
studies
analyzed
in
the
Handbook
for
use
as
an
upper­
percentile
intake
rate
(
USEPA,
1997a).
In
addition,
the
value
was
recommended
in
the
Technical
Support
Document
for
setting
water
quality
criteria
for
human
health
in
the
Great
Lakes
Region
(
USEPA,
1995).
Based
on
the
study
data
from
Ershow
and
Cantor
(
1989,
summarized
below),
EPA
also
recommends
a
2
liters
per
day
intake
for
women
of
childbearing
age.

The
value
of
2
liters/
day
has
been
estimated
as
being
somewhere
between
the
75th
and
the
100th
percentiles,
as
reported
by
different
studies
of
drinking
water
intake.
Thus,
using
this
higher
than
average
value
in
combination
with
recommended
default
body
weights
and
fish
intake
rates
would
protect
most
individuals
in
the
population.
However,
certain
individuals
who
work
or
exercise
in
hot
climates
may
consume
water
at
rates
significantly
higher
than
2
liters/
day.
Some
of
the
most
highly
exposed
individuals,
such
as
migrant
workers,
may
not
be
captured
in
national
surveys
of
drinking
water
intake.
Several
studies
that
have
estimated
drinking
water
intake
are
described
below.

One
study
by
the
National
Cancer
Institute
(
NCI)
estimated
intake
from
tap
water
(
which
includes
water
directly
from
the
tap
and
tap
water
added
to
foods
and
beverages
during
preparation)
using
data
from
the
NFCS
(
Ershow
and
Canter,
1989).
For
11,700
adults
ages
20
­
64
years
old,
this
study
reports
50th,
75th,
and
90th
percentile
tap
water
intakes
of
1.3,
1.7,
and
2.3
liters/
day,
respectively.
Table
2.3.5
includes
the
distribution
of
intake
values
by
age
from
this
study.

NCI
determined
drinking
water
intake
values
from
a
study
in
which
9,000
individuals
were
questioned
in
a
population­
based,
case­
control
study
investigating
a
possible
relationship
between
bladder
cancer
and
drinking
water
(
Cantor
et
al.,
1987).
This
study
estimated
an
overall
average
tap
water
consumption
rate
of
1.39
liters
of
water
per
day.
The
100th
percentile
consumption
rate
was
estimated
to
be
about
1.96
liters
per
day
as
shown
in
Table
2.3.6.

A
survey
of
drinking
water
literature
by
the
National
Academy
of
Sciences
(
NAS)
has
calculated
the
average
per
capita
water
consumption
to
be
1.64
liters
per
day.
NAS
estimates
that
daily
water
consumption
may
vary
with
physical
exercise
and
fluctuations
in
temperature
and
humidity.
It
is
reasonable
to
assume
those
living
in
arid,
hot
climates
will
consume
higher
levels
of
water.
However,
NAS
adopted
the
2
liters/
day
volume
to
represent
the
intake
of
the
majority
of
water
consumers
(
NAS,
1977).
In
another
survey,
the
Food
and
Drug
Administration's
(
FDA)
Total
Diet
Study
estimated
rates
for
water
and
water
used
to
make
drinks
and
soups
for
two
groups
of
adults
to
be
1.04
and
1.26
liters
per
day
with
an
average
of
1.15
liters
per
day.
Finally,
EPA
estimates
based
on
the
U.
S.
Department
of
Agriculture's
(
USDA)
1977­
78
Nationwide
Food
Table
2.3.5:
Tap
Water
Intake
(
g/
day)
for
Both
Sexes
from
Ershow
and
Cantor
(
1989)
ab
Sex
Age
(
yr)
Mean
Standard
Deviation
Percentile
Distribution
50
75
90
95
99
Both
Sexes
<
0.5
272
247
240
332
640
800
c
0.5­
0.9
328
265
268
480
688
764
c
1­
3
646
390
567
820
1162
1419
1899
4­
6
742
406
660
972
1302
1520
1932
7­
10
787
417
731
1016
1338
1556
1998
11­
19
965
562
867
1246
1701
2026
2748
20­
64
1366
728
1252
1737
2268
2707
3780
65+
1459
643
1367
1806
2287
2636
3338
All
1193
702
1081
1561
2092
2477
3415
Males
<
0.5
250
232
240
320
569
757
c
0.5­
0.9
322
249
264
408
634
871
c
1­
3
683
406
606
867
1228
1464
2061
4­
6
773
414
693
1033
1336
1530
1900
7­
10
802
437
738
1046
1391
1609
2055
11­
19
1050
605
942
1364
1856
2179
2967
20­
64
1460
798
1339
1841
2485
2949
4083
65+
1570
704
1448
1952
2460
2790
3712
All
1250
759
1123
1634
2205
2673
3760
Females
<
0.5
293
259
240
358
672
800
c
0.5­
0.9
333
281
278
500
712
759
c
1­
3
606
368
532
783
1114
1339
1806
4­
6
709
395
622
930
1231
1491
1932
7­
10
772
395
726
992
1299
1475
1888
11­
19
882
503
799
1147
1540
1825
2424
20­
64
1297
664
1207
1655
2147
2491
3359
65+
1382
584
1309
1687
2167
2472
3071
All
1147
648
1049
1505
1988
2316
3097
a
Does
not
include
pregnant
women,
lactating
women,
or
breast­
fed
children.
b
Individual
values
may
not
add
to
totals
due
to
rounding.
c
Value
not
reported
due
to
insufficient
number
of
observations.
85
Consumption
Survey
identified
daily
beverage
intakes
ranging
from
1.48
to
1.73
liters
per
day.
Both
the
FDA
and
USDA
studies
were
cited
in
USEPA
(
1997a).
Based
on
these
studies,
EPA
estimated
an
average
adult
drinking
water
consumption
rate
to
be
1.41
liters
per
day
and
the
90th
percentile
value
to
be
2.35
liters
per
day
(
USEPA,
1997a).

Table
2.3.6:
Frequency
Distribution
of
Tap
Water
Consumption
Rates*

Consumption
Rate
(
L/
day)
Cumulative
Frequency
(%)

0.80
19.2
0.81
­
1.12
39.6
1.13
­
1.33
59.7
1.45
­
1.95
79.9
1.96
100.0
*
Represents
consumption
in
a
"
typical"
week.
Source:
Cantor
et
al.
(
1987)

Developmental
Effects
Exposure
Scenarios
As
noted
above,
for
some
contaminants,
RfDs
based
on
health
effects
in
children
are
of
primary
concern.
Because
infants
and
small
children
have
a
higher
water
consumption
per
body
weight
compared
to
adults,
a
higher
water
consumption
rate
per
body
weight
may
be
needed
for
comparison
with
doses
from
relevant
toxicity
studies.
Use
of
these
higher
water
consumption
rates
when
setting
criteria
based
on
health
effects
associated
with
children
should
result
in
adequate
protection
for
infants
and
children.
Estimating
a
mean
drinking
water
intake
for
children
ages
0­
14
years
old,
which
combines
drinking
water
intake
for
five
age
groups
within
the
larger
age
group
of
0­
14
years
from
Ershow
and
Cantor
(
1989)
and
weighting
by
current
population
estimates
(
from
U.
S.
Bureau
of
the
Census,
1996)
results
in
a
drinking
water
intake
of
approximately
750
ml.
As
a
slightly
more
protective
measure
than
using
750
ml,
EPA
proposes
a
drinking
water
intake
of
1
liter.
This
value
is
equivalent
to
about
the
75th
percentile
value,
of
960
ml
for
children
ages
1­
10
years
old
(
Ershow
and
Cantor,
1989).
The
distribution
of
drinking
water
intakes
for
age
groups
within
the
0­
14
year­
old
group
from
Ershow
and
Cantor
(
1989)
is
included
in
Table
2.3.5.
This
value
is
also
appropriate
to
use
for
evaluating
smaller
children
ages
1­
3
years
old
and
has
been
used
as
a
default
by
EPA's
water
program
office
for
small
children
in
past
regulatory
efforts.

Inhalation
and
Dermal
Exposure
A
number
of
water
contaminants
are
volatile
and
thus
diffuse
from
water
into
the
air
where
they
may
be
inhaled.
In
addition,
drinking
water
is
used
for
bathing
and
ambient
waters
for
swimming
86
and
thus,
there
is
at
least
the
possibility
that
some
contaminants
in
water
may
be
dermally
absorbed.

Dermal
absorption
and
the
inhalation
of
volatilized
drinking
water
contaminants
may
be
responsible
for
significant
increases
in
exposure
over
and
above
that
due
to
ingestion.
However,
this
issue
is
quite
complicated.
A
significant
fraction
of
the
water
that
is
ingested
is
either
boiled
or
allowed
to
stand
prior
to
ingestion.
In
both
cases,
it
is
reasonable
to
assume
that
volatilization
will
decrease
the
concentration
of
volatile
contaminants
in
the
water
that
is
actually
ingested.
In
addition,
because
volatilization
can
decrease
the
concentration
of
volatile
contaminants
in
the
water
that
comes
in
contact
with
the
skin,
it
follows
that
volatilization
can
decrease
the
extent
of
dermal
absorption.

Thus,
volatilization
may
increase
exposure
via
inhalation
and
decrease
exposure
via
ingestion.
The
net
effect
of
volatilization
and
dermal
absorption
upon
total
exposure
to
volatile
contaminants
in
water
is
unclear.
Although
several
approaches
can
be
found
in
the
literature,
including
various
models
that
have
been
used
by
EPA,
the
Agency
currently
does
not
have
a
proposed
methodology
for
explicitly
incorporating
inhalation
(
i.
e.,
from
volatilization)
and
dermal
absorption
exposures
from
household
water
uses
in
the
derivation
of
health­
based
criteria
(
i.
e.,
MCLGs
or
AWQC).
The
Agency
is
currently
exploring
the
effect
of
volatilization
and
dermal
absorption
upon
exposure
to
drinking
water
contaminants.
For
example,
the
Agency
has
a
joint
agreement
with
the
International
Life
Sciences
Institute
(
ILSI)
to
develop
guidance
on
estimating
exposures
of
inhalation
and
dermal
absorption
from
contaminants
in
water.
It
is
anticipated
that
this
guidance
would
be
incorporated
into
this
methodology
when
it
is
available.

2.3.2.3
Fish
Intake
Rates
Fish
intake
rates
(
expressed
in
grams/
day)
are
used
in
the
equations
to
derive
AWQC.
Throughout
this
section,
the
terms
"
fish
intake"
or
"
fish
consumption"
are
used.
They
generally
refer
to
the
consumption
of
finfish
and
shellfish,
and
the
national
survey
described
in
this
section
(
the
CSFII)
includes
both.
States
and
Tribes
should
ensure
that
when
selecting
local
or
regionally­
specific
studies,
both
types
are
included
when
the
population
exposed
are
consumers
of
both
types.
If
the
population
of
concern
are
also
believed
to
consume
aquatic
plants
from
the
water
body,
this
source
should
be
accounted
for
with
the
estimate
of
other
exposures
(
i.
e.,
the
relative
source
contribution
analysis).
Ideally,
fish
intake
rates
should
be
representative
of
individuals
who
eat
fish
from
a
given
water
body
for
which
AWQC
are
to
be
set.
In
addition,
priority
should
be
given
to
identifying
and
protecting
the
most
highly
exposed
fish
eaters
in
the
area.
Although
highly
exposed
populations
cannot
be
precisely
defined
and
may
differ
depending
on
the
water
body
to
be
protected,
such
fish
eaters
may
generally
be
separated
into
two
groups:
(
1)
sportfishers,
defined
generally
as
the
group
of
anglers
who
eat
the
fish
they
catch
recreationally;
and
(
2)
subsistence
fishers,
individuals
who
rely
on
fish
for
a
large
part
of
their
protein
intake.
A
more
detailed
description,
as
well
as
examples,
of
these
highly
exposed
groups
follows.
87
Sportfishers
may
vary
widely
in
their
catch
and
consumption
rates.
Some
may
eat
fish
for
short
periods
throughout
the
year
or
during
certain
fishing
seasons.
Others
may
fish
for
much
longer
periods
during
a
year.
Although
sportfishers
may
primarily
fish
recreationally
and
only
supplement
their
regular
diets
with
the
fish
they
catch,
some
sportfishers
may
eat
large
amounts
of
fish
throughout
the
year.

Populations
which
have
been
identified
as
eating
a
larger
portion
of
sport­
caught
fish
than
the
general
population
(
e.
g.,
Native
Americans)
yet
are
not
recreational
fishers
are
distinguished
from
the
above
group
of
sportfishers.
Such
fishers
(
called
subsistence
fishers
here)
may
rely
on
catching
and
eating
fish
to
meet
nutritional
needs
or
because
of
cultural
traditions.
Subsistence
fishers
may
catch
fish
year
round
(
CRITFC,
1994)
or
preserve
fish
to
eat
throughout
the
year.
Some
of
these
fishers,
such
as
Asian­
Americans,
often
consume
portions
of
the
fish
that
recreational
fishers
do
not
often
consume
(
including
liver,
kidneys,
brains,
and
eyes).
In
addition,
fish
may
be
prepared
whole,
providing
greater
exposure
to
contaminants
(
e.
g.,
organs
and
remains
are
often
used
as
soup
stock)
(
Pestana,
1994;
Shubat,
1994;
Allbright,
1994;
Cung,
1994;
Nehls­
Lowe,
1994;
University
of
Wisconsin
SeaGrant,
1994;
Den,
1994;
Young,
1994;
Lorenzana,
1994).
Subsistence
fishers
are
often
(
although
not
always)
low
income
individuals,
and
may
reside
in
either
urban
or
rural
areas.

Several
ethnic
groups
have
been
identified
as
having
members
who
subsist
on
fish.
Several
specific
groups
of
Native
American
fishers
have
been
identified
in
the
Northwest
and
the
Great
Lakes
Region
(
Kmiecik,
1994;
CRITFC,
1994;
Den,
1994;
Young,
1994;
Eng,
1994).
Asian­
American
fishers
are
a
group
that
includes
numerous
populations
such
as
Laotian,
Hmong,
Cambodian,
and
Vietnamese,
each
with
differing
consumption
patterns
and
cultural
traditions.
Asian­
American
fishers
in
particular
may
eat
a
larger
portion
of
the
fish
than
generally
recommended,
including
consumption
of
additional
organs
or
the
whole
fish
(
Pestana,
1994;
Shubat,
1994;
Allbright,
1994;
Cung,
1994;
Nehls­
Lowe,
1994;
University
of
Wisconsin
SeaGrant,
1994;
Den,
1994;
Young,
1994;
Lorenzana,
1994).

When
estimating
fish
intake
for
the
population
of
concern,
EPA
recommends
that
central
tendency
values
(
i.
e.,
median
or
mean
values)
or
higher
percentile
values
from
studies
of
fish
consumption
relevant
to
the
identified
group
be
used
in
the
derivation
of
criteria.
Values
lower
than
the
median
or
mean
should
not
be
used
because
the
identified
populations
would
not
be
adequately
protected.
Furthermore,
when
considering
median
values
from
fish
consumption
studies,
States
need
to
ensure
that
the
distribution
is
based
on
survey
respondents
who
reported
consuming
fish
because
surveys
based
on
both
consumers
and
non­
consumers
typically
result
in
median
values
of
zero.

Because
fish
consumption
habits
may
vary
among
different
types
of
populations
and
among
States,
EPA
prefers
that
States
use
information
on
fish
consumption
rates
directly
relevant
to
the
population
being
addressed.
However,
such
information
may
not
always
be
available.
Thus,
EPA
proposes
the
hierarchy
of
preferences
for
consideration
of
fish
consumption
data
described
below.
Although
Preferences
#
1
and
#
2
are
likely
to
result
in
higher
intake
rates
than
the
default
recommendations
of
Preference
#
4,
if
the
converse
is
true
(
i.
e.,
if
site­
specific
or
similar
geographic/
population
studies
indicate
lower
intake
rates
than
the
recommended
defaults),
States
88
may
choose
the
lower
intake
rates
determined
from
the
first
two
preferences.
However,
if
a
State
chooses
values
(
whether
central
tendency
or
high
end)
that
particularly
target
highly
exposed
consumers,
they
should
be
compared
to
high­
end
fish
intake
rates
for
the
general
population
to
make
sure
that
the
highly
exposed
consumers
within
the
general
population
would
also
be
protected
by
the
chosen
intake
rates.
As
discussed
in
the
Federal
Register
presentation
of
the
Methodology,
it
is
recommended
that
cooked
weight
values
of
intakes
be
used.

Preference
#
1:
Use
of
Local
Information
Once
a
State
has
identified
the
particular
population,
which,
if
a
protected
subgroup,
will
also
afford
acceptable
protection
to
the
entire
population,
EPA
recommends
that
States
use
results
from
fish
intake
surveys
conducted
in
the
geographic
area
where
the
State
is
located
to
estimate
fish
intake
rates
(
measured
in
grams/
day)
that
are
likely
to
most
closely
represent
the
defined
populations
being
addressed.
Generally,
the
more
specific
the
data
are
to
the
individuals
who
use
the
water
body
of
interest,
the
better
the
data
are
considered
to
be
for
estimating
accurate
fish
intake
rates.

Information
on
local
fish
consumption
habits
may
not
be
already
available
to
States.
Thus,
if
time
and
money
permit,
States
are
encouraged
to
conduct
their
own
surveys
in
order
to
obtain
estimates
of
fish
consumption
(
in
grams/
day)
and
to
characterize
fisher
populations
within
the
State,
and
specifically,
the
locality
of
interest.
The
EPA
guidance
manual
entitled
Guidance
for
Conducting
Fish
and
Wildlife
Consumption
Surveys
(
USEPA,
1997b)
may
be
useful
in
planning
and
conducting
surveys.
This
guidance
document
reviews
five
methods
of
obtaining
fish
consumption
data:

°
Recalled
information
collected
by
telephone.

°
Recalled
information
collected
by
in­
person
interviews.

°
Recalled
information
from
self­
administered
mailed
questionnaires.

°
Diaries
maintained
by
anglers.

°
On­
site
creel
censuses
(
obtaining
harvest
data
collected
on­
site
from
single
anglers).
89
The
advantages
and
disadvantages
of
each
method
are
addressed,
and
suggestions
about
procedures
to
solve
problems
associated
with
each
survey
method
are
given.

In
addition,
Consumption
Surveys
for
Fish
and
Shellfish
lists
several
suggestions
regarding
the
type
of
information
to
collect
when
conducting
these
surveys.
Examples
of
this
information
include:
(
1)
sociodemographic
characteristics
such
as
age
of
the
angler,
number
of
household
members,
and
pregnancy
or
lactation
status
of
women
in
the
household;
(
2)
fishing
activities
including
seasonal
and
temporal
distribution
of
fishing
activities,
whether
the
angler
fishes
for
sport
or
consumption,
and
the
type
of
fish
captured
(
whether
bottom
feeders
or
pelagic);
and
(
3)
preparation
and
consumption
patterns
including
portions
of
the
fish
consumed,
methods
of
preparation
prior
to
cooking,
and
procedures
for
cooking.
Such
information
can
be
used
to
investigate
patterns
of
consumption
among
high­
risk
groups,
and
can
aid
in
most
accurately
characterizing
consumption
rate
information
using
as
few
assumptions
as
possible.

The
document
presents
a
variety
of
guidelines
for
conducting
surveys,
and
is
intended
to
provide
methods
for
efficiently
and
cost­
effectively
collecting
information
necessary
for
valid
statistical
analyses
of
risks
to
subsistence
and
recreational
anglers.
States
should
refer
to
USEPA
(
1997b)
for
more
detailed
information
on
methods
of
conducting
fish
consumption
surveys.

A
couple
of
issues
not
addressed
in
detail
in
USEPA
(
1997b)
should
be
emphasized
when
planning
a
fish
consumption
survey.
The
first
issue
involves
identification
of
subsistence
fisher
populations.
Because
it
may
be
difficult
to
identify
subsistence
fisher
populations
solely
through
traditional
approaches
such
as
mail
or
phone
surveys,
it
may
be
necessary
for
surveyors
to
use
other
methods
to
target
these
populations.
A
couple
of
methods
may
be
of
use.
One
method
involves
contact
with
community
organizations
that
represent
these
populations
(
e.
g.,
Indian
tribal
organizations)
that
have
already
established
a
relationship
with
community
members.
In
addition,
creel
clerks
(
those
who
interview
fishers
at
specific
fishing
locations)
may
be
good
sources
of
information
on
fisher
demographics
because
they
have
direct
contact
with
individuals
at
fishing
sites
(
Shubat,
1993).
It
is
important
to
anticipate
cultural
and
language
requirements
of
each
ethnic
group
and
to
try
and
follow
the
community­
based
approach
indicated
above.
Asians
and
Pacific
Islanders
are
currently
the
fastest
growing
minority
population
in
the
U.
S.
For
many
first
and
second
generation
immigrants
and
refugees,
survey
methods
which
utilize
creel,
mail­
in,
telephone
or
doorto
door
techniques
are
ineffective
in
obtaining
reliable
data
characterizing
fish
and
seafood
consumption
patterns
(
Nakano,
1996;
USEPA,
1996).
Informal
studies
indicate
a
preference
for
bottom
dwelling
fish;
therefore,
Asian
and
Pacific
Islander
surveys
should
include
an
appropriate
species
list
(
Soukhaphonh
et
al.,
1996).

A
second
issue
important
to
emphasize
is
that,
if
States
intend
to
consider
health
effects
resulting
from
acute
exposures
when
setting
AWQC,
surveyors
may
wish
to
obtain
information
regarding
maximum
amounts
of
fish
that
may
be
eaten
at
a
meal.
Because
many
surveys
are
designed
to
obtain
information
simply
on
the
number
of
fish
meals
eaten
by
an
individual
over
a
specific
time
period,
rather
than
the
size
of
the
fish
meals,
maximum
meal
sizes
may
not
generally
be
obtained
by
a
fish
consumption
survey.
As
noted
above,
such
large
acute
exposures
may
be
especially
90
problematic
to
children,
people
with
special
susceptibilities,
and
pregnant
women.
In
addition,
such
high
doses
may
be
more
likely
to
occur
at
specific
times
during
the
year,
such
as
periods
when
certain
types
of
fish
are
available
or
during
specific
events
(
e.
g.,
summer
vacation,
Native
American
religious
festivals,
or
fishing
tournaments).
Thus,
surveyors
may
wish
to
consider
obtaining
information
on
such
maximum
intake
rates
and
determine
whether
these
rates
are
likely
to
occur
during
specific
times
during
the
year.

Preference
#
2:
Use
of
Surveys
from
Similar
Geographic
Areas
and
Population
Groups
For
those
States
and
Tribes
that
do
not
have
resources
available
to
conduct
a
survey
of
consumption
rates
of
local
populations
and
when
such
information
is
otherwise
not
available,
EPA's
second
preference
in
determining
fish
intake
rates
is
for
States
and
Tribes
to
use
results
from
existing
fish
intake
surveys
that
reflect
similar
geography
and/
or
population
groups.
For
instance,
States
or
Tribes
with
subsistence
fisher
populations
may
wish
to
use
consumption
rates
from
studies
that
focus
specifically
on
these
groups,
or,
at
minimum,
use
rates
that
represent
high­
end
values
from
studies
that
measured
consumption
rates
for
a
range
of
types
of
fishers
(
e.
g.,
recreational/
sport
fishers,
subsistence,
minority
populations).
A
State
or
Tribe
in
a
particular
region
of
the
country
may
consider
using
rates
from
studies
that
surveyed
the
same
region;
for
example,
a
State
or
Tribe
that
has
a
climate
that
allows
year­
round
fishing
may
underestimate
consumption
if
rates
are
used
from
studies
taken
in
regions
where
individuals
fish
for
only
one
or
two
seasons
per
year.
A
State
or
Tribe
that
has
a
high
percentage
of
a
particular
age
group
(
such
as
elderly
individuals,
who
have
been
shown
to
have
higher
rates
in
certain
surveys)
may
wish
to
use
age­
specific
consumption
rates,
which
are
available
from
some
surveys.

Fish
intake
rates
estimated
from
available
surveys
that
have
investigated
the
fish
consumption
habits
of
individuals
are
described
below
and
presented
in
Tables
2.3.7
and
2.3.9.
These
surveys
are
divided
into
the
two
previously
identified
groups
of
highly
exposed
fisher
populations
(
sportfishers
and
subsistence
fishers)
described
above.
Although
the
surveys
are
divided
into
these
two
categories
for
ease
of
presentation,
it
should
be
noted
that
these
two
categories
cannot
always
be
strictly
delineated.
In
particular,
there
may
be
individuals
included
in
the
sportfisher
surveys
that
exhibit
habits
indicative
of
subsistence
fishers
(
i.
e.,
eating
fish
as
a
large
part
of
their
diet).
Also,
some
members
of
identified
subsistence
populations
may
not
subsist
on
fish
as
a
major
portion
of
their
diets.

These
surveys
use
a
variety
of
methods.
The
methods
used
in
the
surveys
to
estimate
the
fish
intake
rates
are
included
in
Tables
2.3.8
and
2.3.10.
A
few
points
should
be
made
about
the
methods
used
in
these
studies
to
estimate
the
consumption
rates.
One
major
issue
regarding
these
rates
is
that
although
they
are
presented
as
grams
per
day
in
Tables
2.3.7
and
2.3.9,
they
should
be
considered
to
be
approximations
of
actual
gram/
day
amounts
only.
For
example,
the
estimates
are
generally
obtained
by
memory
recall,
not
strict
daily
log­
keeping
of
grams
eaten
per
day.
In
addition,
surveys
generally
ask
respondents
to
estimate
the
number
of
meals
they
have
eaten
over
a
given
period
of
91
time.
Although
some
surveys
include
questions
about
approximate
size
of
the
meals,
others
do
not
ask
any
questions
about
the
actual
size
of
the
meals
eaten
during
that
time
and,
instead,
assume
all
meals
are
a
given
size
(
most
often
227
grams,
or
a
half
pound).

A
second
major
issue
to
be
addressed
is
that
the
estimates
of
fish
intake
may
vary
across
surveys
for
reasons
which
depend
on
the
type
of
fish
included
in
the
survey.
For
instance,
surveys
may
report
consumption
of
only
certain
types
of
fish.
Some
surveys
have
focused
primarily
on
either
freshwater
or
saltwater
fish,
whereas
others
have
collected
information
on
both
types.
In
addition,
some
surveys
have
queried
individuals
about
whether
they
have
eaten
recreational
fish
only,
whereas
others
have
questioned
respondents
about
intake
of
commercial
fish,
or
both.

Methods
of
averaging
fish
consumption
information
also
differ
among
studies.
Some
studies
average
the
consumption
rates
over
all
individuals,
regardless
of
whether
they
ate
fish,
while
other
surveys
average
the
information
only
for
those
individuals
who
reported
eating
fish.
For
example,
Cox,
Vaillancourt,
and
Hayton
(
1993)
report
consumption
rates
averaged
for
the
fish­
eating
population,
whereas
the
Alabama
Department
of
Environmental
Management
(
1993)
report
a
rate
averaged
for
both
individuals
who
eat
fish
and
those
who
do
not
eat
fish.

As
discussed
in
the
Federal
Register
notice,
fish
consumption
surveys
also
vary
in
terms
of
whether
reported
rate
values
are
for
cooked
fish,
uncooked
fish,
or
whether
the
study
is
unclear
as
to
which
is
reported.
States
and
Tribes
should
check
to
see
if
the
survey
study
clearly
identifies
whether
weights
represent
cooked
or
uncooked
fish.

Many
of
the
differences
in
survey
methods
are
highlighted
in
the
text
and
accompanying
Tables
2.3.8
and
2.3.10.
However,
States
should
consult
the
individual
surveys
to
obtain
the
most
complete
descriptions
of
the
study
and
resulting
consumption
rates.
USEPA's
Guidance
for
Conducting
Fish
and
Wildlife
Consumption
Surveys
(
USEPA,
1997b)
includes
detailed
descriptions
of
the
various
methods
for
conducting
surveys,
including
their
strengths
and
limitations.

Sportfishers
As
noted
above,
sportfishers
differ
with
respect
to
their
catch
and
consumption
habits.
Surveys
of
the
general
sportfishing
population
may
include
those
who
primarily
fish
for
recreational
purposes
or
eat
fish
for
a
small
portion
of
the
year
but
may
also
include
some
individuals
who
eat
fish
as
a
main
staple
in
their
diets.
Results
of
sportfisher
surveys
are
described
in
the
following
paragraphs,
and
are
included
in
Tables
2.3.7
and
2.3.8.

Alabama
Fishers.
The
Alabama
Department
of
Environmental
Management
(
1993)
conducted
a
survey
from
August
1992
to
July
1993
on­
site
at
various
fishing
locations.
In
this
survey,
1,586
individuals
were
interviewed
and
asked
about
the
number
of
fish
that
were
caught
and
kept
for
consumption.
Demographic
information
on
age,
gender,
income,
and
region
was
also
collected.
Two
survey
methods,
which
differed
in
determining
meal
size
of
the
fish
catch
that
was
to
be
eaten,
were
used
to
estimate
consumption
rates.
Mean
and
95th
percentile
consumption
rates
92
for
the
harvest
method
were
45.8
and
50.7
grams/
day,
respectively,
and
the
rates
for
the
serving
method
were
43.1
and
50.9
grams/
day,
respectively.
Both
were
averaged
over
a
year.
Although
consumption
rates
were
not
found
to
vary
across
major
ethnic
groups,
some
specific
subpopulations
had
higher
than
mean
consumption
as
a
function
of
age
and
income.
Black
anglers
with
incomes
less
than
$
15,000
ate
a
mean
of
63
grams/
day,
and
anglers
over
50
years
old
consumed
a
mean
of
76
grams/
day
of
sport­
caught
fish
(
Alabama
DEM,
1993).

California
Fishers.
The
Santa
Monica
Bay
Restoration
Project
contracted
with
the
Southern
California
Coastal
Water
Research
Project
and
MBC
Applied
Environmental
Sciences
to
conduct
a
seafood
consumption
study
from
September
1991
to
August
1992
(
SCCWRP
and
MBC,
1994).
The
purpose
of
the
study
was
to
characterize
recreational
anglers
fishing
in
the
Santa
Monica
Bay,
including
identifying
ethnic
subgroups
of
the
population
with
the
highest
consumption.
Information
on
household
income
was
also
evaluated.
The
survey
form
included
a
census
and
a
questionnaire.
Twenty­
nine
sites
were
surveyed
on
99
days
of
sampling,
with
2,376
anglers
included
in
the
census
and
over
1,200
interviews
(
71%).
Of
these,
555
anglers
(
45%)
provided
enough
information
to
be
used
to
derive
consumption
rates.
The
overall
median
and
mean
consumption
rates
were
21.4
and
49.6
grams/
day,
respectively,
with
the
highest
median
and
mean
consumption
rates
(
85.7
and
137.3
grams/
day)
in
the
Other
category
(
i.
e.,
primarily
Pacific
Island
origin).
Among
ethnic
groups,
the
90th
percentile
rates
ranged
from
64.3
to
173.6
grams/
day,
with
the
Hispanic
category
having
the
lowest
and
the
Other
category
having
the
highest
rates.
With
respect
to
income,
the
study
showed
that
the
lowest
income
group
(<$
5,000/
year)
had
the
highest
median
consumption
rate
(
32.1
grams/
day)
but
the
highest
income
group
(>$
50,000/
year)
had
the
highest
mean
consumption
rate
(
58.9
grams/
day)
and
the
highest
90th
percentile
(
128.6
grams/
day).
However,
it
should
be
noted
that
two­
thirds
of
the
survey
population
was
comprised
of
higher
income
anglers.

Louisiana
Fishers.
The
Louisiana
Department
of
Environmental
Quality
conducted
a
seafood
consumption
survey
in
1993
(
Dellenbarger,
et
al.,
1993).
A
telephone
survey
was
conducted
of
1,100
households
in
Houma,
LA,
a
coastal
community.
Households
sampled
were
stratified
by
ethnic
characteristics;
however,
the
households
were
otherwise
randomly
selected.
Other
high­
end
consumers
were
individuals
over
50
years
old,
who
consumed
a
mean
value
of
40
grams/
day.
Rates
for
all
types
of
fish
consumed
were
65
grams/
day,
comprised
of
17
grams/
day
of
fresh
water
fish,
15
grams/
day
of
saltwater
fish,
and
33
grams/
day
of
shellfish.
These
rates
include
both
sport­
caught
and
commercial
fish
and
are
averaged
for
only
those
people
who
ate
fish
and
seafood.

New
York
Fishers.
Based
on
a
survey
of
4,530
anglers,
the
New
York
State
Department
of
Environmental
Conservation
(
Connelly,
et
al.,
1990)
estimated
that
consumption
of
fish
(
all
types)
by
New
York
State
anglers
averaged
about
45.2
meals
per
year,
or
28.1
grams
per
day
(
assuming
227
grams
per
meal).
Averages
are
also
listed
by
age
of
the
angler,
income
group,
and
the
area
within
New
York
State
where
the
angler
lives.
The
highest
average
was
recorded
for
the
two
Long
Island
counties
of
Nassau
and
Suffolk,
whose
populations
consumed
a
combined
mean
value
of
37
grams/
day
of
fish.
Other
high­
end
consumers
were
individuals
over
50
years
old,
who
consumed
a
mean
value
of
40
grams/
day.
93
Table
2.3.7:
Sportfishersa
Fish
Intake
Data
Fisher
Groupb
Fish
Intake
Rates
(
g/
day)
Fish
Typec
mean
median
80%
ile
90%
ile
95%
ile
Alabama
fishers1
45.8
50.7
F+
S
R+
C
Louisiana
(
coastal)
fishers2
65
F+
S
R+
C
New
York
fishers3
28.1
F
R
New
York
(
Hudson
River)
fishers4
23
(
typical)

Michigan
fishers5
14.5
30
62
80
F+
S
R
Michigan
fishers6
18.3
50
(
approx.)
70
(
approx.)
F+
S
R+
C
Michigan
fishers7
44.7
F
R
Wisconsin
fishers
(
10
counties)
8
12.3
37.3
F
R
Wisconsin
fishers
(
10
counties)
8
26.1
63.4
F
R+
C
Ontario
fishers9
22.5
F
R
Ontario
fishers10
31
(
average)

Los
Angeles
Harbor
fishers11
37
120.8
225
338.8
Washington
State
(
Commencement
Bay)
fishers12
23
54
S
R
Washington
State
(
Columbia
River)
fishers13
7.7
F+
S
R+
C
Maine
fishers
(
inland
waters)
14
6.4
2.0
13
26
Washington
State
(
Columbia
River)
fishers15
1.8
NOTES:
aSportfishers
may
include
individuals
who
eat
fish
as
a
large
portion
of
their
diets
bFisher
groups
refer
to
the
same
headings
as
those
that
appear
in
the
text
cFish
Type:
F=
Freshwater,
S
=
Saltwater
(
may
indicate
either
estuarine
or
marine
waters),
R
=
Recreationally
caught
SOURCES:
1AL
Dept.
Env.
Mgt.,
1993
2Dellenbarger,
et
al.,
1993
3Connelley,
et
al.,
1990
4Barclay,
1993
5West,
et
al.,
1993
6West,
et
al.,
1989
7Humphrey,
1976
8Fiore,
et
al.,
1989
9Cox,
Vaillancourt,
and
Hayton,
1993
10Sonstegard,
1985
11Puffer,
et
al.,
1982
12Pierce,
et
al.,
1981
13Honstead,
et
al.,
1971
14Ebert
et
al.,
1993
15Soldat,
1970
94
Table
2.3.8:
Sportfishersa
Survey
Methods
Fisher
Groupb
Methods
(
See
Key)
c
Number
Surveyed
Contact
Method
Instrument
Reporting
Method
Catch
vs.

Consumption
Individual
vs.
Household
Data
Available
Duration
Alabama
fishers1
1,586
on­
site
int
log
catch
individual
age,
eth,
inc,
reg,
sex
12
mos
Louisiana
(
coastal)
fishers2
1,100
randomd
tele
recall
consumption
household
age,
edu,
eth,
inc,
oth
1
mos
New
York
fishers3
4,530
license
mail/
follow
up
by
tele
recall
catch
individual
age,
inc,
reg
12
mos
New
York
(
Hudson
River)
fishers4
336
on­
site
int
recall
consumption
Michigan
fishers5
2,684
license
mail
recall
consumption
household
age,
edu,
eth,
inc,
reg,
sex
12
mos
Michigan
fishers6
1,104
license
mail
recall
consumption
household
age,
edu,
eth,
inc,
reg,
sex
6
mos
Michigan
fishers7
182
license
log
catch
individual
24
mos
Wisconsin
fishers
(
10
counties)
8
801
license
mail
recall
consumption
individual
age,
edu,
eth,
reg,
sex
Wisconsin
fishers
(
10
counties)
8
801
license
mail
recall
consumption
individual
age,
edu,
eth,
reg,
sex
Ontario
fishers9
494
license
mail
recall
consumption
individual
age,
reg,
sex
summer,
fall
Ontario
fishers10
Los
Angeles
Harbor
fishers11
1,059
on­
site
int
recall
catch
individual
age,
eth
12
mos
Washington
State
(
Commencement
Bay)
fishers12
508
license
int/
follow
up
by
tele
recall
catch
individual
summer,
fall
Table
2.3.8:
Sportfishersa
Survey
Methods
Fisher
Groupb
Methods
(
See
Key)
c
Number
Surveyed
Contact
Method
Instrument
Reporting
Method
Catch
vs.

Consumption
Individual
vs.
Household
Data
Available
Duration
95
Washington
State
(
Columbia
River)
fishers13
10,900
license
int
recall
consumption
household
12
mos
Maine
fishers
(
inland
waters)
14,
e
Washington
State
(
Columbia
River)
fishers15,
e
KEY:
Contact
Method:
Census/
Random/
Fish
Licenses/
On­
Site/
Tribal
Members
Instrument:
Personal
Interview/
Mail
Survey/
Telephone
Survey
Log/
Recall:
Respondents
recorded
consumption
information
in
a
log
or
recalled
consumption
information
during
interview
Catch/
Consumption:
Catch:
Original
data
from
catch
rates
extrapolated
to
consumption
rates
Consumption:
Data
obtained
on
consumption
patterns
Individual/
Household:
Consumption
information
obtained
either
for
individuals
or
for
households
Data
Available:
Study
may
have
data
on:
Age/
Education/
Ethnicity/
Income/
Region/
Sex/
Other
NOTES:
aSportfishers
may
include
some
individuals
who
eat
fish
as
a
large
portion
of
their
diets.
bFisher
groups
refer
to
the
same
headings
as
those
that
appear
in
the
text.
c
Blank
cells
indicate
information
is
not
available.
dA
"
stratified
random"
approach
was
used
to
obtain
information
with
adequate
representation
of
the
population
of
interest.
eData
available
only
from
draft
documents.
Consequently,
detailed
information
was
not
available
at
the
time
of
publications.
Additional
information
will
be
provided
in
future
revisions
of
this
document.

SOURCES:
1AL
Dept
Env
Mgt,
1993
2Dellenbarger,
et
al.,
1993
3Connelley,
et
al.,
1990
4Barclay,
1993
5West,
et
al.,
1993
6West,
et
al.,
1989
7Humphrey,
1976
8Fiore,
et
al.,
1989
9Cox,
Vaillancourt,
and
Hayton,
1993
10Sonstegard,
1985
11Puffer,
et
al.,
1982
12Pierce,
et
al.,
1981
13Honstead,
et
al,
1971
14Ebert
et
al.,
1993
15Soldat,
1970
96
Barclay
(
1993)
conducted
direct
interviews
with
336
shore­
based
anglers
on
the
Hudson
River
at
sites
including
the
upper
Hudson,
mid­
Hudson,
and
lower
Hudson
sites,
at
both
urban
and
rural
sites.
These
surveys
were
conducted
between
June
and
November
of
1991
and
April
and
July
of
1992.
Because
the
survey
did
not
reach
anglers
in
boats
or
all
river
areas,
the
authors
of
the
survey
note
that
the
results
cannot
be
directly
extrapolated
to
the
entire
population
of
Hudson
River
anglers.
Over
58
percent
of
the
individuals
eat
their
catch.
The
survey
reports
that
the
average
frequency
of
fish
consumption
reported
was
3
meals
over
the
previous
month,
but
did
not
ask
respondents
about
the
size
of
their
fish
meals.
Assuming
227
grams
(
8
ounces)
of
fish
would
be
eaten
per
meal
and
assuming
4.3
weeks
per
month,
the
results
translate
to
an
average
fish
consumption
rate
of
23
grams/
day.

Michigan
Fishers.
West
et
al.
(
1993)
completed
a
survey
of
Michigan
fishers
over
a
one
year
period.
For
this
survey,
2,684
individuals
who
purchased
fishing
licenses
responded
to
mailed
surveys.
Consumption
of
commercial
and
sport­
caught
fish
was
estimated
through
a
7­
day
recall,
and
data
were
separated
demographically
by
age,
education,
ethnicity,
income,
region
and
gender.
Mean
consumption
was
estimated
to
be
14.5
grams/
day.
The
80th
percentile
was
30
grams/
day,
90th
percentile
consumption
rate
was
62
grams/
day
and
the
95th
percentile
rate
was
80
grams/
day.
Several
specific
subpopulations
surveyed
in
this
study
had
higher
than
average
consumption
rates.
Minority
fishers
(
primarily
black
and
non­
reservation
Native
Americans)
with
annual
incomes
less
than
$
25,000
averaged
the
highest
consumption
rate
of
all
Michigan
angler
groups
surveyed,
consuming
a
mean
of
43.1
grams/
day
of
sport­
caught
fish.

An
older
survey
by
West
et
al.
(
1989)
evaluated
Michigan
fishers
as
part
of
a
revision
of
exposure
pathways
for
the
Michigan
Toxic
Substance
Control
Commission.
This
earlier
study
was
only
conducted
over
a
six
month
period,
but
its
results
were
corroborated
by
the
more
recent
survey
data.
The
population
studied
was
sport
anglers,
and
consumption
of
both
self­
caught
and
commercial
fish
was
considered.
The
survey
relied
on
seven­
day
recall
in
order
to
estimate
mean
fish
consumption;
the
percentage
of
respondents
consuming
no
fish
was
high
(
56.6
percent).
The
study
concluded
that
mean
fish
consumption
for
Michigan
sport
anglers
and
their
families
is
16.1
grams/
day,
after
adjustment
for
non­
response
bias.
The
90th
percentile
consumption
is
approximately
50
grams/
day,
the
95th
percentile
is
about
70
grams/
day,
and
the
maximum
reported
fish
consumption
is
over
200
grams/
day.

The
Michigan
Department
of
Natural
Resources
conducted
a
survey
of
381,000
sport­
fishers
in
1974
(
Humphrey,
1976,
as
cited
in
Rupp
et
al.,
1979).
This
survey
obtained
mean
catch
of
36
lbs.
of
fish
per
year
(
44.7
grams
per
day)
for
consumption.

Wisconsin
Fishers.
In
a
survey
of
anglers
in
Wisconsin,
the
annual
mean
number
of
sportcaught
meals
was
18.
Using
the
assumed
fish
meal
size
of
8
ounces
(
227
grams)
from
this
survey,
the
estimated
mean
daily
consumption
of
sport­
caught
fish
in
Wisconsin
is
about
11
grams/
day.
When
respondents
who
consumed
no
sport­
caught
fish
were
excluded,
the
mean
daily
sport­
caught
fish
intake
was
12
grams/
day
(
Fiore
et
al.,
1989).
The
95th
percentile
value,
counting
only
those
individuals
who
consumed
any
sport­
caught
fish,
was
determined
to
be
37
grams/
day.
97
Ontario
Fishers.
Another
study
was
completed
by
Ontario
sports
fishers
in
1992
(
Cox,
Vaillancourt,
and
Hayton,
1993).
Questionnaires
were
inserted
randomly
into
10,000
copies
of
1992
Guide
to
Eating
Ontario
Sports
Fish,
and
494
replies
were
received.
Questions
regarding
fish
preferences
and
catch
rate,
consumption
rate
and
portion
sizes,
and
use
of
consumption
advisories
were
asked.
A
mean
daily
consumption
of
22.5
grams/
day
was
calculated
based
on
estimated
average
meal
size
and
frequency
of
eating
sport­
caught
sportfish.
Anecdotal
evidence
provided
by
one
researcher
studying
the
Ontario
sportfishers
during
an
earlier
survey
from
1985
(
Sonstegard,
1985
as
cited
in
Kleiman,
1985)
found
that
an
average
sportfisher
consumed
a
mean
of
31
grams/
day,
while
a
high­
end
consumer
ate
62
grams/
day
(
the
percentile
value
was
not
specified).
The
maximum
amount
consumed
was
over
310
grams/
day.

Idaho
Fishers.
One
study
was
conducted
in
the
Lake
Coeur
d'Alene
region
in
Idaho
(
West,
1993;
Richter
and
Rondinelli,
1989).
933
individuals
were
surveyed,
including
Native
Americans
living
both
on
and
off
reservations,
individuals
selected
randomly
from
individuals
with
fishing
licenses
in
Idaho,
and
volunteers
who
were
recruited
for
the
study.
Tribal
members
were
surveyed
in
person,
while
others
were
surveyed
primarily
by
telephone.
All
respondents
were
asked
to
recall
fish
consumption
patterns.
This
study
was
conducted
over
a
period
of
three
months,
so
data
must
be
extrapolated
to
the
rest
of
the
year.
Consumption
rates
for
the
licensed
fisher
population
ranged
from
16
to
27
grams/
day.

Los
Angeles
Harbor
Fishers.
From
January
to
December
of
1980,
1059
interviews
with
sportfishers
were
conducted
in
several
fishing
areas
of
the
Los
Angeles
Harbor
area
(
Puffer
et
al.,
1982).
No
fisher
was
sampled
more
than
once.
Data
was
collected
on
the
following:
amount
of
fish
caught
on
the
day
of
the
interview,
the
primary
use
of
the
fish
(
whether
eaten
by
the
fisher's
family,
given
away,
thrown
back,
etc.),
frequency
of
fishing,
and
other
variables.
Based
on
this
data
and
assuming
that
only
an
edible
portion
(
1/
4
to
1/
2)
of
the
caught
fish
would
be
eaten,
median
and
90th
percentile
consumption
rates
of
37
grams
per
day
and
225
grams
per
day
were
determined.
The
95th
percentile
was
338.8
grams/
day.
Consumption
rates
were
also
estimated
by
age,
race,
and
species
caught.
This
study
indicates
that
median
consumption
rates
for
Orientals/
Samoans
are
71
grams/
day
and
113
grams/
day
for
individuals
over
65
years
old.

Washington
State
Fishers.
Interviews
were
conducted
with
fishers
in
Commencement
Bay,
Washington
from
July
to
late
November;
304
interviews
were
conducted
in
summer
and
204
were
conducted
in
the
fall
(
Pierce,
1981).
Data
were
collected
on
size
and
amount
of
specific
species
caught,
size
of
the
fishers'
families,
frequency
of
fishing,
and
planned
use
of
the
fish.
The
fishers
were
later
called
about
whether
the
fish
had
been
eaten.
USEPA
(
1989a)
used
these
data
to
estimate
a
median
consumption
rate
value
of
23
grams
per
day
and
a
90th
percentile
of
54
grams
per
day
(
also
reported
in
USEPA,
1997a).
The
authors
note
that
although
a
survey
of
night/
dawn
fishing
was
conducted
only
once,
fish
caught
at
this
time
could
represent
a
significant
part
of
the
total
fish
caught
from
the
bay.
Therefore,
these
values
may
underestimate
fish
consumption.

Honstead
et
al.
(
1971,
as
cited
in
Rupp
et
al.,
1979)
conducted
a
study
of
the
consumption
patterns
of
sportfishers
on
the
Columbia
River
in
the
Tri­
City
area
of
Hanford,
Washington.
This
98
survey
monitored
10,900
persons,
each
of
which
were
members
of
households
where
a
Columbia
River
angler
resides.
The
surveyors
required
respondents
to
recall
the
number
of
fish
meals
consumed
over
a
12­
month
period
and,
using
an
estimate
of
200
grams
per
meal,
calculated
the
mean
annual
consumption
to
be
2.8
kg
per
year
(
7.7
grams/
day).

Lake
Ontario
Fishers.
Connelly
et
al.
(
1996)
surveyed
1,202
Lake
Ontario
anglers
through
mail
questionnaires,
diaries,
and
telephone
interviews.
The
mail
questionnaires
were
based
on
a
12
month
recall
of
1991
fishing
trips;
the
diaries
involved
self­
recording
of
1992
fishing
trips.
Of
the
1,202
participants,
853
returned
a
diary
or
provided
diary
information
by
telephone.
Participants
were
instructed
to
record
in
the
diary
the
species
of
fish
eaten,
meal
size,
method
by
which
fish
was
acquired
(
sport­
caught
or
other),
fish
preparation
and
cooking
techniques
used,
and
the
number
of
household
members
eating
the
meal.
Due
to
changes
in
health
advisories
for
Lake
Ontario
which
resulted
in
less
Lake
Ontario
fishing,
only
43
percent,
or
366
persons
indicated
that
they
fished
Lake
Ontario
in
1992.
The
mean
fish
intake
from
all
sources
was
17.9
grams/
day
and
from
sport­
caught
sources
was
4.9
grams/
day.
The
median
rates
were
14.1
grams/
day
for
all
sources
and
2.2
grams/
day
for
sport­
caught;
the
95th
percentiles
were
42.3
grams/
day
and
17.9
grams/
day
for
all
sources
and
sport­
caught,
respectively.
Residents
of
large
cities
and
younger
people
had
lower
intake
rates
on
average.
The
authors
note
that
although
diaries
tend
to
provide
more
accurate
information
than
studies
based
on
12
month
recall,
a
considerable
portion
of
the
respondents
participated
in
the
study
for
only
a
portion
of
the
year
and
some
errors
may
have
been
generated
in
extrapolating
the
results
to
an
entire
year.

Alaska
Communities.
Wolfe
and
Walker
(
1987)
analyzed
a
data
set
from
98
Alaska
communities
(
four
large
urban
population
centers
and
94
small
communities)
for
harvests
of
fish,
land
mammals,
marine
mammals,
and
other
wild
resources.
The
data
set
was
developed
by
various
researchers
in
the
Alaska
Department
of
Fish
and
Game,
Division
of
Subsistence,
between
1980
and
1985.
Respondents
were
asked
to
estimate
the
quantities
of
particular
species
that
were
harvested
and
used
by
members
of
their
households
during
the
previous
12
month
period.
Urban
sport
fish
harvests
were
derived
from
a
survey
that
was
mailed
to
a
randomly
selected
statewide
sample
of
anglers.
For
the
four
urban
centers,
fish
harvests
ranged
from
6.2
grams/
day
to
26.2
grams/
day.
The
range
for
the
94
small
communities
was
31
grams/
day
to
1,541
grams/
day.
For
the
94
communities,
the
median
per
capita
fish
harvest
was
162
grams/
day.
Dressed
weight,
the
portion
brought
into
the
kitchen
for
use,
varied
by
species
and
community,
but
in
general
was
70
to
75
percent
of
total
fish
weight.
The
authors
used
a
factor
of
.5
to
convert
harvest
to
intake
rates,
yielding
a
median
per
capita
consumption
rate
in
the
94
small
communities
of
81
grams/
day,
and
a
range
of
15.5
to
770
grams/
day.

Savannah
River
Fishers.
Turcotte
(
1983)
estimated
fish
consumption
from
the
Savannah
River
in
Georgia
based
on
total
harvest,
population
studies,
and
a
Georgia
fishery
survey.
The
angler
survey
data,
which
included
the
number
of
fishing
trips
per
year
as
well
as
the
number
and
weights
of
fish
harvested
per
trip,
were
used
to
estimate
the
average
consumption
rate
in
the
angler
population.
The
study
found
an
average
consumption
rate
of
31
grams/
day
and
a
maximum
rate
of
58
grams/
day.
99
Alabama
Fishers.
Meredith
and
Malestuto
(
1996)
studied
anglers
in
29
locations
in
Alabama
to
estimate
freshwater
fish
consumption.
The
purpose
of
their
study
was
to
compare
two
methods
of
estimating
fish
consumption:
the
harvest
or
krill
survey,
and
the
serving­
size
method.
The
two
techniques
yielded
comparable
estimates
of
mean
fish
intake:
43
and
46
grams/
day,
respectively.

Florida
Fishers
Who
Receive
Food
Stamps.
As
part
of
a
larger
effort,
the
Florida
Department
of
Environmental
Regulation
attempted
to
identify
fish
consumption
rates
of
anglers
who
were
thought
to
consume
higher
rates
of
fish.
Interviews
with
twenty­
five
households'
primary
seafood
preparers
were
conducted
at
each
of
five
food
stamp
centers
per
quarter
for
an
entire
year.
The
respondents
were
asked
to
recall
fish
consumption
at
home
within
the
previous
seven
days.
Sekerke
et
al.
(
1994)
found
that
adult
males
in
the
study
consumed
60
grams/
day
of
finfish
and
50
grams/
day
of
shellfish;
adult
females
consumed
40
grams/
day
and
30
grams/
day,
respectively,
of
finfish
and
shellfish.

Subsistence
Fishers
Subsistence
fishers
consume
fish
as
a
major
staple
of
their
diet.
As
noted
above,
subsistence
fishers
often
have
higher
consumption
rates
than
other
fisher
groups;
however,
consumption
rates
vary
considerably
among
subsistence
fishers.
Consequently,
generalizations
should
not
be
made
about
this
fisher
group.
If
studies
contained
in
this
section
are
used
to
estimate
exposure
patterns
for
a
subsistence
population
of
concern,
care
should
be
taken
to
match
the
dietary
and
population
characteristics
of
the
two
populations
as
closely
as
possible.
Several
surveys
evaluating
the
consumption
patterns
of
subsistence
fishers
have
been
initiated
in
the
last
several
years.
Some
of
these
have
been
completed
and
many
more
are
currently
being
carried
out,
with
results
expected
in
the
near
future.
Although
many
of
these
surveys
provide
only
a
range
of
consumption
rates,
a
great
deal
of
qualitative
information
has
been
gained
through
these
surveys,
both
about
the
individual
populations
that
were
studied
and
about
effective
survey
methods
for
different
groups
of
subsistence
fishers.
The
consumption
rates
reported
by
these
surveys
are
presented
below.
Results
of
these
surveys
and
the
methods
used
to
collect
the
data
are
summarized
in
Tables
2.3.9
and
2.3.10.

Great
Lakes
Tribes.
The
Great
Lakes
Indian
Fish
and
Wildlife
Commission
conducted
a
survey
of
spear
fishing
for
walleye
among
Native
Americans
living
on
reservations
in
the
Great
Lakes
Region
(
Kmiecik,
1994).
This
study
was
designed
to
evaluate
the
concern
about
mercury
among
spear
fishers
in
the
tribes
of
the
Great
Lakes
Region.
The
results
of
this
study
showed
that
people
were
modifying
their
behavior
about
where
to
fish
and
types
and
sizes
of
fish
to
keep
based
on
concerns
about
mercury.
Although
consumption
rates
had
no
baseline
for
comparison
prior
to
mercury
concerns,
many
respondents
indicated
that
they
modified
their
consumption
of
fish
due
to
concerns
about
mercury
contamination.
Despite
these
possible
decreases
in
fish
consumption,
the
rates
of
consumption
of
walleye
were
still
extremely
high;
the
mean
value
was
351
grams/
day,
while
the
maximum
amount
consumed
was
1,426
grams/
day.
These
daily
consumption
rates
were
calculated
by
multiplying
the
average
portion
size,
as
reported
by
the
respondents,
by
the
respondents'
average
consumption
of
2.75
meals
per
week
(
that
is
the
average
of
each
season's
meals/
wk),
and
100
then
dividing
by
7
days/
wk.
Assuming
individuals
may
have
been
eating
other
fish
in
addition
to
walleye,
the
rates
may
be
higher
than
these
values.

Idaho
Fishers.
As
described
above,
a
study
conducted
in
the
Lake
Coeur
d'Alene
region
in
Idaho
surveyed
Native
Americans,
individuals
with
fishing
licenses,
and
volunteers
(
West,
1993;
Richter
and
Rondinelli,
1989).
This
study
was
conducted
over
a
period
of
three
months,
so
data
must
be
extrapolated
to
the
rest
of
the
year.
Consumption
rates
of
tribal
members
ranged
from
28
to
49
grams/
day.

Columbia
River
Tribes.
One
of
the
most
comprehensive
surveys
of
fishing
patterns
among
Native
Americans
has
been
conducted
by
the
Columbia
River
Inter­
Tribal
Fisheries
Commission.
The
study
surveyed
four
of
the
tribes
living
in
the
Columbia
River
Basin
(
CRITFC,
1994).
From
four
tribes
both
on
and
off
the
reservation,
717
individuals
were
surveyed
in
person
regarding
their
consumption
patterns
of
self­
caught
fish,
wild
game,
and
wild
rice.
The
responses
were
based
on
memory
recall,
and
the
survey
was
conducted
over
a
full
year.
Mean
consumption
from
this
study
was
calculated
as
58.7
grams/
day
and
the
95th
percentile
is
170
grams/
day.

American
Samoan
Fishers.
A
number
of
surveys
have
been
conducted
in
American
Samoa
that
have
attempted
to
assess
the
potential
risk
of
industrial
development
in
the
major
harbor
on
the
main
island
of
American
Samoa
where
most
of
the
population
also
lives
and
fishes
(
Den,
1994;
Young,
1994;
Eng,
1994).
The
local
EPA
conducted
a
pilot
scoping
survey
to
assess
the
extent
of
the
contamination
in
the
fisheries
resources
and
heavy
metal
poisoning
in
blood
and
urine
of
sample
populations;
a
brief
survey
of
consumption
rates
was
included
in
this
survey.
The
results
of
this
study
(
the
toxicity
of
the
harbor
and
the
fisheries
resources)
encouraged
the
local
EPA
to
apply
for
a
study
to
be
conducted
by
the
Centers
for
Disease
Control
(
CDC).
Results
indicated
fish
consumption
at
a
rate
of
approximately
12
grams/
day
(
Ponwith,
1991;
ATSDR,
1995).

Wisconsin
Chippewa
Indians.
Peterson
et
al.
(
1994)
investigated
the
extent
of
exposure
of
Chippewa
Indians
who
consume
fish
caught
in
northen
Wisconsin
lakes
to
methylmercury.
The
study,
conducted
in
May
1990,
included
175
randomly
selected
and
152
nonrandomly
selected
participants.
The
authors
reported
that
both
groups
had
similar
fish
consumption
rates.
Participants
were
asked
to
complete
a
questionnaire
describing
their
routine
fish
consumption
and,
more
extensively,
their
fish
consumption
during
the
previous
two
months.
Results
from
the
survey
showed
a
mean
fish
consumption
of
1.2
meals
per
week.
This
includes
fish
from
all
sources.
The
consumption
figure
translates
to
a
fish
intake
of
20
grams/
day,
using
117
grams/
meal
as
the
average
weight
of
fish
consumed
per
fish
meal
in
the
general
population.
Consumption
varied
seasonally,
with
the
highest
consumption
during
April
and
May,
the
spearfishing
season
for
walleye.
During
peak
consumption
months,
males
and
respondents
under
35
consumed
more
fish
than
females
and
respondents
35
and
over.

Miccousukee
Indian
Tribes.
The
Centers
for
Disease
Control
(
1993)
administered
dietary
questionnaires
to
2
children
and
183
adults
from
the
Miccousukee
Indian
Tribes
of
South
Florida.
The
survey
found
that
31
percent
ate
fish
from
the
Everglades
during
the
previous
six
months;
57
101
percent
consumed
marine
fish
during
the
previous
six
months.
The
median
consumption
of
local
fish
was
3.5
grams/
day;
the
maximum
consumption
was
168
grams/
day.
Blue
gill
was
the
most
common
species
of
local
fish
consumed;
largemouth
bass
were
consumed
in
greatest
quantity.

Wisconsin
Tribes.
A
1992
EPA
report
entitled
Tribes
at
Risk
(
The
Wisconsin
Tribes
Comparative
Risk
Project)
reported
an
average
total
fish
intake
for
Native
Americans
living
in
Wisconsin
of
35
grams/
day.
The
average
daily
intake
of
locally
harvested
fish
was
31.5
grams.

Tribes
of
Puget
Sound.
In
November
1994
Toy
et
al.
(
1995)
completed
a
study
of
fish
consumption
among
190
adult
members
of
the
Tulalip
and
Squaxin
Island
Tribes
of
Puget
Sound.
The
study
was
conducted
between
February
and
May
1994.
Fish
consumption
practices
were
assessed
using
dietary
recall
methods,
food
models,
and
a
food
frequency
questionnaire.
Fish
consumed
were
categorized
into
anadromous
fish
(
e.
g.,
king
salmon
and
sockeye
salmon),
pelagic
fish
(
e.
g.,
cod
and
pollock),
bottom
fish
(
e.
g.,
halibut
and
sole),
and
shell
fish
(
e.
g.,
manila
clams,
scallops,
and
mussels).
Anadromous
fish
and
shell
fish
were
consumed
in
greatest
quantities.

The
50th
percentile
consumption
rate
for
all
fish
combined
for
the
Tulalip
Tribe
was
0.55
grams/
kg
body
weight/
day
and
0.52
grams/
kg
body
weight/
day
for
the
Squaxin
Tribe.
If
an
average
body
weight
is
assumed
to
be
70
kg,
the
daily
fish
consumption
rate
for
adults
in
the
Tulalip
Tribe
was
38.5
grams/
day
and
36.4
grams/
day
for
the
Squaxin
Tribe.
The
weighted
combined
median
daily
fish
consumption
for
both
tribes
was
37.1
grams.

Native
Americans
near
Clear
Lake,
California.
Harnly
et
al.
(
1997)
found
that
Native
Americans
living
near
Clear
Lake,
California
consumed
an
average
of
84
grams/
day
of
fish
(
60
grams/
day
of
sport
fish
plus
24
grams/
day
of
commercial
fish).
The
most
popular
species
of
sportfish
were:
catfish,
perch,
hitch,
bass,
and
carp.
Commercial
species
most
commonly
eaten
were:
snapper,
tuna,
salmon,
crab,
and
shrimp.

Hawaiian
Islands.
The
Mercury
Study
Report
to
Congress
(
1997)
cites
a
number
of
studies
on
the
commercial
utilization
of
seafood
[
i.
e.,
Higuchi
and
Pooley
(
1985)
and
Hudgins
(
1980)]
and
analyses
of
epidemiology
[
i.
e.,
Wilkens
and
Hankin
(
1996)]
which
provide
a
basis
to
describe
general
patterns
of
fish
consumption
among
Hawaiians.
These
studies
indicate
that,
on
average,
Hawaiians
consumed
30.5
grams/
day
of
fish
in
1972
and
24.0
grams/
day
in
1974.
A
1987
State
of
Hawaii
study
of
400
residents
cited
by
the
authors
found
that
shrimp
and
mahimahi
were
the
most
popular
seafoods.

Alaska
Natives.
Nobmann
et
al.
(
1992)
performed
a
nutrient
analysis
of
the
food
consumed
in
eleven
communities
that
represented
different
ethnic
and
socioeconomic
regions
of
Alaska.
The
survey
sample
included
351
adults
aged
21­
60
years.
Information
was
obtained
using
24
hour
dietary
recalls
during
five
seasons
over
an
18­
month
period.
The
mean
daily
intake
of
fish
and
shellfish
of
Alaska
Natives
was
109
grams/
day.
102
Table
2.3.9:
Subsistence
Fishersa
Consumption
Data
Fisher
Groupb
Fish
Intake
Rates
(
g/
day)
Fish
Typec
mean
95%
ile
max
Great
Lakes
tribes1
351
1426
F
R
Columbia
River
tribes2
58.7
170
F
R
Florida
residents
receiving
food
stamps3
23
F+
S
Florida
Asian
residents3
59
F+
S
R+
C
High­
end
Caucasian
consumers
on
Lake
Michigan4
54
132
Wisconsin
tribes5
31.5
Chippewa
tribes
in
Wisconsin6
55
Native
Alaskan
Adults7,
c
109
F+
S
NOTES:
aSubsistence
fishers
include
groups
(
such
as
the
Florida
residents
receiving
food
stamps)
that
may
eat
sport­
caught
fish
at
high
rates
but
do
not
subsist
on
the
fish
as
a
large
part
of
their
diet
bFisher
groups
refer
to
the
same
headings
as
those
that
appear
in
the
text
cFish
Type:
F=
Freshwater,
S
=
Saltwater
(
may
indicate
either
estuarine
or
marine
waters),
R
=
Recreationally
caught.

SOURCES:
1Kmiecik,
1994
2CRITFC,
1994
3Degner
et
al.,
1994
4Hovinga,
1992;
1993
5USEPA,
1992
6Peterson
et
al.,
1995
7Nobmann
et
al.,
1992
103
Table
2.3.10:
Subsistence
Fishersa
Survey
Methods
Fisher
Typeb
Methods
(
See
Key)
c
Number
Surveyed
Contact
Method
Instrument
Reporting
Method
Catch
vs.
Consumption
Individual
vs.
Household
Data
Available
Duration
Great
Lakes
tribes1
69
tribe
mail
recall
consumption
individual
NA
2
mos
Columbia
River
tribes2
717
tribe/
random
interview
recall
consumption
individual
age,
eth,
reg,
sex
12
mos
Florida
residents
receiving
food
stamps3
500
Florida
Asian
residents3
120
randome
telephone
recall
consumption
individual
age,
eth,
reg,
sex,
income
12
mos
(
of
total
study,
not
recall)

High­
end
Caucasian
consumers
on
Lake
Michigan4,
d
115
Wisconsin
tribes5,
d
Chippewa
tribes
in
Wisconsin6,
d
323
tribe/
random
interview
recall
consumption
individual
sex,
employmt.,
age,
education
1
mo
Native
Alaskan
Adults7,
d
351
recall
consumption
18
mos
KEY:
Contact
Method:
Census/
Random/
Fish
Licenses/
On­
Site/
Tribal
Members
Instrument:
Personal
Interview/
Mail
Survey/
Telephone
Survey
Log/
Recall:
Respondents
recorded
consumption
information
in
a
log
or
recalled
consumption
information
during
interview
Catch/
Consumption:
Catch:
Original
data
from
catch
rates
extrapolated
to
consumption
rates.
Consumption:
Data
obtained
on
consumption
patterns
Individual/
Household:
Consumption
information
obtained
either
for
individuals
or
for
households
Data
Available:
Study
may
have
data
on:
Age/
Education/
Ethnicity/
Income/
Region/
Sex/
Other
NOTES:
aSubsistence
fishers
include
groups
(
such
as
the
Florida
residents)
that
may
eat
sport­
caught
fish
at
high
rates
but
not
subsist
on
fish
as
a
large
part
of
their
diets.
bFisher
groups
refer
to
the
same
headings
as
those
that
appear
in
the
text.
cBlank
cells
indicate
information
is
not
available.
dData
available
only
from
draft
documents.
eNumber
sampled
per
county
was
proportionate
to
population
in
county
compared
to
the
entire
State.

SOURCES:
1Kmiecik,
1994
2CRITFC,
1994
3Degner
et
al.,
1994
4Hovinga,
1992;
1993
5USEPA,
1992
6Peterson
et
al.,
1995
7Nobmann
et
al.,
1992
104
Surveys
in
Progress
The
Wisconsin
Department
of
Health
is
currently
conducting
a
study
of
the
Hmong
populations
of
Sheboygan
and
Manitowac
(
Nehls­
Lowe,
1994;
University
of
Wisconsin
Sea
Grant,
1994).
These
surveys
have
resulted
from
a
larger
project
that
was
designed
to
inform
the
Asian­
American
populations
of
the
potential
dangers
of
eating
too
much
contaminated
fish.

The
EAGLE
project
(
EAGLE,
1991;
Cole,
1994)
is
a
Canadian
collaboration
of
the
Assembly
of
First
Nations
and
Health
and
Welfare
Canada.
This
project
is
a
several
year
study
of
the
health
of
First
Nation
communities
throughout
the
northern
Great
Lakes
region.
Consumption
patterns
by
these
communities
of
local
food
sources
have
been
obtained,
and
preliminary
results
of
this
project
have
been
compiled.
The
results
will
not
be
ready,
however,
until
sometime
in
1997
(
Wheatley,
1996).

Another
study
is
underway
among
the
Ojibway
peoples
(
Chippewa)
of
the
upper
Great
Lakes
region
(
Dellinger,
1993
and
1996)
and
is
currently
being
finalized.
This
study
is
designed
primarily
to
study
the
correlation
between
fish
consumption
habits,
body
burdens,
and
neurobehavioral
effects.

EPA
Regions
9
and
10
have
begun
studies
of
the
Asian­
American/
Pacific
Islander
communities
in
Washington
and
California
(
Den,
1994;
Young,
1994;
Eng,
1994;
Lorenzana,
1994).
In
order
to
most
effectively
reach
the
communities
that
they
wish
to
survey,
Region
10
awarded
the
project
to
a
local
Asian­
American
community.
Specifically
called
the
Asian
Pacific
American
Seafood
Consumption
Study
and
conducted
via
the
Refugee
Federation
Service
Center
and
the
University
of
Washington,
the
community
group
designed
the
study
with
input
from
technical
advisors
(
statisticians,
toxicologists,
epidemiologists),
agency
representatives
and
various
community
groups
(
USEPA,
1996).
Personal
interview
surveys
conducted
in
the
King
County
area
of
Seattle,
Washington
were
completed
during
the
of
summer
1997,
and
a
report
is
expected
by
September
1998
(
Lorenzana,
1998).
A
study
of
the
Laotian
community
in
the
San
Francisco
Bay
Area
(
specifically,
west
Contra
Costa
County)
was
funded
by
EPA
Region
9
and
conducted
via
the
Asian
Pacific
Environmental
Network,
a
non­
profit
organization
that
coordinates
environmental
health
projects.
This
represents
a
community­
based
survey
where
the
survey
questions
were
designed
with
input
from
the
Pacific
Asian
community,
interested
agencies
and
academia,
and
where
the
actual
survey
was
conducted
by
an
elder
and
junior
person
from
each
of
the
various
ethnic
groups
composing
the
Laotian
community.
Data
collection
was
completed
during
the
Summer
of
1997,
and
a
draft
report
developed.
This
underwent
subsequent
peer
review
and
the
report
was
finalized
in
March
1998
(
Den,
1998).

Preference
#
3:
Use
of
Distributional
Data
from
National
Food
Consumption
Surveys
If
information
from
existing
regional
studies
is
not
relevant
to
a
given
State,
EPA's
third
preference
is
that
States
use
distributional
information
for
intake
of
fresh/
estuarine
species
for
different
population
groups
from
national
food
consumption
surveys.
EPA
has
analyzed
one
such
105
national
survey,
the
combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII).
EPA
recommends
use
of
the
CSFII
(
see
Tables
2.3.11
through
2.3.22),
but
believes
similar
nationally­
based
surveys
are
appropriate
for
consideration
(
see
Table
2.3.1
on
sources
of
exposure
information).
The
1989
through
1991
CSFII
data
are
the
most
recent
analyzed
by
EPA
for
developing
estimates.
As
more
current
data
become
available,
these
estimates
may
be
revised
by
EPA.

In
addition
to
providing
nationally­
based
information,
which
offers
a
greater
quantity
of
data
points,
the
CSFII
information
is
also
presented
here
for
regional
break­
outs
from
the
same
data
set.
States
may
wish
to
consider
these
regional
values
if
they
have
at
least
some
information
as
indicated
with
Preferences
#
1
and
#
2,
and
if
they
believe
that
the
consumption
rates
of
the
particular
population
of
concern
differ
from
the
national
rates.
However,
if
a
State
has
not
identified
a
separate
well­
defined
population
of
highly­
exposed
consumers
and
believes
that
the
national
data
from
the
CSFII
are
representative,
EPA
recommends
these
national
data.
Although
the
regional
break­
outs
are
provided
for
the
States
to
consider,
EPA
believes
that
there
is
less
confidence
in
the
regional
estimates
due,
in
part,
to
the
relatively
small
sample
size
that
results
when
these
break­
outs
are
made
(
see
Tables
2.3.12
through
2.3.20).
For
example,
the
Mountain
Region
is
indicative
of
a
very
small
sample
size
with
few
respondents
who
reported
consumption
during
the
study
period
and
is
skewed
by
a
few
high
consumers
(
e.
g.,
a
mean
of
3.23
grams/
day
and
a
90th
percentile
of
0.48
grams/
day).
It
is,
therefore,
not
recommended
that
these
breakouts
be
used
by
themselves
to
represent
regional/
State
intakes.
In
addition,
the
geographic
divisions,
as
created,
may
not
accurately
reflect
the
consumption
patterns
of
each
State
within
a
given
division.
For
example,
whereas
the
Pacific
geographic
division
(
i.
e.,
California,
Oregon,
Washington)
may
be
reasonable
to
use
for
West
Coast
populations,
it
is
doubtful
that
the
values
for
the
West
South
Central
division
apply
equally
to
each
State
(
Table
2.3.18).
That
is,
fish
consumption
in
Louisiana
may
be
vastly
different
than
fish
consumption
in
Oklahoma.

A
detailed
set
of
fish
consumption
tables
from
the
CSFII
is
presented
in
Appendix
A
of
this
document.
The
tables
indicate
consumption
rates
for
adults,
children
under
14,
women
of
childbearing
age
(
considered
to
be
ages
15­
44),
as
well
as
per
capita
values.
Both
average
and
acute
values
are
presented
for
the
adults
and
per
capita
groups,
whereas
only
acute
values
are
given
for
children
under
14
and
women
of
childbearing
age.
The
procedures
for
determining
average
and
acute
consumption
are
described
on
pages
107
and
117,
respectively.
Appendix
A
includes
the
regional
breakouts
that
are
also
listed
in
Tables
2.3.12
to
2.3.20.
All
of
the
aforementioned
tables
are
presented
in
both
grams/
day
and
in
mg/
kg/
day.
Finally,
the
Appendix
includes
species
breakouts
by
mean
consumption
for
each
of
the
four
major
groups
in
grams/
day.

The
U.
S.
Department
of
Agriculture
conducts
the
CSFIIs,
through
which
dietary
intake
data
is
collected
for
selected
years
from
April
of
one
year
to
March
of
the
next
(
USEPA,
1998).
About
25
percent
of
the
interviews
are
conducted
in
a
calendar
quarter.
These
data
are
collected
from
the
48
conterminous
States
over
3
consecutive
days.
On
the
first
day
of
the
survey,
participants
give
information
to
an
in­
home
interviewer,
and
on
the
second
and
third
days,
data
are
taken
from
selfadministered
dietary
records.
Meals
consumed
both
at
home
and
away
from
home
are
recorded.
However,
it
was
not
possible
to
distinguish
between
the
intake
of
fish
locally
caught
and
that
which
106
was
not.
Although
the
assumption
that
all
freshwater/
estuarine
fish
consumed
comes
from
a
particular
water
body
is
somewhat
conservative,
EPA
believes
that
this
is
a
reasonable
assumption
to
ensure
adequate
protection
from
such
fish
subject
to
contamination.

The
CSFII
1989­
1991
did
not
draw
samples
from
Alaska
or
Hawaii.
As
these
two
States
could
potentially
contain
a
larger
percentage
of
subsistence
fishers
than
the
population
from
the
48
conterminous
States,
the
absence
of
data
from
these
two
States
could
result
in
a
slight
underestimate
of
per
capita
fish
consumption
for
the
entire
population.
This
underestimate
is
probably
insignificant
given
that
the
populations
of
Alaska
and
Hawaii
are
quite
small
compared
with
that
of
the
total
conterminous
States
(
USEPA,
1998).
However,
as
indicated
above,
Alaska
and
Hawaii
are
encouraged
to
make
decisions
on
fish
intake
via
Preferences
#
1
and
#
2,
if
possible,
to
ensure
the
most
accurate
estimates.

The
CSFII
survey
is
a
national
multi­
stage,
stratified­
cluster
area
probability
sample.
The
48
conterminous
States
were
divided
into
60
strata.
Within
these
strata,
counties,
cities,
and
areas
within
cities
were
grouped
into
relatively
homogeneous
units
called
primary
sampling
units
(
PSUs).
Two
of
these
units
were
sampled,
with
replacement,
from
each
of
the
strata.
Each
PSU
was
sampled
with
probability
proportional
to
its
1985
projected
population.
These
units
were
further
divided
into
area
segments,
from
which
predetermined
numbers
of
households
were
selected
for
participation.
Each
household
within
an
area
segment
had
equal
probability
of
selection
(
USEPA,
1998).
To
allow
the
data
for
the
three
survey
years
to
be
combined,
the
area
segments
for
each
of
the
years
were
drawn
from
the
same
PSUs
(
USEPA,
1998).

Each
of
the
surveys
consists
of
"
basic"
and
"
low­
income"
samples.
Individuals
in
all
households,
regardless
of
income,
were
eligible
for
inclusion
in
the
basic
sample.
In
the
low­
income
sample,
only
households
with
gross
income
at
or
below
130
percent
of
the
Federal
poverty
threshold
were
eligible
for
inclusion.
Both
samples
are
included
in
the
distributional
estimates
using
data
from
the
three
survey
years.

Response
rates
for
the
three
survey
years
and
for
the
low­
income
and
basic
surveys
varied
from
40
to
53
percent.
USDA
corrected
the
survey
weights
for
non­
response.

Of
the
6,000
food
categorizations
in
the
CSFII
surveys,
465
relate
to
fish.
Survey
respondents
with
3
days
of
dietary
intake
data
reported
consumption
across
284
of
these
fish­
related
food
codes.
The
amount
of
a
fish­
related
food
code
reported
was
adjusted
according
to
information
in
the
USDA
recipe
file
to
reflect
the
proportion
of
fish
in
the
recipe.
For
example,
if
fish
was
80
percent
of
the
recipe,
then
consumption
of
100
grams
of
the
food
code
was
adjusted
to
80
grams
to
represent
the
amount
of
fish
consumed.

Food
codes
were
assigned
to
either
a
freshwater/
estuarine
or
marine
habitat.
Food
codes
containing
flatfish
(
i.
e.,
flounder,
smelt,
halibut,
plaice,
and
sole),
clams,
scallops,
crabs
(
with
the
exception
of
king
crab)
and
salmon
were
apportioned
between
the
freshwater/
estuarine
and
marine
107
habitats
based
on
the
proportions
of
freshwater/
estuarine
and
marine
species
landed
during
1989,
1990,
and
1991
reported
by
the
National
Marine
Fisheries
Service
(
NMFS,
1995­
96).

In
some
cases
habitat
assignments
are
based
on
NMFS
data
and
life­
cycle
considerations.
If
a
particular
species
is
listed
by
NMFS
as
commercially
harvested
in
marine
waters,
but
is
known
to
spend
at
least
part
of
it's
life­
cycle
in
estuarine
or
freshwater
habitats,
further
evaluation
was
undertaken
to
determine
the
significance
of
a
species'
life­
cycle
with
respect
to
exposure
to
chemical
contaminants
in
freshwater
and
estuarine
waters.
Species
with
life­
cycles
utilizing
both
freshwater/
estuarine
and
marine
habitats
identified
as
contributing
significantly
to
the
CSFII
fish
consumption
rate
determination
include
shrimp
and
salmon.

Shrimp,
which
are
harvested
in
both
marine
waters
and
freshwater/
estuarine
waters,
spend
their
juvenile
years
up
to
sexual
maturity
in
freshwater/
estuarine
habitats.
At
sexual
maturity,
shrimp
are
nearly
adult
size
and
generally
begin
migration
to
marine
waters
where
they
spend
the
remainder
of
their
adult
life.
Once
shrimp
reach
sexual
maturity,
they
are
nearly
full
grown
and
available
for
commercial
harvesting
from
both
estuarine
and
marine
waters.
Though
shrimp
are
harvested
from
both
estuarine
and
marine
waters,
they
have
been
assigned
to
the
freshwater/
estuarine
habitat
designation.
This
is
because
shrimp
are
harvestable
from
estuarine
waters
or
immediately
after
migrating
to
marine
waters
(
Zein­
Eldin
and
Renaud,
1986;
Kutkuhn,
n.
d.).

The
six
species
of
anadromous
salmon
found
in
North
American
waters
spend
their
first
3
months
to
3
years
in
freshwater/
estuarine
habitats
before
migrating
to
marine
waters.
At
the
time
of
out
migration
to
marine
waters,
juveniles
measure
up
to
5
inches
in
length.
All
six
species
will
then
spend
1­
5
years
maturing
as
adults
in
open
sea
before
migrating
back
to
freshwater
lakes,
rivers
and
streams
for
spawning.
Depending
on
the
species,
spawning
may
occur
from
one
to
eight
weeks
after
entering
freshwater
habitats
(
some
unique
populations
of
sockeye
salmon
may
spend
up
to
6
months
in
lakes
prior
to
spawning).
Additionally
(
with
the
exception
of
these
sockeye
populations)
most
salmon
fast,
thus
spending
their
energy
making
the
trip
to
their
spawning
destination.
Because
these
six
species
of
salmon
spend
essentially
their
entire
adult
life
in
open
seas
prior
to
commercial
harvesting
from
marine
waters,
all
salmon
have
been
designated
marine
habitat
with
the
exception
of
1
percent
of
the
total
U.
S.
harvest
which
accounts
for
salmon
which
are
farmed
raised
or
harvested
from
landlocked
populations
(
Groot
and
Margolis,
1991).

Consumption
for
the
given
USDA
food
code
with
an
unknown
habitat
designation
was
allocated
across
the
freshwater/
estuarine
and
marine
habitat
types
in
the
same
percentages
as
those
observed
across
food
codes
from
known
habitat
types.

Average
daily
individual
consumptions
for
a
given
fish­
by­
habitat
category
were
calculated
by
summing
the
amount
of
fish
eaten
by
the
individual
across
three
reporting
days
for
all
fish­
related
food
codes
in
a
given
fish­
by­
habitat
category.
The
total
individual
consumption
was
then
divided
by
three
to
obtain
an
average
daily
consumption.
The
three­
day
individual
food
consumption
data
collection
period
is
one
during
which
a
majority
of
sampled
individuals
did
not
consume
any
finfish
or
shellfish.
The
non­
consumption
of
finfish
or
shellfish
by
a
majority
of
individuals,
combined
with
108
consumption
data
from
high­
end
consumers,
resulted
in
a
wide
range
of
observed
fish
consumption.
This
range
of
fish
consumption
data
would
tend
to
produce
distributions
of
fish
consumption
with
larger
variances
than
would
be
associated
with
a
longer
survey
period,
such
as
30
days.
The
larger
variances
would
reflect
greater
dispersion,
which
results
in
larger
upper­
percentile
estimates,
as
well
as
wider
confidence
intervals
associated
with
parameter
estimates.
It
follows
that
the
estimates
of
the
upper
percentiles
of
per
capita
fish
consumption
based
on
three
days
of
data
will
be
conservative
with
regards
to
risk
(
USEPA,
1998).

For
each
type
of
criteria
(
chronic,
acute,
or
consideration
of
developmental
effects),
percentile
values
from
distributional
data
on
intakes
are
presented
below
for
consumption
of
freshwater
and
estuarine
fish,
as
well
as
for
consumption
of
all
fish
(
including
marine
species).

Chronic
Criteria
Table
2.3.11
and
Exhibit
2.3.1
include
distributional
data
on
intake
rates
of
fresh/
estuarine
fish
for
adults
18
years
and
older.
These
intake
values
represent
"
as
consumed"
weights;
that
is,
they
are
primarily
cooked
weight
intakes
but
also
include
any
raw
fish
consumption
(
e.
g.,
raw
shellfish)
reported.
These
estimates
were
determined
by
averaging
information
from
both
consumers
and
nonconsumers
of
fish
over
the
three
days
of
the
survey.
This
survey
did
not
specifically
ask
questions
on
whether
a
respondent
eats
fish
or
how
often
and,
therefore,
it
is
not
possible
to
identify
consumers
from
non­
consumers.
Since
the
CSFII
reporting
period
is
only
three
days,
long­
term
consumption
distributions
cannot
be
well
characterized
using
the
CSFII
data.
EPA
is
recommending
adult
intakes
(
i.
e.,
specifically
based
on
individuals
age
18
years
and
over)
for
the
general,
sport
fisher,
and
subsistence
fisher
populations
to
be
consistent
with
the
fact
that
the
assumptions
used
for
drinking
water
intake
and
body
weight
are
also
based
on
adults.
These
values
represent
reasonable
intake
rates
for
long­
term
exposure
that
result
in
chronic
effects.
As
shown
in
Table
2.3.11,
the
arithmetic
mean
for
adults
is
5.59
g/
day;
the
median
is
0
g/
day.
The
90th
percentile
value
is
17.80
g/
day,
the
95th
percentile
is
39.04
g/
day,
and
the
99th
percentile
is
86.30
g/
day.
Ninety
percent
confidence
intervals
for
the
mean
and
90
percent
bootstrap
intervals
for
the
median
and
percentile
values
are
also
recorded
in
Table
2.3.11.
EPA
determined
confidence
interval
estimates
for
the
percentile
estimates
by
using
Efron's
percentile
bootstrap
technique
(
USEPA,
1998).
Exhibit
2.3.1
shows
the
cumulative
distribution
(
via
histogram),
which
States
may
wish
to
use
to
estimate
intake
rates
at
different
percentile
values
from
those
values
presented
in
Table
2.3.11.
109
Table
2.3.11:
Daily
Estimates
of
Fish
Consumption
(
Finfish
and
Shellfish):
Individuals
of
Age
18
and
Over
in
the
U.
S.
Population
(
g/
day)

Statistic
Estimate
90%
Interval*

Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
5.59
4.91
6.28
50th
Percentile
0
0.00
0.00
90th
Percentile
17.80
14.89
20.63
95th
Percentile
39.04
36.13
42.16
99th
Percentile
86.30
81.99
96.67
All
Fish
(
including
marine)

Mean
18.01
16.85
19.17
50th
Percentile
0.00
0.00
0.00
90th
Percentile
60.64
57.06
64.63
95th
Percentile
86.25
80.29
91.00
99th
Percentile
142.96
134.23
154.15
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications
Source:
CSFII
(
1989­
1991)

Geographic
data
are
included
for
areas
of
the
United
States,
as
determined
by
the
U.
S.
Department
of
Commerce
for
the
1980
Census
of
Population.
Because
of
small
sample
size,
these
data
are
provided
for
all
individuals
rather
than
for
those
only
>
18
years
old.
The
regions
are
broken
out
as
follows:

New
England:
Connecticut,
Maine,
Massachusetts,
New
Hampshire,
Rhode
Island,
Vermont
Middle
Atlantic:
New
Jersey,
New
York,
Pennsylvania
South
Atlantic:
Delaware,
District
of
Columbia,
Florida,
Georgia,
Maryland,
North
Carolina,
South
Carolina,
Virginia,
West
Virginia
East
North
Central:
Illinois,
Indiana,
Michigan,
Ohio,
Wisconsin
110
East
South
Central:
Alabama,
Kentucky,
Mississippi,
Tennessee
West
North
Central:
Iowa,
Kansas,
Minnesota,
Missouri,
Nebraska,
North
Dakota,
South
Dakota
West
South
Central:
Arkansas,
Louisiana,
Oklahoma,
Texas
Mountain:
Arizona,
Colorado,
Idaho,
Montana,
Nevada,
New
Mexico,
Utah,
Wyoming
Pacific:
California,
Oregon,
Washington
As
stated
on
page
104,
States
and
Tribes
should
consider
these
data
in
combination
with
other
regional
studies
and
not
by
themselves
because
of
the
lack
of
confidence
due
to
the
small
sample
size.

Tables
2.3.12
through
2.3.20
include
these
breakouts
for
finfish
and
shellfish,
again
representing
as
consumed
intakes:

Table
2.3.12:
Distribution
of
Finfish
and
Shellfish
Consumption:
New
England
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
4.94
50th
Percentile
0.00
90th
Percentile
17.58
95th
Percentile
30.11
99th
Percentile
72.04
All
Fish
(
including
marine)

Mean
21.90
50th
Percentile
0.00
90th
Percentile
73.56
95th
Percentile
91.33
99th
Percentile
145.86
Source:
CSFII
(
1989­
1991)
111
Table
2.3.13:
Distribution
of
Finfish
and
Shellfish
Consumption:
Middle
Atlantic
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
3.79
50th
Percentile
0.00
90th
Percentile
12.13
95th
Percentile
25.29
99th
Percentile
61.72
All
Fish
(
including
marine)

Mean
18.68
50th
Percentile
0.00
90th
Percentile
61.26
95th
Percentile
80.54
99th
Percentile
153.23
Source:
CSFII
(
1989­
1991)
112
Table
2.3.14:
Distribution
of
Finfish
and
Shellfish
Consumption:
South
Atlantic
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
4.92
50th
Percentile
0.00
90th
Percentile
16.72
95th
Percentile
30.45
99th
Percentile
77.54
All
Fish
(
including
marine)

Mean
16.33
50th
Percentile
0.00
90th
Percentile
57.62
95th
Percentile
83.39
99th
Percentile
130.78
Source:
CSFII
(
1989­
1991)
113
Table
2.3.15:
Distribution
of
Finfish
and
Shellfish
Consumption:
East
North
Central
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
2.88
50th
Percentile
0.00
90th
Percentile
5.10
95th
Percentile
18.24
99th
Percentile
58.24
All
Fish
(
including
marine)

Mean
13.21
50th
Percentile
0.00
90th
Percentile
47.50
95th
Percentile
72.05
99th
Percentile
114.31
Source:
CSFII
(
1989­
1991)
114
Table
2.3.16:
Distribution
of
Finfish
and
Shellfish
Consumption:
East
South
Central
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
10.66
50th
Percentile
0.00
90th
Percentile
37.64
95th
Percentile
58.41
99th
Percentile
165.12
All
Fish
(
including
marine)

Mean
16.63
50th
Percentile
0.00
90th
Percentile
52.26
95th
Percentile
69.94
99th
Percentile
165.12
Source:
CSFII
(
1989­
1991)
115
Table
2.3.17:
Distribution
of
Finfish
and
Shellfish
Consumption:
West
North
Central
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
4.48
50th
Percentile
0.00
90th
Percentile
4.41
95th
Percentile
25.84
99th
Percentile
104.32
All
Fish
(
including
marine)

Mean
12.85
50th
Percentile
0.00
90th
Percentile
42.99
95th
Percentile
63.05
99th
Percentile
141.07
Source:
CSFII
(
1989­
1991)
116
Table
2.3.18:
Distribution
of
Finfish
and
Shellfish
Consumption:
West
South
Central
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
7.04
50th
Percentile
0.00
90th
Percentile
23.85
95th
Percentile
55.46
99th
Percentile
112.68
All
Fish
(
including
marine)

Mean
13.06
50th
Percentile
0.00
90th
Percentile
49.69
95th
Percentile
74.77
99th
Percentile
114.22
Source:
CSFII
(
1989­
1991)
117
Table
2.3.19:
Distribution
of
Finfish
and
Shellfish
Consumption:
Mountain
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
3.23
50th
Percentile
0.00
90th
Percentile
0.48
95th
Percentile
20.90
99th
Percentile
78.60
All
Fish
(
including
marine)

Mean
11.20
50th
Percentile
0.00
90th
Percentile
39.32
95th
Percentile
58.55
99th
Percentile
95.84
Source:
CSFII
(
1989­
1991)
118
Table
2.3.20:
Distribution
of
Finfish
and
Shellfish
Consumption:
Pacific
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
3.93
50th
Percentile
0.00
90th
Percentile
10.16
95th
Percentile
26.46
99th
Percentile
68.74
All
Fish
(
including
marine)

Mean
16.81
50th
Percentile
0.00
90th
Percentile
55.87
95th
Percentile
83.44
99th
Percentile
122.64
Source:
CSFII
(
1989­
1991)

Developmental
Criteria
Table
2.3.21
presents
as
consumed
weight
distributional
data
for
children
ages
0
to
14
who
are
"
acute"
consumers
of
fish.
Exhibit
2.3.2
graphically
shows
a
more
complete
distribution
of
values
for
these
consumers.
The
term
"
acute
consumer"
does
not
refer
to
adverse
health
effects
or
toxicity
studies.
It
refers
to
the
subset
of
survey
responses
where
fish
was
actually
consumed.
That
is,
the
distributional
data
from
the
CSFII
for
these
"
acute
consumers"
was
determined
by
using
only
data
from
the
individuals
who
ate
fish
during
the
survey
period.
"
Acute
consumers"
were
defined
as
individuals
who
reported
consumption
of
a
fish­
related
food
code
at
least
once
in
the
three­
day
reporting
period.
In
addition,
if
an
individual
consumed
fish
for
two
of
the
three
days,
the
average
daily
consumption
for
that
individual
was
calculated
by
summing
the
two
daily
consumption
values
and
dividing
by
two.
As
noted
above,
these
data
may
be
most
appropriate
to
use
for
evaluating
exposures
for
children
because
they
generally
have
higher
intake
rates
per
body
weight
than
adults.
EPA
did
not
generate
intervals
around
the
estimates
because
variance
estimation
algorithms
require
data
from
at
least
two
primary
sampling
units
(
PSUs)
per
stratum,
and
this
criterion
was
not
always
met
for
these
acute
consumers
(
USEPA,
1998).
14
Acute
consumer
refers
to
respondents
who
reported
consuming
fish
during
the
3­
day
survey
period.

119
Table
2.3.21:
Daily
Estimates
of
Fish
Consumption:
Finfish
and
Shellfish
­
Acute
Consumers,
14
Children
0
to
14
Years
Old
in
the
U.
S.
Population
Statistic
Estimate
(
g/
day)

Fresh/
Estuarine
Mean
45.73
50th
Percentile
28.35
90th
Percentile
108.36
95th
Percentile
136.24
99th
Percentile
214.62
All
Fish
(
including
marine)

Mean
74.80
50th
Percentile
56.49
90th
Percentile
153.70
95th
Percentile
178.08
99th
Percentile
337.46
Source:
CSFII
(
1989­
1991)
15
Acute
consumer
refers
to
respondents
who
reported
consuming
fish
during
the
3­
day
survey
period.

121
The
CSFII
data
indicate
that
for
median,
mean,
and
upper
percentile
intakes,
rates
for
children
are
higher
per
body
weight
than
rates
for
adults,
with
differences
of
up
to
8.6
g/
kg­
day
at
the
99th
percentile
(
See
Appendix
A).

For
in­
utero
developmental
effects,
intake
rates
for
women
of
childbearing
age
may
be
most
appropriate.
Thus,
Table
2.3.22
presents
the
distribution
of
as
consumed
fish
intake
values
for
women
ages
15­
44
years
old
who
are
acute
consumers,
as
described
above.
Exhibit
2.3.3
graphically
shows
a
more
complete
distribution
of
values
for
these
acute
consumers.

Table
2.3.22:
Daily
Estimates
of
Fish
Consumption:
Acute
Consumers,
15
Women
15
to
44
Years
Old
in
the
U.
S.
Population
(
g/
day)

Statistic
Estimate
Fresh/
Estuarine
Mean
61.40
50th
Percentile
35.22
90th
Percentile
148.83
95th
Percentile
185.44
99th
Percentile
363.56
All
Fish
(
including
marine)

Mean
88.80
50th
Percentile
69.95
90th
Percentile
170.01
95th
Percentile
212.56
99th
Percentile
361.04
Source:
CSFII
(
1989­
1991)
123
Preference
#
4:
Use
of
Default
Intake
Rates
from
the
CSFII
The
1980
AWQC
National
Guidelines
recommended
a
fish
intake
rate
of
6.5
grams/
day,
based
on
the
mean
consumption
rate
of
freshwater
and
estuarine
finfish
and
shellfish
from
30­
day
diary
results
reported
in
the
1973­
74
National
Purchase
Diary
Survey.
These
updated
guidelines
recommend
several
default
intake
rates
depending
on
the
population
and
type
of
effect
(
chronic
or
developmental)
that
is
being
considered.

Default
Intake
Rate
for
Chronic
Effects
Although
EPA
prefers
that
States
use
one
of
the
above
methods
to
determine
fish
intake,
this
section
presents
two
default
intake
rates
that
EPA
believes
represent
appropriate
fish
intake
values
for
different
population
groups,
and
are
appropriate
for
determining
intake
related
to
contaminants
that
may
cause
chronic
effects.
EPA
recommends
using
the
following
intake
rates
(
of
freshwater
and
estuarine
finfish
and
shellfish)
based
on
information
for
all
adults
from
the
CSFII:
17.80
g/
day
for
the
general
adult
population
and
sport
fishers,
and
86.30
g/
day
for
subsistence
fishers.
These
values
represent
the
intake
of
freshwater/
estuarine
finfish
and
shellfish
as
consumed.
By
applying
17.80g/
day
as
a
default
for
the
general
adult
population,
EPA
intends
to
select
an
intake
rate
that
is
protective
of
a
majority
of
the
population.
EPA
further
considers
that,
although
these
rates
are
reflective
of
high­
end
consumers
in
the
general
population
and
do
not
directly
reflect
intakes
specific
to
sportfishers
and
subsistence
fishers,
they
are
indicative
of
the
average
consumption
among
sport
fishers
and
subsistence
fishers,
respectively.
Specifically,
comparison
of
the
CSFII
intake
rates
with
results
from
state
and
regional
surveys
indicate
that
these
rates
may
be
appropriate
for
the
defined
sportfisher
and
subsistence
fisher
populations
(
refer
to
study
summaries
for
sportfishers
and
subsistence
fishers
under
Preference
#
2).
As
noted
above,
however,
sportfisher
and
subsistence
fisher
populations
are
generalized
terms
and
each
group
may
encompass
a
variety
of
types
of
individuals.
Thus,
States
should
try
to
use
intake
rates
more
specific
to
the
population
addressed
before
considering
the
default
intake
rates
suggested
here
(
ensuring
that
the
rates
chosen
meet
the
minimum
discussed
on
page
86).

Default
Intake
Rate
for
Developmental
Effects
For
a
few
fish
contaminants,
health
effects
in
children
are
of
primary
concern
(
e.
g.,
cholinesterase
inhibitors).
Because
children
have
a
higher
fish
consumption
rate
per
body
weight
compared
to
adults,
using
a
higher
fish
consumption
rate
per
body
weight
may
be
necessary
for
setting
AWQC
to
assure
adequate
protection
for
children
from
toxicants
that
cause
such
effects.
EPA
advises
that
in
absence
of
local
data
or
other
data
approximating
local
information
on
values
appropriate
for
children's
intake,
States
use
a
value
of
108.36
g/
day.
This
value
represents
the
90th
percentile
from
the
combined
1989­
1991
CSFII
surveys
for
acute
consumption
(
defined
above
under
Preference
#
3,
in
the
Developmental
Criteria
subsection)
of
freshwater/
estuarine
finfish
and
shellfish
for
children
ages
0
to
14.
The
value
represents
only
those
children
from
the
CSFII
survey
who
ate
fish
during
the
3­
day
survey
period,
and
the
intake
was
averaged
over
the
number
of
days
during
which
fish
was
actually
consumed.
It
is
recommended
that
this
value
be
used
with
a
body
weight
of
16
Superfund
guidance
suggests
that
an
average
mouthful
of
water
may
be
50
ml
[
SUPERFUND
Risk
Assessment
Guidelines
(
USEPA,
1989b)].

124
28
kilograms
(
discussed
above)
to
protect
individuals
from
adverse
effects
of
eating
fish
when
RfDs
are
based
on
health
effects
in
children.

Developmental
effects
may
be
of
concern
for
children
or
women
of
childbearing
age.
To
provide
additional
protection
from
adverse
effects
when
pregnant
women
are
of
particular
concern,
a
default
intake
rate
of
148.83
g/
day,
specific
to
women
of
childbearing
age
(
15­
44
years
old),
is
suggested
for
setting
AWQC
to
protect
against
such
developmental
effects.
This
value
represents
approximately
the
90th
percentile
of
acute
consumption
of
freshwater/
estuarine
finfish
and
shellfish
for
women
in
this
age
group
from
the
CSFII
survey.
As
with
the
rate
for
children,
this
value
represents
only
those
women
who
consumed
fish
during
the
3­
day
survey
period.

2.3.2.4
Incidental
Ingestion
The
drinking
water
ingestion
rate
of
2
liters/
day
is
used
only
for
setting
AWQC
for
those
water
bodies
designated
as
public
water
supply
sources.
Individuals
exposed
to
water
from
water
bodies
that
are
not
listed
as
public
water
supply
sources
would
not
be
likely
to
ingest
2
liters/
day
from
these
waters.
However,
even
if
a
water
body
is
not
used
for
public
drinking
water
supplies,
it
is
possible
that
an
individual
may
incidentally
ingest
some
amount
of
water
if
he
or
she
swims,
fishes
or
boats
in
the
water
body.
Literature
on
recreational
exposure
combined
with
assumptions
about
the
average
mouthful
of
water
ingested
for
every
hour
of
total
body
contact
can
be
used
to
determine
an
incidental
ingestion
rate.
EPA
recommends
an
incidental
ingestion
rate
of
10
ml/
day
based
on
data
from
studies
below
when
developing
chronic
criteria.
The
criteria
that
would
be
calculated
using
incidental
ingestion
would
include
water
bodies
that
are
designated
to
be
used
for
recreational
purposes
only.

Incidental
ingestion
can
be
determined
by
estimating
the
number
of
hours
that
an
individual
may
be
in
contact
with
water
during
recreation
and
multiplying
this
value
by
an
average
mouthful
of
water
assumed
to
be
swallowed
during
each
hour
of
recreational
exposure
which
results
in
total
body
contact
with
water.
The
estimate
of
10
ml/
day
is
based
on
an
assumption
that
an
individual
may
be
in
total
contact
with
water
for
123
hours
a
year
(
which
represents
an
hour
of
exposure
per
day
throughout
four
summer
months)
and
may
ingest
30
ml
of
water
per
hour
of
total
contact
(
State
of
Michigan,
1985).
16
The
value
is
calculated
by
multiplying
30
ml/
hour
by
123
hours
a
year
and
dividing
by
365.
This
value
has
been
proposed
for
use
in
the
proposed
Water
Quality
Guidance
for
the
Great
Lakes
(
58
FR
20869).

Other
studies
of
recreational
exposure
suggest
much
variation
in
this
rate,
with
some
similar
estimates
of
exposure
as
a
result
of
water
skiing,
swimming,
boating,
and
fishing
activities.
These
studies
are
discussed
below.

EPA
has
reported
exposure
durations
for
swimming,
water
skiing,
boating,
and
fishing.
EPA
recently
estimated
a
national
average
frequency
of
swimming
of
7
days/
year
with
a
2.6
hour
duration
125
(
USEPA,
1989b).
This
value
may
be
compared
with
an
earlier
EPA
publication
(
USEPA,
1979),
which
estimated
an
average
annual
frequency
of
9
days/
year
with
a
2
hour
duration
of
exposure
per
day.
EPA
estimated
that
approximately
20
million
individuals
participated
in
water
skiing
(
which,
like
swimming,
involves
total
body
contact
with
water)
for
a
total
of
260
million
hours
per
year.
This
averages
to
14
hours
of
exposure
per
participant.
USEPA
(
1979)
also
listed
individuals
that
participated
in
other
water
activities.
Sixty­
eight
million
people
were
involved
nationally
in
boating
with
an
average
duration
of
24
hours
per
participant
per
year
and
54
million
people
fished
with
122
hours
per
participant
per
year.
Total
body
contact
with
water
during
boating
and
fishing
was
identified
as
40
percent
and
20
percent,
respectively
(
USEPA,
1979).
These
values
were
used
to
adjust
the
exposure
duration
for
participants
in
these
activities
to
yield
exposures
on
a
total
contact
basis.
The
resulting
total
contact
exposures
per
year
for
swimming,
water
skiing,
boating
and
fishing
were
calculated
as
18,
14,
10,
and
24
hours,
respectively.
Adding
all
exposures
yields
66
hours
of
total
body
contact
exposure
from
recreational
activities.

Several
recreational
surveys
have
been
conducted
in
Michigan
which
indicate
up
to
105
hours
of
total
water
exposure.
Estimating
total
exposure
suggests
total
hours
of
exposure
per
year
that
are
higher
than
the
national
average.
The
calculation
of
these
exposures
involves
assuming
an
individual
participates
in
all
activities
for
the
number
of
days
listed
in
the
1981
Michigan
Travel
and
Recreation
Survey
and
for
the
duration
of
hours
per
participation
as
identified
in
the
1976
Recreation
survey.
In
addition
to
these
assumptions,
exposure
was
adjusted
by
the
percentage
of
total
body
contact
exposure
involved
in
the
activity.
This
adjustment
was
made
assuming
the
same
percentages
for
total
body
contact
used
in
USEPA
(
1979).
The
calculation
of
total
body
exposure
resulting
from
these
activities
is
indicated
in
Table
2.3.23.

Although
the
default
value
of
10
ml/
day
for
chronic
ingestion
is
appropriate
for
situations
in
which
exposure
occurs
daily
for
about
four
months,
States
and
Tribes
in
warmer
climates
may
wish
to
use
higher
incidental
ingestion
rates
for
chronic
criteria
to
protect
individuals
who
may
swim
in
lakes
or
rivers
for
a
greater
portion
of
the
year.
For
example,
Louisiana
uses
89
ml/
day
to
account
for
exposure
due
to
incidental
ingestion
when
developing
criteria
for
non­
drinking
water
supplies.
The
assumptions
used
by
the
Louisiana
Department
of
Environmental
Quality
in
determining
the
89
ml/
day
are
described
in
Louisiana
DEQ
(
1989,
1994).

In
addition
to
chronic
values
for
incidental
ingestion,
States
and
Tribes
may
wish
to
use
an
incidental
ingestion
rate
for
evaluating
contaminants
that
cause
adverse
health
effects
from
shorterterm
exposures
based
on
the
amount
of
water
that
may
be
ingested
in
a
given
hour
of
recreational
activity.
126
Table
2.3.23:
Yearly
Total
Hours
of
Total
Body
Contact
as
Determined
by
Michigan
Recreational
Surveys
Activity
Days
per
Participant
Hours
per
Participation
Body
Contact
Adjustment
Hours
of
Exposure
Swimming
13.3
2.1
(
ave.)
1.0
27.9
Fishing
14.3
3.7
(
ave.)
0.2
10.6
Power
Boating
24.5
(
total)
3.2
0.4
31.4
Water
Skiing
9.6
1.5
1.0
14.4
Sailing
10.4
(
total)
3.2
0.4
13.3
Canoeing
4.8
3.9
0.4
7.5
TOTAL
105.1
Source:
Wells
(
1990)

2.3.3
Quantification
of
Exposure
In
typical
exposure
assessments,
the
magnitude,
frequency,
and
duration
of
exposure
is
quantified
for
a
given
population
and
specifically
selected
exposure
pathways.
After
selecting
exposure
concentration
values
in
each
environmental
medium
to
be
addressed
(
e.
g.,
water,
food),
pathway­
specific
intake
rates
are
subsequently
selected.
Given
an
assumption
that
exposure
occurs
over
a
period
of
time,
dividing
the
exposure
assumption
by
the
period
of
time
will
give
an
average
exposure
rate
per
unit
time.
Alternatively,
exposure
can
be
estimated
by
normalizing
both
the
time
and
body
weight
factors,
expressed
in
units
of
mg
chemical/
kg
body
weight­
day.
(
This
is
discussed
in
the
Federal
Register
notice
and
comment
is
requested
on
this
alternative.)

The
term
"
intake"
used
with
this
methodology
describes
the
daily
exposure
estimate
(
normalized
for
a
lifetime)
and
is
expressed
in
units
of
mg
chemical/
liter.
Specifically,
for
purposes
of
establishing
AWQC
which
are,
by
and
large,
based
on
chronic
health
effects
data
and
are
intended
to
be
protective
of
the
general
population
over
a
lifetime
of
exposure,
the
criteria
calculations
are
made
in
terms
of
a
person's
daily
exposure.
That
is,
the
AWQC
represent
an
acceptable
daily
exposure
over
a
lifetime
for
which
no
adverse
health
effects
associated
with
that
chemical
are
expected
to
occur.
Hence,
the
expression
of
the
AWQC
is
in
mg
chemical/
day.
The
AWQC
calculation
includes
an
assumption
of
body
weight.

When
selecting
contaminant
concentration
values
in
environmental
media
and
exposure
intake
values
for
the
Relative
Source
Contribution
(
RSC)
analysis,
it
is
important
to
realize
that
each
value
selected
(
including
those
intakes
recommended
as
default
assumptions
in
the
AWQC
equation)
is
associated
with
a
distribution
of
values
for
that
parameter.
Determining
how
various
subgroups
fall
127
within
the
distributions
of
overall
exposure
and
how
the
combination
of
exposure
variables
defines
what
population
is
being
protected
is
a
complicated
and,
perhaps,
unmanageable
task,
depending
on
the
amount
of
information
available
on
each
exposure
factor
included.
Many
times,
the
default
assumptions
used
in
EPA
risk
assessments
are
derived
from
the
evaluation
of
numerous
studies
and
are
generally
considered
to
represent
a
particular
population
group
or
some
national
average.
Therefore,
describing
with
certainty
the
exact
percentile
of
a
particular
population
that
is
protected
with
a
resulting
criteria
is
often
not
possible.

General
recommendations
for
selecting
values
to
be
used
in
exposure
assessments
for
both
individual
and
population
exposures
are
discussed
in
EPA's
Guidelines
for
Exposure
Assessment
(
USEPA,
1992).
The
ultimate
choice
of
the
contaminant
concentration
values
used
in
the
RSC
estimate
and
the
exposure
intake
rates
requires
the
use
of
professional
judgment.
In
particular,
when
combining
variable
values
for
the
AWQC
estimate,
the
basis
of
the
health
effect
(
e.
g.,
chronic)
and
the
population
(
e.
g.,
general
population)
must
be
kept
in
mind;
for
example,
combining
a
90th
percentile
intake
with
a
5th
percentile
body
weight
is
not
appropriate
because
it
is
not
likely
that
the
smallest
person
would
have
the
highest
intake
and
it
would
not
be
appropriate
with
such
a
chronic
effect,
general
population
scenario.
Similar
judgments
must
be
made
for
less­
than­
lifetime
health
effects
and
different
target
population
groups.
The
following
are
general
recommendations.
States
and
Tribes
have
the
flexibility
to
consider
other
parameters
based
on
site­
specific
information
or
other
risk
management
considerations.

Contaminant
concentration.
The
concentration
values
for
all
media
used
in
the
RSC
analysis
are
arithmetic
means
when
calculations
are
made
for
the
general
population.
These
are
used
to
represent
reasonable
central
tendency
estimates
for
a
typically
exposed
person.
Higher
concentration
values
may
be
considered
when
making
evaluations
for
more
highly
exposed
population
groups
(
e.
g.,
subsistence
fishers)
whose
patterns
of
exposure
with
fish
consumption
are
not
the
same
as
the
general
population.
However,
higher
contaminant
concentration
values
should
not
be
used
for
all
media
(
e.
g.,
other
dietary
or
air
intake
assumptions)
unless
it
is
clear
that
the
specific
population
group
is
likely
to
experience
higher
concentrations
from
other
media
as
well.
For
example,
in
the
RSC
analysis,
choosing
a
higher
concentration
value
for
estimating
fish
exposure
with
a
subsistence
fisher,
should
not
mean
automatically
using
high
concentration
values
for
other
foods
(
such
as
vegetables,
fruit,
etc.)
or
for
air
exposures.

Body
weight.
By
and
the
large,
the
AWQC
will
be
based
on
the
arithmetic
mean
of
the
adult
body
weight
for
the
general
population.
If
the
health
effect
of
concern
is
one
that
specifically
occurs
in
children,
the
arithmetic
average
child
body
weight
is
recommended.
The
same
recommendation
of
an
arithmetic
mean
is
made
for
women
of
childbearing
age.

Dietary
intake
(
non­
fresh/
estuarine
fish
intake)
and
inhalation.
Values
recommended
for
these
assumptions,
which
are
a
part
of
the
RSC
analysis,
are
based
on
the
arithmetic
means
from
the
information
sources
utilized.
Specifically,
the
dietary
intake
assumptions
are
taken
from
the
Food
and
Drug
Administration's
Total
Diet
Study
program
(
Pennington,
1983)
and
the
inhalation
rate
is
based
on
a
study
conducted
by
the
International
Commission
on
Radiological
Protection
(
ICRP,
1981),
128
which
has
been
historically
used
in
EPA
risk
assessments.
These
studies
are
representative
of
the
overall
U.
S.
population.

Fresh/
estuarine
fish
intake
and
drinking
water.
The
intake
rates
recommended
for
these
two
parameters
are
higher
than
the
arithmetic
means
for
the
U.
S.
population.
The
choice
of
default
intake
assumptions
for
these
parameters
represent
a
risk
management
decision
under
the
goals
of
the
Clean
Water
Act
to
establish
AWQC
that
are
protective
of
a
majority
of
the
population
through
the
exposure
routes
of
water
and
fish
consumption.
The
default
drinking
water
intake
rate
represents
the
84th
percentile
value
from
the
study
on
which
it
is
based.
The
default
intake
rate
for
fish
consumption
of
the
general
population
represents
the
90th
percentile
value
from
the
study
on
which
it
is
based.
However,
it
should
be
kept
in
mind
that
the
study
does
not
enable
accounting
for
fish
consumers
only
and,
therefore,
the
intake
assumption
likely
represents
less
than
the
90th
percentile
of
the
population
potentially
at
risk
from
this
exposure
route.

EPA
considers
the
national
AWQC
recommendations
to
be
protective
of
a
majority
of
the
general
population
and
believes
that
it
has
used
appropriate
professional
judgment
in
recommending
these
criteria.
EPA
encourages
States
and
Tribes
to
use
local
or
more
site­
specific
exposure
intake
and
concentration
assumptions
that
they
believe
would
appropriately
protect
the
overall
population,
including
highly
exposed
subgroups.
The
exposure
assessment
procedures
used
in
this
methodology,
which
includes
the
RSC
Exposure
Decision
Tree
recommendation,
do
not
prohibit
the
use
of
Monte
Carlo
analysis.
States
and
Tribes
may
consider
using
such
probabilistic
techniques
when
they
have
access
to
data
that
are
adequate
enough
to
provide
meaningful
results
from
such
analyses.
Again,
the
selection
of
a
point
off
the
overall
distribution
of
exposures
(
which
represents
a
combination
of
other
distributions)
is
a
decision
that
involves
professional
judgment.

2.3.4
Consideration
of
Non­
Water
Sources
of
Exposure
When
Setting
AWQC
In
the
1980
AWQC
National
Guidelines,
different
approaches
for
addressing
non­
water
exposure
pathways
were
used
in
setting
AWQC
for
the
protection
of
human
health
depending
upon
the
toxicological
endpoint
of
concern.
For
those
substances
for
which
the
appropriate
toxic
endpoint
was
linear
carcinogenicity,
only
the
two
water
sources
(
i.
e.,
drinking
water
consumption
and
freshwater/
estuarine
fish
ingestion)
were
considered
in
the
derivation
of
the
AWQC.
Non­
water
sources
and
marine
fish
ingestion
were
not
considered
explicitly.
The
rationale
for
this
approach
is
that
in
the
case
of
linear
carcinogens
the
AWQC
is
being
determined
with
respect
to
the
incremental
lifetime
risk
posed
by
a
substance's
presence
in
water,
and
is
not
being
set
with
regard
to
an
individual's
total
risk
from
all
sources
of
exposure.

In
the
case
of
substances
for
which
the
AWQC
is
set
on
the
basis
of
a
nonlinear
carcinogen
or
a
noncancer
endpoint
where
a
threshold
is
assumed
to
exist,
non­
water
exposures
were
considered
when
deriving
the
AWQC
under
the
1980
AWQC
National
Guidelines.
In
effect,
the
1980
AWQC
National
Guidelines
specified
that
the
AWQC
be
calculated
based
on
no
more
than
that
portion
of
the
ADI
that
remains
after
contributions
from
other
expected
sources
of
exposure
have
been
accounted
for.
The
ADI
is
equivalent
to
the
RfD,
which
is
discussed
in
Section
2.2.
The
rationale
129
AWQC
'
[
70]
[
ADI
&
(
DT
%
IN)]
÷
[
2
%
0.0065R]

(
Equation
2.3.1)
for
this
approach
has
been
that
for
pollutants
exhibiting
threshold
effects,
the
objective
of
the
AWQC
is
to
ensure
that
an
individual's
total
exposure
does
not
exceed
that
threshold
level.
It
is
useful
to
note
that
while
the
1980
Guidelines
recommended
taking
inhalation
and
nonfish
dietary
sources
into
account
in
setting
the
AWQC
for
threshold
contaminants,
in
practice
the
data
on
these
other
sources
were
not
available.
Therefore,
the
AWQC
usually
were
derived
such
that
they
accounted
for
all
of
the
ADI
(
RfD).

EPA
is
proposing
that
only
a
portion
of
the
RfD
or
Pdp/
SF
be
used
in
setting
AWQC
in
order
to
account
for
other
sources
of
exposure
for
threshold
toxicants,
including
both
noncarcinogens
and
nonlinear
carcinogens.
Toxicological
issues
related
to
noncarcinogens
and
nonlinear
carcinogens
are
discussed
in
detail
in
Sections
2.2
and
2.1,
respectively.
For
carcinogens
that
act
in
a
linear
fashion,
non­
water
sources
would
not
be
taken
into
account
when
setting
AWQC.
The
rationale
is
the
same
as
that
given
in
the
1980
Guidelines,
namely,
that
the
AWQC
is
being
determined
for
the
incremental
lifetime
risk
posed
by
a
substance's
presence
in
water
and
not
for
an
individual's
total
risk
from
all
exposure
sources.

For
noncarcinogens
for
which
non­
water
exposures
were
considered,
the
1980
methodology
included
the
following
general
formula
for
setting
the
criterion:

where
AWQC
is
the
criterion
in
units
of
mg/
L;
ADI
is
the
Acceptable
Daily
Intake
(
now
Reference
Dose,
RfD)
in
units
of
mg/
kg­
day;
DT
is
non­
freshwater
and
­
estuarine
fish
dietary
intake
in
mg/
kgday
IN
is
inhalation
intake
in
mg/
kg­
day;
70
is
human
body
weight
in
kg;
2
is
the
drinking
water
consumption
in
L/
day;
0.0065
is
fish
ingestion
in
kg/
day;
and
R
is
the
bioconcentration
factor
in
L/
kg.
As
indicated
by
the
above
equation,
the
1980
AWQC
National
Guidelines
used
a
"
subtraction"
approach
to
account
for
non­
water
exposure
sources
when
calculating
AWQC
for
noncarcinogenic,
threshold
pollutants.
That
is,
the
amount
of
the
ADI
(
RfD)
"
available"
for
water
sources
was
determined
by
first
subtracting
out
contributions
from
non­
water
sources.
A
similar
subtraction
approach
was
used,
albeit
inconsistently,
in
the
derivation
of
drinking
water
MCLG
values
in
the
early
and
mid­
1980s;
more
recently,
however,
the
derivation
of
MCLGs
has
incorporated
what
has
been
termed
the
"
percentage"
approach.

EPA
has
considered
several
alternative
approaches
to
account
for
non­
water
sources
and
to
resolve
past
inconsistencies
in
its
method.
All
approaches
are
discussed
in
detail
in
a
separate
document
available
in
the
public
docket
for
this
proposal
(
Borum,
unpublished).
The
result
of
discussions
on
these
approaches
was
a
consensus
by
the
Relative
Source
Contribution
Policy
Workgroup
to
recommend
the
Decision
Tree
Approach
for
internal
Agency
review.
This
was
considered
the
best
option
of
the
alternatives
presented.
To
account
for
exposures
from
other
media
when
setting
an
AWQC,
the
exposure
decision
tree
for
determining
proposed
RfD
or
Pdp/
SF
allocations
represents
a
method
of
comprehensively
assessing
a
chemical
for
regulatory
development.
130
This
method
considers
the
adequacy
of
available
exposure
data,
levels
of
exposure,
relevant
sources/
media
of
exposure,
and
regulatory
agendas
(
i.
e.,
multiple
regulatory
actions
for
the
same
chemical).
The
decision
tree
addresses
most
of
the
disadvantages
associated
with
the
exclusive
use
of
either
the
percentage
or
subtraction
approaches,
because
they
are
not
arbitrarily
chosen
prior
to
determining
the
following:
specific
population(
s)
of
concern,
whether
these
populations
are
relevant
to
multiple­
source
exposures
for
the
chemical
in
question
(
i.
e.,
whether
the
population
is
actually
or
potentially
experiencing
exposure
from
multiple
sources),
and
whether
levels
of
exposure,
regulatory
agendas
or
other
circumstances
make
allocation
of
the
RfD
or
Pdp/
SF
desirable.
Both
subtraction
and
percentage
methods
are
potentially
utilized
under
different
circumstances
with
the
Decision
Tree
Approach,
and
the
decision
tree
is
recommended
with
the
idea
that
there
is
enough
flexibility
to
use
other
procedures
if
information
on
the
contaminant
in
question
suggests
it
is
not
appropriate
to
follow
the
decision
tree
(
e.
g.,
if
multiple
sources
of
exposure
do
not
exist
for
the
population
of
concern).
EPA
recognizes
that
there
may
be
other
valid
approaches
in
addition
to
the
exposure
decision
tree
and
the
others
identified
in
the
Federal
Register
(
FR)
notice.
EPA
is
specifically
recommending
the
Exposure
Decision
Tree
for
use
with
this
methodology.

As
stated
in
the
FR,
current
internal
policy
discussions
include
the
application
of
this
approach
to
all
program
offices
to
the
extent
practicable
when
conducting
exposure
assessments.
As
such,
the
broader
goals
are
to
ensure
more
comprehensive
evaluations
of
exposure
Agency­
wide
and
consistent
allocations
of
the
RfD
or
Pdp/
SF
for
criteria­
setting
purposes
when
appropriate.

2.3.4.1
Exposure
Decision
Tree
Approach
Although
the
following
discussion
of
the
Exposure
Decision
Tree
Approach
is
included
in
the
Federal
Register
notice,
it
is
repeated
here
for
the
benefit
of
the
reader,
and
for
use
in
evaluating
the
example
below.

When
data
regarding
exposure
sources
of
a
given
chemical
are
adequate,
the
decision
tree
is
designed
to
allow
for
accurate
predictions
of
exposure
for
the
population(
s)
of
concern.
When
there
are
less
data,
there
is
an
even
greater
need
to
make
sure
that
public
health
protection
is
achieved.
A
series
of
qualitative
alternatives
is
proposed.
Specifically,
the
decision
tree
makes
use
of
chemical
information
when
actual
monitoring
data
are
inadequate.
It
considers
information
on
the
chemical/
physical
properties,
uses
of
the
chemical,
and
environmental
fate
and
transformation,
as
well
as
the
likelihood
of
occurrence
in
various
media.
Review
of
such
information,
when
available,
and
concurrence
on
a
reasonable
exposure
characterization
for
the
chemical
would
result
in
a
health­
based
criterion
that
is
more
accurate
in
predicting
exposures
than
a
default
of
20
percent.
Although
the
20
percent
default
is
still
proposed
when
information
is
not
adequate,
the
need
for
using
a
default
is
greatly
reduced.

As
stated
above,
the
recommendation
is
made
with
the
understanding
that
there
may
be
situations
where
the
decision
tree
procedure
is
not
practicable
or
may
be
simply
irrelevant
after
considering
the
properties,
uses
and
sources
of
the
chemical
in
question.
It
is
important
to
have
the
flexibility
to
choose
other
procedures
that
are
more
appropriate
for
setting
health­
based
criteria
or,
131
perhaps,
allocating
the
RfD
or
Pdp/
SF,
as
long
as
reasons
why
the
regulatory
action
should
follow
a
different
course
are
clearly
presented.
Often,
however,
the
multiple
source
nature
of
chemicals
is
likely
to
merit
a
decision
tree
evaluation
for
the
purpose
of
setting
human
health
criteria
or
standards
for
a
given
chemical.
The
decision
to
perform,
or
not
to
perform,
an
allocation
could
actually
be
made
at
several
points
during
the
decision
tree
process.
Working
through
the
whole
process
may
be
most
helpful
for
determining
why
another
approach
should
be
used.
While
combined
exposures
above
the
RfD
(
Pdp/
SF)
may
or
may
not
be
an
actual
health
risk,
a
combination
of
health
standards
exceeding
the
RfD
(
Pdp/
SF)
may
not
be
sufficiently
protective.
Maintaining
total
exposure
below
the
RfD
(
Pdp/
SF)
is
a
reasonable
health
goal
and
there
are
circumstances
where
health­
based
criteria
for
a
chemical
should
not
exceed
the
RfD
(
Pdp/
SF),
either
alone
(
if
only
one
criterion
is
relevant)
or
in
combination.
"
Relevancy"
here
means
determining
whether
more
than
one
criterion,
standard,
or
other
guidance
is
being
planned,
performed
or
is
in
existence
for
the
chemical
in
question.

It
is
clear
that
this
will
be
an
interactive
process;
input
by
exposure
assessors
will
be
provided
to,
and
received
from,
risk
managers
throughout
the
process,
given
that
there
may
be
significant
implications
regarding
control
issues
(
i.
e.,
cost/
feasibility),
environmental
justice
issues,
etc.
In
cases
where
the
decision
tree
is
not
chosen,
communication
and
concurrence
about
the
decision
rationale
and
the
alternatively
proposed
criteria
are
of
great
importance.

Exhibit
2.3.4
presents
the
Exposure
Decision
Tree.
Descriptions
of
the
boxes
within
the
decision
tree
are
separated
by
the
following
process
headings
to
facilitate
an
understanding
of
the
major
considerations
involved.

Problem
Formulation
Initial
decision
tree
discussion
centers
around
the
first
two
boxes:
identification
of
population(
s)
of
concern
(
Box
1)
and
identification
of
relevant
exposure
sources
and
pathways
(
Box
2).
The
term
"
problem
formulation"
refers
to
evaluating
the
population(
s)
and
sources
of
exposure
in
the
manner
described
above
(
i.
e.,
the
potential
for
the
population
of
concern
to
experience
exposures
from
multiple
sources
for
the
chemical
in
question),
such
that
the
data
for
the
chemical
in
question
consider
each
source/
medium
of
exposure
and
its
relevancy
to
the
identified
population(
s).
Evaluation
includes
determining
whether
the
levels,
multiple
regulatory
actions,
or
other
circumstances
make
allocation
of
the
RfD
or
Pdp/
SF
reasonable.
The
initial
discussion
has
also
included
agreement
on
the
exposure
parameters
chosen,
intakes
chosen
for
each
route
and
any
environmental
justice
or
other
social
issues
that
aid
in
determining
the
population
of
concern.
The
term
"
data,"
as
used
here
and
discussed
throughout
this
section,
refers
to
ambient
sampling
data
(
whether
from
Federal,
regional,
State
or
area­
specific
studies)
and
not
internal
human
exposure
measurements.
133
0.95
.
j
r
&
1
i
'
0
n
i
0.9i
0.1n
&
i
(
Equation
2.3.2)
Data
Adequacy
In
Box
3,
it
is
necessary
that
adequate
data
exist
for
the
relevant
sources/
pathways
of
exposure
if
one
is
to
avoid
using
default
procedures.
In
fact,
distributional
data
may
exist
for
some
or
most
of
the
sources
of
exposure.
At
a
minimum,
the
central
tendency
and
high­
end
values
are
considered
necessary
to
determine
an
appropriate
estimate
of
exposure
when
using
actual
data.
It
is
critically
important
to
describe
and
provide
guidance
for
the
data
adequacy
issue,
or
the
approach
could
be
considered
arbitrary.

There
are
numerous
factors
to
consider
in
order
to
determine
whether
a
dataset
is
adequate.
These
include:
(
1)
sample
size
(
i.
e.,
the
number
of
data
points);
(
2)
whether
the
dataset
is
a
random
sample
representative
of
the
target
population
(
if
not,
estimates
drawn
from
it
may
be
biased
no
matter
how
large
the
sample);
(
3)
the
magnitude
of
the
error
that
can
be
tolerated
in
the
estimate
(
estimator
precision);
(
4)
the
sample
size
needed
to
achieve
a
given
precision
for
a
given
parameter
(
e.
g.,
a
larger
sample
is
needed
to
precisely
estimate
an
upper
percentile
than
a
mean
or
median);
(
5)
an
acceptable
analytical
method
detection
limit;
and
(
6)
the
functional
form
and
variability
of
the
underlying
distribution,
which
determines
the
estimator
precision
(
e.
g.,
whether
the
distribution
is
normal
or
lognormal
and
whether
the
standard
deviation
is
1
or
10).
Lack
of
information
may
prevent
assessment
of
each
of
these
factors;
monitoring
study
reports
often
fail
to
include
background
information
or
enough
summary
statistics
(
and
rarely
the
raw
data)
to
completely
characterize
data
adequacy.
Thus,
a
case­
by­
case
determination
of
data
adequacy
is
likely.

That
being
stated,
there
are
some
criteria,
as
proposed
below,
that
lead
to
a
rough
rule­
ofthumb
on
what
constitutes
an
"
adequate"
sample
size
for
exposure
assessment.
The
primary
objective
is
to
estimate
an
upper
percentile
point
(
e.
g.,
say
the
90th)
and
a
central
tendency
value
of
some
exposure
distribution
based
on
a
random
sample
from
the
distribution.
Assuming
that
the
distribution
of
exposure
is
unknown,
a
nonparametric
estimate
of
the
90th
percentile
is
required.
The
required
estimate,
based
on
a
random
sample
of
n
observations
from
a
target
population,
is
obtained
by
ranking
the
data
from
smallest
to
largest
and
selecting
the
observation
whose
rank
is
1
greater
than
the
largest
integer
in
the
product
of
.9
times
n.
For
example,
in
a
data
set
of
25
points,
the
nonparametric
estimate
of
the
90th
percentile
is
the
23rd
largest
observation.

In
addition
to
this
point
estimate,
it
is
useful
to
have
an
upper
confidence
bound
on
the
90th
percentile.
To
find
the
rank
of
the
order
statistic
that
gives
an
upper
95
percent
confidence
limit
on
the
90th
percentile,
the
smallest
value
of
r
that
satisfies
the
following
formula
is
determined:
134
For
relatively
small
data
sets,
the
above
formula
will
lead
to
selecting
the
largest
observation
as
the
upper
confidence
limit
on
the
90th
percentile.
However,
the
problem
with
using
the
maximum
is
that,
in
many
environmental
datasets,
the
largest
observation
is
an
outlier
and
would
provide
an
unrealistic
upper
bound
on
the
90th
percentile.
It
would,
therefore,
be
preferable
if
the
sample
size
n
were
large
enough
so
that
the
formula
yielded
the
second
largest
observation
as
the
confidence
limit.

This
motivates
establishing
the
following
criterion
for
setting
an
"
adequate"
sample
size:
pick
the
smallest
n
such
that
the
nonparametric
upper
95
percent
confidence
limit
on
the
90th
percentile
is
the
second
largest
value.
Application
of
the
above
formula
with
r
set
to
n­
1
yields
n
=
45
for
this
minimum
sample
size.

For
the
upper
95
percent
confidence
limit
to
be
a
useful
indicator
of
a
maximum
exposure
it
must
not
be
overly
conservative
(
too
large
relative
to
the
90th
percentile).
It
is,
therefore,
of
interest
to
estimate
the
expected
magnitude
of
the
ratio
of
the
upper
95
percent
confidence
limit
to
the
90th
percentile.
This
quantity
generally
cannot
be
computed,
since
it
is
a
function
of
the
unknown
distribution.
However,
to
get
a
rough
idea
of
its
value,
consider
the
particular
case
of
a
normal
distribution.
If
the
coefficient
of
variation
is
between
0.5
and
2.0
(
i.
e.,
the
standard
deviation
divided
by
the
mean)
the
expected
value
of
the
ratio
in
samples
of
45
will
be
approximately
1.17
to
1.31;
i.
e.,
the
upper
95
percent
confidence
limit
will
be
only
about
17
to
31
percent
greater
than
the
90th
percentile
on
the
average.

It
should
be
noted
that
the
nonparametric
estimate
of
the
95
percent
upper
confidence
limit
based
on
the
second
largest
value
can
be
obtained
even
if
the
data
set
has
only
two
detects
(
it
is
assumed
that
the
two
detects
are
greater
than
the
detection
limit
associated
with
all
non­
detects).
This
is
an
argument
for
using
nonparametric
rather
than
parametric
estimation,
since
use
of
parametric
methods
would
require
more
detected
values.
On
the
other
hand,
if
non­
detects
were
not
a
problem
and
the
underlying
distribution
were
known,
a
parametric
estimate
of
the
90th
percentile
would
generally
be
more
precise.

As
stated
above,
adequacy
is
also
determined
by
determining
whether
the
samples
are
relevant
to
and
representative
of
the
population
at
risk.
Data
may,
therefore,
be
adequate
for
some
decisions
and
inadequate
for
others;
this
determination
requires
some
professional
judgment.

If
the
answer
to
Box
3
is
no,
then
the
decision
tree
falls
into
Box
4.
As
suggested
by
the
separate
boxes,
the
available
data
that
will
be
reviewed
as
part
of
Box
4
do
not
meet
the
requirements
necessary
for
Box
3.
In
Box
4,
any
data
that
are
available
(
information
about
the
chemical/
physical
properties,
uses,
and
environmental
fate
and
transformation,
as
well
as
any
other
information
that
would
characterize
the
likelihood
of
exposure
from
various
media
for
the
chemical)
are
evaluated
to
make
a
qualitative
determination
of
the
relation
of
one
exposure
source
to
another.
Although
this
information
will
always
be
presented
at
the
outset,
it
is
proposed
that
this
information
also
be
used
to
estimate
the
health­
based
criteria.
The
estimate
should
be
rather
conservative,
given
that
it
is
not
based
on
actual
monitoring
data
(
or
data
that
has
been
considered
to
be
inadequate
for
a
more
135
accurate
quantitative
estimate).
Therefore,
there
are
greater
uncertainties,
and
accounting
for
variability
is
not
really
possible.
With
such
information,
a
determination
can
be
made
as
to
whether
there
are
significant
known
or
potential
uses/
sources
other
than
the
source
of
concern
(
Box
8).
If
there
are
not,
then
it
is
recommended
that
50
percent
of
the
RfD
or
Pdp/
SF
can
be
safely
allocated
to
the
source
of
concern
(
Box
9).
While
this
leaves
half
of
the
RfD
or
Pdp/
SF
unallocated,
it
is
recommended
as
the
maximum
allocation
due
to
the
lack
of
data
needed
to
more
accurately
quantify
actual
or
potential
exposures.
If
the
answer
to
the
question
in
Box
8
is
yes,
and
some
information
is
available
on
each
source
of
exposure
(
Box
10A),
apply
the
procedure
in
either
Box
14
or
Box
15
(
depending
on
whether
one
or
more
criterion
is
relevant
to
the
chemical),
using
a
50
percent
ceiling
(
Box
10C),
again
due
to
the
lack
of
adequate
data.
If
the
answer
to
the
question
in
Box
10A
is
no,
then
use
20
percent
of
the
RfD
or
Pdp/
SF
(
Box
10B).

If
the
answer
to
the
question
in
Box
4
is
no;
that
is,
there
are
not
sufficient
data/
information
to
characterize
exposure,
it
may
be
best
to
defer
action
on
the
chemical
until
better
information
becomes
available
(
Boxes
5
&
6).
If
this
is
not
possible,
then
the
"
default"
assumption
of
20
percent
of
the
RfD
or
Pdp/
SF
(
Box
7)
should
be
used.
Box
7
is
not
likely
to
be
used
very
much,
given
that
the
information
described
in
Box
4
should
be
available
in
most
cases.
However,
EPA
intends
to
use
it
as
the
default
value
that
has
also
been
used
in
past
water
program
regulations.

Regulatory
Actions
If
there
are
adequate
data
available
to
describe
the
central
tendencies
and
high
ends
from
each
exposure
source/
pathway,
then
the
levels
of
exposure
relative
to
the
RfD
or
Pdp/
SF
are
compared
(
Box
11).
If
the
levels
of
exposure
for
the
chemical
in
question
are
not
near
(
currently
defined
as
greater
than
80
percent),
at,
or
in
excess
of
the
RfD
or
Pdp/
SF,
then
a
subsequent
determination
is
made
(
Box
13)
as
to
whether
there
is
more
than
one
regulatory
action
relevant
for
the
given
chemical
(
i.
e.,
more
than
one
criteria,
standard
or
other
guidance
being
planned,
performed
or
in
existence
for
the
chemical).
The
subtraction
method
is
considered
acceptable
when
only
one
criterion
is
relevant
for
a
particular
chemical.
In
these
cases,
other
sources
of
exposure
can
be
considered
"
background"
and
can
be
subtracted
from
the
RfD
(
Pdp/
SF).
When
more
than
one
criterion
is
relevant
to
a
particular
chemical,
apportioning
the
RfD
(
Pdp/
SF)
via
the
percentage
method
is
considered
appropriate
to
ensure
that
the
combination
of
criteria,
and
thus
the
potential
for
resulting
exposures,
do
not
exceed
the
RfD
(
Pdp/
SF).

Allocation
Decisions
If
the
answer
to
this
question
(
Box
13)
is
no,
then
the
recommended
method
for
setting
a
health­
based
criterion
is
to
utilize
a
subtraction
calculation
(
Box
14).
Specifically,
subtract
out
appropriate
intake
values
for
each
exposure
source
other
than
the
source
of
concern,
based
on
the
variability
in
occurrence
levels
for
that
source.
This
aspect
implies
that
a
case­
by­
case
determination
of
the
variability
and
the
resulting
intake
chosen
will
be
made,
as
each
chemical
evaluated
can
be
expected
to
have
different
variabilities
associated
with
each
source
of
intake.
As
a
default,
high­
end
intakes
(
approximating
the
90th
to
98th
percentiles
of
exposure)
could
be
subtracted
out.
However,
136
there
is
concern
that
an
estimate
adding
98th
percentile
values
for
all
sources
could
be
above
any
actually
exposed
population
or
individual.
Therefore,
scientific
judgment
is
needed
in
selecting
intake
values,
including
the
appropriateness
for
the
population
of
concern.
The
subtraction
method
would
also
include
an
80
percent
ceiling
and
a
20
percent
floor.

If
the
answer
to
the
question
in
Box
13
is
yes,
then
the
recommended
method
for
setting
health­
based
criteria
is
to
allocate
the
RfD
or
Pdp/
SF
among
those
sources
for
which
health­
based
criteria
are
being
set
(
Box
15).
Two
main
options
for
allocating
the
RfD
or
Pdp/
SF
are
presented
in
this
box.
Option
1
is
the
percentage
approach
(
with
a
ceiling
and
floor).
This
option
simply
refers
to
the
percentage
of
overall
exposure
contributed
by
an
individual
exposure
source.
For
example,
if
for
a
particular
chemical,
drinking
water
were
to
represent
half
of
total
exposure
and
diet
were
to
represent
the
other
half,
then
the
drinking
water
contribution
(
known
as
the
"
relative
source
contribution"
or
RSC)
would
be
50
percent.
The
health­
based
criteria
would,
in
turn,
be
set
at
50
percent
of
the
RfD
or
Pdp/
SF.
This
option
also
utilizes
an
appropriate
combination
of
intake
values
for
each
exposure
source
based
on
the
variability
in
occurrence
levels
of
each
source.
This
will
also
be
determined
on
a
case­
by­
case
basis.
Option
2
would
involve
the
subtraction
of
exposure
levels
from
all
sources
of
exposure
from
the
RfD
or
Pdp/
SF
and
apportioning
the
free
space
among
those
sources
for
which
health­
based
criteria
are
being
set.
There
are
several
ways
to
do
this:
1)
divide
the
free
space
among
the
sources
with
preference
given
to
the
source
likely
to
need
the
most
increase
(
e.
g.,
because
of
intentional
uses
or
because
of
physical/
chemical
properties
like
solubility
in
water);
2)
divide
the
free
space
in
proportion
to
the
"
base"
amount
used
(
e.
g.,
the
source
accounting
for
60
percent
of
exposure
gets
60
percent
of
the
free
space
­
this
is
identical
to
the
percentage
method;
the
outcome
is
the
same);
and
3)
divide
the
free
space
based
on
current
variability
of
exposure
from
each
source
(
i.
e.,
such
that
more
free
space
is
allocated
to
the
source
that
varies
the
most).
The
resulting
criterion
would
then
be
equal
to
the
amount
of
free
space
allocated
plus
the
amount
subtracted
for
that
source.
Note:
The
allocation
options
continue
to
be
discussed
within
EPA
as
part
of
an
Agency­
wide
Pilot
Study
group.
Although
some
preferences
have
been
discussed,
along
with
strengths
and
shortcomings
of
each
option,
it
is
still
being
deliberated.
The
Agency
welcomes
comments
on
these
options.

Finally,
if
the
answer
in
Box
11
is
yes,
that
is,
if
the
levels
of
exposure
for
the
chemical
in
question
are
near
(
currently
defined
as
greater
than
80
percent),
at,
or
in
excess
of
the
RfD
or
Pdp/
SF,
then
the
estimates
of
exposures
and
related
uncertainties,
potential
allocations,
toxicity­
related
information,
control
issues,
and
other
information
are
to
be
presented
to
managers
for
a
decision
(
Box
12).
The
high
levels
referred
to
in
Box
11
may
be
due
to
one
source
contributing
that
high
level
(
while
other
sources
contribute
relatively
little)
or
due
to
more
than
one
source
contributing
levels
that,
in
combination,
approach
or
exceed
the
RfD
or
Pdp/
SF.
This
presentation
may
inevitably
be
necessary
due
to
the
control
issues
(
i.
e.,
cost
and
feasibility
concerns)
that
may
be
involved,
especially
when
multiple
criteria
are
at
issue.
In
practice,
risk
managers
are
routinely
a
part
of
any
decisions
regarding
regulatory
actions
and
will
be
involved
with
any
recommended
outcome
of
the
exposure
decision
tree
or,
for
that
matter,
any
alternative
to
the
exposure
decision
tree.
However,
because
exposures
that
approach
or
exceed
the
RfD
or
Pdp/
SF
and
the
feasibility
of
controlling
different
137
sources
of
exposure
are
complicated
issues,
risk
managers
will
need
to
be
directly
involved
in
formulating
any
allocation
decisions.

Just
as
with
the
other
outcomes
in
the
exposure
decision
tree,
a
recommendation
for
setting
a
health­
based
value
(
or
values,
depending
on
the
number
of
relevant
sources)
for
chemicals
that
apply
to
Box
12
is
also
appropriate.
It
is
likely
that
risk
managers
will
want
some
input
from
the
exposure
assessors
even
if
exposures
are
above
the
RfD
or
Pdp/
SF
and
control
issues
apply.
Therefore,
in
these
cases,
recommendations
can
still
be
offered
and
should
be
performed
as
with
Boxes
13,
14,
and
15.
The
recommendation
should
be
made
based
on
health­
based
considerations
only,
just
as
when
the
chemical
in
question
was
not
a
Box
12
situation.
If
the
chemical
is
relevant
to
one
regulatory
action
only,
the
other
sources
of
exposure
could
be
subtracted
from
the
RfD
or
Pdp/
SF
to
determine
if
there
is
any
leftover
amount
for
setting
a
criterion.
If
the
chemical
is
a
multiple
criteria
issue,
then
a
recommended
allocation
could
be
made,
even
though
it
is
possible
that
all
sources
would
need
to
be
reduced.
Regardless
of
the
outcome
of
Box
11,
all
allocations
made
(
via
the
methods
of
Boxes
14
or
15)
should
include
a
presentation
of
the
uncertainty
in
the
estimate
and
in
the
RfD
or
Pdp/
SF
for
a
more
complete
characterization.

The
process
for
a
Box
12
situation,
versus
a
situation
that
is
not,
differs
in
that
the
presentations
for
Boxes
14
and
15
are
based
on
a
concurrence
of
allocations
(
following
the
review
of
available
information
and
a
determination
of
appropriate
exposure
parameters)
in
the
absence
of
control
issues
that
would
result
in
more
selective
reductions.
With
Box
12,
one
or
several
criteria
possibilities
("
scenarios")
may
be
presented
for
comparison
along
with
implications
of
the
effects
of
various
control
options.
It
would
be
most
appropriate
to
present
the
information
in
this
manner
to
risk
managers
given
the
complexity
of
these
additional
issues,
rather
than
the
more
definitive
proposals
that
are
not
associated
with
Box
12
situations.

Results
of
both
Boxes
14
and
15
rely
on
the
80
percent
ceiling
and
20
percent
floor.
The
80
percent
ceiling
was
implemented
to
ensure
that
the
health­
based
goal
will
be
low
enough
to
provide
adequate
protection
for
individuals
whose
total
exposure
to
a
contaminant
is,
due
to
any
of
the
exposure
sources,
higher
than
currently
indicated
by
the
available
data.
This
also
increases
the
margin
of
safety
to
account
for
possible
unknown
sources
of
exposure.
The
20
percent
floor
has
been
traditionally
rationalized
to
prevent
a
situation
where
small
fractional
exposures
are
being
controlled.
That
is,
below
a
point
it
is
more
appropriate
to
reduce
other
sources
of
exposure,
rather
than
promulgating
standards
for
de
minimus
reductions
in
overall
exposure.
The
idea
of
adding
flexibility
with
the
floor
to
go
lower
(
perhaps
to
zero)
if
necessary
in
cases
where
total
exposure
exceeds
the
RfD
or
Pdp/
SF
and
additional
reductions
are
warranted
has
also
been
discussed.
The
Agency
welcomes
comments
on
this
issue.

2.3.4.2
Notes
on
Use
of
the
Exposure
Decision
Tree
Approach
for
Setting
AWQC
Because
two
different
types
of
AWQC
are
proposed
(
based
on
either
(
1)
fish
ingestion
only
or
(
2)
both
fish
and
water
ingestion),
special
circumstances
arise
under
the
decision
tree
approach
138
AWQC
'
[
RfD
&
(
DW
%
DT
%
IN)]
C
BW
FI
C
BAF
(
Equation
2.3.3)

AWQC
'
RfD
@
RSC
fish
@
BW
FI
@
BAF
(
Equation
2.3.4)
when
accounting
for
the
drinking
water
portion
of
exposure.
These
circumstances
relate
to
whether
it
is
a
type
(
1)
or
(
2),
and
whether
one
or
more
health­
based
criterion
is
being
considered.
These
four
instances
are
described
below.

When
a
criterion
is
being
set
based
on
fish
ingestion
only,
and
when
only
one
health­
based
criterion
(
i.
e.,
AWQC)
is
relevant
for
the
chemical,
ingestion
from
drinking
water
would
be
considered
a
non­
ambient
water
source
and
would
be
subtracted
from
the
RfD
(
or
subtracted
out
with
nonlinear
carcinogens;
i.
e.,
the
Pdp/
SF)
in
the
numerator
of
the
equation
to
determine
the
AWQC,
as
follows:

where:

RfD
=
Reference
dose
(
mg/
kg­
day)
DW
=
Contaminant
intake
from
drinking
water
(
mg/
kg­
day)
IN
=
Contaminant
intake
from
air
(
mg/
kg­
day)
DT
=
Contaminant
intake
from
non­
fresh/
estuarine
fish
and
other
dietary
intake
(
i.
e.,
all
other
dietary
sources)
(
mg/
kg­
day)
BW
=
Body
weight
(
kg)
FI
=
Fish
consumption
rate
(
kg)
BAF
=
Bioaccumulation
factor
(
L/
kg)

The
terms
DW,
DT,
and
IN
represent
the
relative
source
contribution
(
RSC)
and
are
indicated
here
as
separate
parameters
to
facilitate
understanding
of
other
common
sources
that
could
be
subtracted
out
(
when
only
one
health­
based
criterion
is
relevant).
In
this
case,
the
occurrence
of
the
contaminant
in
treated
drinking
water
would
be
the
most
relevant
concentration
data
for
determining
intake
from
drinking
water,
because
it
is
assumed
that
individuals
get
their
drinking
water
from
the
tap.

When
a
criterion
is
being
set
based
on
fish
ingestion
only,
and
more
than
one
health­
based
criterion
is
being
set,
then
an
appropriate
RSC
allocation
procedure,
using
either
Option
1
or
Option
2
in
Box
15
(
Exhibit
2.3.4)
would
be
performed.
This
calculation
is
expressed
by
the
following
equation:
139
AWQC
'
[
RfD
&
(
DT
%
IN)]
@
BW
DI
%
(
FI
@
BAF)

(
Equation
2.3.5)

AWQC
'
RfD
@
RSC
fish
%
water
@
BW
DI
%
(
FI
@
BAF)

(
Equation
2.3.6)
where:

RSC
fish
=
Relative
source
contribution
for
fish
as
determined
by
Option
1
or
Option
2
in
Box
15
(
of
Exhibit
2.3.4)
and
including
only
the
portion
of
the
intake
ascribed
to
contaminated
fish
intake
All
other
parameters
are
the
same
as
above.

As
noted
in
the
definition
of
RSC
fish,
only
the
amount
of
contaminant
intake
from
eating
contaminated
freshwater
and
estuarine
finfish
and
shellfish
would
be
included
in
the
RSC
allocation.
Marine
fish
intake
would
normally
be
accounted
for
as
part
of
the
dietary
intake
component
of
the
RSC
calculation.
Again,
intake
from
treated
drinking
water
would
be
considered
separately
as
a
nonambient
water
source.

If
a
criterion
is
being
set
based
on
fish
and
water
ingestion,
and
only
one
health
standard
is
being
set,
then
the
following
equation
applies:

where:

DI
=
Drinking
water
consumption
rate
(
in
L/
day)

All
other
parameters
are
the
same
as
those
in
Equation
2.3.3.

In
this
case,
drinking
water
consumption
is
not
considered
in
the
non­
water
sources
of
intake
because
the
criterion
is
being
set
for
both
fish
and
water
ingestion.
Thus,
only
air
and
dietary
intake
are
subtracted
from
the
RfD
or
Pdp/
SF
(
here,
the
parameters
DT
and
IN
represent
the
RSC
to
be
subtracted
out).

Finally,
in
a
situation
where
a
criterion
is
being
set
for
both
fish
and
water
ingestion,
and
more
than
one
health­
based
criterion
is
to
be
set,
then
the
following
equation
is
applicable:
140
where:

RSC
fish+
water
=
The
relative
source
contribution
for
fish
and
water
as
determined
by
Option
1
or
Option
2
in
Box
15
(
of
Exhibit
2.3.4)
and
including
the
portion
of
the
intake
for
contaminated
fish
and
water
intake
DI
=
Drinking
water
consumption
rate
(
in
L/
day)

All
other
parameters
are
the
same
as
Equation
2.3.3.

In
this
case,
the
concentration
of
a
chemical
in
ambient
water
is
the
relevant
exposure
source
to
include
in
the
RSC
fish+
water,
because
use
of
this
criterion
assumes
that
an
individual
may
ingest
such
concentrations
of
water
daily.

Guidance
has
been
provided
on
the
type
of
studies
that
should
be
considered
for
estimating
fish
consumption
(
Preferences
#
1
through
#
4)
and
numerous
studies
have
been
summarized.
Recommended
values
have
also
been
presented
for
drinking
water
intakes
and
body
weights.
However,
these
are
just
some
of
the
parameters
that
will
be
needed
in
order
to
perform
estimates
of
overall
exposure
to
a
chemical.
While
it
is
not
the
intention
of
this
document
to
provide
an
exhaustive
list
of
sources
of
information,
Table
2.3.1
does
provide
suggestions
for
sources
of
information
on
exposure
intake
parameters
and
contaminant
data.

Although
the
consumption
of
marine
species
of
fish
is
not
a
direct
component
of
an
ambient
water
quality
criterion,
there
is
certainly
a
reason
to
account
for
ingestion
exposures
to
marine
species,
as
they
may
significantly
contribute
to
total
human
exposure.
That
is,
although
the
AWQC
derivation
may
be
set
for
both
fish
and
water
ingestion,
it
is
set
to
protect
humans
from
exposure
to
the
contaminant
in
fresh
and
estuarine
species
only.
Therefore,
to
protect
humans
who
additionally
consume
marine
species
of
fish,
the
marine
portion
should
be
considered
as
part
of
the
"
other
sources
of
exposure"
when
calculating
an
RSC
value.
Specifically,
the
DT
parameter
should
account
for
all
non­
fresh/
estuarine
fish
dietary
intake,
thus
allowing
the
common
consumption
of
marine
species
to
be
accounted
for
as
well
as
all
other
ingested
foods.
Regarding
the
dietary
information
available
from
the
Food
and
Drug
Administration's
(
FDA)
Total
Diet
Study
Program
(
as
cited
in
Table
2.3.1),
EPA
believes
that
the
FDA
estimates
are
acceptable
to
account
for
exposure
to
the
major
marine
fish
species
in
the
typical
U.
S.
diet
(
e.
g.,
tuna,
cod,
haddock).
However,
States
may
utilize
more
comprehensive
marine
species
estimates
(
e.
g.,
using
marine
species
intake
estimates
from
the
CSFII
survey
and
marine
fish
contaminant
concentration
data)
provided
they
ensure
that
marine
fish
intake
is
not
double­
counted
with
the
other
dietary
intake
estimate
used
(
e.
g.,
the
FDA
program).

In
all
four
of
the
equations
above
(
2.3.3
through
2.3.6),
the
proposed
80
percent
ceiling
and
20
percent
floor
apply.
However,
if
exposures
approach
or
exceed
the
RfD
or
Pdp/
SF,
then
additional
risk
management
decisions
will
be
necessary
regarding
which
exposure
sources
may
most
practically
be
further
reduced
(
given
control
and
feasibility
limitations)
beyond
the
decision
tree
approach
to
RSC.
141
2.3.4.3
Setting
AWQC
for
Chemical
X
Using
the
Decision
Tree
Approach
This
example
describes
the
application
of
the
Exposure
Decision
Tree
Approach
(
described
above
and
outlined
in
Exhibit
2.3.4)
to
account
for
sources
of
exposure
to
a
generic
Chemical
X
when
setting
AWQC.
Two
different
criteria
are
evaluated:
criteria
that
include
fish
intake
only
(
and
are
applicable
to
recreational
waters)
and
criteria
that
include
both
fish
and
drinking
water
intake
(
and
are
applicable
to
waters
designated
as
public
water
supplies).
As
noted
above,
different
exposure
sources
are
used,
depending
on
whether
criteria
are
based
on
assumptions
about
consumption
of
both
fish
and
water
or
of
fish
only.
In
the
case
of
estimating
a
fish­
only
criterion,
an
incidental
ingestion
rate
of
water
of
0.01
L/
day
from
recreational
activities
is
assumed
and
effectively
replaces
the
DI
intake
assumption
of
2
L/
day.

The
following
sections
describe
the
processes
of
accounting
for
sources
of
exposure
and
the
data
needed
to
apply
these
processes
to
the
Exposure
Decision
Tree.
Specifically,
the
sections
describe
the
sources
and
uses
of
the
chemical,
the
population
of
concern,
the
data
available
on
contamination
in
exposure
media
and
uptake
from
that
media,
the
adequacy
of
exposure
information,
and
the
derivation
of
the
AWQC
using
the
decision
tree
approach.
As
stated
previously,
the
underlying
objective
is
to
maintain
total
exposure
below
the
RfD
(
Pdp/
SF)
by
accounting
for
other
sources
of
exposure
and,
therefore,
using
only
a
portion
of
either
the
RfD
or
Pdp/
SF
in
setting
AWQC.

Sources
and
Uses
of
Chemical
X
There
are
no
known
natural
sources
of
Chemical
X
in
the
environment.
This
chemical
has
been
extensively
used
as
a
solvent
in
many
industrial
processes
and
has
some
uses
as
a
pesticide.
Current
releases
of
Chemical
X
to
the
environment
may
occur
from
these
numerous
processes
and
from
its
pesticide
use,
and
possibly
from
poorly
maintained
hazardous
waste
sites,
illegal
dumping,
and
disposal
of
Chemical
X
in
municipal
landfills
rather
than
hazardous
waste
landfills.
In
addition,
Chemical
X
may
remain
in
the
environment
from
past
releases.
Small
amounts
may
be
found
in
outdoor
and
indoor
air,
soil
surfaces,
and
surface
water.
Chemical
X
in
surface
waters
and
sediments
bioaccumulates
in
fish
(
the
determined
bioaccumulation
factor
for
fish
is
120,000).

Population
of
Concern
The
first
step
in
determining
how
to
set
AWQC
for
Chemical
X
when
considering
exposure
contributions
from
all
environmental
media
is
to
define
the
population
of
concern
for
the
chemical
(
see
Box
1
in
Exhibit
2.3.4).
The
population
of
concern
may
be
a
group
that
is
either
more
toxicologically
sensitive
or
more
highly
exposed
compared
with
the
general
population.
For
Chemical
X,
a
particular
population
of
concern
is
subsistence
fishers
who
eat
large
quantities
of
self­
caught
fish.
These
fishers
may
eat
fish
for
a
large
portion
of
the
year,
and
may
include
such
groups
as
Native
Americans,
immigrants
who
rely
on
fishing
(
particularly
Asian­
Americans),
and
poor
populations
(
USEPA,
1994b).
These
individuals,
who
are
highly
exposed
to
self­
caught
fish,
may
have
exposures
142
that
are
much
higher
than
exposures
to
the
general
population.
In
addition
to
subsistence
fishers,
other
individuals
with
higher
than
average
exposures
are
those
who
engage
in
recreational
fishing
(
i.
e.,
sport­
caught
fish)
and
eat
their
catch.
For
this
example,
exposures
are
evaluated
for
more
highly­
exposed
fish
consumers
within
the
population
who
may
represent
subsistence
fishers.
Subsistence
fishers
and
sport
fishers
are
compared
with
exposures
of
persons
from
the
general
U.
S.
population.
However,
use
of
the
default
assumptions
discussed
in
Section
2.3.2.3
result
in
the
same
estimate
for
sport
fishers
and
the
general
population.

Data
Used
to
Assess
Exposure
to
Chemical
X
This
section
discusses
data
available
for
the
relevant
exposure
sources
and
pathways
for
Chemical
X
(
Box
2
of
Exhibit
2.3.4).
Exposure
may
occur
from
several
environmental
media,
including
ambient
surface
water,
drinking
water,
commercial
food
products,
and
air.
Human
exposures
are
estimated
by
combining
information
on
concentrations
of
Chemical
X
in
environmental
media
with
intake
rates
of
these
environmental
media.
The
largest
exposure
for
subsistence
fishers,
sport­
fishers,
and
for
the
general
population
appears
to
be
from
ingestion
of
fish.

Exposures
from
Raw
Surface
Water
When
setting
AWQC
for
protection
against
intake
of
pollutants
from
both
fish
and
water,
ambient
concentrations
in
surface
waters
(
that
have
not
been
treated
for
drinking
water)
are
used
to
assess
exposure
resulting
from
drinking
the
water
directly
from
these
sources.
In
addition
to
the
need
for
assessing
exposures
from
drinking
water
from
surface
water
sources,
available
concentrations
in
fish
may
be
used
to
assess
intake
from
eating
contaminated
fish.

Exposure
Resulting
from
Drinking
Water
Directly
from
Water
Bodies.
Information
on
concentrations
in
waters
as
well
as
intake
rates
of
water
are
needed
to
assess
exposure
from
surface
waters.
Several
surveys
have
measured
concentrations
of
Chemical
X
in
ambient
surface
waters.
A
majority
of
these
surveys
have
been
conducted
in
U.
S.
lakes.
In
a
study
of
chemical
concentrations
in
surface
water
in
one
lake,
average
chemical
concentrations
in
1988,
1990,
and
1992
were
0.33,
0.32,
and
0.18
ng/
L.
These
concentrations
are
based
on
both
dissolved
phase
and
particulate
phase
concentrations.
These
authors
also
show
that,
from
1980
to
1992,
the
total
concentrations
in
the
water
column
decreased
with
a
first
order
rate
constant
of
0.20/
yr.
These
authors
note
that
the
lake
is
relatively
unimpacted
by
point
sources
of
Chemical
X
and
receives
most
of
its
loadings
from
the
atmosphere.

Water
samples
were
collected
from
another
lake
from
June
to
October,
1989.
In
three
periods
throughout
this
time,
samples
were
collected
at
four
or
five
sites.
Taking
the
arithmetic
mean
of
these
dissolved
phase
concentrations
results
in
a
value
of
2.8
ng/
L,
and
a
95th
percentile
value
of
±
7.2
ng/
L.
Although
the
dissolved
concentrations
were
reported
at
each
site,
the
authors
also
give
some
composite
information
on
Chemical
X
concentrations
in
the
water.
The
average
of
total
Chemical
X
for
sites
18
and
21
was
1.7
±
0.6
ng/
L,
the
average
for
site
14
was
5.5
±
2.4
ng/
L,
and
the
average
143
for
sites
4
and
10
was
15.6
±
11.2
ng/
L.
Two
sites
were
close
to
a
heavily
industrialized
river
which
is
an
important
source
of
Chemical
X
to
the
lake.

Other
studies
have
been
conducted
in
earlier
years
in
several
proximally
located
lakes.
One
collected
samples
in
1980
and
reported
the
average
concentration
in
the
first
lake
to
be
1.8
ng/
L,
with
concentrations
of
3.2
ng/
L
in
near
shore
samples
and
1.2
ng/
L
in
open
lake
samples.
Mean
concentrations
ranging
from
0.63
to
3.3
ng/
L
were
detected
in
another
study
of
the
second
lake
for
the
years
1978
to
1983.
Another
study
reported
a
mean
level
of
0.49
ng/
L
in
water
columns
of
a
third
lake
in
1981.
From
1977
to
1981,
373
river
samples
from
9
locations
near
one
of
the
lakes
were
collected.
The
overall
mean
concentration
was
300
ng/
L,
with
a
detection
limit
of
50
ng/
L.
The
authors
did
not
specifically
state
the
number
of
positives.

Surveys
in
other
areas
of
the
U.
S.
have
also
been
conducted.
Both
surface
water
and
subsurface
water
drainage
were
investigated
in
one
area
of
California
during
1977.
No
samples
contained
detectable
levels
of
the
chemical,
and
the
detection
limit
was
not
reported.
Chemical
X
was
collected
from
a
bay
in
Texas
in
an
area
of
suspected
contamination.
Concentrations
ranged
from
<
0.01
to
70
ng/
L.
The
authors
report
an
average
of
3.1
ng/
L
but
do
not
describe
whether
or
how
the
average
accounted
for
the
non­
detected
values.

Because
the
first
summarized
U.
S.
lake
study
indicated
a
decrease
in
Chemical
X
concentrations
throughout
the
period
from
1980
to
1992,
the
most
recent
studies
are
the
most
useful
to
this
analysis,
especially
where
older
studies
were
conducted
in
areas
of
suspected
contamination.
The
information
from
the
studies
that
measured
concentrations
in
the
late
1980s
and
early
1990s
(
from
the
first
two
summarized
studies)
were
used
to
estimate
average
and
high­
end
values
for
Chemical
X.
The
first
study
reports
a
value
of
0.18
ng/
L
for
1992,
based
on
the
concentration
of
the
total
chemical
in
the
water
column;
this
value
is
used
as
an
average
value
for
this
analysis.
As
indicated
above,
the
data
came
from
an
uncontaminated
lake.
We
assume
that
these
values
are
appropriate,
assuming
that
most
water
bodies
in
the
United
States
have
not
been
impacted
by
point
sources
of
contamination.
However,
using
this
mean
value
may
underestimate
the
true
mean
of
concentrations
in
ambient
waters
throughout
the
United
States.
For
the
high­
end
estimate,
the
value
of
15.6
ng/
L
from
the
second
study
is
used.
This
value
was
not
the
highest
value
seen
from
this
study,
because
it
is
an
average
using
data
from
two
sites,
but
it
was
taken
from
the
most
contaminated
area
within
the
lake.
Although
the
data
are
reported
for
both
dissolved
and
total
chemical
fractions,
data
on
the
total
chemical
is
used
to
match
the
data
from
the
first
study,
which
is
based
on
the
total
chemical.
These
estimates
are
only
crude
estimates
of
central
and
high­
end
concentrations
in
water,
because
they
are
based
only
on
information
from
these
lakes.

Combining
the
above
values
with
the
drinking
water
intake
of
2
liters/
day
yields
a
central
tendency
intake
rate
of
5.1
x
10­
9
mg/
kg­
day
and
a
high­
end
value
of
4.4
x
10­
7
mg/
kg­
day.

Exposure
from
Eating
Contaminated
Fish:
Concentrations
in
Fish.
Measurements
of
chemical
concentration
in
fish
in
U.
S.
waters
are
available
from
several
surveys.
One
national
study,
begun
in
1986,
measured
Chemical
X
in
fish
at
nearly
400
sites.
A
majority
of
these
locations
(
314)
144
were
affected
by
a
variety
of
point
and
nonpoint
sources
of
pollution,
39
locations
were
from
the
United
States
Geological
Survey
sites,
and
35
areas
represented
background
contaminant
levels.
Game
fish
for
human
consumption
were
analyzed
as
fillets,
and
bottom
feeders
were
analyzed
as
whole­
body
samples.
The
mean
concentration
of
Chemical
X
found
in
samples
of
this
study
was
1.89
µ
g/
g
(
wet
weight).
The
median
value
determined
was
0.21
µ
g/
g
and
the
maximum
value
found
was
124.0
µ
g/
g.

National
distributions
of
Chemical
X
in
fish
are
also
available
from
an
ongoing
study.
The
purpose
of
this
study
is
to
determine
differences
in
concentrations
of
organochlorines
at
different
geographic
locations,
and
to
estimate
changes
in
these
concentrations
over
time.
The
most
recent
information
available
is
for
1984,
in
which
112
sites
were
sampled.
These
sites
were
selected
to
represent
all
major
river
basins
in
the
United
States.
Eleven
sites
were
common
to
both
national
studies.
Composite
samples
from
the
ongoing
study
consisted
of
five
fish
and
were
collected
at
each
site
for
two
bottom
feeder
species
and
one
predator
species;
the
whole
bodies
of
these
fish
were
collected
for
analysis.
The
geometric
mean
value
determined
from
the
1984
survey
is
0.39
µ
g/
g,
and
the
maximum
value
is
6.7
µ
g/
g.
Earlier
data
from
this
survey,
collected
between
1980
and
1981,
shows
a
geometric
mean
chemical
residue
of
0.53
µ
g/
g,
and
a
maximum
value
of
11.3
µ
g/
g.

Concentrations
in
marine
fish
have
been
measured
in
a
nationwide
shellfish
study.
Total
chemical
concentrations
in
whole
tissue
of
bivalves
(
mussels
and
oysters)
collected
during
1986
ranged
from
0.009
to
6.8
µ
g/
g
(
dry
weight).

Regional
studies
of
chemical
contamination
in
fish
have
also
been
conducted.
In
New
York,
chemical
concentrations
in
standard
fillets
of
striped
bass
were
measured,
and
were
shown
to
decline
between
1984
and
1990.
In
1990,
the
arithmetic
mean
was
1.3
µ
g/
g
measured
on
a
wet
weight
basis.
In
1983,
levels
of
0.6
to
72
µ
g/
g,
measured
on
a
lipid
basis,
were
found
in
fish
from
major
tributaries
and
embayments
of
the
Great
Lakes.
In
cooked
fish
from
one,
median
concentrations
ranged
from
0.17
to
3.0
µ
g/
g.

Exposure
from
Eating
Contaminated
Fish:
Consumption
Rates
of
Fish.
Consumption
rates
of
sport­
caught
fish
vary,
depending
on
whether
the
rate
is
determined
for
the
general
population
or
for
individuals
who
receive
a
large
portion
of
their
dietary
intake
from
sport­
caught
fish.
For
the
general
U.
S.
population,
the
proposed
default
national
non­
marine
fish
consumption
rate
for
adults
of
17.80
grams/
day
has
been
estimated
from
information
using
three
years
of
data
from
the
nationallybased
Continuing
Survey
of
Food
Intake
for
Individuals
(
CSFII)
conducted
by
USDA.
The
CSFII
is
conducted
annually,
and
dietary
intake
data
from
the
48
conterminous
states
are
collected
over
3­
day
survey
periods
(
USEPA,
1998).
The
estimates
based
on
CSFII
used
information
for
both
adult
consumers
and
non­
consumers
of
fish,
and
represent
intake
of
all
fish
whether
store­
bought
or
sportcaught
This
survey
is
described
in
more
detail
under
Preference
#
3
(
page
103).

Data
on
national
distributions
of
fish
intake
by
sportfishers
are
not
available.
Although
several
surveys
have
measured
consumption
of
fish
by
sportfishers
in
particular
areas,
these
studies
are
limited
to
particular
geographic
regions
and
do
not
approximate
a
national
distribution.
Because
of
17
For
simplicity,
the
example
uses
the
86.30
g/
day
subsistence
fisher
assumption
only.
The
alternative
default
subsistence
fisher
intake
value
of
39.04
g/
day
would
result
in
lower
estimates
of
Chemical
X
intake
from
fresh/
estuarine
fish
and
lower
(
less
stringent)
AWQC.
These
differences
are
footnoted
in
Tables
2.3.24
and
2.3.28.

145
the
lack
of
information
specific
to
national
estimates
for
sportfishers,
17.80
grams/
day,
which
approximates
the
90th
percentile
from
the
CSFII,
is
used
here
to
represent
the
average
consumption
rate
of
the
sportfisher
population.
This
value
is
used
to
estimate
intake
for
derivation
of
national
criteria.

Data
on
national
distributions
of
intake
by
subsistence
fishers
are
also
not
available.
Some
studies
that
have
specifically
targeted
subsistence
fishers
have
been
conducted
in
certain
geographic
areas.
In
addition,
sportfisher
surveys
have
included
information
on
specific
subpopulations
who
have
high
consumption
rates
and
may
subsist
on
fish
for
a
large
part
of
the
year.
Because
of
the
lack
of
national
distributions,
this
example,
which
is
conducted
to
represent
an
average
intake
estimate
of
subsistence
fishers,
uses
the
default
value
of
86.30
grams/
day,
based
on
the
95th
percentile
from
the
CSFII.
17
Combining
data
on
consumption
rates
with
concentration
data
from
the
ongoing
national
study
to
estimate
exposure
from
Chemical
X
yields
central
tendency
and
high­
end
intake
estimates
from
consumption
of
fish
for
an
average
individual
from
the
general,
sportfisher,
and
subsistence
fisher
populations,
as
indicated
in
Table
2.3.24.
Because
the
high­
end
values
in
each
case
use
the
maximum
contaminant
value
from
the
national
study,
this
high­
end
intake
of
Chemical
X
represents
a
value
higher
than
the
90th
percentile
intake
from
the
chemical
in
contaminated
fish.

Consumption
of
sport­
caught
fish
may
replace
consumption
of
other
commercial
meats
and
fish.
Thus,
Chemical
X
intake
resulting
from
consumption
of
commercial
foods
was
adjusted
to
account
for
this
replacement.
This
adjustment
is
described
below,
under
the
section
describing
dietary
intake
of
commercial
foods.

Exposure
from
Treated
Drinking
Water
In
cases
where
AWQC
are
set
based
on
fish
intake
only,
drinking
water
intake
is
accounted
for
as
a
separate
exposure.
In
these
instances,
information
on
treated
drinking
water,
if
available,
is
the
relevant
information
to
use
when
accounting
for
other
sources
of
exposure.
National
and
regional
studies
have
measured
Chemical
X
contamination
in
both
ground­
water
and
surface
water
sources
of
drinking
water.
Information
from
these
studies
can
be
combined
with
information
on
intake
rates
of
water
to
estimate
total
intake
of
Chemical
X
from
this
source.

In
a
regional
study
of
contamination
of
drinking
water,
Chemical
X
was
measured
from
the
mid­
1970s
to
early
1985
in
tap
water,
raw
water,
and
finished
water.
Chemical
X
concentrations
were
either
not
detected,
or
they
were
found
at
levels
close
to
the
limit
of
detection.
18
If
the
alternate
default
intake
assumption
of
39.04
g/
day
was
used
instead,
the
subsistence
fisher
central
tendency
and
high­
end
estimates
would
be
approximately
2.2
x
10­
4
mg/
kg­
day
and
3.7
x
10­
3
mg/
kg­
day,
respectively.

146
Table
2.3.24:
Chemical
X
Intakes
from
Eating
Fresh/
Estuarine
Fish
for
Three
Types
of
Individuals
Central
Tendency
Estimate
(
mg/
kg­
day)
High­
End
Estimate
(
mg/
kg­
day)

General
Population
9.9
x
10­
5
1.7
x
10­
3
General
Sportfishers
9.9
x
10­
5
1.7
x
10­
3
Subsistence
Fishers18
4.8
x
10­
4
8.3
x
10­
3
A
discussion
of
the
adequacy
of
these
data
for
determining
exposure
estimates
follows
this
section.
Although
data
on
surface
water
are
limited,
the
detection
limit
from
the
first
subset
of
the
first
national
study
summarized
is
used
as
a
crude
estimate
of
average
human
exposure.
A
high­
end
estimate
may
be
about
1.4
µ
g/
L,
the
highest
value
seen
in
this
study.
Concentrations
may
have
decreased
since
these
data
were
collected.
Because
of
this
possible
decrease
and
because
1.4
µ
g/
L
was
the
highest
value
seen,
the
value
may
represent
a
value
higher
than
the
98th
percentile,
if
such
concentrations
are
still
seen.

Ground
Water.
National
studies
of
Chemical
X
in
ground
water
showed
either
no
detectable
levels
of
the
chemical,
or
very
few
positive
values.
In
the
same
three­
sampling
national
study,
18
ground­
water
supplies
sampled
in
the
first
subset
found
no
positive
Chemical
X
concentrations.
Only
one
finished
ground­
water
sample
out
of
18
in
each
of
the
other
samplings
contained
detectable
chemical
levels,
at
0.1
µ
g/
L.
The
second
national
study
found
no
detectable
chemical
levels
in
ground
water.

Two
state
studies
found
detectable
chemical
concentrations
in
ground
water.
One
study
measured
Chemical
X
concentrations
in
163
wells
across
the
state,
including
public
and
private
drinking
water
wells.
Many
of
the
wells
sampled
were
from
highly­
populated,
industrialized
areas.
Chemical
X
concentrations
were
found
in
32
wells,
and
ranged
from
0.06
to
1.27
µ
g/
L.
The
other
study,
a
pesticide
hazard
assessment
survey,
was
conducted
from
1983
to
1984.
They
found
Chemical
X
in
2
out
of
143
samples
from
10
counties.
The
two
detectable
concentrations
were
0.269
and
2.3
µ
g/
L,
and
the
detection
limit
was
0.25
µ
g/
L.

Other
regional
ground­
water
studies
either
found
no
detectable
levels
of
Chemical
X,
or
did
not
report
the
Chemical
X
concentrations
in
the
detected
samples.
In
one,
drinking
water
wells
in
12
towns
were
sampled
for
Chemical
X
in
1984
and
1985.
With
a
detection
limit
of
3.3
µ
g/
L,
no
concentrations
of
the
chemical
were
found
in
42
well
locations.
In
another,
a
survey
of
ground­
water
supplies
found
positive
chemical
concentrations
at
less
than
8
percent
of
the
96
locations
sampled.
147
DWConc
'
(
0.67)
(
(
SWConc)
%
(
0.33)
(
(
GWConc)

(
Equation
2.3.27)
Data
on
positive
samples
from
the
three­
sampling
national
study
are
very
limited.
Information
from
the
first
state
ground­
water
study
was
used
to
estimate
central
tendency
and
high­
end
values
for
Chemical
X
in
ground
water.
By
assuming
that
the
detected
concentration
values
are
equally
distributed
between
0.06
to
1.27
µ
g/
L
and
that
the
non­
detected
samples
had
concentrations
of
0.03
µ
g/
L,
an
average
of
0.16
µ
g/
L
was
determined
from
this
study.
To
provide
a
crude
estimate
of
high
exposure,
the
high
value
of
1.27
µ
g/
L
from
the
same
study
may
be
used.
Because
these
data
come
from
only
one
state,
the
values
are
not
representative
of
national
distributions
of
Chemical
X
in
ground­
water
sources.
Thus,
these
data
do
not
adequately
represent
a
national
estimate
of
risk.

Estimating
Exposure
from
Surface
Water
and
Ground­
Water
Concentrations.
To
estimate
exposures
from
drinking
water
sources,
these
chemical
concentrations
in
surface
water
and
ground
water
sources
are
averaged
to
determine
estimates
of
exposure
to
Chemical
X
in
drinking
water.
To
do
this,
we
used
the
percent
of
the
U.
S.
population
served
by
systems
using
surface
water
and
ground
water
and
determined
a
weighted
average
concentration
in
drinking
water
by
using
the
following
equation:

where:

DWConc
=
Average
or
high­
end
drinking
water
concentration
from
both
surface
water
and
ground
water
0.67
=
The
fraction
of
the
U.
S.
population
served
by
public
surface
water
supplies
SWConc
=
Average
or
high­
end
Chemical
X
concentration
in
surface
waters
0.33
=
The
fraction
of
the
U.
S.
population
served
by
public
ground
water
supplies
GWConc
=
Average
or
high­
end
Chemical
X
concentration
in
ground
waters
The
weighted
average
value
determined
above
was
multiplied
by
an
estimate
of
daily
drinking
water
intake
of
2
liters/
day
and
divided
by
adult
body
weight
of
70
kilograms
to
estimate
exposure
in
units
of
mg/
kg­
day.
The
resulting
estimates
of
intake
from
drinking
water
are
3.4
x
10­
6
mg/
kg­
day
as
a
central
tendency
value,
and
3.9
x
10­
5
mg/
kg­
day
as
a
high­
end
estimate.

It
should
be
noted
that
these
estimates
are
larger
than
the
estimates
from
ambient
surface
water
sources
(
by
about
three
orders
of
magnitude
for
the
central
tendency
estimate
and
two
orders
148
of
magnitude
for
high­
end
estimates).
This
may
be
because
the
drinking
water
samples
were
collected
a
significant
number
of
years
prior
to
the
collection
of
the
raw
surface
water
samples.
In
addition,
the
lack
of
data
for
both
raw
surface
water
and
for
treated
drinking
water
may
also
be
a
factor
in
the
differences
between
the
intake
estimates.

Dietary
Intake
from
Commercial
Foods
Estimates
of
dietary
intake
of
Chemical
X
from
commercial
foods
combine
measurements
of
Chemical
X
concentrations
in
store­
bought
foods
and
daily
intake
rates
of
various
food
items.
Data
available
on
exposure
estimates
and
concentrations
in
foods
are
described
below.

Two
sources
of
information
on
dietary
intakes
of
Chemical
X
from
commercial
foods
and
concentrations
of
Chemical
X
in
commercial
food
are
available.
The
estimates
of
intake
used
in
this
analysis
and
presented
in
Table
2.3.25
use
the
second
source
of
information
described
here.
The
first
source
of
information
is
an
estimate
by
FDA
of
the
adult
dietary
intakes,
which
was
determined
by
combining
Chemical
X
concentrations
detected
in
12
Total
Diet
Studies
(
TDS)
conducted
over
the
time
period
between
April
1982
and
April
1985
with
intake
rates
of
different
food
items.
The
TDS
measure
concentrations
of
various
contaminants
in
234
foods
purchased
from
supermarkets
or
grocery
stores
throughout
the
United
States
and
are
collected
four
or
five
times
a
year.
Using
these
12
TDS
samplings,
the
FDA
determined
mean
daily
intakes
ranging
from
0.038
to
0.054
µ
g/
day
for
males
and
0.026
to
0.040
µ
g/
day
for
females.
In
addition
to
the
FDA
estimates
of
exposure
using
information
from
the
12
TDS
samplings,
the
second
source
of
information
includes
food
concentrations
of
Chemical
X
available
from
44
TDS
samplings
conducted
from
April,
1982
to
November
1993,
including
the
12
described
above.
From
the
sampling
conducted
over
this
full
range
of
years
(
1982
to
1993),
Chemical
X
concentrations
have
been
found
in
30
of
the
234
food
items
sampled.
For
each
of
the
30
food
items,
Chemical
X
was
detected
in
one
to
three
of
the
44
TDS
sampling
collections.
However,
some
of
these
reported
values
are
trace
amounts
of
Chemical
X,
which
represent
the
best
estimates
of
those
who
analyzed
the
data,
but
are
below
quantifiable
limits.
Thus,
only
eleven
samples
are
above
quantifiable
limits.
These
concentrations
can
be
combined
with
information
on
age­
and
sex­
specific
intake
rates
of
different
food
items
found
in
Pennington
(
1983)
to
determine
overall
exposure.

For
this
analysis,
as
noted
above,
concentration
data
from
the
full
range
of
years
(
1982
to
1993)
was
used
to
estimate
exposures.
For
each
food
item,
the
detected
Chemical
X
levels
were
averaged
with
the
samples
in
which
Chemical
X
was
not
detected
to
estimate
an
average
Chemical
X
concentration
for
a
given
food
item.
The
nondetected
levels
were
given
a
value
of
zero.
To
estimate
a
high­
end
Chemical
X
concentration,
the
highest
values
seen
from
each
food
item
were
used.

These
concentration
data
were
then
combined
with
information
on
age­
and
sex­
specific
dietary
intakes
of
different
food
items
to
estimate
total
adult
intake
of
Chemical
X
from
commercial
foods.
The
daily
intake
of
individual
food
items
was
taken
from
Pennington
(
1983),
which
reports
daily
intake
rates
of
individual
food
items
for
four
population
groups
(
males
aged
25­
30
and
60­
65
149
years
old
and
females
aged
25­
30
and
60­
65
years
old).
To
represent
the
full
range
of
adult
ages,
we
assumed
that
the
dietary
intake
of
25­
30
year­
olds
represents
dietary
habits
of
individuals
aged
18
to
54
years
old,
and
that
the
consumption
rate
of
60­
65
year­
olds
represents
the
consumption
rate
for
adults
aged
55
years
and
older.
The
percent
of
these
wider
age
ranges
(
18
to
54
years;
55
years
and
older)
in
the
United
States
population
and
information
that
half
of
each
age
group
consists
of
males
and
half
consists
of
females
(
US
DOC,
1992)
were
then
used
to
determine
an
age­
and
sex­
weighted
overall
average
consumption
rate
for
each
food
item.

Table
2.3.25:
Intake
of
Chemical
X
from
Commercial
Food
Items
by
Three
Types
of
Individuals
Central
Tendency
Estimate
(
mg/
kg­
day)
High­
End
Estimate
(
mg/
kg­
day)

General
Population
1.13
x
10­
6
4.10
x
10­
5
General
Sportfishers
1.13
x
10­
6
4.10x
10­
5
Subsistence
Fishers
1.06
x
10­
6
3.86
x
10­
5
Source:
Based
on
FDA
data.

To
estimate
total
exposure
from
food,
the
Chemical
X
concentrations
in
the
food
items
(
mg/
g)
were
then
multiplied
by
the
consumption
rate
of
each
food
item
(
g/
day)
and
then
divided
by
70
kilograms
to
determine
intake
of
Chemical
X
expressed
in
mg/
kg­
day.
These
values
for
each
food
item
were
then
summed,
resulting
in
total
mean
and
high­
end
intake
of
Chemical
X
from
the
diet.
These
total
intakes
were
then
further
adjusted
for
average
individuals
from
the
general
population,
sportfishers,
and
subsistence
fishers
to
exclude
the
amount
of
a
chemical
that
is
ingested
through
freshwater/
estuarine
fish
intake.
For
the
general
population,
it
was
assumed
that
the
amount
of
freshwater/
estuarine
fish
intake
(
17.8
g)
that
was
introduced
earlier
may
replace
17.8
grams
of
commercial
meat
consumed
in
the
diet.
Thus,
the
Chemical
X
intake
from
commercial
meat
was
adjusted
by
the
ratio
of
freshwater/
estuarine
fish
consumption
to
total
consumption
of
commercial
meat
(
17.8g/
205g
=
0.087).
In
other
words,
the
Chemical
X
contribution
from
commercial
meat
was
decreased
by
8.7
percent
for
the
average
individual.
Similar
adjustments
were
made
for
sportfishers
and
for
subsistence
fishers.
For
sportfishers,
the
amount
of
Chemical
X
intake
from
commercial
meat
was
also
decreased
by
8.7
percent,
and
for
subsistence
fishers,
the
amount
was
decreased
by
19
percent.
These
assumptions
were
made
with
the
idea
that
the
"
typical"
dietary
composition
based
on
the
FDA
analysis
should
be
adjusted
to
reflect
a
fish
consumer's
diet.
It
is
included
in
this
example
as
a
reasonable
adjustment
for
exposure
assessors
to
consider.
EPA
acknowledges
that,
with
some
fish
consumer
groups,
a
much
more
significant
adjustment
may
be
more
appropriate
and
States
and
Tribes
are
encouraged
to
consider
the
dietary
choices
of
their
target
population,
if
information
is
available.
The
resulting
intake
rates
of
Chemical
X
from
commercial
foods
are
included
in
Table
2.3.25.
These
intake
estimates
assume
concentrations
of
Chemical
X
only
in
the
food
items
described
above,
which
equate
to
approximately
six
percent
of
the
diet
(
i.
e.,
assumed
contamination
of
161.50
grams
of
food).
These
assumptions
are
presented
in
Appendix
G,
which
lists
the
individual
food
19These
values
were
determined
by
multiplying
the
air
concentrations
by
a
daily
air
intake
of
20
m3
and
dividing
by
the
average
adult
body
weight
of
70
kg.

150
items
and
Chemical
X
concentrations.
The
assumed
total
daily
intake
of
foods
discussed
above,
and
based
on
Pennington
(
1983),
is
2,582
g/
day.

Intake
from
Air
Outdoor
Air.
National
data
on
Chemical
X
concentration
distributions
in
air
are
not
available.
Monitoring
data
from
EPA,
however,
are
available
for
several
states.
From
six
sites
in
one
state,
the
average
value
is
determined
to
be
2.14
ppb
by
volume,
with
a
maximum
value
of
3.9
ppb.
Converting
from
values
in
ppb
to
ug/
m3
results
in
an
average
value
of
0.028
µ
g/
m3,
and
a
maximum
of
0.05
µ
g/
m3.
Additional
studies
have
reported
ambient
concentrations
in
several
regions
of
the
United
States.
One
report
summarized
ambient
air
data
collected
from
several
studies
conducted
in
various
regions
of
the
United
States.
These
studies,
all
published
in
the
1970s,
show
a
range
of
concentrations
from
a
low
2.1­
9.4
µ
g/
m3
in
one
state
to
a
high
value
of
100
µ
g/
m3,
which
is
an
average
value
using
data
from
three
other
states.
In
a
separate
report,
concentrations
of
0.007
µ
g/
m3
in
one
state
and
0.004
µ
g/
m3
in
another
state
were
seen
during
the
summer
of
1978.
Another
state
study
showed
that
during
the
summer
of
1985,
the
ambient
concentration
was
0.002
µ
g/
m3.
One
other
state
study
conducted
from
1979
to
1980,
showed
average
atmospheric
concentration
of
Chemical
X
as
0.0003
µ
g/
m3.
Data
from
an
EPA
urban
air
data
base
shows
a
mean
value
from
about
0.002
to
0.007
µ
g/
m3,
with
a
minimum
value
of
0.0005
µ
g/
m3
and
a
maximum
value
of
greater
than
0.03
µ
g/
m3.

Data
from
the
more
recent
studies
cited
above
are
combined
to
estimate
exposure.
Averaging
the
average
values
from
recent
studies
yields
a
central
tendency
air
concentration
value
of
0.008
µ
g/
m3.
The
high­
end
value
may
be
more
than
0.03
µ
g/
m3
and
may
be
0.05
µ
g/
m3.
These
data
are
from
limited
geographic
regions
and
are
not
indicative
of
areas
in
which
Chemical
X
concentrations
have
not
been
detected.
Therefore,
the
average
values
are
likely
to
be
overestimates
of
the
actual
national
average
estimates.
However,
because
data
are
not
readily
available
on
the
number
of
areas
where
air
concentrations
have
been
measured
but
are
below
detection,
these
values
are
used
as
crude
estimates
of
central
and
high­
end
values
of
intake
of
2.3
x
10­
6
mg/
kg­
day
and
1.4
x
10­
5
mg/
kg­
day,
respectively.
19
Indoor
Air.
Research
also
suggests
that
indoor
air
concentrations
may
be
significantly
higher
than
outdoor
air.
One
study
measured
Chemical
X
levels
in
seven
buildings.
All
types
of
buildings
examined
had
concentrations
that
were
significantly
higher
than
outdoor
concentrations.
A
second
study
measured
the
magnitude
of
the
difference
between
indoor
and
outdoor
air.
This
study
found
that
normal
indoor
air
concentrations
of
Chemical
X
were
at
least
one
order
of
magnitude
higher
than
outdoor
concentrations.
Although
these
data
suggest
that
indoor
air
may
have
higher
concentrations
than
outdoor
air,
the
study
which
measured
the
difference
between
indoor
and
outdoor
levels
was
done
in
only
three
buildings.
Indoor
levels
are
likely
to
have
decreased
via
a
reduction
in
indoor
uses
151
of
Chemical
X.
In
addition,
because
this
study
measured
the
differences
for
only
three
buildings,
the
estimate
is
fairly
uncertain.
Thus,
we
have
not
included
separate
indoor
air
exposures
in
the
current
analysis.
Instead,
the
analysis
models
only
outdoor
ambient
air
concentrations.
A
more
recent
study
of
one
building,
however,
does
give
an
indication
of
levels
of
indoor
air.
This
study
indicated
levels
of
0.018
and
0.017
µ
g/
m3
at
two
locations
were
found
during
1989­
1991.

Adequacy
of
Exposure
Data
The
exposure
data
must
be
evaluated
as
to
whether
they
are
adequate
to
estimate
central
tendencies
and
high­
end
values
for
each
particular
exposure
medium
(
see
Box
3
of
Exhibit
2.3.4).
Although
crude
exposure
estimates
have
been
presented
in
the
previous
section,
the
use
of
these
data
for
estimating
reliable
central
and
high­
end
values
of
exposure
are
limited.
This
section
outlines
the
problems
with
these
data,
and
indicates
why
these
data
are
considered
inadequate
in
terms
of
Box
3
for
estimating
the
total
dose
from
Chemical
X
in
the
population
of
concern
to
compare
with
the
RfD
for
Chemical
X.
Because
data
are
determined
to
be
inadequate
to
describe
central
and
high­
end
values
well
for
the
relevant
exposure
sources,
one
of
two
processes
are
used
to
set
standards
in
the
environmental
media
of
concern.
Depending
on
the
process,
either
default
values
are
used
as
the
allowable
dose
from
a
given
exposure
medium
or
the
available
data
are
used
to
determine
mediaspecific
allowable
doses
via
a
more
conservative
allocation
(
starting
with
Box
4
of
Exhibit
2.3.4).

As
noted
earlier
under
the
description
of
the
Exposure
Decision
Tree,
several
factors
must
be
considered
when
evaluating
data
adequacy
for
allocating
the
RfD
among
media.
One
of
the
main
factors
to
consider
is
the
number
of
samples
in
the
data
set
being
used
to
describe
a
particular
exposure
medium.
Although
there
are
no
universal
rules
about
adequate
sample
sizes,
the
proposed
rules
of
thumb
discussed
earlier
on
page
146
are
used
here.
For
estimating
a
90th
percentile
value
using
a
non­
parametric
method,
45
samples
are
needed,
of
which
at
least
five
must
be
above
detection
limits
to
determine
the
90th
percentile
value.
Fewer
samples
are
usually
adequate
for
estimating
mean
and
median
values.
In
addition
to
evaluation
of
sample
size,
the
other
aforementioned
factors
should
be
assessed
for
a
full
evaluation
of
data
adequacy
[
i.
e.,
representativeness
of
the
sample,
the
accuracy
in
the
analytical
procedures,
and
the
sensitivity
of
the
measurement
relative
to
the
environmental
levels
of
concern
(
i.
e.,
whether
detection
limits
are
low
enough
such
that
concentrations
can
be
detected
in
most
samples
within
a
data
set)].

Intake
of
Drinking
Water
from
Raw
Surface
Water
Sources
For
this
analysis,
the
two
most
recent
studies
were
used
to
represent
central
and
high­
end
estimates
of
concentrations.
The
central
tendency
estimate
was
determined
using
the
most
recent
data
previously
indicated.
The
number
of
data
points
that
made
up
this
value
was
five.
Because
the
high­
end
estimate
of
15.6
ng/
L
was
not
determined
as
a
90th
percentile
value,
the
sample
size
used
to
determine
the
value
was
not
evaluated
in
the
context
of
the
number
needed
to
determine
a
90th
percentile.
152
It
is
important
to
consider
several
factors
in
determining
whether
the
data
are
adequate.
First,
both
samples
are
current
and
thus
more
representative
of
Chemical
X
concentrations
than
older
data.
However,
the
data
are
from
two
lakes
only,
and
thus,
do
not
represent
a
national
distribution
of
data.
In
addition,
the
data
used
for
the
average
and
the
high­
end
values
are
taken
from
two
surveys,
and
thus,
differences
exist
between
these
studies
such
as
the
number
of
chemical
analogues
generally
detected.
Neither
study
reports
the
detection
limits,
or
whether
any
of
the
values
were
below
detection.

Based
on
the
lack
of
information
regarding
detection
limits,
limited
geographic
representation
of
the
data,
and
low
sample
sizes,
it
was
determined
that
data
are
inadequate
to
obtain
central
tendency
and
high­
end
estimates
of
exposure.
Although
such
estimates
are
presented,
they
represent
only
crude
numbers.

Freshwater/
Estuarine
Fish
Intake
The
most
recent
data
appear
adequate
to
use
in
estimating
typical
and
high­
end
exposures
from
contaminants
in
fish.
The
purpose
of
the
national
study
used
was
to
determine
the
geographical
extent
of
chemical
contamination.
Thus,
data
were
collected
from
all
watersheds
in
the
United
States
and
were
collected
in
1984.
The
sample
size
of
this
study
seems
large
enough
to
adequately
represent
the
geometric
mean
value.
As
noted
above,
the
minimum
number
of
samples
needed
to
adequately
represent
the
90th
percentile
of
a
given
exposure
(
using
non­
parametric
methods
to
estimate
acceptable
sample
size)
was
determined
to
be
45.
The
sample
size
needed
to
adequately
determine
the
median
value
would
be
smaller
than
the
size
needed
to
determine
the
90th
percentile.
Because
the
geometric
mean
may
be
assumed
to
be
equivalent
to
the
median,
it
is
assumed
that
the
number
of
samples
used
in
the
study
is
adequate
to
estimate
the
geometric
mean.
In
addition
to
a
minimum
sample
size
needed
to
estimate
the
geometric
mean,
a
minimum
number
of
positive
values
is
also
needed
to
determine
the
median
(
or
geometric
mean).
Although
it
is
not
known
how
many
samples
are
above
detection,
91
percent
of
the
stations
sampled
had
Chemical
X
concentrations
above
detection.
Thus,
for
purposes
of
this
example,
it
was
assumed
that
a
majority
of
samples
had
Chemical
X
concentrations
above
detection.

For
the
estimates
of
fish
consumption
rates,
the
large
sample
size
and
national
representation
of
the
CSFII
survey
make
it
a
useful
survey
for
measurement
of
fish
consumption,
if
the
assumption
is
made
that
the
consumption
rates
from
the
CSFII
study
(
which
measured
consumption
of
both
sport­
caught
and
commercial
fish)
apply
to
consumption
of
freshwater/
estuarine
fish.

Intake
of
Treated
Drinking
Water
from
Surface
Water
Sources
None
of
the
studies
of
Chemical
X
concentrations
in
drinking
water
from
surface
water
sources
is
ideal
for
estimating
exposure
through
drinking
water
from
surface
water
sources.
The
first
national
study
reviewed
may
offer
the
best
information
to
use
in
estimating
exposure
because
Chemical
X
concentrations
from
surface
water
sources
were
taken
from
many
cities
across
the
country
and
because
the
survey
reported
detection
limits.
However,
because
of
the
large
number
of
153
nondetected
samples,
central
exposure
values
cannot
be
determined
without
making
assumptions
about
the
concentrations
in
the
undetected
values.
In
addition,
the
data
are
older
and,
therefore,
may
not
represent
current
concentrations.
The
more
recent
national
study
did
not
detect
any
Chemical
X
concentrations
and
did
not
report
the
detection
limits.
Without
knowing
the
detection
limit,
it
is
impossible
to
make
any
assumption
about
the
concentrations
in
the
undetected
samples,
unless
it
is
assumed
that
the
concentrations
are
zero.
Because
of
these
problems,
these
data
are
considered
inadequate
for
estimating
exposure
from
surface
water
supplies
of
drinking
water.

Intake
of
Treated
Drinking
Water
from
Ground
Water
Sources
Data
on
exposures
from
ground
water
sources
of
drinking
water
are
difficult
to
use
because
few
detected
samples
have
been
found.
As
with
the
surface
water
sources,
it
is
impossible
to
make
assumptions
about
undetected
samples
measured
in
the
study
chosen.
For
data
from
the
first
national
study,
the
number
of
undetected
samples
and
the
time
period
during
which
the
study
was
conducted
make
it
difficult
to
estimate
central
estimates
of
exposure
from
this
study
with
any
level
of
confidence.
The
state
study
chosen
collected
about
160
samples,
of
which
30
had
concentrations
above
the
detection
limit.
However,
this
study
was
done
at
the
time
that
uses
of
Chemical
X
stopped
and
the
concentrations
were
measured
in
industrialized
settings
within
this
one
state.
Because
some
studies
reported
so
many
nondetected
values,
and
others
used
only
regional
data,
these
data
are
considered
inadequate
for
estimating
reliable
central
estimates
of
exposure
to
Chemical
X
in
ground
water
sources
of
drinking
water.

Sources
of
Food
Intake
The
limited
number
of
positive
samples
found
in
the
44
diet
collections
make
estimating
exposure
using
these
data
difficult.
Without
knowing
exact
detection
limits,
it
is
difficult
to
make
assumptions
about
concentrations
for
the
undetected
samples.
In
addition,
for
any
given
food
item,
generally
only
one
value
was
above
the
quantifiable
limit
for
Chemical
X.
(
Two
positive
samples
were
found
for
one
food
item.)
Thus,
neither
adequate
central
or
high­
end
concentrations
could
be
estimated.
Because
of
these
problems,
it
was
determined
that
the
data
are
not
adequate
for
estimating
national
distributions
of
Chemical
X
concentrations
in
food.

Sources
of
Air
Intake
These
data
are
too
limited
for
adequately
estimating
national
exposure
to
Chemical
X
from
air.
Several
of
the
studies
were
performed
in
the
1970s
when
companies
still
manufactured
Chemical
X.
In
addition,
more
recent
nationally
representative
exposure
estimates
are
not
available.
Finally,
sample
size
was
not
available
for
many
of
these
studies.
Thus,
it
was
determined
that
these
data
are
inadequate
to
estimate
exposure
from
air
sources
of
Chemical
X.
154
Setting
AWQC
Under
the
Exposure
Decision
Tree
Approach,
either
the
available
exposure
data
or
default
values
are
evaluated
against
the
toxicological
dose
that
should
result
in
no
adverse
health
effects
from
exposure
to
Chemical
X.
The
toxicological
dose
used
to
evaluate
the
exposure
and
the
method
of
allocation
are
described
below.

The
toxicological
dose
is
determined
first,
as
it
is
the
parameter
to
which
the
other
factors
are
applied.
Chemical
X
has
been
shown
to
cause
more
than
one
type
of
toxicity.
Two
chronic
RfDs
have
been
established
for
Chemical
X,
based
on
the
oral
route.
The
lowest
value
is
2.0
x
10­
5
mg/
kgday
and
is
based
on
clinical
and
immunological
studies
performed
on
monkeys.
The
adverse
health
effects
found
in
this
study
include
decreased
antibody
response
to
injected
sheep
red
blood
cells
by
three
principal
cells
of
the
immune
system,
exudate
from
the
eye,
inflammation
of
eyelid
glands,
and
changes
in
finger
and
toe
nails.
Because
non­
water
exposures
are
considered
for
cases
in
which
pollutants
cause
threshold
effects,
this
example
uses
the
chronic
RfD
value
of
2.0
x
10­
5
mg/
kg­
day
to
evaluate
chronic
toxicity
effects.

In
addition
to
these
chronic
effects,
Chemical
X
has
been
shown
to
result
in
adverse
health
effects
based
on
results
from
short
duration
studies.
EPA
recommends
that
where
such
effects
have
been
identified,
these
should
be
considered
and
compared
with
exposure
estimates
which
use
intake
rates
that
may
plausibly
be
ingested
within
a
short
time.
A
literature
review
shows
an
acute
study
with
a
LOAEL
of
244
mg/
kg­
day
for
developmental
(
fetal)
effects.
Assuming
uncertainty
factors
of
10
for
animal­
to­
human,
intrahuman,
and
LOAEL
to
NOAELs
results
in
a
value
of
0.244
mg/
kg­
day.
To
determine
whether
to
evaluate
the
chronic
or
developmental
effects,
the
differences
between
short­
term
exposures
and
the
"
RfD
DT"
were
compared
with
differences
between
chronic
exposure
and
the
chronic
RfD.

Specifically,
health
effects
and
relevant
intake
assumptions
for
a
target
population
of
pregnant
women
were
considered
for
this
example
due
to
the
fetal
developmental
effects
indicated
above.
It
was
assumed
that
a
pregnant
woman
might
ingest
a
one­
time
high
dose
or
multiple
high
doses
of
fish
within
a
short
time
period.
Data
show
that
such
doses
may
be
much
higher
than
average
fish
ingestion.
However,
it
is
unlikely
that
high­
end
intakes
from
other
media
would
occur
simultaneously.
Thus,
the
comparison
used
high
intake
assumptions
for
fish
intake
only.
The
information
on
"
acute"
fish
intake
rates
(
as
defined
in
Section
2.3.2.3,
Preference
#
3)
available
from
the
CSFII
includes
assumptions
for
women
of
childbearing
age
(
ages
15­
44
years
old).
The
90th
percentile
value
of
"
acute"
intake
(
see
Section
2.3.2.3
for
a
discussion
of
these
intake
values
determined
in
the
CSFII)
is
148.8
g/
day,
which
was
used
in
the
comparison.
The
body
weight
value
of
65
kilograms
from
Ershow
and
Cantor
(
1989)
was
used
and
is
applicable
to
pregnant
women.

Comparing
differences
between
this
shorter­
term
exposure
and
the
developmental
effect
RfD
"
RfD
DT"
with
the
differences
between
chronic
exposure
and
the
chronic
RfD
indicated
that
shorterterm
exposure
compared
with
the
developmental
RfD
is
lower
than
the
chronic
exposure
versus
the
chronic
RfD.
Chronic
exposures
using
intake
from
public
drinking
waters
rather
than
ambient
water
155
(
1.1
x
10­
4
mg/
kg­
day)
are
about
five
and
a
half
times
higher
than
the
chronic
RfD
(
2
x
10­
5
mg/
kgday
whereas
the
shorter­
term
exposures
(
1.6
x
10­
3
mg/
kg­
day)
are
much
lower
than
the
developmental
"
RfD
DT"
(
0.244
mg/
kg­
day).
Thus,
only
chronic
exposures
are
considered
in
this
example.

Using
the
process
to
set
AWQC
outlined
in
Exhibit
2.3.4,
the
available
exposure
data
(
although
limited)
are
used
to
determine
allowable
doses
for
each
medium.
This
allocation
is
performed
because
there
is
more
than
one
source/
use
of
the
chemical
(
Box
8)
and
because
there
is
some
information
available
to
characterize
all
sources
of
exposure
(
Box
10A).
Because
the
exposure
estimates
are
determined
not
to
be
adequate
enough
to
represent
central
and
high­
end
estimates,
the
allowable
dose
for
any
one
source
can
only
be
as
high
as
50
percent
of
the
total
allowable
dose.
The
allowable
dose
is
also
limited
by
the
floor
20
percent
of
the
allowable
dose
(
Box
10C).
Total
exposures
compared
with
the
RfD
are
included
in
Table
2.3.26.
Each
exposure
source
as
a
percent
of
total
exposure,
as
well
as
the
default
values
(
also
as
percentages)
of
these
exposures
used
in
criteria
allocation,
are
included
in
Table
2.3.27.
These
allowable
doses
from
each
medium
are
determined
for
three
types
of
individuals
discussed
earlier
(
average
individuals
from
the
general
population,
sportfishers,
and
subsistence
fishers).
Crude
contaminant
concentration
values
are
available
for
high
exposure
estimates
and
for
central
tendency
estimates.
Thus,
a
decision
must
be
made
about
whether
to
use
the
high­
end
or
central
value
when
setting
standards
for
a
given
environmental
medium.
Guidance
is
currently
being
developed
to
address
the
use
of
central
tendency
versus
high­
end
values.
For
this
example,
central
estimates
of
contaminant
concentrations
are
used
for
the
general
population,
and
high­
end
concentration
estimates
of
fish
are
used
for
sport
and
subsistence
fishers.
[
Because
the
variability
in
the
exposure
estimates
is
based
predominantly
on
the
variability
of
concentrations
in
the
exposure
media,
the
high­
end
values
do
not
reflect
use
of
high­
end
versus
central
tendency
consumption
rates.
Rather,
differences
in
consumption
rates
(
which
apply
only
to
fish
consumption)
are
reflective
of
the
defaults
used
for
the
different
populations
of
fishers.]
States
may
wish
to
use
different
percentile
values
for
the
general
population
and
other
fishers
based
on
concentrations
of
contaminants
in
their
area.

For
the
three
groups
of
fish
consumers
being
evaluated
(
subsistence
fishers,
sportfishers,
and
individuals
from
the
general
population),
two
criteria
are
relevant:
the
AWQC
and
health
tolerances
set
for
pesticide
use.
Thus,
an
allocation
based
on
the
percentage
approach
(
and
allocating
the
free
space)
is
done.
Because
total
exposure
is
greater
than
the
RfD,
there
is
no
free
space
to
be
allocated.
Thus,
the
percentage
approach
was
used
without
allocating
free
space.
20
These
estimates
were
made
using
high­
end
values
of
fish
exposure
(
i.
e.,
the
reported
high­
end
contaminant
concentration,
along
with
the
default
consumption
rates)
for
these
populations.
High­
end
values
were
also
assumed
for
the
estimates
made
with
ambient
water
data
(
i.
e.,
high­
end
contaminant
concentrations).
If
central
tendency
contaminant
concentrations
had
been
used,
the
subsistence
fisher
percent
of
total
exposure
attributable
to
fish
and
water,
and
fish­
only
would
be
99.3%
and
98.6%,
respectively.
If
central
tendency
contaminant
concentrations
had
been
used
for
the
sportfisher,
the
estimates
would
equal
those
for
the
general
population.

156
Table
2.3.26:
Total
Exposure
Compared
with
the
RfD
Total
Exposures
with
Ambient
water
(
mg/
kg­
day)
Total
Exposures
with
Drinking
water
(
mg/
kg­
day)

General
population
1.0
x
10­
4
1.1
x
10­
4
Sportfisher20
1.7
x
10­
3
1.7
x
10­
3
Subsistence
fisher20
8.3
x
10­
3
8.3
x
10­
3
RfD
2.0
x
10­
5
2.0
x
10­
5
Table
2.3.27:
Exposure
Information
­­
Percent
of
Total
Exposures
and
Default
Exposure
Percentages
for
Three
Types
of
Individuals
Fish
and
Water
Criterion
Fish­
Only
Criterion
Exposure
as
a
Percent
of
Total
Exposure
Default
Value
Exposure
as
a
Percent
of
Total
Exposure
Default
Value
Subsist.
Sport
Gen.
Subsist.
Sport
Gen.

Fish
99.9
99.8
96.6
50%
99.9
99.6
93.5
50%

Water
0.04
0.2
3.2
20%

Diet
0.01
0.07
1.1
20%
0.01
0.07
1.1
20%

Air
0.03
0.1
2.2
20%
0.03
0.1
2.2
20%

Total
exposure
compared
with
the
RfD
is
included
in
Table
2.3.26.
All
exposures
are
greater
than
the
RfD.
For
both
criteria
and
all
three
types
of
individuals,
the
percent
of
exposure
from
eating
either
(
1)
fish
and
water
or
(
2)
fish­
only
is
very
high
(>
90
percent
of
the
fisher's
total
exposure).
The
ceiling
of
50
percent
is
used
for
the
RSC
allocation
for
the
ambient
water
quality
criterion.

The
value
of
50
percent
is
then
multiplied
by
the
total
allowable
dose
(
2.0
x
10­
5
mg/
kg­
day)
to
determine
allowable
doses
for
all
three
types
of
individuals,
as
noted
in
Table
2.3.28.
For
each
of
the
criteria,
the
allowable
dose
of
1
x
10­
5
mg/
kg­
day
is
used
to
determine
the
AWQC.
Criteria
based
on
(
1)
fish
and
water
intake,
and
(
2)
fish
intake
only
are
calculated
and
presented
in
this
table.
For
21
If
the
alternate
default
intake
assumption
of
86.3
g/
day
was
used
instead,
the
subsistence
fisher
AWQC
estimates
for
fish
and
water,
and
fish
only,
would
be
1.5
x
10­
7
mg/
L
157
each
type
of
fisher,
the
fish
and
water
criterion
values
do
not
differ
from
the
fish
only
criterion
values.
Other
exposure
factors
from
the
equation
used
in
the
calculation
are:
body
weight=
70
kg;
drinking
water
intake=
2
L/
day
(
or
incidental
ingestion
of
0.01
L/
day);
fish
intake
rates
of
0.01780
kg/
day
for
the
general
and
sportfisher
populations
and
0.08630
kg/
day
for
subsistence
fishers;
and
a
bioaccumulation
factor
of
120,000.

Table
2.3.28:
AWQC
for
Three
Types
of
Individuals
Fish
and
Water
Criterion
Fish
Only
Criterion
Default
Allowable
Dose
(
50%
of
RfD)
1.0
x
10­
5
mg/
kg­
day
1.0
x
10­
5
mg/
kg­
day
AWQC:
Subsistence
Fisher21
Sportfisher
General
Population
6.8
x
10­
8
mg/
L
3.3
x
10­
7
mg/
L
3.3
x
10­
7
mg/
L
6.8
x
10­
8
mg/
L
3.3
x
10­
7
mg/
L
3.3
x
10­
7
mg/
L
Presenting
Information
to
Risk
Managers
Although
the
above
example
utilizes
the
20
percent
floor
for
the
other
sources
of
exposure,
it
is
clear
that
a
combination
of
allocations
of
the
RfD,
if
used,
would
exceed
the
80
percent
ceiling
and
in
the
case
of
the
fish­
only
criterion,
would
exceed
100
percent
of
the
RfD.
This
example
also
illustrates
the
potential
need
for
flexibility
to
lower
the
floor
(
perhaps
to
zero?)
due
to
exposures
in
exceedance
of
the
RfD.

Because
total
exposures
from
all
environmental
media
are
greater
than
the
dose
without
an
appreciable
risk
of
toxicological
effects,
several
pieces
of
information
can
be
presented
to
risk
managers
for
their
review
in
deciding
how
to
apportion
the
dose
of
2.0
x
10­
5
mg/
kg­
day
among
exposure
sources.
These
data
include
information
about
the
toxicity
of
the
chemical
(
including
uncertainty
in
the
estimate),
information
about
exposures,
and
issues
involving
control
of
Chemical
X.
Because
exposures
and
uncertainties
in
these
estimates
were
discussed
in
previous
sections,
they
will
not
be
discussed
again
here.
Additionally,
control
technology
issues
will
not
be
discussed
here,
as
they
are
not
directly
related
to
estimating
exposure
via
the
Exposure
Decision
Tree
Approach.
However,
additional
toxicity
information
is
presented
below.
158
The
toxicity
data
supporting
the
RfD
of
2.0
x
10­
5
mg/
kg­
day
should
be
described
in
order
to
give
risk
managers
an
idea
about
the
confidence
in
the
value.
Some
evaluative
information
is
available
from
the
Integrated
Risk
Information
System.
As
noted
above,
the
critical
endpoints
upon
which
the
RfD
is
based
include
decreased
antibody
response
to
injected
sheep
red
blood
cells,
exudate
from
the
eye,
inflammation
of
eyelid
glands,
and
changes
in
finger
and
toe
nails.
A
total
uncertainty
factor
of
300
is
applied
to
the
Lowest
Observed
Adverse
Effect
Level
(
LOAEL)
from
the
critical
study.
The
total
number
of
uncertainty
factors
account
for:
sensitive
individuals
within
the
population
(
a
10­
fold
factor);
extrapolation
from
monkeys
to
humans
(
a
3­
fold
factor);
use
of
a
LOAEL
instead
of
a
NOAEL
(
a
partial
factor);
and
use
of
a
subchronic
rather
than
a
chronic
study
(
a
3­
fold
factor).
The
overall
confidence
in
the
RfD
is
medium
because
the
confidence
in
the
principal
study
based,
in
turn,
on
the
confidence
in
the
data
base
are
also
considered
medium.

In
addition
to
toxicity
data,
information
on
ways
to
control
exposure
in
different
environmental
media
can
be
presented
to
risk
managers
to
aid
them
in
determining
the
relative
ease
of
controlling
exposure
from
different
environmental
media.
Ideally,
this
information
would
include
expected
incremental
costs
of
treatment
needed
per
unit
decrease
of
Chemical
X
and
other
feasibility
issues
associated
with
controlling
Chemical
X
in
different
environmental
media.

Exposure
from
consuming
freshwater/
estuarine
fish
represents
the
single
largest
exposure
to
Chemical
X.
Other
sources
of
exposure
represent
smaller
percentages
of
total
exposure,
but
all
highend
exposures
except
air
exposures
also
exceed
the
RfD
individually.

2.3.4.4
Bioavailability
of
Substances
from
Different
Routes
of
Exposure
For
many
chemicals,
the
rate
of
absorption
can
differ
substantially
from
ingestion
compared
to
inhalation.
There
is
also
available
information
for
some
chemicals
which
demonstrates
appreciable
differences
in
gastrointestinal
absorption
depending
on
whether
the
chemical
is
ingested
from
water,
soil,
or
food.
For
some
contaminants,
plant
and
animal
food
products
may
also
have
appreciably
different
absorption
rates.
Regardless
of
the
allocation
approach
used,
EPA
proposes
using
existing
data
on
differences
in
bioavailability
between
water,
air,
soils,
and
different
foods
for
estimating
total
exposure
and
in
allocating
the
RfD.
The
Agency
has
developed
such
exposure
estimates
for
cadmium
(
USEPA,
1994b).
In
the
absence
of
data,
EPA
will
assume
equal
rates
of
absorption
from
different
routes
and
sources
of
exposure.

Information
on
absorption
rates
for
Chemical
X
is
not
available.
Available
studies
only
generally
describe
varying
fractions
of
the
chemical
in
different
media
due
to
varying
rates
of
volatilization,
solubility,
and
adsorption.
Discussions
about
chemical
transformations
in
the
environment
and
by
human
metabolism
are
similarly
vague.
In
the
absence
of
such
data,
it
is
assumed
for
this
example
that
Chemical
X
is
fully
absorbed
and
that
the
rates
of
absorption
from
different
routes
and
sources
of
exposure
are
equal.
159
2.3.5
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May.
22According
to
the
1980
AWQC
National
Guidelines,
laboratory­
measured
or
predicted
bioconcentration
factors
were
used
when
field­
measured
bioconcentration
factors
(
equivalent
to
what
are
now
called
field­
measured
BAFs)
were
not
available.

166
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24(
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P.
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28.

Zein­
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1986.
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48(
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16.

2.4.
Use
of
BAFs
in
the
Derivation
of
AWQC
2.4.1
Introduction
Aquatic
organisms
are
known
to
accumulate
certain
types
of
chemicals
in
their
bodies.
Uptake
of
these
chemicals
may
occur
from
exposure
to
contaminated
water,
consumption
of
contaminated
food,
and
exposure
to
other
sources
such
as
contaminated
bottom
sediment.
This
chemical
uptake
process
is
called
bioaccumulation.
For
some
chemicals,
such
as
certain
highly
hydrophobic
chemicals,
uptake
through
the
food
chain
can
be
the
most
important
route
of
exposure.
As
organisms
in
higher
trophic
levels
feed
on
organisms
in
lower
trophic
levels,
tissue
concentrations
of
these
chemicals
increase
through
the
trophic
levels
so
that
the
concentrations
in
the
highest
trophic
level
organisms
may
be
many
orders
of
magnitude
higher
than
levels
in
the
environment.
The
trophiclevel
increase
in
contaminated
concentrations
is
called
biomagnification,
and
may
result
in
serious
adverse
health
effects
for
consumers
of
the
highest
trophic
levels
of
fish.

To
protect
humans
from
harmful
exposures
to
bioaccumulative
chemicals,
EPA
proposes
to
use
bioaccumulation
factors
(
BAFs)
in
deriving
AWQC.
These
BAFs
are
ratios
of
the
contaminant
concentration
in
tissue
to
the
concentration
in
water,
taking
into
account
uptake
through
contaminated
food,
sediment,
and
water.
Chemicals
with
larger
BAFs
reflect
greater
accumulation
in
fish
tissues
compared
to
chemicals
with
lower
BAFs.
The
BAFs
may
be
of
such
large
magnitude
that
the
resulting
ambient
water
quality
criterion
will
be
strongly
influenced
by
the
BAF.

In
contrast
to
the
current
guidelines,
the
1980
AWQC
National
Guidelines
for
deriving
human
health
criteria
relied
on
an
alternate
type
of
ratio,
the
biconcentration
factor
(
BCF),
to
derive
AWQC.
22
In
contrast
to
the
BAF,
the
BCF
measures
uptake
of
chemicals
into
fish
that
have
been
exposed
only
through
water
(
not
through
food
or
sediment).
Because
BAFs
account
for
uptake
from
all
sources
of
waterborne
exposure
of
a
chemical
to
an
organism
(
through
food,
water,
and
sediment),
EPA
believes
the
use
of
the
BAF
to
be
superior
to
the
BCF
for
deriving
human
health
AWQC.
167
2.4.1.1
Bioaccumulation
and
Bioconcentration
Concepts
Bioaccumulation
reflects
the
uptake
and
retention
of
a
chemical
by
an
aquatic
organism
from
all
surrounding
media
(
e.
g.,
water,
food,
sediment).
Bioconcentration
refers
to
the
uptake
and
retention
of
a
chemical
by
an
aquatic
organism
from
water
only.
Both
bioaccumulation
and
bioconcentration
can
be
viewed
simply
as
the
result
of
competing
rates
of
chemical
uptake
and
depuration
(
chemical
loss)
by
an
aquatic
organism.
However,
the
rates
of
uptake
and
depuration
can
be
affected
by
numerous
factors
including
the
physical
and
chemical
properties
of
the
chemical,
the
physiology
and
biology
of
the
organism,
environmental
conditions,
ecological
factors
such
as
food
web
structure,
and
the
amount
and
source
of
the
chemical.
When
the
rates
of
chemical
uptake
and
depuration
are
equal,
the
distribution
of
the
chemical
between
the
organism
and
its
source(
s)
is
said
to
be
at
equilibrium
or
at
steady
state.
For
a
constant
chemical
exposure,
the
time
required
to
achieve
steady
state
conditions
varies
according
to
the
properties
of
the
chemical
and
other
factors.
For
example,
some
chemicals
require
a
long
time
to
reach
steady
state
conditions
between
environmental
compartments
(
e.
g.,
many
months
for
certain
highly
hydrophobic
chemicals)
while
others
reach
steady
state
relatively
quickly
(
e.
g.,
hours
to
days
for
certain
hydrophilic
chemicals).

The
concept
of
steady
state
or
equilibrium
conditions
is
very
important
when
assessing
or
evaluating
bioaccumulation
or
bioconcentration
and
applying
these
principles
in
real
world
situations,
such
as
the
derivation
of
ambient
water
quality
criteria.
For
some
chemicals
and
organisms
that
require
relatively
long
time
periods
to
reach
steady
state,
changes
in
water
column
chemical
concentrations
may
occur
on
a
much
more
rapid
time
scale
compared
to
the
corresponding
changes
in
an
organism's
tissue
concentrations.
Thus,
if
the
system
departs
substantially
from
steady
state
conditions,
the
ratio
of
the
tissue
concentration
to
the
water
concentration
may
have
little
resemblance
to
the
steady­
state
ratio
and
have
little
predictive
value
of
long­
term
bioaccumulation
potential.

For
highly
bioaccumulative
pollutants
in
dynamic
systems,
reliable
BAFs
can
be
determined
only
if,
among
other
factors,
water
column
concentrations
are
averaged
over
a
sufficient
period
of
time
(
e.
g.,
a
duration
approximating
the
amount
of
time
predicted
for
the
pollutant
to
reach
steadystate
In
addition,
adequate
spatial
averaging
of
both
tissue
and
water
column
concentrations
is
required
to
develop
reliable
BAFs
for
use
in
deriving
human
health
ambient
water
quality
criteria.

For
this
reason,
a
bioaccumulation
factor
is
defined
in
this
guidance
as
representing
the
ratio
(
in
L/
kg)
of
a
concentration
of
a
substance
in
tissue
to
its
concentration
in
the
surrounding
water
in
situations
where
the
organism
and
its
food
are
exposed
and
the
ratio
does
not
change
substantially
over
time.
A
bioconcentration
factor
is
considered
to
represent
the
uptake
and
retention
of
a
substance
by
an
aquatic
organism
from
the
surrounding
water
only,
through
gill
membranes
or
other
external
body
surfaces,
in
situations
where
the
tissue­
to­
water
ratio
does
not
change
substantially
over
time.

This
chapter
provides
the
technical
basis
and
rationale
for
EPA's
proposed
procedures
for
determining
BAFs
for
toxic
substances.
Section
2.4.2
lists
pertinent
definitions
used
throughout
the
168
BAF
'
C
t
C
w
(
Equation
2.4.1)
chapter,
Sections
2.4.3
through
2.4.5
describe
issues
and
procedures
relevant
to
estimating
BAFs
for
nonpolar
organic
chemicals,
Section
2.4.6
describes
procedures
relevant
to
the
derivation
of
BAFs
for
inorganic
chemicals,
and
Section
2.4.7
discusses
the
derivation
of
BAFs
for
two
example
chemicals.
Issues
associated
with
applying
fish
consumption
rate
information
to
trophic
level­
specific
BAFs
are
discussed
in
Section
2.4.8.

2.4.2
Definitions
Baseline
BAF
(
BAF
R
f
d).
For
organic
chemicals,
a
BAF
(
in
L/
kg­
lipid)
that
is
based
on
the
concentration
of
freely
dissolved
chemical
in
the
ambient
water
and
the
lipid
normalized
concentration
in
tissue;
for
inorganic
chemicals,
a
BAF
that
is
based
on
the
wet
weight
of
the
tissue.

Baseline
BCF
(
BCF
R
f
d).
For
organic
chemicals,
a
BCF
(
in
L/
kg­
lipid)
that
is
based
on
the
concentration
of
freely
dissolved
chemical
in
the
ambient
water
and
the
lipid
normalized
concentration
in
tissue;
for
inorganic
chemicals,
a
BCF
that
is
based
on
the
wet
weight
of
the
tissue.

Bioaccumulation.
The
net
accumulation
of
a
substance
by
an
organism
as
a
result
of
uptake
from
all
environmental
sources.

Bioaccumulation
Factor
(
BAF).
The
ratio
(
in
L/
kg­
tissue)
of
the
concentration
of
a
substance
in
tissue
to
its
concentration
in
the
ambient
water,
in
situations
where
both
the
organism
and
its
food
are
exposed
and
the
ratio
does
not
change
substantially
over
time.
The
BAF
is
calculated
as:

where:
C
t
=
Concentration
of
the
chemical
in
the
wet
tissue
(
either
whole
organism
or
specified
tissue)
C
w
=
Concentration
of
chemical
in
water
Bioconcentration.
The
net
accumulation
of
a
substance
by
an
aquatic
organism
as
a
result
of
uptake
directly
from
the
ambient
water,
through
gill
membranes
or
other
external
body
surfaces.

Bioconcentration
Factor
(
BCF).
The
ratio
(
in
L/
kg­
tissue)
of
the
concentration
of
a
substance
in
tissue
of
an
aquatic
organism
to
its
concentration
in
the
ambient
water,
in
situations
where
the
organism
is
exposed
through
the
water
only
and
the
ratio
does
not
change
substantially
over
time.
The
BCF
is
calculated
as:
169
BCF
'
C
t
C
w
(
Equation
2.4.2)

BSAF
'
C
R
C
soc
(
Equation
2.4.3)

BMF(
TL,
n)
'
C
R
(
TL,
n)

C
R
(
TL,
n
&
1)
where:
C
t
=
Concentration
of
the
chemical
in
the
wet
tissue
(
either
whole
organism
or
specified
tissue)
C
w
=
Concentration
of
chemical
in
water
Biota­
Sediment
Accumulation
Factor
(
BSAF).
The
ratio
(
kg
of
sediment
organic
carbon
per
kg
of
lipid)
of
the
lipid­
normalized
concentration
of
a
substance
in
tissue
of
an
aquatic
organism
to
its
organic
carbon­
normalized
concentration
in
surface
sediment,
in
situations
where
the
ratio
does
not
change
substantially
over
time,
both
the
organism
and
its
food
are
exposed,
and
the
surface
sediment
is
representative
of
average
surface
sediment
in
the
vicinity
of
the
organism.
The
BSAF
is
defined
as:

where:
C
R
=
The
lipid­
normalized
concentration
of
the
chemical
in
tissues
of
the
biota
(
F
g/
g
lipid)
C
soc
=
The
organic
carbon­
normalized
concentration
of
the
chemical
in
the
surface
sediment
(
F
g/
g
sediment
organic
carbon)

Biomagnification.
The
increase
in
tissue
concentration
of
poorly
depurated
materials
in
organisms
along
a
series
of
predator­
prey
associations,
primarily
through
the
mechanism
of
dietary
accumulation.

Biomagnification
Factor
(
BMF).
The
ratio
(
unitless)
of
the
tissue
concentration
of
a
predator
organism
at
a
particular
trophic
level
to
the
tissue
concentration
in
its
prey
organism
at
the
next
lowest
trophic
level,
for
a
given
waterbody
and
chemical
exposure.
For
organic
chemicals,
a
BMF
can
be
calculated
using
lipid­
normalized
concentrations
in
the
tissue
of
organisms
at
two
successive
trophic
levels
as:
170
BMF(
TL,
n)
'
C
t
(
TL,
n)

C
t
(
TL,
n
&
1)

C
fd
w
'
(
f
fd)
@
(
C
t
w)

(
Equation
2.4.4)
where:
C
R
(
TL,
n)
=
Lipid­
normalized
concentration
in
appropriate
tissue
of
predator
organism
at
trophic
level
"
n"

C
R
(
TL,
n­
1)
=
Lipid­
normalized
concentration
in
appropriate
tissue
of
prey
organism
at
the
next
lowest
trophic
level
from
the
predator.

For
inorganic
chemicals,
a
BMF
can
be
calculated
using
chemical
concentrations
in
the
tissue
of
organisms
at
two
successive
trophic
levels
as:

where:
C
t
(
TL,
n)
=
Concentration
in
appropriate
tissue
of
predator
organism
at
trophic
level
"
n"
(
may
be
either
wet
weight
or
dry
weight
concentration
so
long
as
both
the
predator
and
prey
concentrations
are
expressed
in
the
same
manner)

C
t
(
TL,
n­
1)
=
Concentration
in
appropriate
tissue
of
prey
organism
at
the
next
lowest
trophic
level
from
the
predator
(
may
be
either
wet
weight
or
dry
weight
concentration
so
long
as
both
the
predator
and
prey
concentrations
are
expressed
in
the
same
manner)

As
explained
in
the
TSD,
BMFs
can
also
be
related
to
(
and
calculated
from)
FCMs
and
baseline
BAFs.

Depuration.
The
loss
of
a
substance
from
an
organism
as
a
result
of
any
active
or
passive
process.

Food­
Chain
Multiplier
(
FCM).
The
ratio
of
a
baseline
BAF
for
an
organism
of
a
particular
trophic
level
to
the
baseline
BCF
(
usually
determined
for
organisms
in
trophic
level
one).

Freely
Dissolved
Concentration.
For
hydrophobic
organic
chemicals,
the
concentration
of
the
chemical
that
is
dissolved
in
ambient
water,
excluding
the
portion
sorbed
onto
particulate
or
dissolved
organic
carbon.
The
freely
dissolved
concentration
is
considered
to
represent
the
most
bioavailable
form
of
an
organic
chemical
in
water
and,
thus,
is
the
form
that
best
predicts
bioaccumulation.
The
freely
dissolved
concentration
can
be
determined
as:
171
BAF
R
'
C
R
C
w
(
Equation
2.4.5)

BCF
R
'
C
R
C
w
(
Equation
2.4.6)
where:
C
w
f
d
=
Freely
dissolved
concentration
of
the
organic
chemical
in
ambient
water
C
w
t
=
Total
concentration
of
the
organic
chemical
in
ambient
water
f
fd
=
Fraction
of
the
total
chemical
in
ambient
water
that
is
freely
dissolved
Lipid­
normalized
Bioaccumulation
Factor
(
BAF
R
)
.
The
ratio
(
in
L/
kg­
lipid)
of
a
substance's
lipid­
normalized
concentration
in
tissue
to
its
concentration
in
the
ambient
water,
in
situations
where
both
the
organism
and
its
food
are
exposed
and
the
ratio
does
not
change
substantially
over
time.
The
lipid­
normalized
BAF
is
calculated
as:

where:
C
R
=
Lipid­
normalized
concentration
of
the
chemical
in
whole
organism
or
specified
tissue
C
w
=
Concentration
of
chemical
in
water
Lipid­
normalized
Bioconcentration
Factor
(
BCF
R
)
.
The
ratio
(
in
L/
kg­
lipid)
of
a
substance's
lipid­
normalized
concentration
in
tissue
of
an
aquatic
organism
to
its
concentration
in
the
ambient
water,
in
situations
where
the
organism
is
exposed
through
the
water
only
and
the
ratio
does
not
change
substantially
over
time.
The
lipid­
normalized
BCF
is
calculated
as:

where:
C
R
=
Lipid­
normalized
concentration
of
the
chemical
in
whole
organism
or
specified
tissue
C
w
=
Concentration
of
chemical
in
water
Lipid­
normalized
Concentration
(
C
R
)
.
The
total
concentration
of
a
contaminant
in
a
tissue
or
whole
organism
divided
by
the
lipid
fraction
in
that
tissue
or
whole
organism.
The
lipid­
normalized
concentration
can
be
calculated
as:
172
C
R
'
C
t
f
R
(
Equation
2.4.7)

C
soc
'
C
s
f
oc
(
Equation
2.4.8)
where:

C
t
=
Concentration
of
the
chemical
in
the
wet
tissue
(
either
whole
organism
or
specified
tissue)
f
R
=
Fraction
lipid
content
in
the
organism
or
specified
tissue
Octanol­
water
Partition
Coefficient
(
Kow).
The
ratio
of
the
concentration
of
a
substance
in
the
n­
octanol
phase
to
its
concentration
in
the
aqueous
phase
in
an
equilibrated
two­
phase
octanolwater
system.
For
log
K
ow,
the
log
of
the
octanol­
water
partition
coefficient
is
a
base
10
logarithm.

Organic
Carbon­
normalized
Concentration
(
Csoc).
For
sediments,
the
total
concentration
of
a
contaminant
in
sediment
divided
by
the
fraction
of
organic
carbon
in
sediment.
The
organic
carbon­
normalized
concentration
can
be
calculated
as:

where:
C
s
=
Concentration
of
chemical
in
sediment
f
oc
=
Fraction
organic
carbon
in
sediment
Uptake.
Acquisition
by
an
organism
of
a
substance
from
the
environment
as
a
result
of
any
active
or
passive
process.

2.4.3
Determining
BAFs
for
Nonpolar
Organics
The
calculation
of
a
BAF
for
a
nonpolar
organic
chemical
(
chemicals
that
do
not
readily
dissolve
in
water)
used
in
the
derivation
of
AWQC
is
a
two­
step
process.
The
first
step
is
to
calculate
a
baseline
BAF
for
the
chemical
of
interest
using
information
from
the
field
site
or
laboratory
where
the
original
data
were
collected
(
i.
e.,
the
lipid
content
of
the
species
collected
and
the
freely
dissolved
fraction
of
the
chemical
in
water
at
the
site
where
the
data
were
collected).
If
information
used
to
estimate
fish
consumption
rates
indicates
that
organisms
are
being
consumed
from
different
trophic
levels,
then
baseline
BAFs
need
to
be
determined
for
each
of
the
relevant
trophic
levels.
173
The
second
step
is
to
calculate
a
BAF
(
or
BAFs)
for
the
chemical
that
will
be
used
in
the
derivation
of
AWQC,
using
information
from
the
location
where
the
aquatic
species
of
interest
are
consumed
(
i.
e.,
the
lipid
content
of
the
aquatic
species
consumed
by
humans
and
the
freely
dissolved
fraction
of
the
chemical
in
water
at
the
site
where
the
aquatic
species
are
being
consumed).
The
difference
between
a
baseline
BAF
and
a
BAF
used
in
the
derivation
of
a
AWQC
is
that
baseline
BAFs
can
be
used
for
extrapolating
from
one
species
to
another
and
from
one
water
body
to
another.
This
is
the
case
because
baseline
BAFs
are
lipid­
normalized,
enabling
extrapolation
for
organic
chemicals
from
one
species
to
another;
and
because
they
are
based
on
the
freely
dissolved
concentration
of
organic
chemicals,
enabling
extrapolation
from
one
water
body
to
another
(
the
importance
of
these
concepts
is
discussed
below).
Baseline
BAFs,
however,
cannot
be
used
directly
in
the
derivation
of
AWQC
because
they
may
not
reflect
the
conditions
in
the
area
of
interest
(
e.
g.,
the
lipid
content
of
the
aquatic
species
consumed
in
the
area
of
interest
and
the
freely
dissolved
fraction
of
the
chemical
in
the
area
of
concern).

Depending
on
the
type
of
information
available
for
a
given
chemical,
different
procedures
may
be
used
to
determine
the
baseline
BAF.
The
most
preferred
BAFs
are
those
derived
using
appropriate
field
data.
Field­
measured
BAFs,
however,
have
not
been
determined
for
all
chemicals.
Thus,
EPA
proposes
a
hierarchy
of
procedures
to
determine
BAF
values.
The
data
preference
for
derivation
of
baseline
BAFs
for
nonpolar
organic
substances
is
as
follows
(
in
order
of
priority):

1.
A
field­
measured
baseline
BAF
derived
from
a
field
study
of
acceptable
quality.

2.
A
predicted
baseline
BAF
derived
from
a
field­
measured
BSAFs
of
acceptable
quality.

3.
A
predicted
baseline
BAF
derived
from
a
laboratory­
measured
BCF
of
acceptable
quality
and
a
food­
chain
multiplier
(
FCM).

4.
A
predicted
baseline
BAF
derived
from
an
acceptable
K
ow
and
a
food­
chain
multiplier.

While
EPA
recommends
the
above
hierarchy
for
determining
final
baseline
BAF
values,
for
comparative
purposes,
baseline
BAFs
should
be
determined
for
each
chemical
by
as
many
of
the
four
methods
as
available
data
allow.
Comparing
baseline
BAFs
derived
using
the
different
methods
recommended
above
can
provide
insight
for
identifying
and
evaluating
any
discrepancies
in
the
BAF
determinations
that
might
occur.
The
information
needed
to
derive
a
baseline
BAF
using
each
of
the
four
methods
is
discussed
in
Section
2.4.4.
Section
2.4.5
discusses
the
information
needed
to
derive
a
BAF
for
use
in
the
calculation
of
AWQC.

2.4.4
Estimating
Baseline
BAFs
for
Nonpolar
Organics
All
the
baseline
BAFs
for
nonpolar
organic
chemicals
should
be
expressed
on
a
freelydissolved
and
lipid­
normalized
basis.
The
procedures
for
adjusting
a
field­
measured
BAF,
field­
174
measured
BSAF,
or
laboratory­
measured
BCF
to
a
freely­
dissolved
and
lipid­
normalized
basis
are
discussed
below.

2.4.4.1
Field­
Measured
Baseline
BAF
EPA's
first
preference
for
deriving
a
BAF
for
nonpolar
organic
substances
is
the
use
of
a
valid
field­
measured
BAF.
Field­
measured
BAFs
are
preferred
to
other
procedures
because
they
inherently
account
for
the
effects
of
metabolism,
biomagnification,
and
other
factors
affecting
bioaccumulation.

The
calculation
of
a
field­
measured
baseline
BAF
requires
information
on:
(
1)
a
fieldmeasured
BAF
based
on
the
total
concentration
of
a
chemical
in
the
tissue
of
the
aquatic
organism
sampled
and
the
total
concentration
of
the
chemical
in
the
ambient
water;
(
2)
the
fraction
of
tissue
that
is
lipid
in
the
aquatic
organism
of
interest;
and
(
3)
either
the
measured
or
estimated
freely
dissolved
fraction
of
the
total
chemical
in
the
ambient
water
where
the
aquatic
species
were
collected
(
estimating
the
freely
dissolved
fraction
for
a
chemical
requires
information
on
the
particulate
and
dissolved
organic
carbon
content
in
the
ambient
water
and
the
K
ow
of
the
chemical
of
interest).
The
equation
for
deriving
a
field­
measured
baseline
BAF
expressed
on
a
freely­
dissolved
and
lipidnormalized
basis
is:

Baseline
BAF
fd
R
'
Measured
BAF
t
T
f
fd
&
1
1
f
R
(
Equation
2.4.9)

where:

Baseline
BAF
R
f
d
=
BAF
expressed
on
a
freely­
dissolved
and
lipid­
normalized
basis
Measured
BAFtT
=
BAF
based
on
total
concentration
in
tissue
(
wet
weight
basis)
and
water
f
R
=
Fraction
of
the
tissue
that
is
lipid
f
fd
=
Fraction
of
the
total
chemical
that
is
freely
dissolved
in
the
ambient
water
For
the
derivation
of
Equation
2.4.9,
see
Appendix
C.

For
each
trophic
level,
a
species
mean
baseline
BAF
is
calculated
as
the
geometric
mean
if
more
than
one
acceptable,
measured
baseline
BAF
is
available
for
a
given
species.
For
each
trophic
level,
a
trophic
level­
specific
BAF
is
calculated
as
the
geometric
mean
of
the
species
mean
measured
baseline
BAFs.
Each
of
the
three
components
for
deriving
the
baseline
BAF
are
described
in
further
detail
below.
175
Measured
BAFtT
To
estimate
a
measured
BAFtT
,
information
is
needed
on
the
total
concentration
of
the
pollutant
in
the
tissue
of
the
organism
and
the
total
concentration
of
the
chemical
in
ambient
water
at
the
site
of
sampling.
The
equation
to
derive
a
measured
BAF
tT
is:

Measured
BAF
tT
=
Total
concentration
of
chemical
in
tissue
(
ug/
Kg
wet
weight)
Total
concentration
of
chemical
in
the
ambient
water
(
ug/
L)

(
Equation
2.4.10)

Guidance
for
Measuring
Field­
Based
BAFs
Application
of
data
quality
assurance
procedures
when
measuring,
estimating,
and
applying
BAFs
is
of
primary
importance.
The
following
procedural
and
quality
assurance
requirements
should
be
met
for
field­
measured
BAFs:

C
The
field
studies
used
should
be
limited
to
those
that
include
fish
at
or
near
the
top
of
the
aquatic
food
chain
(
i.
e.,
in
trophic
levels
3
and/
or
4).
In
situations
where
consumption
of
lower
trophic
level
organisms
represents
an
important
exposure
route,
such
as
certain
types
of
shellfish
at
trophic
level
2,
the
field
study
should
also
include
appropriate
target
species
at
this
trophic
level.

C
The
trophic
level
of
the
fish
species
should
be
determined,
taking
into
account
the
life
stage(
s)
consumed
and
food
web
structure
at
the
location(
s)
of
interest.

C
Collection
of
bioaccumulation
field
data
at
a
specific
site
for
which
criteria
are
to
be
applied
and
with
the
species
of
concern
are
preferred.

C
If
data
cannot
be
collected
from
every
site
for
which
criteria
are
to
be
derived,
the
site
of
the
field
study
should
not
be
so
unique
that
the
BAF
cannot
be
extrapolated
to
other
locations
where
the
criteria
and
values
will
be
applied.

C
Samples
of
the
appropriate
resident
species
and
the
water
in
which
they
reside
should
be
collected
and
analyzed
using
appropriate,
sensitive,
accurate,
and
precise
methods
to
determine
the
concentrations
of
bioaccumulative
chemicals
present.

C
For
organic
chemicals,
the
percent
lipid
should
be
either
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
BAF
to
permit
the
measured
concentration
of
chemical
in
the
organism's
edible
tissues
to
be
lipid­
normalized.
176
C
The
concentration
of
the
chemical
in
the
water
should
be
measured
in
a
way
that
can
be
related
to
particulate
organic
carbon
(
POC)
and
dissolved
organic
carbon
(
DOC),
as
further
described
in
the
forthcoming
section
on
POC
and
DOC
concentrations
(
page
14).

C
For
organic
chemicals
with
log
K
ow
greater
than
four,
the
concentrations
of
POC
and
DOC
in
the
ambient
water
should
be
either
measured
or
reliably
estimated.

C
For
inorganic
chemicals
where
lipid
normalization
does
not
apply,
BAFs
should
be
used
only
if
they
are
expressed
on
a
wet
weight
basis;
BAFs
reported
on
a
dry
weight
basis
can
be
used
only
if
they
are
converted
to
a
wet
weight
basis
using
a
conversion
factor
that
is
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
BAF.

EPA
recommends
the
use
of
field­
measured
BAFs
as
the
first
preferred
method
for
determining
BAFs
because
they
incorporate
numerous
site­
specific
factors
that
can
affect
bioaccumulation
(
food
web
structure,
temporal
and
spatial
variation
in
contaminant
levels,
and
metabolism
of
the
contaminant).
However,
in
order
to
ensure
that
the
resulting
BAFs
accurately
reflect
contaminant
bioaccumulation
and
subsequent
exposure
to
the
target
human
population,
the
measurement
of
field­
based
BAFs
must
be
performed
carefully
and
should
consider
several
factors
that
can
lead
to
variability
and
uncertainty
in
BAF
estimates.
Several
of
these
factors
are
summarized
below.
Further
discussion
of
these
and
other
factors
is
provided
in
USEPA
(
1995a;
1995b).
EPA
is
developing
additional
guidance
on
performing
field
studies
for
determining
BAFs
and
will
provide
this
guidance
for
review
upon
its
completion.

Selection
of
Target
Species.
The
choice
of
the
target
species
for
contaminant
analysis
is
one
critical
aspect
in
determining
a
valid
and
representative
field­
measured
BAF
for
establishing
AWQC
designed
to
protect
human
health.
Selection
of
the
target
species
should
be
made
with
knowledge
of
the
key
exposure
route(
s)
involved
in
bioaccumulation
of
the
contaminant
of
interest
(
e.
g.,
uptake
from
water,
food,
sediment/
pore
water).
Several
important
factors
to
consider
when
selecting
target
species
for
contaminant
monitoring
have
been
summarized
by
EPA
in
their
document:
Fish
Sampling
and
Analysis:
A
Guidance
Document
for
Issuing
Fish
Consumption
Advisories
(
USEPA,
1993),
and
are
recommended
for
consideration
when
identifying
target
species
in
BAF
studies.
While
the
objectives
of
fish
consumption
advisory
studies
and
BAF
studies
are
not
entirely
identical,
many
of
the
principles
described
in
USEPA
(
1993)
also
apply
to
the
determination
of
BAFs
from
field
studies.

It
is
of
primary
importance
that
the
target
species
selected
be
among
those
species
commonly
consumed
in
the
study
area
and
those
of
commercial,
recreational
or
sustenance
fishing
value.
In
addition,
the
potential
for
bioaccumulation
of
the
contaminant(
s)
of
interest
should
be
considered.
Knowledge
of
the
food
web
structure,
likely
exposure
routes,
and
contaminant
properties
(
e.
g.,
K
ow
for
organics)
is
important
for
evaluating
a
species'
bioaccumulation
potential.
Species
occupying
177
trophic
level
three
(
e.
g.,
forage
fish)
or
four
(
e.
g.,
predator
fish)
are
recommended
for
selection
in
BAF
studies
because
they
have
consistently
been
among
the
highest
bioaccumulators
in
the
aquatic
food
web,
particularly
for
highly
hydrophobic
chemicals.
If
possible,
the
target
finfish
species
should
include
at
least
one
species
of
bottom
feeding
fish
species
(
trophic
level
three)
and
one
top
predator
species
(
trophic
level
four).
Including
species
with
different
dietary
preferences
will
help
account
for
the
effect
of
food
web
structure
on
bioaccumulation,
the
effect
of
which
can
vary
with
the
properties
of
the
chemical
(
i.
e.,
in
some
cases,
bottom
feeders
can
have
higher
BAFs
and
in
other
cases
lower
BAFs
compared
to
top
predator
(
piscivorous)
species).
Organisms
occupying
trophic
level
two
(
e.
g.,
clams,
oysters)
should
also
be
sampled
if
information
indicates
that
consumption
of
such
organisms
is
likely
to
be
an
important
exposure
route
to
contaminants.
In
addition,
the
geographic
distribution
of
the
species
should
be
considered
in
relation
to
the
target
human
population
intended
for
protection.
Further
information
pertaining
to
the
selection
of
target
aquatic
species
for
contaminant
analysis
for
fish
advisories
is
provided
in
USEPA
(
1993).

Choice
of
Sampling
Sites.
Selection
of
sampling
sites
and
the
frequency
at
which
they
are
sampled
should
take
into
account
numerous
factors,
many
of
which
relate
to
the
spatial
and
temporal
variability
in
the
contaminant
concentrations
in
the
target
aquatic
species
and
environmental
media.
If
the
proper
temporal
and
spatial
intervals
are
not
selected,
such
measurements
can
lead
to
erroneous
estimates
of
bioaccumulation.
The
sites
should
be
representative
of
those
from
which
the
target
human
population
are
expected
to
be
exposed.
In
addition,
the
sampling
sites
need
to
be
representative
of
the
area
of
movement
of
the
target
species.
This
is
particularly
important
for
migratory
species
which
may
only
spend
a
portion
of
the
time
in
the
study
area
of
interest.

Temporal
and
spatial
variability
can
be
particularly
high
for
water
concentrations
of
contaminants.
Thus,
individual
water
samples
taken
at
one
point
in
time
may
not
adequately
reflect
average
exposure
to
the
target
species.
Water
concentrations
should
be
averaged
over
the
approximate
time
it
takes
for
the
target
species
to
reach
steady
state,
which
varies
depending
on
the
toxicokinetics
of
the
contaminants
in
relation
to
the
target
organism.
For
example,
chemicals
with
high
K
ow
values
are
expected
to
reach
steady­
state
in
top
trophic
level
organisms
much
slower
than
chemicals
with
low
K
ow
values,
and
thus,
require
greater
temporal
averaging
of
water
column
concentrations
for
estimating
BAFs.
Other
factors
to
consider
when
determining
the
frequency
of
sampling
include
the
home
range
of
the
target
species,
its
life
history,
and
the
pattern
of
contaminant
release
(
episodic
vs.
continuous
releases).
Selection
of
sampling
sites
should
also
consider
temporal
and
spatial
variations
in
food
web
structure
that
may
occur
across
the
study
area.
The
desired
level
of
statistical
power
should
also
be
considered
when
determining
the
number
of
sampling
sites
and
replicates.

Biological
Considerations.
When
sampling
target
species
for
BAF
determinations
used
in
deriving
human
health
criteria,
several
biological
attributes
of
the
target
species
should
also
be
considered.
For
example,
the
size/
age
of
the
organism
can
affect
the
extent
of
bioaccumulation
in
the
organism.
Young
fish
can
exhibit
lower
accumulation
of
some
contaminants
due
to
growth
dilution.
In
addition,
the
reproductive
status
(
e.
g.,
pre/
post
spawning)
can
alter
the
body
burden
of
contaminants,
with
significant
contaminant
loss
observed
due
to
maturation
and
release
of
sperm
or
178
eggs.
Seasonal
variations
in
lipid
content
can
also
lead
to
differences
in
accumulation
of
contaminants.
In
general,
the
size
of
the
target
species
should
be
representative
of
the
size
being
consumed
by
the
target
human
population.
If
this
size
range
is
broad,
stratifying
sampling
strategies
by
size
class
is
necessary,
particularly
when
taking
composite
samples.
The
timing
of
sampling
should
include
the
period
of
most
frequent
harvesting
of
the
species.
Additional
discussion
of
these
and
other
attributes
to
consider
when
sampling
finfish
and
shellfish
for
contaminant
monitoring
is
provided
in
USEPA
(
1993).

Measurement
of
Other
Important
Parameters.
For
nonpolar
organic
chemicals,
lipid
content
of
the
target
species
should
be
measured
in
the
same
tissue
in
which
the
contaminant
was
measured
to
permit
lipid
normalization.
This
will
usually
be
fillet
for
finfish
and
edible
tissue
for
shellfish.
In
addition,
POC
and
DOC
should
be
measured
in
the
water
samples
in
order
to
estimate
the
freely
dissolved
fraction.
For
inorganic
chemicals,
the
bioavailability
of
various
forms
of
the
chemical
should
be
considered
when
deciding
upon
the
analyte
being
measured
for
the
BAF
determination.
Where
appropriate,
BAFs
should
be
expressed
for
specific
forms
of
the
contaminant.
For
example,
methylmercury
is
known
to
be
more
bioavailable
than
inorganic
forms
of
mercury,
and
the
relative
proportions
of
each
can
vary
significantly
over
space
and
time.
Thus,
BAFs
determined
for
total
mercury
without
knowledge
of
the
relative
proportion
of
various
organic
and
inorganic
forms
of
mercury
are
more
uncertain
in
their
applicability
to
other
sites
and
times
than
BAFs
measured
for
specific
forms
of
mercury.
Other
parameters
such
as
temperature,
pH,
dissolved
oxygen,
conductivity/
salinity,
total
suspended
sediments,
and
sediment
grain
size
should
also
be
measured,
as
they
may
alter
the
bioavailability
and
subsequent
bioaccumulation
of
contaminants
by
aquatic
organisms.
EPA
will
provide
additional
guidance
on
the
design
and
conduct
of
field
BAF
studies
in
its
forthcoming
guidance
document,
which
is
expected
to
undergo
external
review
in
the
fall
of
1998.

Freely
Dissolved
Fraction
of
the
Chemical
in
Water
(
ffd)

Nonpolar
organic
chemicals
can
exist
in
water
in
several
different
forms,
including
freely
dissolved
chemicals
in
the
water
column,
chemicals
bound
to
particulate
matter,
and
chemicals
bound
to
dissolved
organic
matter
in
the
water.
The
form
of
the
chemical
has
been
shown
to
affect
bioaccumulation,
with
the
freely
dissolved
form
of
a
chemical
considered
to
be
the
best
expression
of
the
bioavailable
form
to
aquatic
organisms.
Because
the
amount
of
chemical
that
is
freely
dissolved
may
differ
among
water
bodies
due
to
differences
in
the
total
organic
carbon
in
the
water,
BAFs
based
on
the
concentration
of
freely
dissolved
chemical
will
provide
the
most
universal
BAF
for
organic
chemicals
when
averaging
BAFs
from
different
studies.
However,
BAFs
based
on
the
total
concentration
of
the
chemical
in
water
(
i.
e.,
the
freely
dissolved
plus
that
sorbed
to
particulate
organic
carbon
and
dissolved
organic
carbon)
can
often
be
measured
more
accurately
than
BAFs
based
on
freely
dissolved
concentrations
in
water.
Therefore,
if
BAFs
based
on
total
water
concentrations
are
reported
in
a
BAF
study,
information
on
the
organic
carbon
content
of
water
(
at
the
site
from
which
the
BAF
was
measured,
if
available)
is
required
to
predict
freely
dissolved
concentrations
used
to
determine
the
baseline
BAF.
Specifically,
the
fraction
freely
dissolved
(
f
fd)
in
Equation
2.4.9
must
be
estimated,
using
information
on
the
chemical's
K
ow
and
both
dissolved
and
179
f
fd
'
1
[
1
%
(
POC
@
K
ow)
%
(
DOC
@
K
ow
10
)]

(
Equation
2.4.11)
particulate
organic
carbon
contents
(
DOC
and
POC)
of
the
water.
The
equation
used
to
estimate
f
fd
is:

where:

POC
=
Concentration
of
particulate
organic
carbon
(
kg/
L)
in
the
ambient
water
DOC
=
Concentration
of
dissolved
organic
carbon
(
kg/
L)
in
the
ambient
water
K
ow
=
N­
octanol
water
partition
coefficient
for
the
chemical
In
this
equation,
the
terms
"
K
ow"
and
"
K
ow/
10"
are
used
to
estimate
the
partition
coefficients
to
POC
and
DOC,
respectively,
which
have
units
of
L/
kg.
The
scientific
basis
supporting
the
derivation
of
this
equation
for
estimating
the
freely
dissolved
fraction
is
provided
in
Appendix
D.

POC
and
DOC
Concentrations.
As
noted
above,
when
converting
from
the
total
concentration
of
a
chemical
to
a
freely
dissolved
concentration,
the
POC
and
DOC
should
be
obtained
from
the
original
study
that
reports
BAFs
based
on
total
concentrations
of
a
chemical
in
water.
However,
if
the
POC
and
DOC
concentrations
are
not
reported
in
the
BAF
study,
then
reliable
estimates
of
POC
and
DOC
might
be
available
from
other
studies
of
the
same
site
used
in
the
BAF
study
or
closely
related
sites
within
the
same
water
body.
When
using
POC/
DOC
data
from
other
studies
of
the
same
water
body
for
the
same
or
very
similar
sites,
care
must
be
taken
to
ensure
that
environmental
conditions
that
may
affect
POC
or
DOC
concentrations
are
similar
to
those
in
the
BAF
study.
Information
on
the
spatial
and
temporal
variability
of
POC
and
DOC
at
the
site
of
interest
(
and
factors
influencing
this
variability)
should
be
used
in
evaluating
the
applicability
of
any
surrogate
POC
or
DOC
data
for
estimating
the
freely
dissolved
fraction.
For
example,
differences
in
hydrological
conditions
between
the
BAF
study
and
the
surrogate
study
(
e.
g.,
high
vs.
low
flow
events,
mixed
vs.
stratified
water
column,
tidal
cycle
differences)
and
the
degree
to
which
such
conditions
influence
POC
and
DOC
concentrations
should
be
evaluated
in
deciding
whether
surrogate
data
provide
reliable
estimates
of
POC
and
DOC
for
the
BAF
study.
Similarly,
differences
in
other
factors
which
may
influence
POC
and
DOC
concentrations,
such
as
the
sampling
season,
sampling
depth,
proximity
to
areas
of
high
DOC
inputs
including
wetlands,
should
be
evaluated
in
determining
the
reliability
of
surrogate
data.
Additional
factors
besides
the
examples
listed
here
may
also
be
important
in
determining
the
reliability
of
surrogate
POC
and
DOC
data
for
estimating
the
baseline
BAF.

Guidance
on
Selecting
Appropriate
Kow
Values
The
conversion
of
total
chemical
concentrations
in
water
to
freely
dissolved
chemical
concentrations,
as
well
as
other
procedures
discussed
in
this
chapter
(
including
the
BSAF
method
and
180
use
of
the
food
chain
model)
rely
on
the
K
ow
for
chemicals.
A
variety
of
techniques
are
available
to
estimate
or
predict
K
ow
values,
some
of
which
are
more
or
less
reliable
depending
on
the
K
ow
of
the
chemical.

As
discussed
in
USEPA
1998a,
EPA
is
proposing
and
taking
comment
on
two
options
on
how
to
select
a
reliable
K
ow
value.
The
first
option
is
EPA's
existing
guidance
published
in
the
Great
Lakes
Water
Quality
Initiative
(
60
FR
15366,
March
23,
1995).
A
second
option
is
more
detailed,
draft
guidance
on
selecting
K
ow
values
which
EPA
has
developed
and
is
currently
undergoing
external
scientific
peer
review.
The
salient
features
of
both
the
GLWQI
K
ow
selection
guidance
(
option
1)
and
EPA's
new,
draft
guidance
(
option
2)
are
presented
below.
Additional
details
of
the
new
draft
K
ow
selection
guidance
(
option
2)
are
provided
in
Appendix
F.

Guidance
on
selecting
reliable
values
of
K
ow
based
on
the
GLWQI
approach
(
option
1)
is
as
follows:

For
chemicals
with
log
K
ow
<
4:

Priority
Technique
1
Slow­
stir
Shake­
flask
Generator­
column
2
Measured
value
from
the
CLOGP
program
3
Reverse­
phase
liquid
chromatography
on
C
18
with
extrapolation
to
zero
percent
solvent
4
Reverse­
phase
liquid
chromatography
on
C
18
without
extrapolation
to
zero
percent
solvent
5
Calculated
by
the
CLOGP
program
For
chemicals
with
log
K
ow
>
4:

Priority
Technique
1
Slow­
stir
Generator­
column
2
Reverse­
phase
liquid
chromatography
on
C
18
with
extrapolation
to
zero
percent
solvent
181
3
Reverse­
phase
liquid
chromatography
on
C
18
without
extrapolation
to
zero
percent
solvent
4
Shake­
flask
5
Measured
value
from
the
CLOGP
program
6
Calculated
by
the
CLOGP
program
If
no
measured
K
ow
is
available,
the
K
ow
must
be
estimated
using
the
CLOGP
program.
Several
general
points
should
be
kept
in
mind
when
using
K
ow
values.
Values
should
be
used
only
if
they
were
obtained
from
the
original
authors
or
from
a
critical
review
that
supplied
sufficient
information.
If
more
than
one
"
best"
K
ow
value
is
available
for
a
chemical
(
i.
e.,
the
highest
priority
value
available),
the
arithmetic
mean
of
the
available
log
K
ow
s
or
the
geometric
mean
of
the
available
K
ow
s
may
be
used.
Because
of
potential
interference
due
to
radioactivity
associated
with
impurities,
values
determined
by
measuring
radioactivity
in
water
and/
or
octanol
should
be
considered
less
reliable
than
values
determined
by
a
K
ow
method
of
the
same
priority
that
employ
non­
radioactive
techniques
except
when
measurements
of
parent
chemical
are
done.
The
values
determined
using
radioactive
methods
should
be
moved
down
one
step
in
the
priority
below
the
values
determined
using
the
non­
radioactive
technique
except
when
measurements
of
parent
chemical
are
done.
Because
the
K
ow
is
an
intermediate
value
in
the
derivation
of
a
BAF,
the
value
used
for
the
K
ow
of
a
chemical
should
not
be
rounded
to
less
than
three
significant
digits.
K
ow
values
that
are
outliers
compared
with
other
values
for
a
chemical
should
not
be
used.

The
salient
features
of
EPA's
new
draft
methodology
(
option
2)
for
selecting
reliable
values
of
K
ow
is
described
below.

I.
Assemble/
evaluate
experimental
and
calculated
data
(
e.
g.,
CLOGP,
LOGKOW,
SPARC)

II.
If
calculated
log
K
ow's
>
8,
A.
Develop
independent
estimates
of
K
ow
using:
1.
Liquid
Chromatography
(
LC)
methods
with
"
appropriate"
standards.
(
See
Appendix
F
for
guidelines
for
LC
application).
2.
Structure
Activity
Relationship
(
SAR)
estimates
extrapolated
from
similar
chemicals
where
"
high
quality"
measurements
are
available.
"
High
quality"
SARs
are
defined
in
Appendix
F
of
the
TSD.
3.
Property
Reactivity
Correlation
(
PRC)
estimates
based
on
other
measured
properties
(
solubility,
etc.).
B.
If
calculated
data
are
in
reasonable
agreement
and
are
supported
by
independent
estimates
described
above,
report
the
average
calculated
value.
Guidance
on
determining
whether
K
ow
values
are
in
"
reasonable
agreement"
are
presented
in
Appendix
F
of
the
TSD.
182
C.
If
calculated/
estimated
data
do
not
agree,
use
professional
judgement
to
evaluate/
blend/
weight
the
calculated
and
estimated
data
to
assign
a
K
ow
value.
D.
Document
rationale
including
relevant
statistics.

III.
If
calculated
log
K
ow's
range
from
6
­
8,
A.
Look
for
"
high
quality"
measurements.
These
will
generally
be
slow
stir
measurements,
the
exception
being
certain
classes
of
compounds
where
micro
emulsions
tend
to
be
less
of
a
problem
(
i.
e.,
PNA's,
shake
flask
measurements
are
good
to
log
K
ow
of
6.5).
B.
If
measured
data
are
available
and
are
in
reasonable
agreement
(
both
measurements
and
calculations),
report
average
measured
value.
C.
If
measured
data
are
in
reasonable
agreement,
but
differ
from
calculated
values,
develop
independent
estimates
and
apply
professional
judgement
to
evaluate/
blend/
weight
the
measured,
calculated
and
estimated
data
to
assign
K
ow
value.
D.
If
measured
data
are
not
in
reasonable
agreement
(
or
if
only
one
measurement
is
available),
use
II
A,
B,
and
C
to
produce
a
`
best
estimate;'
use
this
value
to
evaluate/
screen
the
measured
K
ow
data.
Report
the
average
value
of
screened
data.
If
no
measurements
reasonably
agree
with
`
best
estimate,'
apply
professional
judgement
to
evaluate/
blend/
weight
the
measured,
calculated
and
estimated
data
to
assign
K
ow.
E.
If
measured
data
are
unavailable,
proceed
through
II
A,
B,
C
and
report
the
`
best
estimate.'
F.
Document
rationale
including
relevant
statistics.

IV.
If
calculated
log
K
ow's
<
6,
A.
Proceed
as
in
III.
Slow
stir
is
the
preferred
method
but
shake
flask
data
can
be
considered
for
all
chemicals
if
sufficient
attention
has
been
given
to
emulsion
problems
in
the
measurement.

The
general
operational
guidelines
for
EPA's
new
draft
methodology
for
selecting
K
ow
values
are
as
follows:

1.
For
chemicals
with
log
K
ow
>
5,
it
is
highly
unlikely
to
find
multiple
"
high
quality"
measurements.
(
Note:
"
high
quality"
is
data
judged
to
be
reliable
based
on
the
guidelines
presented
in
Appendix
F
of
the
TSD).

2.
"
High
Quality"
measured
data
are
preferred
over
estimates,
but
due
to
the
scarcity
of
`
high
quality'
data,
the
use
of
estimates
is
important
in
assigning
K
ow
'
s.

3.
K
ow
measurements
by
slow
stir
are
extendable
to
108.
Shake
flask
K
ow
measurements
are
extendable
to
106
with
sufficient
attention
to
micro
emulsion
effects;
for
classes
183
of
chemicals
that
are
not
highly
sensitive
to
emulsion
effects
(
i.
e.,
PNA's)
this
range
may
extend
to
106.5.

4.
What
is
to
be
considered
reasonable
agreement
in
log
K
ow
data
(
measured
or
estimated)
depends
primarily
on
the
log
K
ow
magnitude.
The
following
standards
for
data
agreement
have
been
set
for
this
guidance:
0.5
for
log
K
ow
>
7;
0.4
for
6
#
log
K
ow
#
7
;
0.3
for
log
K
ow
<
6.

5.
Statistical
methods
should
be
applied
to
data
as
appropriate
but
application
is
limited
due
to
the
scarcity
of
data,
and
the
determinate/
methodic
nature
of
most
measurement
error(
s).

The
various
techniques
are
summarized
as
follows:

C
The
slow­
stir
method
requires
adding
the
test
chemical
to
a
reaction
flask
which
contains
a
water
and
octanol
phase.
The
chemical
partitions
to
these
two
phases
under
conditions
of
slow
stirring
the
flask.
After
the
phases
are
allowed
to
separate,
the
concentration
of
the
test
chemical
in
each
phase
is
determined
(
Brooke
et
al.,
1986).
This
method
is
easy
to
use
and
can
be
replicated
with
a
high
degree
of
confidence.
Emulsions,
which
can
contaminate
the
aqueous
phase
and
influence
the
observed
K
ow
values,
can
be
prevented,
and
high
K
ow
values
can
be
obtained
easily
(
de
Bruijn
et
al.,
1989).
In
general,
there
is
reasonable
agreement
between
the
slow
stir
method
and
literature
data
obtained
using
the
generator­
column
method.
For
log
K
ow
values
less
than
4.5,
data
agree
well
with
K
ow
s
determined
based
on
the
shake­
flask
method
(
de
Bruijn
et
al.,
1989).

C
The
shake­
flask
method
also
involves
adding
the
chemical
to
a
reaction
flask
with
a
mixture
of
octanol
and
water.
In
this
method,
however,
the
flask
is
shaken
to
obtain
partitioning
of
the
chemical
between
the
octanol
and
water
phases
(
OECD,
1981).
Several
researchers
have
found
that
the
shake­
flask
technique
is
acceptable
only
for
chemicals
with
log
K
ow
s
less
than
certain
values.
Some
researchers
found
that
the
shake­
flask
technique
has
been
reported
to
be
acceptable
only
for
chemicals
which
have
log
K
ow
s
less
than
4
(
Karickhoff
et
al.,
1979;
Konemann
et
al.,
1979;
Braumann
and
Grimme,
1981;
Harnisch
et
al.,
1983;
Brooke
et
al.,
1990).
Others
have
found
that
the
technique
is
acceptable
for
chemicals
with
slightly
higher
log
K
ow
values.
Brooke
et
al.
(
1986)
compared
techniques
and
decided
that
the
shake­
flask
technique
is
acceptable
for
chemicals
with
log
K
ow
s
up
to
5,
whereas
Chessells
et
al.
(
1991)
stated
that
this
technique
is
acceptable
for
log
K
ow
values
up
to
about
5.5.

C
The
generator­
column
method
involves
filling
a
column
with
an
inert
material
(
silanized
Chromosorb
W
or
glass
beads)
that
is
coated
with
water­
saturated
octanol
and
contains
the
test
chemical.
Pumping
water
through
the
column
results
in
an
aqueous
solution
in
equilibrium
with
the
octanol
phase.
The
water
that
leaves
the
184
Composite
K
ow
'
1
DOC
10
%
POC
j
n
i
'
1
C
t
w
j
n
i
'
1
C
fd
w
&
1
column
is
extracted
with
specifically
either
an
organic
solvent
or
a
C
18
column
that
is
then
eluted
with
hexane
or
methanol
(
DeVoe
et
al.,
1981;
Woodburn
et
al.,
1984;
Miller
et
al.,
1984).

C
The
reverse­
phase
liquid
chromatography
method
involves
adding
the
test
chemical
in
a
polar
mobile
phase
(
such
as
water
or
water­
methanol)
to
a
hydrophobic
porous
stationary
phase
(
the
C
18
n­
alkanes
covalently
bound
to
a
silica
support).
The
chemical
partitions
between
the
column
and
the
polar
aqueous
phase.
K
ow
values
are
estimated
from
linear
equations
between
the
K
ow
and
retention
indices
that
are
derived
for
reference
chemicals
(
Konemann
et
al.,
1979;
Veith
et
al.,
1979;
McDuffie,
1981;
Garst
and
Wilson,
1984).

C
The
CLOGP
program
is
a
computer
program
that
contains
measured
K
ow
values
for
some
chemicals
and
can
calculate
K
ow
values
for
additional
chemicals
based
on
similarities
in
chemical
structure
between
chemicals
with
measured
K
ow
values
and
chemicals
for
which
K
ow
s
are
to
be
determined.
The
method
used
to
calculate
the
K
ow
values
is
described
in
Hansch
and
Leo
(
1979).

C
SPARC
(
SPARC
Performs
Automated
Reasoning
in
Chemistry)
is
a
mechanistic
model
developed
at
the
Ecosystems
Research
Division
of
the
National
Exposure
Research
Laboratory
of
the
Office
of
Research
and
Development
of
the
U.
S.
Environmental
Protection
Agency
by
Sam
Karickhoff,
Lionel
Carreira,
and
coworkers

In
some
situations,
available
data
may
require
determination
of
a
single
K
ow
value
for
a
class
of
chemicals
or
a
or
mixture
of
closely
related
chemicals
(
e.
g.,
when
toxicity
data
are
class­
or
mixture­
specific).
However,
it
is
not
possible
to
determine
experimentally
a
valid
K
ow
for
a
substance
that
is
a
mixture
of
chemicals
(
e.
g.,
PCBs,
toxaphene,
chlordane).
For
calculating
the
composite
freely
dissolved
fraction
used
to
adjust
a
composite
field­
measured
BAF
to
a
composite
baseline
BAF,
a
composite
K
ow
value
of
the
mixture
can
be
calculated
based
on
the
sum
of
the
total
concentration
of
the
mixture
components
in
water
(
e.
g.,
individual
congeners
for
PCBs),
the
sum
of
the
dissolved
mixture
components
in
water,
and
the
DOC
and
POC
from
the
site
for
which
the
BAF
was
measured.
The
equation
used
to
derive
the
composite
K
ow
for
use
in
determining
a
composite
baseline
BAF
is:

where:
185
i
=
1,
2,
...
n
individual
mixture
components
(
e.
g.,
congeners
for
PCBs).
C
w
t
=
total
concentration
of
the
mixture
component
in
water.

C
w
f
d
=
freely
dissolved
concentration
of
the
mixture
component
in
water.

Notably,
calculation
of
a
composite
K
ow
is
just
one
of
a
series
of
steps
involved
in
deriving
composite
baseline
BAFs
and
AWQC
BAFs
for
chemical
mixtures
for
which
single
toxicity
values
apply.
These
steps
include:
(
1)
first
determining
a
composite
initial
total
BAF
(
analogous
to
the
total
field­
measured
BAF
for
individual
chemicals),
(
2)
determining
a
composite
baseline
BAF,
using
the
composite
freely
dissolved
fraction
and
composite
K
ow
derived
using
the
equation
above,
(
3)
calculating
a
composite
AWQC
BAF
from
the
composite
baseline
BAF
based
on
the
composite
freely
dissolved
fraction
at
the
AWQC
site(
s).
This
last
step
requires
calculation
of
a
second
composite
K
ow
which
is
used
to
determine
the
composite
freely
dissolved
fraction
at
the
AWQC
site(
s)
using
POC
and
DOC
data
from
the
AWQC
site(
s).
Additional
details
of
the
steps
required
in
determining
a
composite
K
ow
for
deriving
composite
baseline
BAFs
and
composite
AWQC
BAFs
(
including
example
calculations
for
PCBs)
are
provided
in
62
FR
117250
(
March
12,
1997).

Lipid
Normalization
of
Data
Partitioning
of
organic
chemicals
into
aquatic
organisms
has
been
shown
to
be
a
function
of
the
lipid
content
of
the
organism
(
Mackay,
1982;
Connolly
and
Pederson,
1988;
Thomann,
1989).
For
this
reason,
EPA
assumes
that
BAFs
and
BCFs
for
lipophilic
organic
chemicals
are
directly
proportional
to
the
percent
lipid
in
the
tissue
or
whole
body
of
the
organism
of
interest.
For
example,
an
organism
with
two
percent
lipid
content
would
accumulate
twice
the
amount
of
a
chemical
as
an
organism
with
one
percent
lipid
content,
all
else
being
equal.
This
assumption
has
been
extensively
evaluated
in
the
literature
and
is
generally
accepted.
To
account
for
the
influence
of
the
lipid
content
on
the
BAF
or
BCF,
EPA
recommends
normalizing
the
BAF
or
BCF
to
the
percent
lipid
in
the
fish.
This
procedure
is
consistent
with
other
EPA
guidance
on
bioaccumulation
(
Stephan
et
al.,
1985;
USEPA,
1991).

To
compare
BAFs
and
BCFs
that
have
been
measured
in
fish
that
have
different
lipid
contents,
EPA
recommends
that
BAFs
and
BCFs
be
normalized
by
dividing
by
the
mean
lipid
fraction
of
the
aquatic
organism.
Whole
body
and
edible
tissue
BAFs
and
BCFs
are
normalized
using
the
respective
whole
body
and
edible
tissue
fraction
lipid
values.
Since
lipid
content
is
known
to
vary
from
one
tissue
to
another
and
from
one
aquatic
species
to
another,
EPA
recommends
the
percent
lipid
used
to
normalize
the
BAF
or
BCF
(
whole
body
or
edible
tissue)
be
obtained
from
the
BAF
or
BCF
study.
Unless
comparability
can
be
determined
across
organisms,
the
fraction
lipid
should
be
determined
for
the
test
organism.
Lipid
content
of
the
fish
tissue
is
affected
by
the
age,
sex
and
diet
of
the
fish,
by
the
season
the
fish
are
sampled,
and
by
differing
environmental
conditions.
Therefore,
it
is
generally
necessary
to
determine
an
average
percent
lipid
value
for
the
test
organisms.
186
EPA
recommends
using
a
gravimetric
method
for
determining
the
percent
lipid
value
(
USEPA,
1995a).
The
method
is
easy
to
use
and
is
employed
by
many
laboratories.
It
should
be
noted
that
the
solvent
used
to
determine
lipid
content
has
been
shown
to
affect
the
percent
lipid
values
measured
in
some
studies
because
different
solvent
systems
extract
differing
fractions
of
total
lipids
(
Lapin
and
Chernova,
1969;
Randall
et
al.,
1991;
and
Cabrini
et
al.,
1992).
These
authors
note
that
percent
lipid
values
can
vary
by
as
much
as
a
factor
of
four
depending
on
the
solvent
system
used.
To
ensure
consistency
among
States,
EPA
recommends
for
lipid
analyses
that
the
method
of
Bligh
and
Dyer
(
1959)
which
uses
chloroform/
methanol
as
an
extraction
solvent
or
the
lower
toxicity
solvent
modification
of
this
the
method
by
Hara
and
Radin
(
1978)
which
uses
hexane/
isopropanol
as
an
extraction
solvent.
Other
extraction
solvents
for
lipid
analyses,
e.
g.,
hexane/
acetone
and
dichloromethane,
might
provide
equivalent
results
when
used
with
appropriate
sample
sizes
and
extraction
times
(
Honeycutt
et
al.,
1995,
de
Boer,
J.,
1988).

In
addition
to
the
effect
of
the
solvent
on
lipid
analysis,
additional
factors
may
affect
variability
of
results
if
they
are
not
adequately
controlled
(
USEPA,
1995a).
Use
of
alcohol
as
a
solvent
may
overestimate
total
lipids
because
non­
lipid
material
may
also
be
extracted.
Several
factors,
including
solvent
contaminants,
lipid
decomposition
from
exposure
to
oxygen
or
light,
and
lipid
degradation
from
changes
in
pH
during
cleanup
can
lead
to
underestimation
of
total
lipids.
Finally,
high
temperature
may
decompose
lipid
material.
Laboratories
should
consider
these
sources
of
error
when
conducting
and
evaluating
results
of
lipid
analyses
(
USEPA,
1995a).

2.4.4.2
Baseline
BAF
Derived
from
Biota­
Sediment
Accumulation
Factors
(
BSAFs)

When
acceptable
field­
measured
values
of
the
BAF
are
not
available
for
a
nonpolar
organic
chemical,
EPA
recommends
the
use
of
a
BSAF
to
predict
the
BAF
as
the
second
procedure
in
the
BAF
data
preference
hierarchy.
Although
BSAFs
may
be
used
for
measuring
and
predicting
bioaccumulation
directly
from
concentrations
of
chemicals
in
surface
sediment,
they
also
can
be
useful
in
estimating
a
BAF
as
noted
by
Cook
et
al.
(
1993).
Because
BSAFs
are
based
on
field
data
and
incorporate
effects
of
metabolism,
biomagnification,
growth,
and
other
factors,
BAFs
estimated
from
BSAFs
will
incorporate
the
net
effect
of
all
these
factors.
The
BSAF
approach
is
particularly
beneficial
for
developing
water
quality
criteria
for
chemicals
such
as
polychlorinated
dibenzo­
pdioxins
dibenzofurans,
and
certain
biphenyl
congeners.
These
chemicals
are
detectable
in
fish
tissues
and
sediments
but
are
difficult
to
measure
in
the
water
column
and
are
subject
to
metabolism.

Predicting
BAFs
from
BSAFs
requires
several
steps.
First,
BSAFs
must
be
measured
for
the
chemical
of
interest
and
for
one
or
more
reference
chemicals
for
which
measured
BAFs
are
also
available.
Second,
the
relationship
between
the
BSAFs
for
the
chemical
of
interest
and
the
reference
chemical
and
the
relationship
between
the
chemicals'
K
ow
values
must
be
determined.
Finally,
information
on
the
BSAF
and
K
ow
relationships
and
the
BAF
of
the
reference
chemical(
s)
should
be
used
to
determine
the
BAF
for
the
chemical
of
interest.
The
following
sections
describe
the
methodology
for
determining
BAFs
from
BSAFs,
the
data
requirements,
and
the
application
and
validation
of
this
procedure
for
estimating
BAFs
using
data
from
Lake
Ontario.
187
BSAF
'
C
R
C
soc
(
Equation
2.4.12)

C
R
'
C
t
f
R
(
Equation
2.4.13)

C
soc
'
C
sed
f
oc
(
Equation
2.4.14)
Determination
of
BSAF
Values
As
shown
in
the
following
equation,
the
BSAF
is
determined
by
relating
lipid­
normalized
concentrations
of
chemicals
in
an
organism
to
organic
carbon­
normalized
concentrations
of
the
chemicals
in
surface
sediment
samples
associated
with
the
average
exposure
environment
of
the
organism.

where:

C
R
=
Lipid­
normalized
concentration
of
the
chemical
in
tissues
of
biota
(
F
g/
g
lipid)

C
soc
=
Organic
carbon­
normalized
concentration
of
the
chemical
in
the
surface
sediment
(
F
g/
g
sediment
organic
carbon)

The
lipid­
normalized
concentration
of
a
chemical
in
an
organism
(
C
R
)
is
determined
by:

where:

C
t
=
Concentration
of
the
chemical
in
the
wet
tissue
(
either
whole
organism
or
specified
tissue)
(
F
g/
g)

f
R
=
Fraction
lipid
content
in
the
organism
The
organic
carbon­
normalized
concentration
of
a
chemical
in
sediment
(
C
soc)
is
determined
by:
23Because
K
R
and
K
soc
are
of
similar
magnitude
and
vary
in
proportion
to
one
another,
the
BSAF
at
equilibrium
is
expected
to
be
at
or
near
unity.

188
BSAF
.
C
fd
b
C
K
R
C
fd
s
C
K
soc
'
D
bs
C
K
R
K
soc
.
D
bs
C
2
(
Equation
2.4.15)
where:

C
sed
=
Concentration
of
chemical
in
sediment
(
F
g/
g
sediment)

f
oc
=
Fraction
organic
carbon
in
sediment
BSAFs
are
most
useful
when
measured
under
steady
state
or
near
steady­
state
conditions
in
which
chemical
concentrations
in
water
are
linked
to
slowly
changing
concentrations
in
sediment.
However,
because
BSAFs
are
rarely
measured
for
ecosystems
which
are
at
equilibrium,
the
BSAF
inherently
includes
a
measure
of
the
"
disequilibrium"
of
the
ecosystem.
This
disequilibrium
can
be
assessed
for
chemicals
with
log
K
ow
>
3
with
the
following
relationship:

where:

C
b
f
d
=
Concentration
of
freely
dissolved
chemical
(
associated
with
water)
in
the
tissues
of
biota
(
F
g/
g
wet
tissue)

C
s
f
d
=
Concentration
of
freely
dissolved
chemical
(
associated
with
pore
water)
in
the
sediment
(
F
g/
g
sediment
organic
carbon)

K
R
=
Lipid­
water
equilibrium
partition
coefficient
(
C
R
/
C
b
f
d)

K
soc
=
Sediment
organic
carbon­
water
equilibrium
partition
coefficient
(
C
soc/
C
s
f
d)

D
bs
=
Disequilibrium
(
fugacity)
ratio
between
biota
and
sediment
(
C
b
f
d/
C
s
f
d)

Measured
BSAFs
may
range
widely
for
different
chemicals
depending
on
K
R
,
K
soc,
and
the
actual
ratio
of
C
b
f
d
to
C
s
f
d.
However,
at
equilibrium,
the
ratio
between
the
freely
dissolved
chemical
in
the
tissue
water
to
sediment
pore
water
(
D
bs)
is
one.
Thus,
the
BSAF
under
equilibrium
conditions
is
equal
to
the
ratio
K
R
/
K
soc
(
which
is
thought
to
range
from
1­
4)
23.
When
chemical
equilibrium
between
sediment
and
biota
does
not
exist,
the
BSAF
will
equal
the
disequilibrium
(
fugacity)
ratio
between
biota
and
sediment
(
D
bs
=
C
b
f
d/
C
s
f
d)
times
the
ratio
of
the
equilibrium
partition
coefficients
(
approximately
2).
189
(
Baseline
BAF
fd
R
)
i
'
(
Baseline
BAF
fd
R
)
r
@
(
BSAF)
i
@
(
K
ow)
i
(
BSAF)
r
@
(
K
ow)
r
(
Equation
2.4.16)
The
deviation
of
D
bs
from
the
equilibrium
value
of
1.0
is
determined
by
the
net
effect
of
all
factors
which
contribute
to
the
disequilibrium
between
sediment
and
aquatic
organisms.
A
disequilibrium
ratio
(
D
bs)
greater
than
one
can
occur
due
to
biomagnification
or
when
surface
sediment
has
not
reached
steady­
state
with
water.
A
disequilibrium
ratio
(
D
bs)
less
than
one
can
occur
as
a
result
of
kinetic
limitations
for
chemical
transfer
from
sediment
to
water,
water
to
food
chain,
and
biological
processes
(
such
as
growth
or
biotransformation
of
the
chemical
in
the
animal
and
its
food
chain).
BSAFs
are
most
useful
when
measured
under
steady­
state
conditions.
BSAFs
measured
for
systems
with
new
chemical
loadings
or
rapid
increases
in
loadings
may
be
unreliable
due
to
underestimation
of
steady­
state
C
soc
s.

Relationship
of
BAFs
to
BSAFs
Differences
between
BSAFs
for
different
organic
chemicals
are
good
measures
of
the
relative
bioaccumulation
potentials
of
the
chemicals.
When
calculated
from
a
common
organism­
sediment
sample
set,
chemical­
specific
differences
in
BSAFs
primarily
reflect
the
net
effect
of
biomagnification,
metabolism,
bioenergetics,
and
bioavailability
factors
on
each
chemical's
disequilibrium
ratio
between
biota
and
sediment.
Thus,
the
relationship
between
the
BSAF
for
the
test
chemical
i
and
the
BSAF
for
the
reference
chemical
r
can
be
used
with
additional
information
(
K
ow
values
for
all
chemicals
and
the
BAF
for
the
reference
chemicals)
to
predict
a
BAF
for
chemical
i.
This
approach
is
consistent
with
previously
proposed
guidance,
in
which
ratios
of
BSAFs
for
PCDDs
and
PCDFs
to
TCDD
were
proposed
for
evaluation
of
TCDD
toxic
equivalency
associated
with
complex
mixtures
of
the
dioxin
and
furan
congeners
(
see
60
FR
15366
for
discussion
of
bioequivalency
factors).

The
calculation
of
the
BAF
from
the
BSAF
is
as
follows:

where:

(
Baseline
BAF
R
f
d)
i
=
BAF
expressed
on
a
freely­
dissolved
and
lipid­
normalized
basis
for
chemical
of
interest
"
i"

(
Baseline
BAF
R
f
d)
r
=
BAF
expressed
on
a
freely­
dissolved
and
lipid­
normalized
basis
for
reference
chemical
"
r"

(
BSAF)
i
=
BSAF
for
chemical
"
i"

(
BSAF)
r
=
BSAF
for
the
reference
chemical
"
r"

(
K
ow)
i
=
octanol­
water
partition
coefficient
for
chemical
"
i"
190
(
K
ow)
r
=
octanol­
water
partition
coefficient
for
the
reference
chemical
"
r"

Appendix
E
presents
the
derivation
of
this
equation
using
the
general
BAF
equation
relating
concentration
in
tissue
to
concentration
in
water,
a
relationship
between
concentrations
in
sediment
organic
carbon
and
water,
and
assumptions
about
equilibrium
between
water
and
sediment.

Note
that
BAF
R
f
ds
calculated
from
BSAFs
will
incorporate
any
errors
associated
with
measurement
of
the
BAF
R
f
d
for
the
reference
chemical
and
the
K
ow
s
for
both
the
reference
and
unknown
chemicals.
Such
errors
can
be
minimized
by
comparing
results
from
several
reference
chemicals
and
assuring
consistent
use
of
freely
dissolved
water
concentration
(
C
w
f
d)
values
which
are
adjusted
for
dissolved
organic
carbon
binding
effects
on
the
fraction
of
each
chemical
that
is
freely
dissolved
(
f
fd)
in
unfiltered,
filtered,
or
centrifuged
water
samples.
Other
errors
may
be
introduced
by
using
values
based
on
non­
steady
state
external
loading
rates
or
chemicals
with
strongly
reduced
C
w
f
d
due
to
rapid
volatilization
from
water.
When
selecting
K
ow
values
for
use
in
estimating
BAFs
from
BSAFs,
consideration
should
be
given
to
the
similarity
of
K
ow
measurement
techniques
between
the
reference
and
target
chemicals,
in
addition
to
the
guidance
previously
described
for
selecting
representative
K
ow
values.

The
trophic
level
to
which
the
baseline
BAF
applies
is
the
same
as
the
trophic
level
of
the
organisms
used
in
the
determination
of
the
BSAF.
For
each
trophic
level,
a
species
mean
baseline
BAF
is
calculated
as
the
geometric
mean
if
more
than
one
acceptable
baseline
BAF
is
predicted
from
BSAFs
for
a
given
species.
For
each
trophic
level,
a
trophic
level­
specific
BAF
is
calculated
as
the
geometric
mean
of
the
acceptable
species
mean
baseline
BAFs
derived
using
BSAFs.

Procedural
and
Quality
Assurance
Requirements
EPA
recommends
certain
requirements
for
measuring
the
data
needed
for
this
procedure.
These
requirements,
described
below,
apply
to
BAF
values
for
the
reference
chemicals,
the
measured
BSAFs
for
all
chemicals,
and
the
K
ow
values
used
in
this
procedure.

The
data
requirements
for
measuring
BAF
values
that
were
noted
in
Section
2.4.4.1
(
Field­
Measured
BAFs)
are
also
applicable
to
the
measurement
of
BAF
R
f
d
values
to
assure
reliable
values
for
the
reference
chemicals.
Data
on
several
reference
chemicals
should
be
obtained
for
use
in
the
analysis
to
ensure
that
predictions
are
more
robust
than
those
that
would
be
obtained
using
only
one
reference
chemical.
The
water
sample
analyses
should
approximate
the
average
exposure
of
the
organism
and
its
food
chain
over
a
time
period
that
is
most
appropriate
for
the
chemical,
organism,
and
ecosystem.
It
is
preferable
to
choose
at
least
some
reference
chemicals
that
have
similar
log
K
ow
s
and
chemical
class
characteristics
as
the
test
chemicals
for
which
the
BAF
is
to
be
determined.
In
addition,
for
consistency
among
reference
chemicals,
each
freely
dissolved
water
concentration
used
to
calculate
a
BAF
R
f
d
should
be
based
on
a
consistent
adjustment
of
the
concentration
of
total
chemical
in
water
for
DOC
and
POC
using
the
relationship
described
in
the
section
titled
"
Freely
Dissolved
Fraction
of
Chemical
in
Water."
191
For
measured
BSAFs,
chemical
concentrations
in
surface
sediment
and
in
biota
and
data
on
the
percent
organic
carbon
in
surface
sediment
samples
are
needed.
The
following
procedural
and
quality
assurance
requirements
should
be
met
for
determining
the
field­
measured
BSAFs:

C
The
field
studies
used
should
be
limited
to
those
conducted
with
fish
at
or
near
the
top
of
the
aquatic
food
chain
(
i.
e.,
in
trophic
levels
3
and/
or
4).
In
situations
where
consumption
of
lower
trophic
level
organisms
represents
an
important
exposure
route,
such
as
certain
types
of
shellfish
at
trophic
level
2,
the
field
study
should
also
include
appropriate
target
species
at
this
trophic
level.

C
Samples
of
surface
sediments
(
0­
1
cm
is
ideal)
should
be
from
locations
in
which
sediment
is
regularly
deposited
and
is
representative
of
average
surface
sediment
in
the
vicinity
of
the
organism.

C
The
K
ow
s
used
should
be
of
acceptable
quality
as
described
in
Section
2.4.4.1
above.

C
The
site
of
the
field
study
should
not
be
so
unique
that
the
resulting
BAF
cannot
be
extrapolated
to
other
locations
where
the
criteria
and
values
will
be
applied.

C
The
percent
lipid
should
be
either
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
BAF.

Application
of
BSAF
Procedure
for
Predicting
Lake
Ontario
and
Green
Bay
BAF
R
f
ds
To
demonstrate
the
use
of
the
BSAF
procedure
to
predict
BAFs,
EPA
has
calculated
BAF
R
f
ds
from
BSAFs
using
two
independent
data
sets
from
Lake
Ontario
and
one
from
Green
Bay.
These
data
sets
come
from
Oliver
and
Niimi
(
1988),
the
EPA
Lake
Ontario
TCDD
Bioaccumulation
Study
(
USEPA,
1990),
and
the
EPA
Green
Bay/
Fox
River
Mass
Balance
Study.
The
first
data
set
(
Oliver
and
Niimi,
1988)
has
been
used
extensively
for
construction
of
food
chain
models
of
bioaccumulation
and
calculation
of
food
chain
multipliers,
biomagnification
factors
and
BAF
R
f
ds
from
chemical
concentrations
determined
in
organisms
and
water.
Oliver
and
Niimi
(
1988)
also
collected
surface
sediment
data
which
allows
calculation
of
lakewide
average
BSAFs.
These
data
were
collected
from
1981
to
1984
for
PCB
congeners
and
other
chlorinated
organics.

The
second
data
set
(
from
the
TCDD
Bioaccumulation
Study)
includes
extensive
samples
of
fish
and
sediment
collected
in
1987
from
Lake
Ontario.
Samples
from
this
study
were
later
analyzed
for
PCDD,
PCDF,
PCB
congeners,
and
some
organochlorine
pesticides
at
EPA.
Although
data
from
the
TCDD
Bioaccumulation
Study
have
not
been
published,
they
are
useful
to
show
a
comparison
with
BAF
R
f
ds
calculated
from
Oliver
and
Niimi
samples
and
to
provide
BAF
R
f
ds
for
additional
organic
chemicals
not
measured
by
Oliver
and
Niimi
(
1988).
192
Four
reference
chemicals
(
the
PCB
congeners
52,
105
and
118
and
DDT)
were
used
for
evaluating
chemicals
from
both
Oliver
and
Niimi
(
1988)
and
the
TCDD
Bioaccumulation
Study
in
order
to
examine
the
variability
introduced
by
the
choice
of
reference
chemical.

The
third
study,
the
Green
Bay/
Fox
River
Mass
Balance
Study,
involved
extensive
sampling
of
water,
sediment,
and
fish
in
Green
Bay
in
1989.
Brown
trout
BAF
R
f
ds
were
calculated
from
PCB
BSAFs
measured
in
the
mid­
bay
region
using
PCB
congeners
52
and
118
as
reference
chemicals.
The
reference
chemical
BAF
R
f
ds
were
determined
using
water
and
brown
trout
data
from
the
same
region.

Tables
2.4.1a
and
2.4.1b
present
the
predicted
BAF
R
f
ds
from
all
three
data
sets
as
well
as
measured
BAF
R
fds
from
Oliver
and
Niimi
(
1988)
and
the
TCDD
Bioaccumulation
Study.
The
geometric
means
of
the
BAF
R
f
d
predicted
using
the
Lake
Ontario
data
(
Oliver
and
Niimi,
1988;
TCDD
Bioaccumulation
Study)
are
reported
in
Table
2.4.2.

There
are
several
assumptions
and
additional
data
used
for
these
evaluations.
First,
the
water
analyses
of
Oliver
and
Niimi
(
1988)
were
adjusted
for
an
estimated
2
mg/
L
residual
dissolved
organic
carbon
concentration
in
the
centrifuged
water
(
assuming
no
residual
particulate
organic
carbon
after
centrifuging)
and
an
estimated
K
doc
=
K
ow/
10
in
order
to
calculate
a
freely
dissolved
water
concentration
from
f
fd
(
see
Section
2.4.4.1
on
total
vs.
freely
dissolved
concentrations
and
Appendix
D
for
calculation
of
water
concentrations).
Concentrations
of
freely
dissolved
PCBs
from
Green
Bay
were
also
calculated
on
the
basis
of
dissolved
organic
carbon
in
the
water
samples
and
an
assumed
K
doc
=
K
ow/
10.
Log
K
ow
values
were
taken
from
a
variety
of
sources.
Log
K
ow
s
for
PCBs
are
those
reported
by
Hawker
and
Connell
(
1988).
Log
K
ow
s
for
PCDDs
and
PCDFs
are
those
estimated
by
Burkhard
and
Kuehl
(
1986)
except
for
the
penta­,
hexa­,
and
hepta­
chlorinated
dibenzofurans
which
were
estimated
on
the
basis
of
assumed
similarity
to
the
trends
reported
for
the
PCDDs
by
Burkhard
and
Kuehl
(
1986).

Evaluation
of
BAF
R
f
ds
Calculated
from
Lake
Ontario
and
Green
Bay
BSAFs
The
validity
of
the
BSAF
method
for
predicting
BAFs
is
evaluated
in
this
section
using
several
approaches:
(
1)
correlating
measured
vs.
predicted
log
BAF
R
f
ds
from
the
same
lake
and
same
study
(
i.
e.,
Lake
Ontario,
Oliver
and
Niimi,
1988),
(
2)
correlating
measured
vs.
predicted
log
BAF
R
f
ds
from
the
same
lake
(
Ontario)
but
separate
studies
(
Oliver
and
Niimi,
1988,
U.
S.
EPA,
1990),
(
3)
comparisons
of
predicted
BAF
R
f
ds
with
K
ow
values,
and
(
4)
comparisons
of
predicted
and
measured
BAF
R
f
ds
from
different
lakes
(
i.
e.,
Lake
Ontario
and
Green
Bay,
Lake
Michigan).
These
comparisons
were
based
on
data
presented
in
Tables
2.4.1a
and
2.4.1b.

Exhibit
2.4.1
illustrates
that
measured
log
BAF
R
f
ds
calculated
using
water
data
from
Oliver
and
Niimi
(
1988)
generally
agree
with
log
BAF
R
f
ds
predicted
from
BSAFs
determined
using
sediment
data
from
the
same
study.
The
correlation
coefficient
(
r)
for
the
correlation
of
BAFs
using
data
from
Tables
2.4.1a
and
2.4.1b
is
0.92
and
indicates
that
the
data
are
well
correlated.
One
deviation
of
predicted
BAFs
from
measured
BAFs
should
be
noted,
however.
For
chlorinated
benzenes
and
193
toluenes,
BAF
R
f
ds
predicted
from
BSAFs
are
underestimated
compared
with
measured
BAF
R
f
ds.
This
underestimation
may
be
due
to
altered
water­
sediment
fugacity
gradient
in
response
to
rapid
volatilization
from
water.
The
better
agreement
between
measured
and
predicted
BAF
R
f
ds
for
PCBs,
on
the
other
hand,
is
facilitated
by
the
lower
volatilization
of
PCBs
from
water.
In
addition
to
the
correlation
shown
in
Exhibit
2.4.1,
the
ratios
between
the
BAFs
(
which
indicates
the
magnitude
of
difference
between
the
values)
were
plotted
as
a
frequency
distribution,
as
shown
in
Exhibit
2.4.2.
The
magnitude
of
difference
between
these
two
BAFs
is
within
a
factor
of
four
in
the
majority
of
cases.

Exhibit
2.4.3
demonstrates
the
predictability
of
BAFs
for
the
same
chemical
but
based
on
BSAF
from
different
studies.
The
predicted
log
BAF
R
f
ds
using
data
from
the
EPA
TCDD
Bioaccumulation
Study
(
U.
S.
EPA,
1990)
(
collected
several
years
after
the
Oliver
and
Niimi
samples
were
collected)
correlate
equally
well
with
the
predicted
log
BAF
R
f
ds
calculated
from
Oliver
and
Niimi
(
1988)
data.
An
r
value
of
0.94
was
obtained
for
the
correlation
of
BAFs
using
data
from
Tables
2.4.1a
and
2.4.1b.
Exhibit
2.4.4
shows
that
for
the
majority
of
chemicals,
predicted
BAFs
from
the
TCDD
Bioaccumulation
Study
are
within
a
factor
of
two
of
predicted
BAFs
from
Oliver
and
Niimi
(
1988);
measured
and
predicted
BAFs
for
all
chemicals
are
within
a
factor
of
ten
of
each
other.

Exhibit
2.4.5
shows
the
relationship
of
log
BAF
R
f
ds
calculated
from
EPA
BSAFs
using
lake
trout
data
from
the
TCDD
Bioaccumulation
Study
(
Cook
et
al.,
1994)
to
log
K
ow
s.
The
bioaccumulative
PCDDs
and
PCDFs
(
2,3,7,8­
chlorinated)
have
BAF
R
f
ds
10­
to
1,000­
fold
less
than
PCBs
with
similar
K
ow
s,
which
is
expected
due
to
PCDD
and
PCDF
metabolism
in
fish.
It
should
be
noted,
however,
that
some
of
the
chlordane
and
nonachlor
BAF
R
f
ds
do
not
have
the
expected
correlations
with
K
ow.
This
is
shown
in
Exhibit
2.4.5
by
the
BAF
R
f
ds
for
five
of
six
chlordanes
and
nonachlors
that
are
much
greater
than
those
for
PCBs
with
the
same
estimated
log
K
ow.
This
finding
is
unexpected
because
PCBs
are
not
metabolized
in
fish
and
would
be
expected
to
have
higher
BAFs
than
other
chemicals
with
the
same
K
ow
values.
Therefore,
the
log
K
ow
values
chosen
here
for
the
chlordanes
and
nonachlors
may
be
significantly
underestimated.

All
of
the
above
correlations
were
based
on
the
BSAF
procedure
using
the
Oliver
and
Niimi
(
1988)
Lake
Ontario
salmonid
BAF
R
f
d
for
PCB
congener
52
as
a
reference
chemical.
As
noted
earlier,
the
BSAF
procedure
is
strengthened
through
use
of
several
reference
chemicals
with
both
a
range
of
K
ow
s
and
ability
to
be
accurately
measured
in
water.
Using
additional
reference
chemicals
(
PCB
congeners
105
and
118
and
DDT)
results
in
correlations
with
other
measured
and
predicted
BAF
R
f
ds
from
Tables
2.4.1a
and
2.4.1b
that
are
very
similar
to
comparisons
seen
using
PCB
congener
52
as
a
reference
chemical.

A
good
test
for
robustness
of
the
BSAF
procedure
for
predicting
BAF
R
f
ds
is
comparison
of
two
independent
data
sets
based
on
different
ecosystems
and
conditions.
Such
a
comparison
can
be
made
for
bioaccumulation
of
PCBs
in
Lake
Ontario
fish
and
Green
Bay
fish.
Although
both
ecosystems
are
specific
to
the
Great
Lakes
area,
Green
Bay
is
a
shallower,
smaller,
and
more
eutrophic
body
of
water
than
Lake
Ontario.
The
correlation
between
the
PCB
log
BAF
R
f
ds
for
brown
trout
predicted
from
BSAFs
using
Green
Bay
data
and
measured
log
BAF
R
f
ds
based
on
Oliver
and
Niimi
(
1988)
data
194
is
shown
in
Exhibit
2.4.6.
An
r
value
of
0.91
was
obtained
for
the
correlation
of
these
log
BAFs
(
using
data
from
Tables
2.4.1a
and
2.4.1b),
showing
that
the
values
are
well
correlated.
Exhibit
2.4.7
shows
that,
most
frequently,
the
BAFs
differ
from
each
other
by
less
than
a
factor
of
two,
and
all
chemical
BAFs
are
within
a
factor
of
ten
of
each
other.
The
correlation
between
predicted
log
BAF
R
f
ds
from
Green
Bay
(
for
brown
trout)
and
predicted
lake
trout
log
BAFs
using
Oliver
and
Niimi
(
1988)
salmonid
and
water
measurements
and
lake
trout
BSAFs
from
the
EPA
TCDD
Bioaccumulation
Study
is
shown
in
Exhibit
2.4.8.
The
r
value
for
the
relationship
between
these
log
BAFs
using
data
from
Tables
2.4.1a
and
2.4.1b
is
0.90.
As
shown
in
Exhibit
2.4.9,
the
most
frequent
difference
in
these
BAFs
is
less
than
or
equal
to
a
factor
of
two.
However,
for
PCB
congener
198,
the
difference
between
BAFs
is
85,
as
indicated
by
the
value
farthest
to
the
right
in
the
exhibit.

Despite
the
complex
exposures
of
Green
Bay
fish
(
which
result
from
movement
and
interaction
of
biota
through
gradients
of
decreasing
PCBs,
nutrients
and
suspended
organic
carbon
extending
from
the
Fox
River
to
the
outer
bay
and
Lake
Michigan),
good
agreement
exists
between
Green
Bay
brown
trout
predicted
log
BAFs
and
both
field­
measured
log
BAF
R
f
ds
and
predicted
log
BAF
R
f
ds
from
BSAFs
from
Lake
Ontario,
using
PCB
52
as
a
reference
chemical.
Although
not
shown,
good
agreement
also
exists
for
the
predicted
BAFs
using
PCB
118
as
the
reference
chemical.
In
addition
to
the
above
comparisons,
correlations
of
predicted
log
BAFs
with
log
K
ow
values
from
Green
Bay
show
relationships
that
are
similar
to
the
log
BAF
­
log
K
ow
relationship
for
predicted
BAF
R
f
ds
from
Lake
Ontario
data.

Based
on
the
above
correlations
and
ratios,
the
BSAF
method
appears
to
work
well
not
only
for
predicting
BAFs
using
data
from
the
same
system
(
Lake
Ontario)
but
also
for
predicting
BAFs
between
systems
(
Green
Bay
vs.
Lake
Ontario).
These
evaluations
support
the
use
of
the
BSAF
method
for
predicting
BAFs.
195
Table
2.4.1a:
Great
Lakes
Trout
BAF
R
R
f
d
s
Calculated
from
Measured
BSAFs/
BAFs
Measured
Values
Predicted
BAF
R
f
d
Chemical
Log
K
ow
BSAF
Ol.
&
Niimi
(
1988)
log
BAF
Ol.
&
Niimi
(
1988)
BSAF
EPA
(
1990)
log
BAF
Ol.
&
Niimi
ref
PCB
52
log
BAF
EPA
ref
PCB
52
log
BAF
Ol.
&
Niimi
ref
PCB
105
log
BAF
EPA
ref
PCB
105
dieldrin
5.3
6.65
7.67
6.95
ddt
6.45
1.09
7.78
1.67
7.87
8.22
7.54
7.5
dde
6.76
4.14
8.35
7.7
8.76
9.19
8.43
8.47
ddd
6.06
0.28
7.00
6.90
6.56
mirex
6.89
1.43
8.13
1.31
8.43
8.55
8.09
7.84
photomirex
6.89
5.48
8.07
9.01
8.68
g­
chlordane
6.0
2.22
6.79
7.73
7.40
t­
chlordane
6.0
2.00
7.85
7.13
c­
chlordane
6.0
4.77
8.23
7.51
t­
nonachlor
6.0
10.5
8.57
7.85
c­
nonachlor
6.0
0.51
7.25
6.54
alpha­
hch
3.78
2.45
4.69
5.55
5.22
gamma­
hch
3.67
0.69
4.93
4.89
4.56
hcbd
4.84
ocs
6.29
0.98
8.07
7.67
7.33
hcb
5.6
0.09
6.40
5.95
5.62
pcb
5.11
0.04
5.81
5.07
4.73
1235tcb
4.56
1245tcb
4.56
1234tcb
4.59
0.01
5.07
4.11
3.78
135tcb
4.17
124tcb
3.99
123tcb
4.1
245tct
4.93
236tct
4.93
pct
6.36
Total­
PCB
6.14
1.85
7.81
7.79
7.46
PCB
5
4.97
PCB
6
5.06
0.36
6.16
5.44
PCB
8
5.07
Table
2.4.1a:
Great
Lakes
Trout
BAF
R
R
f
d
s
Calculated
from
Measured
BSAFs/
BAFs
Measured
Values
Predicted
BAF
R
f
d
Chemical
Log
K
ow
BSAF
Ol.
&
Niimi
(
1988)
log
BAF
Ol.
&
Niimi
(
1988)
BSAF
EPA
(
1990)
log
BAF
Ol.
&
Niimi
ref
PCB
52
log
BAF
EPA
ref
PCB
52
log
BAF
Ol.
&
Niimi
ref
PCB
105
log
BAF
EPA
ref
PCB
105
196
PCB
12
5.22
0.44
6.41
5.69
PCB
13
5.29
PCB
16
5.16
5.92
PCB
17
5.25
0.15
5.52
0.99
5.80
6.79
5.47
6.07
PCB
18
5.24
0.26
5.75
0.1
6.05
5.79
5.71
5.07
PCB
22
5.58
0.21
6.39
0.27
6.28
6.56
5.95
5.84
PCB
25
5.67
0.25
0.33
6.44
6.74
6.11
6.02
PCB
26
5.66
1.72
0.44
7.28
6.85
6.94
6.13
PCB
32
5.44
0.18
6.76
6.09
5.75
PCB
33
5.60
0.15
5.32
0.49
6.15
6.84
5.82
6.12
PCB
40
5.66
0.10
6.55
0.18
6.06
6.46
5.72
5.74
PCB
42
5.76
0.52
7.49
6.86
6.53
PCB
44
5.75
0.48
6.96
0.4
6.82
6.90
6.48
6.18
PCB
45
5.53
0.22
6.42
5.70
PCB
46
5.53
0.57
0.02
6.67
5.38
6.34
4.66
PCB
49
5.85
0.69
7.13
7.07
6.74
PCB
52
5.84
0.61
7.01
0.42
7.01
7.01
6.67
6.29
PCB
53
5.62
1.84
6.51
7.27
6.93
PCB
63
6.17
0.82
7.63
6.91
PCB
64
5.95
0.73
7.51
7.20
6.86
PCB
66
6.20
0.85
7.79
7.52
7.18
PCB
74
6.20
3.45
7.66
0.61
8.12
7.53
7.79
6.81
PCB
77
6.36
0.29
7.37
6.65
PCB
81
6.36
0.67
7.73
7.01
PCB
82
6.20
2.45
8.13
0.18
7.97
7.00
7.64
6.28
PCB
83
6.26
1.33
7.93
7.21
PCB
84
6.04
3.04
8.28
7.91
7.57
PCB
85
6.30
1.45
7.89
1.29
7.85
7.96
7.51
7.24
PCB
87
6.29
1.37
7.97
7.25
PCB
91
6.13
1.25
6.92
0.64
7.61
7.48
7.28
6.76
PCB
92
6.35
1.43
8.11
7.89
7.55
Table
2.4.1a:
Great
Lakes
Trout
BAF
R
R
f
d
s
Calculated
from
Measured
BSAFs/
BAFs
Measured
Values
Predicted
BAF
R
f
d
Chemical
Log
K
ow
BSAF
Ol.
&
Niimi
(
1988)
log
BAF
Ol.
&
Niimi
(
1988)
BSAF
EPA
(
1990)
log
BAF
Ol.
&
Niimi
ref
PCB
52
log
BAF
EPA
ref
PCB
52
log
BAF
Ol.
&
Niimi
ref
PCB
105
log
BAF
EPA
ref
PCB
105
197
PCB
95
6.13
1.40
7.25
7.66
7.33
PCB
97
6.29
0.28
7.28
6.56
PCB
99
6.39
0.68
7.39
1.51
7.61
8.12
7.27
7.40
PCB
100
6.23
1.78
8.03
7.31
PCB
101
6.38
2.45
7.45
1.06
8.15
7.95
7.82
7.23
PCB
105
6.65
2.70
8.13
4.49
8.47
8.85
8.13
8.13
PCB
110
6.48
1.53
7.79
0.82
8.05
7.94
7.71
7.22
PCB
118
6.74
4.09
8.15
1.72
8.74
8.52
8.40
7.80
PCB
119
6.58
3.83
8.71
7.99
PCB
126
6.89
3.21
8.94
8.22
PCB
128
6.74
3.61
2.78
8.68
8.73
8.35
8.01
PCB
129
6.73
1.75
1.13
8.36
8.33
8.02
7.61
PCB
130
6.8
2.15
8.68
7.96
PCB
132
6.58
0.87
7.56
7.90
7.57
PCB
136
6.22
10.87
7.37
8.64
8.30
PCB
138
6.83
4.25
8.30
8.84
8.51
PCB
141
6.82
2.75
8.32
1.74
8.64
8.61
8.31
7.89
PCB
146
6.89
3.22
8.73
1.25
8.78
8.53
8.45
7.81
PCB
149
6.67
2.33
7.99
0.93
8.42
8.19
8.09
7.47
PCB
151
6.64
3.38
8.51
1.65
8.55
8.40
8.22
7.69
PCB
153
6.92
4.22
8.32
1.91
8.93
8.75
8.59
8.03
PCB
156
7.18
3.97
9.16
8.83
PCB
158
7.02
1.52
8.75
8.03
PCB
167
7.27
0.69
8.66
7.94
PCB
171
7.11
2.71
8.93
8.59
PCB
172
7.33
1.36
9.01
8.29
PCB
174
7.11
1.54
8.74
1.25
8.68
8.75
8.35
8.03
PCB
177
7.08
3.53
9.01
1.91
9.01
8.91
8.68
8.19
PCB
178
7.14
4.48
2.76
9.18
9.13
8.84
8.41
PCB
180
7.36
3.78
8.58
3.26
9.32
9.42
8.99
8.70
PCB
183
7.20
5.62
9.03
2.68
9.33
9.17
9.00
8.46
Table
2.4.1a:
Great
Lakes
Trout
BAF
R
R
f
d
s
Calculated
from
Measured
BSAFs/
BAFs
Measured
Values
Predicted
BAF
R
f
d
Chemical
Log
K
ow
BSAF
Ol.
&
Niimi
(
1988)
log
BAF
Ol.
&
Niimi
(
1988)
BSAF
EPA
(
1990)
log
BAF
Ol.
&
Niimi
ref
PCB
52
log
BAF
EPA
ref
PCB
52
log
BAF
Ol.
&
Niimi
ref
PCB
105
log
BAF
EPA
ref
PCB
105
198
PCB
185
7.11
1.55
2.24
8.68
9.01
8.35
8.29
PCB
189
7.71
0.71
9.11
8.39
PCB
194
7.80
1.53
8.56
2.47
9.37
9.74
9.03
9.02
PCB
195
7.56
1.90
9.22
8.89
PCB
197
7.3
1.1
8.89
8.17
PCB
198
7.62
6.55
9.98
9.26
PCB
201
7.62
1.53
1.13
9.19
9.22
8.85
8.50
PCB
205
8.00
0.34
0.48
8.91
9.23
8.58
8.51
PCB
206
8.09
0.47
0.34
9.15
9.17
8.81
8.45
PCB
207
7.74
0.66
0.89
8.95
9.24
8.61
8.52
PCB
209
8.18
0.14
0.03
8.70
8.20
8.36
7.48
PCB
24+
27
5.40
0.25
0.12
6.17
6.02
5.83
5.30
PCB
28+
31
5.67
0.52
6.89
0.19
6.77
6.50
6.43
5.78
PCB
37+
42
5.8
0.62
7.14
6.42
PCB
47+
48
5.82
1.23
7.18
0.65
7.29
7.17
6.95
6.46
PCB
41+
64+
71
5.87
0.46
7.08
6.36
PCB
56+
60
6.11
0.31
7.15
6.43
PCB
70+
76
6.17
1.49
7.56
0.61
7.72
7.50
7.39
6.78
PCB
66+
95
6.17
0.53
7.44
6.72
PCB
56+
60+
81
6.19
0.55
7.96
7.32
6.98
PCB
84+
92
6.2
1.22
7.83
7.11
PCB
87+
97
6.29
2.45
8.08
8.06
7.73
PCB
137+
176
6.8
1.16
8.41
7.69
PCB
138+
163
6.91
2.23
8.81
8.09
PCB
156+
171+
202
7.18
1.25
8.82
8.10
PCB
182+
187
7.19
3.80
8.43
9.15
8.81
PCB
157+
200
7.23
1.56
8.97
8.25
PCB
170+
190
7.37
2.06
9.20
4.17
9.06
9.53
8.73
8.81
PCB
195+
208
7.64
0.72
9.04
8.33
PCB
196+
203
7.65
1.56
9.26
1.12
9.23
9.25
8.89
8.53
2378­
TCDD
7.02
0.059
7.34
6.62
Table
2.4.1a:
Great
Lakes
Trout
BAF
R
R
f
d
s
Calculated
from
Measured
BSAFs/
BAFs
Measured
Values
Predicted
BAF
R
f
d
Chemical
Log
K
ow
BSAF
Ol.
&
Niimi
(
1988)
log
BAF
Ol.
&
Niimi
(
1988)
BSAF
EPA
(
1990)
log
BAF
Ol.
&
Niimi
ref
PCB
52
log
BAF
EPA
ref
PCB
52
log
BAF
Ol.
&
Niimi
ref
PCB
105
log
BAF
EPA
ref
PCB
105
199
12378­
PeCDD
7.5
0.054
7.78
7.06
123478­
HxCDD
7.8
0.018
7.60
6.88
123678­
HxCDD
7.8
0.0073
7.21
6.49
123789­
HxCDD
7.8
0.0081
7.26
6.54
1234678­
HpCDD
8.2
0.0031
7.24
6.52
OCDD
8.6
0.00074
7.02
6.30
2378­
TCDF
6.5
0.047
6.72
6.00
12378­
PeCDF
7.0
0.013
6.66
5.94
23478­
PeCDF
7.0
0.095
7.52
6.81
123478­
HxCDF
7.5
0.0045
6.70
5.98
123678­
HxCDF
7.5
0.011
7.09
6.37
123789­
HxCDF
7.5
0.037
7.61
6.90
234678­
HxCDF
7.5
0.04
7.65
6.93
1234678­
HpCDD
8.0
0.00065
6.36
5.64
1234789­
HpCDD
8.0
0.023
7.91
7.19
OCDF
8.8
0.00099
7.34
6.62
a.
Oliver
and
Niimi
(
1988).
BAF
R
f
d
calculated
from
measured
BAFs
using
freely
dissolved
equation
2.4.11,
DOC=
2.0
mg/
L,
POC=
0.0
mg/
L,
K
doc=
K
ow/
10,
K
poc=
K
ow.
Predicted
BAFs
based
on
equation
2.4.16.
b.
U.
S.
EPA
1990.
(
TCDD
Bioaccumulation
Study).
Predicted
BAFs
based
on
equation
2.4.16.
c.
Green
Bay/
Fox
River
Mass
Balance
Study
(
As
described
in
Great
Lakes
Water
Quality
Initiative
Technical
Support
Document
for
the
Procedure
to
Determine
Bioaccumulation
Factors.
EPA­
820­
B­
95­
005.
March
1995.)
200
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
dieldrin
5.3
7.23
7.30
ddt
6.45
7.78
7.78
7.29
7.85
dde
6.76
8.67
8.75
8.18
8.82
ddd
6.06
6.80
6.31
mirex
6.89
8.33
8.11
7.84
8.18
photomirex
6.89
8.92
8.43
g­
chlordane
6
7.64
7.14
t­
chlordane
6
7.41
7.48
c­
chlordane
6
7.78
7.85
t­
nonachlor
6
8.13
8.20
c­
nonachlor
6
6.81
6.88
alpha­
hch
3.78
5.46
4.97
gamma­
hch
3.67
4.80
4.31
hcbd
4.84
ocs
6.29
7.57
7.08
hcb
5.6
5.86
5.37
pcb
5.11
4.97
4.48
1235tcb
4.56
1245tcb
4.56
1234tcb
4.59
4.02
3.53
135tcb
4.17
124tcb
3.99
123tcb
4.1
245tct
4.93
236tct
4.93
pct
6.36
Total­
PCB
6.14
7.70
7.21
PCB
5
4.97
0.14
4.88
5.12
PCB
6
5.06
5.72
5.79
1.7
6.05
6.29
PCB
8
5.07
0.14
4.98
5.22
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
201
PCB
12
5.22
5.97
6.04
PCB
13
5.29
PCB
16
5.16
PCB
17
5.25
5.71
6.35
5.22
6.42
0.75
5.89
6.13
PCB
18
5.24
5.95
5.34
5.46
5.41
0.64
5.81
6.05
PCB
22
5.58
6.19
6.12
5.70
6.19
0.39
5.94
6.18
PCB
25
5.67
6.35
6.29
5.86
6.36
0.73
6.30
6.54
PCB
26
5.66
7.18
6.41
6.69
6.48
0.95
6.40
6.64
PCB
32
5.44
5.99
5.50
PCB
33
5.60
6.06
6.39
5.57
6.46
0.29
5.83
6.07
PCB
40
5.66
5.96
6.02
5.47
6.09
0.69
6.26
6.50
PCB
42
5.76
6.77
6.28
PCB
44
5.75
6.72
6.46
6.23
6.53
PCB
45
5.53
5.98
6.05
1.16
6.36
6.60
PCB
46
5.53
6.58
4.94
6.09
5.01
0.61
6.08
6.32
PCB
49
5.85
6.98
6.49
3.34
7.14
7.38
PCB
52
5.84
6.91
6.57
6.42
6.64
4.74
7.28
7.52
PCB
53
5.62
7.17
6.68
2.12
6.71
6.95
PCB
63
6.17
7.19
7.26
4.37
7.57
7.81
PCB
64
5.95
7.10
6.61
PCB
66
6.20
7.42
6.93
3.1
7.46
7.70
PCB
74
6.20
8.03
7.09
7.54
7.16
2.46
7.36
7.60
PCB
77
6.36
6.93
7.00
4.12
7.74
7.98
PCB
81
6.36
7.29
7.36
11.6
8.19
8.43
PCB
82
6.20
7.88
6.56
7.39
6.63
4.05
7.57
7.81
PCB
83
6.26
7.49
7.56
5.67
7.78
8.02
PCB
84
6.04
7.81
7.32
7.2
7.66
7.90
PCB
85
6.30
7.75
7.51
7.26
7.59
7.25
7.92
8.16
PCB
87
6.29
7.53
7.60
6.13
7.84
8.08
PCB
91
6.13
7.52
7.04
7.02
7.11
8.44
7.82
8.06
PCB
92
6.35
7.79
7.30
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
202
PCB
95
6.13
7.57
7.08
PCB
97
6.29
6.84
6.91
6.42
7.86
8.10
PCB
99
6.39
7.51
7.67
7.02
7.74
7.18
8.01
8.25
PCB
100
6.23
7.58
7.65
1.71
7.23
7.47
PCB
101
6.38
8.06
7.51
7.57
7.58
10.01
8.14
8.38
PCB
105
6.65
8.37
8.41
7.88
8.48
5.35
8.14
8.38
PCB
110
6.48
7.95
7.50
7.46
7.57
4.15
7.86
8.10
PCB
118
6.74
8.64
8.08
8.15
8.15
4.96
8.20
8.44
PCB
119
6.58
8.27
8.34
3.03
7.83
8.07
PCB
126
6.89
8.50
8.57
PCB
128
6.74
8.59
8.29
8.10
8.36
10.21
8.51
8.75
PCB
129
6.73
8.26
7.89
7.77
7.96
PCB
130
6.8
8.24
8.31
11.21
8.61
8.85
PCB
132
6.58
7.81
7.32
PCB
136
6.22
8.55
8.05
PCB
138
6.83
8.75
8.26
PCB
141
6.82
8.55
8.16
8.06
8.24
9.30
8.55
8.79
PCB
146
6.89
8.69
8.09
8.20
8.16
10.0
8.66
8.90
PCB
149
6.67
8.33
7.74
7.84
7.81
8.7
8.37
8.61
PCB
151
6.64
8.46
7.96
7.97
8.03
9.7
8.39
8.63
PCB
153
6.92
8.84
8.31
8.34
8.38
5.35
8.41
8.65
PCB
156
7.18
9.07
8.58
PCB
158
7.02
8.31
8.38
PCB
167
7.27
8.21
8.28
16.0
9.24
9.48
PCB
171
7.11
8.83
8.34
PCB
172
7.33
8.57
8.64
PCB
174
7.11
8.59
8.31
8.10
8.38
4.46
8.52
8.76
PCB
177
7.08
8.92
8.47
8.43
8.54
8.04
8.75
8.99
PCB
178
7.14
9.08
8.69
8.59
8.76
PCB
180
7.36
9.23
8.98
8.74
9.05
10.96
9.16
9.40
PCB
183
7.20
9.24
8.73
8.75
8.80
6.5
8.78
9.02
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
203
PCB
185
7.11
8.59
8.56
8.10
8.63
3.23
8.38
8.62
PCB
189
7.71
8.67
8.74
3.45
9.01
9.25
PCB
194
7.80
9.27
9.30
8.78
9.37
3.29
9.08
9.32
PCB
195
7.56
9.13
8.64
PCB
197
7.3
8.45
8.52
PCB
198
7.62
9.54
9.61
0.46
8.05
8.29
PCB
201
7.62
9.10
8.78
8.60
8.85
4.79
9.06
9.30
PCB
205
8.00
8.82
8.79
8.33
8.86
3.09
9.25
9.49
PCB
206
8.09
9.05
8.73
8.56
8.80
0.95
8.83
9.07
PCB
207
7.74
8.85
8.79
8.36
8.86
1.3
8.62
8.86
PCB
209
8.18
8.60
7.76
8.11
7.83
0.19
8.22
8.46
PCB
24+
27
5.40
6.07
5.58
5.58
5.65
1.55
6.35
6.59
PCB
28+
31
5.67
6.68
6.05
6.18
6.12
0.67
6.26
6.50
PCB
37+
42
5.8
6.70
6.77
6.75
7.39
7.63
PCB
47+
48
5.82
7.19
6.73
6.70
6.80
7.86
7.47
7.71
PCB
41+
64+
71
5.87
6.64
6.71
2.55
7.04
7.28
PCB
56+
60
6.11
6.71
6.78
1.14
6.93
7.17
PCB
70+
76
6.17
7.63
7.05
7.14
7.12
1.2
7.01
7.25
PCB
66+
95
6.17
7.00
7.07
3.1
7.43
7.67
PCB
56+
60+
81
6.19
7.22
6.73
1.15
7.02
7.26
PCB
84+
92
6.2
7.39
7.46
7.25
7.82
8.06
PCB
87+
97
6.29
7.97
7.48
6.3
7.85
8.09
PCB
137+
176
6.8
7.97
8.04
1.43
7.72
7.96
PCB
138+
163
6.91
8.36
8.43
11.94
8.75
8.99
PCB
156+
171+
202
7.18
8.38
8.45
10.70
8.97
9.21
PCB
182+
187
7.19
9.05
8.56
9.38
8.92
9.16
PCB
157+
200
7.23
8.53
8.60
8.66
8.93
9.17
PCB
170+
190
7.37
8.97
9.09
8.48
9.16
4.10
8.74
8.98
PCB
195+
208
7.64
8.60
8.67
1.01
8.41
8.65
PCB
196+
203
7.65
9.13
8.80
8.64
8.87
4.24
9.04
9.28
2378­
TCDD
7.02
6.90
6.97
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
204
12378­
PeCDD
7.5
7.34
7.41
123478­
HxCDD
7.8
7.16
7.23
123678­
HxCDD
7.8
6.77
6.84
123789­
HxCDD
7.8
6.81
6.88
123334678­
Hp
8.2
6.80
6.87
OCDD
8.6
6.57
6.64
2378­
TCDF
6.5
6.28
6.35
12378­
PeCDF
7.0
6.22
6.29
23478­
PeCDF
7.0
7.08
7.15
123478­
HxCDF
7.5
6.26
6.33
123678­
HxCDF
7.5
6.65
6.72
123789­
HxCDF
7.5
7.17
7.24
234678­
HxCDF
7.5
7.21
7.28
Table
2.4.1b:
Great
Lakes
Trout
BAF
R
ds
Calculated
from
Measured
BSAFs/
BAFs
Predicted
Values
Chemical
Log
K
ow
log
BAF
Ol.
&
Niimia
ref
DDT
log
BAF
EPAb
ref
DDT
log
BAF
Ol.
&
Niimia
ref
PCB
118
log
BAF
EPAb
ref
PCB
118
BT­
BSAF
EPA­
G
Bayc
log
BAF
EPA­
G
Bayc
ref
PCB
52
log
BAF
EPA­
G
Bayc
ref
PCB
118
205
1234678­
HpCD
8.0
5.92
5.99
1234789­
HpCD
8.0
7.47
7.54
OCDF
8.8
6.90
6.97
a.
Oliver
and
Niimi
(
1988).
BAF
R
f
d
calculated
from
measured
BAFs
using
freely
dissolved
equation
2.4.11,
DOC=
2.0
mg/
L,
POC=
0.0
mg/
L,
K
doc=
K
ow/
10,
K
poc=
K
ow.
Predicted
BAFs
based
on
equation
2.4.16.
b.
U.
S.
EPA
1990.
(
TCDD
Bioaccumulation
Study).
Predicted
BAFs
based
on
equation
2.4.16.
c.
Green
Bay/
Fox
River
Mass
Balance
Study
(
As
described
in
Great
Lakes
Water
Quality
Initiative
Technical
Support
Document
for
the
Procedure
to
Determine
Bioaccumulation
Factors.
EPA­
820­
B­
95­
005.
March
1995.)
206
Table
2.4.2:
Mean
BAF
R
f
ds
from
Lake
Ontario
BSAFs
for
Salmonids
Chemical
log
Kow
Number
Mean
Mean
BAFs
log
BAF
R
f
d
BAF
R
f
d
dieldrin
5.30
4
7.29
1.93e+
07
ddt
6.45
8
7.73
5.33e+
07
dde
6.76
8
8.66
4.56e+
08
ddd
6.06
4
6.64
4.39e+
06
mirex
6.89
8
8.17
1.49e+
08
photomirex
6.89
4
8.76
5.74e+
08
g­
chlordane
6.00
4
7.48
3.00e+
07
t­
chlordane
6.00
4
7.46
2.91e+
07
c­
chlordane
6.00
4
7.84
6.95e+
07
t­
nonachlor
6.00
4
8.18
1.53e+
08
c­
nonachlor
6.00
4
6.87
7.43e+
06
alpha­
hch
3.78
4
5.30
2.00e+
05
gamma­
hch
3.67
4
4.64
4.34e+
04
hcbd
4.84
ocs
6.29
4
7.41
2.58e+
07
hcb
5.60
4
5.70
5.01e+
05
pcb
5.11
4
4.81
6.47e+
04
1235tcb
4.56
1245tcb
4.50
1234tcb
4.59
4
3.86
7.25e+
03
135tcb
4.17
124tcb
3.99
123tcb
4.10
245tct
4.93
236tct
4.93
pct
6.36
PCBs
5
4.97
6
5.06
4
5.78
6.02e+
05
8
5.07
12
5.22
4
6.03
1.06e+
06
13
5.29
16
5.16
17
5.25
8
5.98
9.52e+
05
18
5.24
8
5.60
3.96e+
05
22
5.58
8
6.10
1.27e+
06
25
5.67
8
6.27
1.87e+
06
26
5.66
8
6.75
5.57e+
06
207
Table
2.4.2:
Mean
BAF
R
f
ds
from
Lake
Ontario
BSAFs
for
Salmonids
(
continued)

Chemical
log
Kow
Number
Mean
Mean
BAFs
log
BAF
R
f
d
BAF
R
f
d
PCBs
32
5.44
4
5.84
6.84e+
05
33
5.60
8
6.18
1.50e+
06
40
5.66
8
5.94
8.72e+
05
42
5.76
4
6.61
4.06e+
06
44
5.75
8
6.54
3.46e+
06
45
5.53
4
6.04
1.09e+
06
46
5.53
8
5.71
5.08e+
05
49
5.85
4
6.82
6.61e+
06
52
5.84
8
6.69
4.90e+
06
53
5.62
4
7.02
1.04e+
07
63
6.17
4
7.25
1.77e+
07
64
5.95
4
6.94
8.80e+
06
66
6.20
4
7.26
1.83e+
07
74
6.20
8
7.51
3.23e+
07
77
6.36
4
6.99
9.68e+
06
81
6.36
4
7.35
2.24e+
07
82
6.20
8
7.17
1.48e+
07
83
6.26
4
7.55
3.53e+
07
84
6.04
4
7.65
4.50e+
07
85
6.30
8
7.58
3.83e+
07
87
6.29
4
7.59
3.89e+
07
91
6.13
8
7.23
1.69e+
07
92
6.35
4
7.64
4.32e+
07
95
6.13
4
7.41
2.55e+
07
97
6.29
4
6.90
7.95e+
06
99
6.39
8
7.54
3.49e+
07
100
6.23
4
7.64
4.40e+
07
101
6.38
8
7.73
5.43e+
07
105
6.65
8
8.34
2.18e+
08
110
6.48
8
7.68
4.74e+
07
118
6.74
8
8.31
2.04e+
08
119
6.58
4
8.33
2.12e+
08
126
6.89
4
8.56
3.63e+
08
128
6.74
8
8.39
2.44e+
08
129
6.73
8
8.03
1.06e+
07
130
6.80
4
8.30
1.98e+
08
132
6.58
4
7.65
4.47e+
07
208
Table
2.4.2:
Mean
BAF
R
d
s
from
Lake
Ontario
BSAFs
for
Salmonids
(
continued)

Chemical
log
Kow
Number
Mean
Mean
BAFs
log
BAF
R
d
BAF
R
d
PCBs
136
6.22
4
8.39
2.44e+
08
138
6.83
4
8.59
3.88e+
08
141
6.82
8
8.31
2.03e+
08
146
6.89
8
8.34
2.18e+
08
149
6.67
8
7.98
9.66e+
07
151
6.64
8
8.16
1.45e+
08
153
6.92
8
8.52
3.31e+
08
156
7.18
4
8.91
8.12e+
08
158
7.02
4
8.37
2.32e+
08
167
7.27
4
8.27
1.87e+
08
171
7.11
4
8.67
4.72e+
08
172
7.33
4
8.63
4.24e+
08
174
7.11
8
8.40
2.51e+
08
177
7.08
8
8.64
4.38e+
08
178
7.14
8
8.83
6.80e+
08
180
7.36
8
9.05
1.13e+
09
183
7.20
8
8.94
8.63e+
08
185
7.11
8
8.53
3.36e+
08
189
7.71
4
8.72
5.30e+
08
194
7.80
8
9.23
1.72e+
09
195
7.56
4
8.97
9.32e+
08
197
7.30
4
8.50
3.20e+
08
198
7.62
4
9.60
3.98e+
09
201
7.62
8
8.89
7.70e+
08
205
8.00
8
8.75
5.64e+
08
206
8.09
8
8.84
6.90e+
08
207
7.74
8
8.77
5.92e+
08
209
8.18
8
8.13
1.35e+
08
24+
27
5.40
8
5.78
5.98e+
07
28+
31
5.67
8
6.31
2.06e+
06
37+
42
5.80
4
6.76
5.70e+
06
47+
48
5.82
8
6.91
8.18e+
06
41+
64+
71
5.87
4
6.70
4.97e+
06
56+
60
6.11
4
6.76
5.82e+
06
70+
76
6.17
8
7.29
1.96e+
07
66+
95
6.17
4
7.06
1.14e+
07
56+
60+
81
6.19
4
7.06
1.16e+
07
84+
92
6.20
4
7.45
2.82e+
07
209
Table
2.4.2:
Mean
BAF
R
d
s
from
Lake
Ontario
BSAFs
for
Salmonids
(
continued)

Chemical
log
Kow
Number
Mean
Mean
BAFs
log
BAF
R
d
BAF
R
d
PCBs
87+
97
6.29
4
7.81
6.46e+
07
137+
176
6.80
4
8.03
1.07e+
08
138+
163
6.91
4
8.42
2.64e+
08
156+
171+
202
7.18
4
8.44
2.76e+
08
182+
187
7.19
4
8.89
7.85e+
08
157+
200
7.23
4
8.59
3.86e+
08
170+
190
7.37
8
8.98
9.53e+
08
195+
208
7.64
4
8.66
4.58e+
08
196+
203
7.65
8
8.92
8.27e+
08
PCDDs
2378­
TCDD
7.02
4
6.95
9.00e+
06
12378­
PeCDD
7.50
4
7.40
2.49e+
07
123478­
HxCDD
7.80
4
7.22
1.65e+
07
123678­
HxCDD
7.80
4
6.83
6.71e+
06
123789­
HxCDD
7.80
4
6.87
7.44e+
06
1234678­
HpCDD
8.20
4
6.85
7.16e+
06
OCDD
8.60
4
6.63
4.29e+
06
PCDFs
2378­
TCDF
6.50
4
6.34
2.16e+
06
12378­
PeCDF
7.00
4
6.28
1.89e+
06
23478­
PeCDF
7.00
4
7.14
1.38e+
07
123478­
HxCDF
7.50
4
6.32
2.07e+
06
123678­
HxCDF
7.50
4
6.70
5.07e+
06
123789­
HxCDF
7.50
4
7.23
1.70e+
07
234678­
HxCDF
7.50
4
7.27
1.84e+
07
1234678­
HpCDF
8.00
4
5.98
9.47e+
05
1234789­
HpCDF
8.00
4
7.53
3.35e+
07
OCDF
8.80
4
6.96
9.10e+
06
0
5
10
15
20
25
30
1
>
1­
2
>
2­
4
>
4­
6
>
6­
8
>
8­
10
>
10­
12
>
12­
14
>
14­
16
>
16­
18
>
18­
20
Range
of
Ratios
Number
of
Occurrences
Exhibit
2.4.2:
Ratios
between
Oliver
and
Niimi
(
1988)
measured
BAFs
and
BAFs
predicted
from
Oliver
and
Niimi
(
1988)
0
5
10
15
20
25
1
>
1­
2
>
2­
4
>
4­
6
>
6­
8
>
8­
10
Range
of
Ratios
Number
of
Occurrences
Exhibit
2.4.4:
Ratios
between
BAFs
predicted
from
EPA
BSAFs
and
BAFs
predicted
from
Oliver
and
Niimi
(
1988)
BSAFs
Exhibit
2.4.7:
Ratios
between
Oliver
and
Niimi
(
1988)
measured
BAFs
and
BAFs
predicted
from
Green
Bay
BSAFs
0
5
10
15
20
25
1
>
1­
2
>
2­
4
>
4­
6
>
6­
8
>
8­
10
Range
of
Ratios
Number
of
Occurrences
Exhibit
2.4.9:
Ratios
BAFs
predicted
from
EPA
BSAFs
and
BAFs
predicted
from
Green
Bay
BSAFs
0
5
10
15
20
25
30
1
>
1­
2
>
2­
4
>
4­
6
>
6­
8
>
8­
10
>
80­
90
Range
of
Ratios
Number
of
Occurrences
24Note:
Equilibrium
partitioning
theory
would
predict
BCF
R
f
d
approximately
equal
to
K
ow,
thus
the
BCF
R
f
d
would
not
be
trophic
level
dependent.

219
2.4.4.3
Baseline
BAF
Derived
from
a
Laboratory­
Measured
BCF
and
Food­
Chain
Multiplier
For
the
third
tier
in
the
data
preference
hierarchy
for
nonpolar
organic
chemicals,
EPA
recommends
the
use
of
a
predicted
BAF
derived
from
a
technically
defensible,
laboratory
measurement
of
the
BCF
and
an
appropriate
food
chain
multiplier
(
FCM).
A
FCM
is
determined
as
the
ratio
of
the
baseline
BAF
(
BAF
R
f
d)
of
an
organism
at
a
particular
trophic
level
to
the
baseline
BCF
(
usually
determined
for
trophic
level
one).
24
FCMs
with
values
greater
than
1.0
indicate
biomagnification
and
typically
apply
to
organic
chemicals
with
K
ow
values
between
4.0
and
9.0.
Laboratory­
measured
BCFs
are
preferred
over
predicted
BCFs
because
laboratory­
measured
BCFs
inherently
account
for
effects
of
chemical
metabolism
on
the
BCF
during
its
measurement.

The
equation
for
calculating
a
baseline
BAF
from
a
laboratory­
measured
BCF
is:

Baseline
BAF
fd
R
'
(
FCM)
Measured
BCF
t
T
f
fd
&
1
1
f
R
(
Equation
2.4.17)

where:

Baseline
BAF
R
f
d
=
BAF
expressed
on
a
freely­
dissolved
and
lipid­
normalized
basis
BCFtT
=
BCF
based
on
total
concentration
in
tissue
and
water
f
R
=
Fraction
of
the
tissue
that
is
lipid
f
fd
=
Fraction
of
the
total
chemical
in
the
test
water
that
is
freely
dissolved
FCM
=
Food­
chain
multiplier
obtained
from
Tables
2.4.4,
2.4.5,
or
2.4.6
by
linear
interpolation
for
the
appropriate
trophic
level
as
necessary
(
or
from
appropriate
field
data)

For
each
trophic
level,
the
species
mean
baseline
BAF
is
calculated
as
the
geometric
mean
if
more
than
one
acceptable
baseline
BAF
is
predicted
from
laboratory­
measured
BCFs
for
a
given
species.
For
each
trophic
level,
the
trophic
level­
specific
BAF
is
calculated
as
the
geometric
mean
of
the
species
mean
baseline
BAFs
based
on
laboratory­
measured
BCFs.
220
Procedural
and
Quality
Assurance
Requirements
for
Measured
BCFs
A
measured
BCF
derived
from
results
of
a
laboratory
exposure
study
is
acceptable
if
the
study
has
met
certain
specific
technical
criteria.
These
criteria
include,
but
are
not
limited
to:

1.
The
test
organism
should
not
be
diseased,
unhealthy,
or
adversely
affected
by
the
concentration
of
the
chemical
because
these
attributes
may
alter
accumulation
of
chemicals
by
otherwise
healthy
organisms.

2.
The
total
concentration
of
the
chemical
in
the
water
should
be
measured
and
should
be
relatively
constant
during
the
steady­
state
time
period.

3.
The
organisms
should
be
exposed
to
the
chemical
using
a
flow­
through
or
renewal
procedure.

4.
For
organic
chemicals,
the
percent
lipid
should
be
either
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
BCF.

5.
For
organic
chemicals
with
log
K
ow
greater
than
four,
the
concentrations
of
POC
and
DOC
in
the
test
solution
should
be
either
measured
or
reliably
estimated.
For
organic
chemicals
with
log
K
ow
less
than
four,
virtually
all
of
the
chemical
is
predicted
to
be
freely
dissolved,
except
in
water
with
extremely
high
DOC
and
POC
concentrations,
which
is
not
characteristic
of
laboratory
dilution
water
used
in
BCF
determinations.

6.
Laboratory­
measured
BCFs
should
be
determined
using
fish
species,
but
BCFs
determined
with
molluscs
and
other
invertebrates
may
be
used
with
caution.
For
example,
because
invertebrates
metabolize
some
chemicals
less
efficiently
than
vertebrates,
a
baseline
BCF
determined
using
invertebrates
is
expected
to
be
higher
than
a
comparable
baseline
BCF
determined
using
fish.

7.
If
laboratory­
measured
BCFs
increase
or
decrease
as
the
concentration
of
the
chemical
increases
in
the
test
solutions
in
a
bioconcentration
test,
the
BCF
measured
at
the
lowest
test
concentration
above
control
concentrations
should
be
used
(
i.
e.,
a
BCF
should
not
be
calculated
from
a
control
treatment).
The
concentrations
of
an
inorganic
chemical
in
a
bioconcentration
test
should
be
greater
than
normal
background
levels
and
greater
than
levels
required
for
normal
nutrition
of
the
test
species
if
the
chemical
is
a
micronutrient,
but
below
levels
that
adversely
affect
the
species.
Bioaccumulation
of
an
inorganic
chemical
might
be
overestimated
if
concentrations
are
at
or
below
normal
background
levels
due
to,
for
example,
nutritional
requirements
of
the
test
organisms.

8.
For
inorganic
chemicals,
BCFs
should
be
used
only
if
they
are
expressed
on
a
wet
weight
basis.
BCFs
reported
on
a
dry
weight
basis
cannot
be
converted
to
wet
weight
221
unless
a
conversion
factor
is
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
BAF.

9.
BCFs
for
organic
chemicals
may
be
based
on
measurement
of
radioactivity
only
when
the
BCF
is
intended
to
include
metabolites,
when
there
is
confidence
that
there
is
no
interference
due
to
metabolites,
or
when
studies
are
conducted
to
determine
the
extent
of
metabolism,
thus
allowing
for
a
proper
correction.

10.
The
calculation
of
the
BCF
must
appropriately
address
growth
dilution.

11.
Other
aspects
of
the
methodology
used
should
be
similar
to
those
described
by
ASTM
(
1990).

In
addition,
the
magnitude
of
the
octanol­
water
partition
coefficient
(
K
ow)
and
the
availability
of
corroborating
BCF
data
should
be
considered.
For
example,
some
chemicals
with
high
log
K
ow
s
may
require
longer
than
28
days
to
reach
steady
state
conditions
between
the
organism
and
the
water
column.
As
with
BAFs,
the
BCFs
should
be
divided
by
the
mean
lipid
fraction
to
express
the
value
on
a
lipid­
normalized
basis.

Food­
Chain
Multipliers
The
food­
chain
multiplier
represents
a
measure
of
a
chemical's
tendency
to
biomagnify
in
aquatic
food
webs.
For
non­
polar
organic
chemicals,
FCMs
can
be
determined
from
bioaccumulation
models
or
directly
from
field
data
(
tissue
residues).

For
model­
derived
FCMs,
EPA
recommends
using
the
food
web
model
by
Gobas
(
1993)
to
determine
FCMs
for
nonpolar
organic
chemicals.
There
are
several
advantages
to
using
the
Gobas
(
1993)
model.
First,
uptake
into
both
benthic
and
pelagic
food
chains
is
measured,
incorporating
exposure
of
organisms
to
chemicals
from
both
the
sediments
and
the
water
column.
Second,
the
input
data
needed
to
run
the
model
can
be
readily
defined.
Third,
the
model­
predicted
BAFs
(
which
are
used
to
derive
the
FCMs)
are
in
agreement
with
field­
measured
BAFs
for
chemicals,
even
those
with
very
high
log
K
ow
s.
Finally,
the
model
predicts
chemical
residues
in
benthic
organisms
using
equilibrium
partitioning
theory,
which
is
consistent
with
EPA's
sediment
quality
criteria
effort.

The
Gobas
(
1993)
model
predicts
the
chemical
residues
in
the
organisms,
which
are
then
used
to
estimate
BAFs
for
each
species
in
the
food
chain:
222
BAF
fd
R
'
C
R
C
fd
w
(
Equation
2.4.18)

FCM
'
BAF
fd
R
K
ow
(
Equation
2.4.19)
where:

=
Lipid­
normalized
BAF
using
the
freely
dissolved
concentration
in
the
water
BAF
fd
R
=
Freely
dissolved
concentration
of
the
chemical
in
the
water
column
C
fd
w
=
Lipid­
normalized
concentration
in
appropriate
tissue
C
R
Food­
chain
multipliers
are
then
calculated
from
the
predicted
BAF
R
f
ds
using
the
following
equation:

where:

K
ow
=
n­
octanol/
water
partition
coefficient
Data
Requirements
to
Predict
the
Food­
Chain
Multiplier.
The
food
chain
model
by
Gobas
(
1993)
requires
specific
data
on
the
structure
of
the
food
chain
and
the
water
quality
characteristics
of
the
water
body
of
interest
including:

C
Feeding
preferences,
weights,
and
lipid
contents
for
each
species
in
the
food
chain.

C
Water
temperature.

C
Organic
carbon
content
of
the
sediment
and
the
water
column.

C
Concentrations
of
the
chemical
in
the
sediment
and
freely
dissolved
concentration
of
the
chemical
in
the
water
column.

C
Densities
of
lipid
and
organic
carbon.

C
Metabolic
transformation
rate
constant.
223
C
K
ow
values,
estimated
using
the
methods
described
in
Section
2.4.4.1
(
subsection
entitled:
Guidance
on
Selecting
Appropriate
K
ow
Values).

It
should
be
noted
that
the
model
of
Gobas
(
1993)
does
not
include
solubility
controls
or
limitations;
thus,
the
concentration
of
the
chemical
in
the
water
used
with
the
model
is
arbitrary
for
determining
the
BAFs,
i.
e.,
the
ratio
of
the
concentration
of
the
chemical
in
the
tissue
to
the
concentration
of
the
chemical
in
the
water
column
(
BAF)
obtained
using
a
1
ng/
L
concentration
of
the
chemical
in
the
water
will
be
equal
to
that
obtained
using
a
150
F
g/
L
concentration
of
the
chemical
for
a
specified
K
ow.

It
should
be
noted
that
the
model
of
Gobas
(
1993)
takes
the
total
concentration
of
the
chemical
in
the
water
and,
before
doing
any
predictions,
calculates
the
freely
dissolved
concentration
of
the
chemical
in
the
water.
The
freely
dissolved
concentration
of
the
chemical
in
the
water
is
then
used
in
all
subsequent
calculations
by
the
model.
By
setting
the
concentration
of
the
DOC
and
POC
to
0
mg/
L,
the
total
concentration
of
the
chemical
input
to
the
model
becomes
equal
to
the
freely
dissolved
concentration
of
the
chemical
in
the
water.
This
allows
the
fixing
of
the
chemical
concentration
relationship
between
sediment
and
water
phases
in
the
model.
BAFs
were
determined
by
dividing
the
chemical
residues
predicted
by
the
model
of
Gobas
(
1993)
by
the
freely
dissolved
concentration
of
the
chemical
in
the
water;
therefore,
they
are
not
influenced
by
the
concentration
of
DOC
input
to
the
model.

Measured
chemical
residues
in
fishes
assigned
to
trophic
level
3
can
be
higher
than
those
in
piscivorous
fishes
(
trophic
level
4)
from
the
same
food
chain.
Potential
causes
of
the
higher
concentrations
(
on
a
lipid
basis)
in
the
trophic
level
3
fish
include
1)
growth
rates
which
are
much
slower
than
rates
for
predator
fishes;
2)
slower
rates
of
metabolism
than
the
predator
fishes
for
the
chemicals
of
interest;
and
3)
feeding
preferences
for
trophic
level
3
fish,
including
predation
on
other
fish.
In
the
development
of
FCMs,
the
feeding
preferences
for
smelt
(
see
Gobas
1993)
consisted
of
a
mixture
of
trophic
level
2
and
3
organisms,
i.
e.,
mysids,
Diporeia
sp.,
and
sculpin.
This
mixture
of
different
trophic
levels
combined
with
bioenergetic
factors
for
the
smelt
caused
the
predicted
concentrations
of
the
chemicals
and
subsequently,
the
derived
FCMs,
to
be
slightly
larger
than
those
for
the
piscivorous
fishes
(
trophic
level
4).
224
Table
2.4.3:
Environmental
Parameters
and
Species
Characteristics
Used
With
the
Model
of
Gobas
(
1993)
for
Deriving
Chain
Multipliers
Environmental
parameters:

Mean
water
temperature:
8
E
C
Organic
carbon
content
of
the
sediment:
2.7%
Organic
carbon
content
of
the
water
column:
0.0
kg/
L
Density
of
lipids:
0.9
kg/
L
Density
of
organic
carbon:
0.9
kg/
L
Metabolic
transformation
rate
constant:
0.0
day­
1
A
socw
=
25
*
log
Kow.
Species
characteristics:
Phytoplankton
Lipid
content:
0.5%
Zooplankton:
Mysids
(
Mysis
relicta)
Lipid
content:
5.0%
Diporeia
sp.
Lipid
content:
3.0%
Sculpin
(
Cottus
cognatus)
Lipid
content:
8.0%
Weight:
5.4
g
Diet:
18%
zooplankton,
82%
Diporeia
sp.
(
pelagic/
benthic
food
web)
100%
Diporeia
sp.
(
all­
benthic
food
web)
100%
zooplankton
(
all­
pelagic
food
web)
Alewives
(
Alosa
pseudoharengus)
Lipid
content:
7.0%
Weight:
32
g
Diet:
60%
zooplankton,
40%
Diporeia
sp.
(
pelagic/
benthic
food
web)
100%
Diporeia
sp.
(
all­
benthic
food
web)
100%
zooplankton
(
all­
pelagic
food
web)
Smelt
(
Osmerus
mordax)
Lipid
content:
4.0%
Weight:
16
g
Diet:
54%
zooplankton,
21%
Diporeia
sp.,
25%
sculpin
54%
Diporeia
sp.,
46%
sculpin
(
all­
benthic
food
web)
68%
zooplankton,
32%
sculpin
(
all­
pelagic
food
web)
Salmonids
(
Salvelinus
namaycush,
Oncorhynchus
mykiss,
Oncorhynchus
velinus
namaycush)
Lipid
content:
11.0%
Weight:
2410
g
Diet:
10%
sculpin,
50%
alewives,
40%
smelt
225
Table
2.4.4:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
Pelagic
and
Benthic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
<
2.0
1.000
1.000
1.000
2.0
1.000
1.005
1.000
2.5
1.000
1.010
1.002
3.0
1.000
1.028
1.007
3.1
1.000
1.034
1.007
3.2
1.000
1.042
1.009
3.3
1.000
1.053
1.012
3.4
1.000
1.067
1.014
3.5
1.000
1.083
1.019
3.6
1.000
1.103
1.023
3.7
1.000
1.128
1.033
3.8
1.000
1.161
1.042
3.9
1.000
1.202
1.054
4.0
1.000
1.253
1.072
4.1
1.000
1.315
1.096
4.2
1.000
1.380
1.130
4.3
1.000
1.491
1.178
4.4
1.000
1.614
1.242
4.5
1.000
1.766
1.334
4.6
1.000
1.950
1.459
4.7
1.000
2.175
1.633
4.8
1.000
2.452
1.871
4.9
1.000
2.780
2.193
5.0
1.000
3.181
2.612
5.1
1.000
3.643
3.162
5.2
1.000
4.188
3.873
5.3
1.000
4.803
4.742
5.4
1.000
5.502
5.821
5.5
1.000
6.266
7.079
5.6
1.000
7.096
8.551
5.7
1.000
7.962
10.209
5.8
1.000
8.841
12.050
5.9
1.000
9.716
13.964
6.0
1.000
10.556
15.996
6.1
1.000
11.337
17.783
6.2
1.000
12.064
19.907
6.3
1.000
12.691
21.677
6.4
1.000
13.228
23.281
6.5
1.000
13.662
24.604
6.6
1.000
13.980
25.645
6.7
1.000
14.223
26.363
Table
2.4.4:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
Pelagic
and
Benthic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
226
6.8
1.000
14.355
26.669
6.9
1.000
14.388
26.669
7.0
1.000
14.305
26.242
7.1
1.000
14.142
25.468
7.2
1.000
13.852
24.322
7.3
1.000
13.474
22.856
7.4
1.000
12.987
21.038
7.5
1.000
12.517
18.967
7.6
1.000
11.708
16.749
7.7
1.000
10.914
14.388
7.8
1.000
10.069
12.050
7.9
1.000
9.162
9.840
8.0
1.000
8.222
7.798
8.1
1.000
7.278
6.012
8.2
1.000
6.361
4.519
8.3
1.000
5.489
3.311
8.4
1.000
4.683
2.371
8.5
1.000
3.949
1.663
8.6
1.000
3.296
1.146
8.7
1.000
2.732
0.778
8.8
1.000
2.246
0.521
8.9
1.000
1.837
0.345
9.0
1.000
1.493
0.226
a
The
FCMs
for
trophic
level
3
are
the
geometric
mean
of
the
FCMs
for
sculpin
and
alewife.
227
Table
2.4.5:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
All­
Pelagic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
<
2.0
1.000
1.000
1.000
2.0
1.000
1.000
1.001
2.1
1.000
1.000
1.001
2.2
1.000
1.000
1.001
2.3
1.000
1.000
1.002
2.4
1.000
1.000
1.002
2.5
1.000
1.001
1.002
2.6
1.000
1.001
1.003
2.7
1.000
1.001
1.003
2.8
1.000
1.001
1.004
2.9
1.000
1.001
1.005
3.0
1.000
1.002
1.006
3.1
1.000
1.002
1.007
3.2
1.000
1.002
1.009
3.3
1.000
1.003
1.011
3.4
1.000
1.004
1.013
3.5
1.000
1.005
1.016
3.6
1.000
1.006
1.021
3.7
1.000
1.007
1.026
3.8
1.000
1.009
1.032
3.9
1.000
1.011
1.040
4.0
1.000
1.014
1.050
4.1
1.000
1.018
1.063
4.2
1.000
1.022
1.078
4.3
1.000
1.028
1.097
4.4
1.000
1.034
1.121
4.5
1.000
1.043
1.150
4.6
1.000
1.053
1.185
4.7
1.000
1.066
1.228
4.8
1.000
1.081
1.280
4.9
1.000
1.099
1.342
5.0
1.000
1.121
1.415
5.1
1.000
1.147
1.502
5.2
1.000
1.176
1.603
5.3
1.000
1.210
1.719
5.4
1.000
1.248
1.851
5.5
1.000
1.289
1.999
5.6
1.000
1.333
2.162
5.7
1.000
1.379
2.337
5.8
1.000
1.425
2.521
5.9
1.000
1.471
2.711
Table
2.4.5:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
All­
Pelagic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
228
6.0
1.000
1.514
2.900
6.1
1.000
1.554
3.083
6.2
1.000
1.589
3.254
6.3
1.000
1.619
3.407
6.4
1.000
1.643
3.536
6.5
1.000
1.660
3.637
6.6
1.000
1.671
3.705
6.7
1.000
1.674
3.738
6.8
1.000
1.669
3.733
6.9
1.000
1.657
3.688
7.0
1.000
1.636
3.602
7.1
1.000
1.606
3.474
7.2
1.000
1.567
3.305
7.3
1.000
1.518
3.094
7.4
1.000
1.458
2.848
7.5
1.000
1.389
2.570
7.6
1.000
1.308
2.270
7.7
1.000
1.219
1.958
7.8
1.000
1.122
1.647
7.9
1.000
1.020
1.349
8.0
1.000
0.915
1.076
8.1
1.000
0.810
0.835
8.2
1.000
0.707
0.631
8.3
1.000
0.610
0.466
8.4
1.000
0.520
0.336
8.5
1.000
0.438
0.237
8.6
1.000
0.366
0.164
8.7
1.000
0.303
0.112
8.8
1.000
0.249
0.075
8.9
1.000
0.204
0.050
9.0
1.000
0.166
0.033
a
The
FCMs
for
trophic
level
3
are
the
geometric
mean
of
the
FCMs
for
sculpin
and
alewife.
229
Table
2.4.6:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
All­
Benthic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
<
2.0
1.000
1.000
1.000
2.0
1.000
1.009
1.001
2.1
1.000
1.010
1.001
2.2
1.000
1.011
1.001
2.3
1.000
1.013
1.002
2.4
1.000
1.015
1.002
2.5
1.000
1.018
1.002
2.6
1.000
1.022
1.003
2.7
1.000
1.026
1.003
2.8
1.000
1.032
1.004
2.9
1.000
1.039
1.005
3.0
1.000
1.048
1.006
3.1
1.000
1.060
1.008
3.2
1.000
1.074
1.010
3.3
1.000
1.092
1.013
3.4
1.000
1.114
1.017
3.5
1.000
1.142
1.022
3.6
1.000
1.177
1.029
3.7
1.000
1.222
1.039
3.8
1.000
1.277
1.053
3.9
1.000
1.347
1.072
4.0
1.000
1.433
1.099
4.1
1.000
1.541
1.138
4.2
1.000
1.676
1.195
4.3
1.000
1.843
1.276
4.4
1.000
2.050
1.392
4.5
1.000
2.306
1.559
4.6
1.000
2.620
1.796
4.7
1.000
3.004
2.131
4.8
1.000
3.470
2.595
4.9
1.000
4.032
3.232
5.0
1.000
4.702
4.087
5.1
1.000
5.492
5.215
5.2
1.000
6.411
6.668
5.3
1.000
7.462
8.501
5.4
1.000
8.643
10.754
5.5
1.000
9.942
13.457
5.6
1.000
11.337
16.617
5.7
1.000
12.800
20.213
5.8
1.000
14.293
24.192
5.9
1.000
15.774
28.468
Table
2.4.6:
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3,
&
4
(
All­
Benthic
Structure)

Log
Kow
Trophic
Level
2
Trophica
Level
3
Trophic
Level
4
230
6.0
1.000
17.202
32.920
6.1
1.000
18.539
37.405
6.2
1.000
19.753
41.764
6.3
1.000
20.822
45.836
6.4
1.000
21.730
49.472
6.5
1.000
22.469
52.544
6.6
1.000
23.037
54.949
6.7
1.000
23.433
56.610
6.8
1.000
23.659
57.472
6.9
1.000
23.717
57.501
7.0
1.000
23.606
56.679
7.1
1.000
23.326
55.007
7.2
1.000
22.873
52.507
7.3
1.000
22.246
49.227
7.4
1.000
21.443
45.254
7.5
1.000
20.467
40.714
7.6
1.000
19.327
35.780
7.7
1.000
18.040
30.657
7.8
1.000
16.629
25.572
7.9
1.000
15.129
20.744
8.0
1.000
13.580
16.359
8.1
1.000
12.026
12.547
8.2
1.000
10.510
9.368
8.3
1.000
9.068
6.822
8.4
1.000
7.732
4.856
8.5
1.000
6.522
3.387
8.6
1.000
5.448
2.321
8.7
1.000
4.513
1.567
8.8
1.000
3.711
1.045
8.9
1.000
3.032
0.689
9.0
1.000
2.465
0.451
a
The
FCMs
for
trophic
level
3
are
the
geometric
mean
of
the
FCMs
for
sculpin
and
alewife.
25These
equations
were
used
to
derive
the
equation
for
ffd
presented
in
Section
2.4.4.1,
and
are
discussed
in
Appendix
D.

231
f
fd
'
1
1
%
DOC
@
K
doc
%
POC
@
K
poc
(
Equation
2.4.21)

C
fd
w
'
C
t
w
@
f
fd
(
Equation
2.4.22)
The
freely
dissolved
concentrations
of
the
chemicals
in
the
water
column
were
calculated
from
the
data
of
Oliver
and
Niimi
(
1988)
using
the
equations
of
Gschwend
and
Wu
(
1985)
and
Cook
et
al.
(
1993)
25
for
freely
dissolved
fraction:

and
freely
dissolved
concentration:

where:

f
fd
=
Fraction
of
the
chemical
which
is
freely
dissolved
in
the
water
DOC
=
Concentration
of
dissolved
organic
carbon
POC
=
Concentration
of
particulate
organic
carbon
K
doc
=
Partition
coefficient
for
the
chemical
between
the
DOC
and
the
freely
dissolved
phase
in
the
water
K
poc
=
Partition
coefficient
for
the
chemical
between
the
POC
phase
and
the
freely
dissolved
phase
in
the
water
C
w
t
=
Total
concentration
of
the
chemical
in
the
water
C
w
f
d
=
Freely
dissolved
concentration
of
the
chemical
in
the
water
The
concentrations
in
the
water
reported
by
Oliver
and
Niimi
(
1988)
were
obtained
by
liquidliquid
extraction
of
aliquots
of
Lake
Ontario
water
which
had
passed
through
a
continuous­
flow
centrifuge
to
remove
POC.
Therefore,
the
concentrations
in
the
water
reported
by
Oliver
and
Niimi
(
1988)
include
both
the
freely
dissolved
chemical
and
the
chemical
associated
with
the
DOC
in
the
water
sample.
The
above
equations
were
used
to
derive
the
freely
dissolved
concentrations
of
the
232
chemicals
in
the
water
by
setting
the
POC
=
0
mg/
L
and
DOC
=
2
mg/
L.
K
ow
s
used
to
derive
the
freely
dissolved
concentrations
have
been
reported
elsewhere
(
USEPA,
1995c).

In
Exhibit
2.4.10,
A
socw
is
plotted
against
K
ow
for
each
chemical
reported
by
Oliver
and
Niimi
(
1988).
Because
the
chemical
residues
by
Oliver
and
Niimi
(
1988)
for
the
foraging
and
piscivorous
fishes
were
almost
entirely
for
the
PCBs
and
pesticides,
a
regression
equation
of
the
form
log
II
=
A
x
log
K
ow
+
B
was
determined
using
this
set
of
chemicals.
Using
the
geometric
mean
regression
technique,
the
slope
(
standard
deviation)
of
this
equation
was
1.07
(
0.078).
This
slope
was
not
significantly
different
from
1.0,
and
thus,
the
relationship
between
the
A
socw/
K
ow
of
the
individual
PCB
and
pesticide
compounds.
The
average
(
standard
deviation)
ratio
was
24.7
(
25.7).
The
following
relationship
was
therefore
selected
to
define
A
socw
in
this
investigation:
A
socw
=
25
*
log
K
ow.

In
addition
to
determining
FCMs
for
organic
substances
using
the
Gobas
(
1993)
model,
EPA
also
recommends
the
use
of
FCMs
derived
from
field
data
where
data
are
sufficient
to
enable
scientifically
valid
and
reliable
determinations
to
be
made.
Currently,
field­
measured
FCMs
are
the
only
method
recommended
for
estimating
FCMs
for
inorganic
substances
because
appropriate
modelderived
estimates
are
not
yet
available.
Similarly,
field­
measured
FCMs
can
also
be
determined
for
organic
substances.
Compared
to
the
model­
based
FCMs
described
previously,
properly
derived
field­
based
FCMs
may
offer
some
advantages
in
some
situations.
For
example,
field­
measured
FCMs
rely
on
measured
contaminant
concentrations
in
tissues
of
biota
and
therefore
inherently
account
for
any
contaminant
metabolism
which
may
occur.
Field­
measured
FCMs
may
also
be
useful
for
estimating
BAFs
for
some
highly
hydrophobic
contaminants
whose
water
column
concentrations
are
very
difficult
to
determine
with
accuracy
and
precision.
Furthermore,
field­
measured
FCMs
may
better
reflect
local
conditions
that
can
influence
bioaccumulation,
such
as
differences
in
food
web
structure,
exposure
pathways,
water
body
type,
and
target
species.
Finally,
use
of
field­
measured
FCMs
in
estimating
BAFs
may
enable
existing
data
on
contaminant
concentrations
in
aquatic
organisms
to
be
used
in
situations
where
companion
water
column
data
are
unavailable
or
are
judged
to
be
unreliable
for
calculating
a
BAF.
234
As
discussed
below
and
in
Appendix
C,
FCMs
are
related
to
and
can
be
determined
from
biomagnification
factors
(
BMF).
For
example:

1.
FCM
TL­
2
=
BMF
TL­
2
2.
FCM
TL­
3
=
(
BMF
TL­
3)
(
FCM
TL­
2)
=
(
BMF
TL­
3)
(
BMF
TL­
2)

3.
FCM
TL­
4
=
(
BMF
TL­
4)
(
FCM
TL­
3)
=
(
BMF
TL­
4)
(
BMF
TL­
3)
(
BMF
TL­
2)

where:

FCM
=
Food
chain
multiplier
for
designated
trophic
level
(
TL2,
TL3,
or
TL4)
BMF
=
Biomagnification
factor
for
designated
trophic
level
(
TL2,
TL3,
or
TL4)

The
basic
difference
between
FCMs
and
BMFs
is
that
FCMs
relate
back
to
trophic
level
one
(
or
trophic
level
two
as
assumed
by
the
Gobas
(
1993)
model),
whereas
BMFs
always
relate
back
to
the
next
lowest
trophic
level.
For
nonpolar
organic
chemicals,
biomagnification
factors
can
be
calculated
from
lipid­
normalized
tissue
residue
concentrations
determined
in
biota
at
a
site
according
to
the
following
equation.

BMF
TL2
=
(
C
R
­
TL2)
/
(
C
R
­
TL1)
BMF
TL3
=
(
C
R
­
TL3)
/
(
C
R
­
TL2)
BMF
TL4
=
(
C
R
­
TL4)
/
(
C
R
­
TL3)

where:

C
R
=
Lipid­
normalized
concentration
of
chemical
in
tissue
of
appropriate
biota
that
occupy
the
specified
trophic
level
(
TL2,
TL3,
or
TL4).

For
inorganic
chemicals,
BMFs
are
determined
as
shown
above,
except
that
tissue
concentrations
expressed
on
a
wet­
weight
basis
and
are
not
lipid
normalized.
In
calculating
fieldderived
BMFs
for
determining
FCMs,
care
must
be
taken
to
ensure
that
the
biota
upon
which
they
are
based
actually
represent
functional
predator­
prey
relationships
at
the
study
site,
and
therefore,
would
accurately
reflect
any
biomagnification
that
may
occur
at
the
site.

As
with
field­
measured
BAFs,
the
potential
advantages
of
using
field
data
for
estimating
bioaccumulation
can
be
offset
by
improper
collection
and
use
of
information.
In
calculating
fieldbased
FCMs,
steps
similar
to
those
recommended
for
determining
field­
measured
BAFs
need
to
be
taken
to
ensure
that
the
resulting
FCMs
accurately
represent
potential
exposures
to
the
target
population
at
the
site(
s)
of
interest.
Some
of
the
general
procedural
and
quality
assurance
requirements
that
are
important
for
determining
field­
measured
FCMs
include:
235
1.
A
food
web
analysis
should
be
conducted
for
the
site
from
which
the
tissue
concentration
data
are
to
be
determined
(
or
have
been
already
been
determined)
to
identify
the
appropriate
trophic
levels
for
the
aquatic
organisms
and
appropriate
predator­
prey
relationships.
To
assist
in
trophic
level
determinations,
EPA
is
in
the
process
of
finalizing
its
draft
trophic
level
and
exposure
analysis
documents
(
USEPA
1995d;
1995e;
1995f)
which
include
trophic
level
analyses
of
numerous
species
in
the
aquatic­
based
food
web.

2.
The
aquatic
organisms
sampled
from
each
trophic
level
should
reflect
the
most
important
exposure
pathways
leading
to
human
exposure
via
consumption
of
aquatic
organisms.
For
higher
trophic
levels
(
e.
g.,
3
and
4),
aquatic
species
should
also
reflect
those
that
are
commonly
consumed
by
humans.

3.
Collection
of
tissue
concentration
field
data
for
a
specific
site
for
which
criteria
are
to
be
derived
and
with
the
specific
species
of
concern
are
preferred.

4.
If
data
cannot
be
collected
from
every
site
for
which
criteria
are
to
be
derived,
the
site
of
the
field
study
should
not
be
so
unique
that
the
FCM
values
cannot
be
extrapolated
to
other
locations
where
the
criteria
and
values
will
apply.

5.
Samples
of
the
appropriate
resident
species
and
the
water
in
which
they
reside
should
be
collected
and
analyzed
using
appropriate,
sensitive,
accurate,
and
precise
methods
to
determine
the
concentrations
of
bioaccumulative
chemicals
present
in
the
tissues.

6.
For
organic
chemicals,
the
percent
lipid
should
be
either
measured
or
reliably
estimated
for
the
tissue
used
in
the
determination
of
the
lipid
normalized
concentration
in
the
organism's
edible
tissues.

7.
The
tissue
concentrations
should
reflect
average
exposure
conditions
over
the
time
period
required
to
achieve
steady­
state
conditions
for
the
contaminant
in
the
target
species
(
usually
trophic
level
three
or
four
organisms).

2.4.4.4
Baseline
BAF
from
Predicted
BCF
and
Food­
Chain
Multiplier
As
the
fourth
tier
in
the
data
preference
hierarchy
for
nonpolar
organics
(
e.
g.,
when
acceptable
field­
measured
BAFs,
BSAFs,
or
laboratory­
measured
BCFs
are
unavailable),
EPA
recommends
multiplying
the
FCM
by
the
K
ow
for
the
chemical
for
estimating
the
baseline
BAF.
This
is
equivalent
to
the
direct
use
of
the
food
chain
bioaccumulation
model
for
estimating
the
BAF
when
modelderived
FCMs
are
used.
For
each
trophic
level,
the
equation
for
calculating
this
baseline
BAF
is:

Baseline
BAF
'
(
FCM)
(
predicted
baseline
BCF)
'
(
FCM)
(
K
ow)

(
Equation
2.4.23)
236
log
BCF
'
1.00
log
K
ow
&
0.08
(
Equation
2.4.24)
where:

Baseline
BAF
=
BAF
expressed
on
a
lipid­
normalized
basis
using
the
freely
dissolved
concentration
of
the
chemical
in
water
FCM
=
Food­
chain
multiplier
obtained
from
Table
2.4.3,
2.4.4,
or
2.4.5
by
linear
interpolation
(
or
from
appropriate
field
data)

K
ow
=
Octanol­
water
partition
coefficient
Use
of
the
K
ow
in
place
of
the
baseline
BCF
is
supported
by
equilibrium
partitioning
theory.
The
linear
relationship
between
the
BCF
and
K
ow
is
also
based
on
the
underlying
assumption
that
the
bioconcentration
process
can
be
viewed
as
a
partitioning
of
a
chemical
between
the
lipids
of
the
aquatic
organisms
and
water
and
that
the
K
ow
is
a
useful
surrogate
for
this
partitioning
process
(
Mackay,
1982).
These
authors
presented
a
thermodynamic
basis
for
the
partitioning
process
for
bioconcentration
and,
in
essence,
the
BCF
on
a
lipid­
normalized
basis
(
and
freely
dissolved
concentration
of
the
chemical
in
the
water)
should
be
similar
if
not
equal
to
the
K
ow
for
organic
chemicals.

In
addition,
empirical
data
support
the
use
of
the
K
ow
in
place
of
the
BCF.
As
indicated
by
Isnard
and
Lambert
(
1988),
numerous
studies
have
demonstrated
a
linear
relationship
between
the
logarithm
of
the
BCF
and
the
logarithm
of
the
octanol­
water
partition
coefficient
(
K
ow)
for
organic
chemicals
for
fish
and
other
aquatic
organisms.
In
addition,
when
the
regression
equations
are
constructed
using
BCFs
reported
on
a
lipid­
normalized
basis,
the
slopes
and
intercepts
are
not
significantly
different
from
one
and
zero,
respectively.
For
example,
de
Wolf
et
al.
(
1992)
adjusted
a
relationship
reported
by
Mackay
(
1982)
to
a
100
percent
lipid
basis
(
lipid
normalized
basis)
and
obtained
the
following
relationship:

For
chemicals
with
large
log
K
ow
s
(
i.
e.,
greater
than
6.0),
reported
BCFs
are
often
not
equal
to
the
K
ow
for
non­
metabolizable
chemicals.
BCFs
for
non­
metabolizable
chemicals
are
equal
to
the
K
ow
when
the
BCFs
are
reported
on
lipid­
normalized
basis,
determined
using
the
freely
dissolved
concentration
of
the
chemical
in
the
exposure
water,
corrected
for
growth
dilution,
determined
from
steady­
state
conditions
or
determined
from
accurate
measurements
of
the
chemical's
uptake
(
k
1)
and
elimination
(
k
2)
rate
constants
from
and
to
the
water,
respectively,
and
determined
using
no
solvent
carriers
in
the
exposure.
Therefore,
EPA
recommends
that
the
K
ow
can
be
used
as
an
approximation
of
the
BCF.

It
is
important
to
recognize
the
BAF
estimated
using
this
method
is
based
on
nonmetabolizable
chemicals.
Thus,
predicted
BAFs
will
be
larger
than
laboratory­
measured
BCFs
for
237
chemicals
that
undergo
some
metabolism.
For
some
chemicals,
such
as
PAHs,
the
predicted
BAF
can
be
higher
than
the
measured
BAF.

2.4.4.5
Metabolism
One
factor
affecting
bioaccumulation
is
metabolism
of
the
chemical
by
aquatic
organisms.
Many
organic
chemicals
that
are
taken
up
by
aquatic
organisms
are
transformed
to
some
extent
by
the
organism's
metabolic
processes,
but
the
rate
of
metabolism
varies
widely
across
chemicals
and
species.
For
most
organic
chemicals,
metabolism
increases
the
depuration
rate
and
reduces
the
BAF
of
the
parent
compound.

The
procedures
to
measure
or
predict
BAFs
differ
in
the
extent
to
which
they
account
for
metabolism.
Field­
measured
BAFs
and
BSAFs
inherently
account
for
any
metabolism
of
the
chemical.
Predicted
BAFs
that
are
obtained
by
multiplying
a
laboratory­
measured
BCF
by
a
modelderived
FCM
take
into
account
the
effect
of
metabolism
on
the
BCF,
but
not
on
the
FCM.
Use
of
field­
derived
FCMs
takes
into
account
metabolism.
A
food
chain
model
prediction
of
the
BAF
(
the
fourth
data
preference
for
nonpolar
organics)
makes
no
allowance
for
chemical
metabolism.
Despite
the
differential
effects
of
metabolism
on
predicted
BAFs,
information
is
not
available
for
predicting
the
effect
of
metabolism
on
predicted
BAFs
that
rely
on
the
food­
chain
multiplier
or
predicted
BCFs.

2.4.4.6
Mixtures
For
chemical
classes
where
sufficient
data
on
the
relative
toxicities
of
individual
members
of
the
class
is
available,
toxicity
equivalency
factors
(
TEF)
can
be
used
to
assess
the
total
toxicity
risk
of
the
mixture
(
for
further
discussion
of
TEFs,
see
Appendix
I,
Section
A.
3.
f
and
Appendix
III,
Section
F.
4
of
the
Federal
Register
notice).
To
date,
adequate
data
to
support
use
of
TEFs
has
been
found
in
only
one
class
of
compounds
(
dioxins)
(
USEPA,
1989).
Because
individual
chemicals
of
a
class
(
e.
g.,
PCDDs
and
PCDFs)
not
only
differ
in
their
relative
toxicities,
but
also
their
relative
bioaccumulation
potentials,
bioaccumulation
equivalency
factors
(
BEF)
can
also
be
used
to
account
such
differences.
Bioaccumulation
equivalency
factors
have
been
developed
for
PCDDs
and
PCDFs
based
on
bioaccumulation
data
for
the
Great
Lakes
(
60
FR
15366).
As
adequate
data
become
available
to
establish
TEFs
for
other
chemical
classes,
the
BEF
methodology
described
in
60
FR
15366
and
U.
S.
EPA
1995c
should
be
considered
for
assessing
combined
risk
from
chemical
mixtures.

2.4.5
BAFs
Used
in
Deriving
AWQC
As
discussed
above
for
nonpolar
organic
chemicals,
after
the
baseline
BAF
has
been
derived
for
a
chemical
using
one
of
the
four
methods,
the
next
step
is
to
calculate
a
BAF
that
will
be
used
in
the
derivation
of
AWQC.
This
requires
information
on:
(
1)
the
baseline
BAF
for
the
chemical
of
interest
using
one
of
the
four
methods
described
above;
(
2)
the
percent
lipid
of
the
aquatic
organisms
consumed
by
humans
at
the
site
of
interest;
and
(
3)
the
freely
dissolved
fraction
of
the
chemical
in
the
ambient
water
of
interest.
238
BAF
for
AWQC(
TL
n)
'
[
(
Baseline
BAF
fd
R
)
TL
n
@
(
f
R
)
TL
n
%
1]
@
(
f
fd)

(
Equation
2.4.25)
2.4.5.1
General
Equation
for
an
AWQC
BAF
For
each
trophic
level,
the
equation
for
deriving
a
BAF
to
be
used
in
deriving
AWQC
is
applicable
to
all
four
methods
and
is:

where:

BAF
for
AWQC
(
TL
n)
=
BAF
at
trophic
level
"
n"
used
to
derive
AWQC
based
on
site
conditions
for
lipid
content
of
consumed
aquatic
organisms
for
trophic
level
"
n"
and
the
freely
dissolved
fraction
in
the
site
water
Baseline
BAF
R
f
d
(
TL
n)
=
BAF
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis
for
trophic
level
"
n"

f
R
(
TL
n)
=
Fraction
lipid
of
aquatic
species
consumed
at
trophic
level
"
n"

f
fd
=
Fraction
of
the
total
chemical
in
water
that
is
freely
dissolved
2.4.5.2
Baseline
BAF
The
baseline
BAFs
used
in
this
equation
are
those
derived
from
the
equations
presented
in
Section
2.4.3
above.

2.4.5.3
Lipid
Content
of
Aquatic
Species
Eaten
by
Humans
The
lipid
content
of
the
aquatic
species
consumed
by
humans
is
required
when
deriving
BAFs
for
a
nonpolar
organic
chemical
that
will
be
used
for
deriving
ambient
water
quality
criteria
(
AWQC).
Information
on
lipid
content
is
needed
because
it
affects
the
extent
of
bioaccumulation
of
nonpolar
organic
chemicals
in
aquatic
organisms
(
Mackay,
1982;
Connolly
and
Pederson,
1988;
Thomann,
1989)
and
therefore,
is
important
in
characterizing
the
potential
contaminant
exposure
to
the
target
population
(
e.
g.,
general
population,
sport
anglers,
subsistence
anglers).

The
lipid
content
of
aquatic
organisms
can
vary
considerably
across
different
species,
across
different
locations
for
a
given
species
across
seasons,
and
across
different
age
classes
(
life
stages)
of
a
species
at
a
given
location.
In
addition
to
lipid
content,
the
types
and
quantity
of
aquatic
species
239
eaten
by
individuals
differ
substantially
throughout
the
United
States.
In
order
to
account
for
some
of
this
variability
in
determining
a
representative
lipid
content
of
consumed
aquatic
species,
EPA
recommends
that
the
lipid
fraction
of
aquatic
organism
be
weighted
by
the
consumption
rate
of
those
aquatic
species
consumed
by
the
target
population
based
on
information
from
the
local
or
regional
survey.
Information
on
consumption
rates
and
lipid
content
is
most
accurately
determined
on
a
local
or
regional
basis
and
is
recommended
as
the
first
choice
for
estimating
lipid
content
of
consumed
species.
Since
baseline
BAFs
are
determined
for
each
trophic
level
and
must
be
adjusted
to
reflect
the
lipid
content
of
consumed
aquatic
species,
EPA
recommends
that
the
consumption­
weighted
lipid
content
of
consumed
aquatic
organisms
also
be
determined
for
each
trophic
level.
If
sufficient
information
is
not
available
to
derive
trophic
level
specific
lipid
contents,
then
States
and
Tribes
may
choose
to
calculate
an
overall
consumption­
weighted
lipid
content
that
combines
data
from
the
relevant
trophic
levels.

EPA
recognizes
that
local
or
regional
fish
consumption
data
are
not
always
available.
Therefore,
EPA
has
derived
default,
national
estimates
of
consumption­
weighted
lipid
content
for
use
in
deriving
national
AWQC,
when
local
or
regional
information
is
unavailable.
If
local
data
on
both
aquatic
species
consumption
rates
and
lipid
contents
are
not
available,
States
may
wish
to
use
national
default
lipid
values
calculated
by
EPA.
Using
the
general
relationship
in
Equation
2.4.26
and
information
on
national
finfish
and
shellfish
consumption
rates
at
various
trophic
levels,
EPA
has
developed
a
national
default
consumption­
weighted
mean
lipid
values
of
2.3%
at
trophic
level
2,
1.5%
at
trophic
level
3,
and
3.1%
at
trophic
level
4
(
expressed
to
two
significant
digits
for
convenience).
The
data
sources,
calculations
and
assumptions
supporting
of
these
national
default
lipid
values
are
described
below.

Data
Sources
To
estimate
a
national
default
consumption­
weighted
percent
lipid
value
for
humans,
information
is
needed
at
a
national
level
on:
(
1)
the
type
and
quantity
of
aquatic
biota
consumed
by
humans;
(
2)
the
percent
lipid
of
the
aquatic
biota
consumed
by
humans;
and
(
3)
the
trophic
level
of
the
consumed
species.
These
data
are
described
below.

Fish
Consumption
Data.
Information
on
the
types
and
quantity
of
aquatic
organisms
consumed
in
the
U.
S.
were
obtained
from
USDA's
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII)
(
USEPA,
1998b).
This
survey
provided
daily
average
per
capita
estimates
of
fish
consumption
for
the
U.
S.
population
for
categories
of
estuarine,
freshwater,
and
marine
fish
and
shellfish.
Although
other
regional
or
local
surveys
were
available,
the
CSFII
was
selected
because
it
provided
consumption
information
on
a
national
basis
and
was
the
most
recent
data
available.
In
this
survey,
consumption
rates
were
divided
into
16
categories
representing
mostly
estuarine
species
and
5
categories
representing
mostly
freshwater
species.
Mean
per
capita
consumption
rates
were
characterized
for
individuals
18
years
and
older.
For
a
detailed
discussion
of
the
use
of
the
CSFII
data
see
the
chapter
on
exposure
in
this
TSD.
Table
2.4.7
displays
the
habitat
classification,
CSFII
consumption
categories,
mean
per
capita
consumption
rates,
and
the
fraction
of
total
estuarine
and
freshwater
consumption
represented
by
each
category
in
the
survey.
240
Lipid
Content
of
Consumed
Species.
The
second
type
of
information
required
in
deriving
national
default
values
for
lipid
content
includes
data
on
the
lipid
content
of
consumed
aquatic
species.
Six
primary
data
sources
were
used
to
estimate
lipid
content.
These
include:
EPA's
STORET
data
base,
EPA's
National
Study
of
Chemical
Residues
in
Fish
(
USEPA,
1992),
two
reviews
from
National
Marine
Fisheries
Service
of
the
National
Oceanic
and
Atmospheric
Administration
(
Sidwell,
1981;
Kryznowek
and
Murphy,
1987),
data
from
the
California
Toxic
Substances
Monitoring
Program
(
TSMP),
Green
Bay
Mass
Balance
Study
(
USEPA,
1992b,
1995g),
and
a
study
of
the
Hudson
River
conducted
by
the
New
York
Department
of
Environmental
Conservation
(
HydroQual,
Inc.,
portions
published
in
Armstrong
and
Sloan,
1988).
Each
of
these
data
sources
are
discussed
in
more
detail
below.

STORET.
Data
from
EPA's
STORET
(
STOrage
and
RETrieval
of
U.
S.
Waterways
Parametric
data)
data
base,
a
waterway­
related
monitoring
data
base,
were
retrieved
by
downloading
tissue
and
sediment
chemistry
data
from
ambient
non­
land­
based
monitoring
stations,
including
lipid
content,
from
1980
through
October,
1993.
Data
are
stored
in
STORET
by
many
government
agencies,
both
federal
and
state.
Most
of
the
data
used
in
this
analysis
were
collected
in
the
Midwest,
in
particular
in
Illinois,
Minnesota,
Michigan,
Iowa,
Kansas,
Missouri,
and
Nebraska.
The
number
of
individual
organisms
per
sample
is
not
known.
Information
on
the
common
and
Latin
name
for
the
species
sampled,
the
tissue
sampled,
the
percent
lipid
content,
the
collection
date
and
location,
and
the
agency
responsible
for
the
data
were
retrieved
from
STORET.
Data
from
the
other
data
bases
used
in
this
analysis
were
confirmed
as
not
being
present
in
STORET,
which
could
have
caused
double­
counting
of
some
samples.
241
Table
2.4.7:
Aquatic
Organism
Categories
and
Average
Consumption
Rates
from
CSFII
Habitat
USDA
CSFII
Category
Estimated
Mean
Consumption
Rate
(
g/
person/
day)
Percentage
of
Total
Consumption
Rate
Estuarine
shrimp
1.72959
30.08%

perch
0.60368
10.50%

estuarine
flatfish
0.52735
9.17%

crab
0.37126
6.46%

flounder
0.29941
5.21%

oyster
0.22555
3.92%

mullet
0.08756
1.52%

croaker
0.06749
1.17%

herring
0.03925
0.68%

smelt
0.03753
0.65%

clam
0.03146
0.55%

scallop
0.00322
0.06%

anchovy
0.00292
0.05%

scup
0.00068
0.01%

sturgeon
0.00054
0.01%

Freshwater
catfish
1.18227
20.56%

trout
0.44946
7.82%

carp
0.05727
1.00%

pike
0.02337
0.41%

freshwater
salmon
0.01096
0.19%

Source:
USDA
combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
and
older
in
the
U.
S.
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.
The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Database
for
individual
food
intake
surveys.
The
number
of
digits
does
not
imply
their
significance.
242
National
Study
of
Chemical
Residues
in
Fish.
This
EPA
study
was
a
one­
time
screening
investigation
to
determine
the
prevalence
of
selected
bioaccumulative
pollutants
in
fish
(
USEPA,
1992a).
Three
to
five
fish
collected
from
one
location
were
used
for
each
composite
sample.
For
each
composite
sample,
two
measurements
of
the
percent
lipid
content
were
obtained,
one
from
the
test
for
dioxins/
furans
and
one
from
the
test
for
other
xenobiotics.
The
average
of
the
two
lipid
values
was
used
to
represent
each
sample
data
point.
Location
and
sampling
date
information
were
available
as
was
the
common
name
of
the
species
collected
and
the
tissue
type
sampled
(
whole
body,
fillet).

NOAA
Data.
Data
on
the
lipid
content
of
estuarine
species
from
the
National
Marine
Fisheries
Service
of
the
National
Oceanic
and
Atmospheric
Administration
were
available
from
two
reviews
(
Sidwell,
1981;
Kryznowek
and
Murphy,
1987).
These
reviews
consist
of
compilations
of
data
from
primary
literature
sources.
Information
on
the
specific
location
and
number
of
individuals
per
value
were
not
available
in
these
reviews.
Data
not
collected
in
North
America
were
excluded,
where
information
was
available
to
make
this
distinction.
Information
was
available
on
common
and
Latin
name,
tissue
type,
and
method
of
preparation
(
e.
g.,
raw,
cooked).
Only
samples
that
were
indicated
as
being
fresh
or
raw,
or
for
which
no
preparation
information
was
available,
were
used
in
the
analysis.
Other
information
such
as
the
number
of
individuals
in
a
sample,
age,
weight,
and
sex
were
not
available.
This
data
source
was
used
to
augment
the
data
from
the
other
data
sources
which
were
very
limited
in
quantity.
For
those
categories
(
catfish,
trout,
carp,
pike,
and
perch)
for
which
we
had
extensive
data
from
the
other
sources,
data
from
NOAA
were
not
used.

California
Toxic
Substances
Monitoring
Program
(
TSMP).
This
program
is
run
by
the
California
Environmental
Protection
Agency,
California
State
Water
Resources
Control
Board,
Division
of
Water
Quality,
Monitoring
and
Assessment
Unit.
The
TSMP
was
organized
to
provide
a
uniform
statewide
approach
to
the
detection
and
evaluation
of
the
occurrence
of
toxic
substances
in
fresh
water,
and
to
a
limited
extent,
in
estuarine
and
marine
waters,
through
the
analysis
of
fish
and
other
aquatic
life.
Samples
are
collected
annually
and
composite
samples
of
six
fish
are
collected
when
possible.
The
data
base
provides
information
on
age,
sample
collection
date,
location,
number
of
organisms
per
sample,
weight,
and
length.
Most
samples
for
the
species
of
interest
are
fillet
samples,
although
some
whole
organism
samples
are
also
present.
Data
were
obtained
via
the
Internet
at
World
Wide
Web
site:
http://
www.
swrcb.
ca.
gov.

Green
Bay
Mass
Balance
Study.
This
study
includes
lipid
content
from
aquatic
species
from
Green
Bay,
Lake
Michigan.
All
data
are
whole
body
samples.
Included
in
the
data
base
are
information
on
collection
date,
zone
of
bay
in
which
the
sample
was
collected,
age,
and
number
of
fish
in
average
or
composite.
Data
were
obtained
from
HydroQual,
Inc.
(
U.
S.
EPA
1992b;
U.
S.
EPA
1995g).
These
data
are
also
available
on
the
World
Wide
Web
at
www.
epa.
gov/
glnpo/
monitor.
html.

Hudson
River
Data.
The
New
York
Department
of
Environmental
Conservation
conducted
sampling
from
which
percent
lipid
content
for
fish
species
from
the
Hudson
River
were
available.
All
data
are
for
muscle
fillet
samples,
and
there
is
one
individual
per
sample.
In
this
data
base,
collection
243
date,
length,
weight,
and
sex
are
available,
as
well
as
location
in
the
form
of
river
mile.
Data
were
obtained
from
HydroQual,
Inc.
(
U.
S.
EPA
1995h;
1995i
and
Armstrong
and
Sloan,
1988).

Data
Analysis
Data
Screening/
Treatment.
Several
steps
were
required
to
prepare
the
data
for
the
calculation
of
average,
consumption­
weighted
lipid
content.
As
described
further
in
the
following
section,
each
record
was
assigned
to
a
CSFII
species
category
based
information
on
the
common
and
Latin
names
given
in
the
various
data
bases
and
information
on
whether
they
could
reasonably
be
expected
to
be
consumed.
Data
for
those
species
that
could
not
be
assigned
to
a
CSFII
species
category
were
omitted.
For
the
STORET
data,
only
those
data
that
contained
the
common
name,
Latin
name,
and
species
code
were
used
so
as
to
maintain
consistency
of
species
names
within
and
across
data
bases.
In
addition,
several
steps
were
taken
to
correct
species
codes
where
known
mistakes
occurred.
An
upper
bound
lipid
content
was
also
set
at
35
percent
to
exclude
extreme
values
that
were
considered
outliers.
Very
few
values
occurred
above
35
percent.

Data
were
screened
by
tissue
types
that
corresponded
to
those
considered
most
commonly
consumed
by
humans.
For
all
finfish
species,
with
the
exception
of
herring,
anchovy,
and
smelt
species,
data
were
considered
only
for
selected
tissues
that
include:
fillet,
fillet/
skin,
muscle,
meat,
and
flesh
samples.
The
great
majority
of
samples
used
for
the
finfish
species
were
fillet
or
fillet/
skin
samples.
For
crab
species,
data
considered
in
the
analysis
included
tissues
designated
as
edible
flesh,
edible
portion,
or
edible
skinned,
in
addition
to
those
described
for
finfish.
For
the
remaining
shellfish
species
(
clam,
oyster,
scallop,
and
shrimp)
and
for
herring,
anchovy
and
smelt,
whole
body
samples
were
considered
in
addition
to
the
tissue
types
used
for
fish
and
crab
species.
These
criteria
were
established
based
on
the
portions
of
a
species
are
believed
to
be
consumed
and
constraints
on
the
availability
of
data.

Selection
of
Species
for
Inclusion
in
Lipid
Analysis.
Given
information
from
the
CSFII
survey
on
the
types
of
aquatic
organisms
consumed
in
the
U.
S.,
the
next
step
in
calculating
the
consumption­
weighted
average
lipid
content
involved
assigning
species
to
the
general
CSFII
categories.
In
most
cases,
information
was
not
available
from
the
CSFII
survey
to
identify
which
species
were
included
for
determining
the
consumption
rates
listed
in
Table
2.4.7.
Therefore,
inclusion
of
species
and
accompanying
lipid
data
into
a
CSFII
category
was
based
on:
(
1)
their
taxonomic
and
publicly­
perceived
linkage
to
a
CSFII
category,
(
2)
consideration
of
their
likelihood
for
being
caught
(
either
recreationally
or
commercially)
and
consumed
in
the
U.
S.,
and
(
3)
their
occurrence
in
either
fresh
or
estuarine
waters
for
at
least
some
portion
of
their
life
cycle.
The
species
included
in
the
lipid
analysis
and
their
relationship
to
the
CSFII
consumption
categories
is
provided
in
Table
2.4.8.
Information
from
numerous
published
sources
were
used
to
help
determine
whether
a
species
met
these
criteria.
Because
several
of
the
CSFII
species
categories
were
broad
in
terms
of
the
types
of
species
that
could
be
included,
some
species
were
assigned
to
multiple
CSFII
survey
categories.
For
example,
flounder
species
fit
into
both
estuarine
flatfish
and
flounder
categories.
In
such
cases,
appropriate
records
were
included
in
both
CSFII
categories.
Notably,
some
species
that
244
are
commonly
caught
and
consumed
but
did
not
fit
into
a
CSFII
category,
such
as
bass
and
walleye,
were
not
included
in
this
analysis.

Lipid
Content
of
Species
in
CSFII
Categories.
Based
on
lipid
data
from
the
aforementioned
sources,
an
average
lipid
content
was
determined
for
each
of
the
species
in
the
CSFII
consumption
categories
(
Table
2.4.8).
Next,
the
overall
average
lipid
content
of
each
CSFII
category
was
determined
as
the
average
of
the
corresponding
individual
species
mean
lipid
values.
Ideally,
if
sufficient
national
consumption
data
were
available
at
the
species
level,
the
overall
average
lipid
value
for
each
CSFII
category
would
be
determined
on
a
consumption­
weighted
basis.
However,
sufficient
national
information
was
not
available
below
the
CSFII
category
level
and
thus,
equal
weights
were
assigned
to
each
species
mean
lipid
value.
For
example,
lipid
contents
were
available
for
several
species
of
trout
(
e.
g.,
rainbow
trout,
brown
trout,
and
others),
whereas
consumption
rates
were
available
from
the
CSFII
only
for
trout
as
a
group.
Thus,
mean
lipid
values
for
all
trout
species
were
averaged
and
combined
with
the
consumption
rate
for
trout
from
the
CSFII.

Trophic
Level
Assignments
to
CSFII
Categories.
National
fish
and
shellfish
consumption
data
from
the
CSFII
(
see
Table
2.4.7)
indicate
that
on
average,
individuals
consume
aquatic
organisms
from
a
variety
of
trophic
levels
(
e.
g.,
oysters
and
clams
in
trophic
level
two,
flounder
and
shrimp
in
trophic
level
three,
perch
and
certain
catfish
species
in
trophic
level
four).
Therefore,
for
the
purposes
of
calculating
national
AWQC
using
the
CSFII
consumption
data,
BAFs
need
to
be
derived
that
are
applicable
to
each
of
these
trophic
levels
and
should
be
adjusted
to
reflect
the
average
lipid
content
of
organisms
consumed
in
each
of
the
trophic
levels.
To
estimate
the
consumption­
weighted
average
lipid
content
in
each
of
the
three
trophic
levels
(
and
to
estimate
consumption
rates
of
aquatic
organisms
within
each
of
the
trophic
levels­­
see
Section
2.4.8),
a
trophic
level
designation
must
be
assigned
to
each
of
the
consumption
rate
categories
of
the
CSFII
shown
in
Table
2.4.7.

In
order
to
estimate
the
trophic
level
status
of
consumed
aquatic
species,
one
should
ideally
rely
on
information
concerning
the
identity,
size,
age,
and
diets
of
individual
aquatic
species
consumed.
This
information
is
useful
because
not
only
can
individual
species
differ
in
their
trophic
status,
but
trophic
level
status
can
also
differ
for
different
sizes
(
ages)
of
individuals
within
a
species
because
diets
often
change
with
age/
size
of
the
organism.
Information
on
the
identity,
and
size
(
age)
of
consumed
aquatic
species
should
be
obtained
from
the
fish
consumption
survey,
if
available.
Information
on
the
diet
of
consumed
aquatic
species
might
be
available
on
a
local
or
regional
basis,
but
more
often
is
scattered
about
in
the
scientific
literature
based
on
studies
of
various
sites
around
the
United
States.
If
local
or
regional
information
is
not
available,
then
EPA
recommends
the
use
of
the
most
recent
version
of
the
document:
Trophic
Level
and
Exposure
Analysis
for
Selected
Piscivorous
Birds
and
Mammals
(
USEPA,
1995d,
1995e,
1995f),
which
contains
information
on
the
dietary
composition
of
numerous
aquatic
species.
This
draft
document
is
currently
being
revised
based
on
peer
review
comments
and
is
expected
to
be
made
final
in
1998.
245
Table
2.4.8:
Lipid
Data
for
Aquatic
Species
Included
in
the
Derivation
of
a
National
Default
Consumption­
Weighted
Lipid
Value
CSFII
Consumption
Category
Common
Name
Scientific
Name
Species
Mean
Lipid
(%)
Sample
Size
CSFII
Mean
Lipid
(%)

Anchovy
Northern
anchovy
Engraulis
mordax
11.70
2
7.25
Striped
anchovy
Anchoa
hepsetus
2.80
1
Carp
Carp
Cyprinus
carpio
4.45
1433
4.45
Catfish
Black
bullhead
Ameiurus
melas
1.12
11
2.36
Brown
bullhead
Ameiurus
nebulosus
2.79
704
Channel
catfish
Ictalurus
punctatus
5.00
1108
White
catfish
Ameiurus
catus
2.15
46
Yellow
bullhead
Ameiurus
natalis
0.75
29
Clam
Butter
clam
Saxidomus
nuttalli
1.22
2
1.68
Geoduck
clam
Panope
generosa
3.20
1
Hard
clam
Mercenaria
mercenaria
1.04
2
Littleneck
clam
Protothaca
staminea
0.75
2
Soft
shell
clam
Mya
arenaria
1.29
5
Venus
clam
Tapes
philippinarum
2.60
1
Crab
Blue
crab
Callinectes
sapidus
2.33
7
1.40
Dungeness
crab
Cancer
magister
1.15
2
King
crab
Paralithodes
camtschatica
0.80
1
Snow
crab
Chionoectes
bairdi
1.30
1
Croaker
Atlantic
croaker
Micropogonias
undulatus
3.30
3
4.77
Spot
Leiostomus
xanthurus
10.35
2
White
croaker
Genyonemus
lineatus
2.33
7
Yellowfin
croaker
Umbrina
roncador
3.11
1
Estuarine
Flatfish
Gulf
flounder
Paralichthys
albigutta
0.80
1
1.48
Rainbow
smelt(
1)
Osmerus
mordax
4.50
88
Southern
flounder
Paralichthys
lethostigma
1.20
1
Starry
flounder
Platichthys
stellatus
0.97
3
Summer
flounder
Paralichthys
dentatus
0.40
1
Winter
flounder
Pseudopleuronectes
americanus
1.00
2
Estuarine
Salmon(
2)
Atlantic
salmon
Salmo
salar
5.65
2
4.55
Chinook
salmon
Oncorhynchus
tshawytscha
2.09
271
Chum
salmon
Oncorhynchus
keta
4.25
2
Coho
salmon
Oncorhynchus
kistuch
2.17
308
Pink
salmon
Oncorhynchus
gorbuscha
5.00
2
Sockeye
salmon
Oncorhynchus
nerka
8.12
4
Flounder
Gulf
flounder
Paralichthys
albigutta
0.80
1
0.87
Southern
flounder
Paralichthys
lethostigma
1.20
1
Starry
flounder
Platichthys
stellatus
0.97
3
Table
2.4.8:
Lipid
Data
for
Aquatic
Species
Included
in
the
Derivation
of
a
National
Default
Consumption­
Weighted
Lipid
Value
CSFII
Consumption
Category
Common
Name
Scientific
Name
Species
Mean
Lipid
(%)
Sample
Size
CSFII
Mean
Lipid
(%)

246
Summer
flounder
Paralichthys
dentatus
0.40
1
Winter
flounder
Pseudopleurnectes
americanus
1.00
2
Freshwater
Salmon
Atlantic
salmon
Salmo
salar
5.65
2
4.22
Kokanee
salmon(
3)
Oncorhynchus
nerka
2.28
3
Chinook
salmon
Oncorhynchus
tshawytscha
2.09
271
Chum
salmon
Oncorhynchus
keta
4.25
2
Coho
salmon
Oncorhynchus
kistuch
2.17
308
Pink
salmon
Oncorhynchus
gorbuscha
5.00
2
Sockeye
salmon
Oncorhynchus
nerka
8.12
4
Herring
Atlantic
herring
Clupea
harengus
13.04
5
10.34
Blueback
herring
Alosa
aestivalis
8.63
3
Pacific
herring
Clupea
pallasi
9.34
8
Mullet
Striped
mullet
Mugil
cephalus
4.49
9
4.49
Oyster
Eastern
oyster
Crassostrea
virginica
1.94
8
1.62
European
oyster
Ostrea
edulis
1.65
2
Olympia
oyster
Ostrea
lurida
0.50
1
Pacific
oyster
Crassostrea
gigas
2.40
8
Perch
White
perch
Morone
americana
5.34
296
3.00
Yellow
perch
Perca
flavescens
0.66
220
Pike
Chain
pickerel
Esox
niger
1.21
5
0.84
Northern
pike
Esox
lucius
0.47
356
Scallop
Atlantic
bay
scallop
Aequipecten
irradians
0.60
1
0.70
Sea
scallop
Placopectens
magellanicus
0.80
2
Scup
Scup
Stenotomus
chrysops
3.70
1
3.70
Shrimp
Brown
shrimp
Penaeus
aztecus
0.93
3
0.75
Northern
pink
shrimp
Pandalus
borealis
0.78
2
Pink
shrimp
Penaeus
duorarum
0.78
2
White
shrimp
Penaeus
setiferus
0.52
2
Smelt
Rainbow
smelt
Osmerus
mordax
4.50
88
4.50
Sturgeon
White
sturgeon
Acipenser
transmontanus
1.09
6
1.09
Trout
Brook
trout
Salvelinus
fontinalis
1.51
7
4.29
Brown
trout
Salmo
trutta
3.81
142
Cutthroat
trout
Salmo
clarki
1.23
16
Lake
trout
Salvelinus
namaycush
10.90
380
Rainbow
trout
Oncorhynchus
mykiss
4.00
123
(
1)
Information
from
the
CSFII
survey
indicated
that
rainbow
smelt
were
included
in
the
calculation
of
fish
consumption
rates
for
estuarine
flatfish.
(
2)
Because
these
species
are
anadromous,
data
on
lipid
content
were
also
included
for
freshwater
salmon
category.
(
3)
Information
from
the
American
Fisheries
Society
publication:
Common
and
Scientific
Names
of
Fishes
from
the
United
States
and
Canada
(
AFS,
1991)
indicates
that
freshwater
stocks
of
sockeye
salmon
are
commonly
referred
to
as
Kokanee.
247
For
the
national
CSFII
survey,
very
limited
data
were
available
to
further
delineate
the
identity
and
size
of
species
consumed
within
each
of
the
CSFII
categories
in
Table
2.4.7.
For
most
of
the
CSFII
categories,
this
lack
of
information
was
not
viewed
as
problematic,
because
rather
unambiguous
assignments
of
trophic
status
could
be
made
to
these
categories
(
e.
g.,
all
oysters
are
considered
to
be
trophic
level
two).
However,
for
other
CSFII
categories,
assignment
of
trophic
status
required
some
reasonable
assumptions
to
be
made
and
therefore
reflect
greater
uncertainty.
The
following
procedures
were
used
in
assigning
trophic
status
to
the
CSFII
consumption
categories.

1.
Data
from
EPA's
draft
trophic
level
document
(
USEPA,
1995d,
1995e,
1995f)
and
other
sources
were
used
to
estimate
the
trophic
level
of
species
that
could
reasonably
be
classified
in
each
of
the
CSFII
consumption
categories.
Species
level
trophic
assignments
were
performed
as
follows.

a.
For
game
fish
that
correspond
to
the
CSFII
categories,
data
were
used
for
edible
size
ranges
(
about
20
cm
[
8
inches]
or
larger).
b.
For
species
where
multiple
size
ranges
were
available,
preferences
was
given
to
the
larger
specimens
in
determining
the
species
trophic
level.
c.
Trophic
level
2
was
assigned
to
a
species
if
appropriate
trophic
level
data
ranged
between
1.6
and
2.4;
trophic
level
3
if
trophic
level
data
ranged
from
2.5
to
3.4;
trophic
level
4
if
trophic
level
data
were
3.5
or
higher.
This
is
consistent
with
the
approach
taken
in
the
Great
Lakes
Water
Quality
Initiative
guidance
(
USEPA,
1995c).

2.
Once
the
species
level
trophic
assignments
were
completed,
this
information
was
used
to
assign
a
trophic
level
to
each
CSFII
consumption
rate
category
as
follows.

a.
In
situations
where
a
CSFII
category
was
represented
by
the
vast
majority
of
species
within
a
single
trophic
level,
that
trophic
level
was
assigned
to
the
CSFII
category
(
e.
g.,
trout,
estuarine
flatfish,
smelt).
b.
In
one
situation
(
catfish),
the
CSFII
consumption
rate
was
equally
divided
between
trophic
level
3
and
4
because
about
half
the
species
were
determined
to
be
trophic
level
3
and
about
half
trophic
level
4.
c.
For
shrimp,
trophic
level
3
was
assigned
based
on
data
for
the
"
general
shrimp
category"
in
USEPA
(
1995f)
because
other
data
(
grass
shrimp,
mysids)
were
for
species
that
are
not
consumed
by
humans.

3.
The
results
of
the
trophic
level
assignments
are
shown
in
Table
2.4.9.
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Anchovy
Northern
anchovy
Engraulis
mordax
­­­
3
­­­
Feeds
on
zooplankton
3
3
Adult
3.2
­­­
Feeds
on
large
zooplankton
Juvenile
3
­­­
Feeds
on
zooplankton
Carp
Common
carp
Cyprinus
carpio
­­­
­­­
2.2­
3.1
Young
feed
on
zoopl.,
adults
on
plants,
molluscs,
crustacea
and
become
more
herbivorous
3
3
10­
23
cm
3
2.8­
3.1
Up
to
23
cm,
feed
primarily
on
benthic
inverts.
>
23
cm
2.4
2.2­
2.6
Larger
carp
(>
23
cm
approx)
feed
primarily
on
plants
and
detritus
(
60­
70%),
benthic
inverts.
(
15­
35%)
and
some
zoopl
(<
15%).
Catfish
Black
bullhead
Ameiurus
melas
­­­
3
2.9­
3.2
Seem
to
consume
zooplankton
and
benthic
inverts.
throughout
life.
Indiv.
>
15
cm
may
consume
some
small
fish,
but
also
plant
materials.
3
3
(
50%)
4
(
50%)

Blue
catfish
Ictalurus
furcatus
­­­
3
­­­
Assumption.
3
Brown
bullhead
Ameiurus
nebulosus
­­­
­­­
2.7­
3.3
Diet
changes
with
size.
3
>
10cm
3.0
2.7­
3.2
>
10cm
feeds
on
20­
30%
plants
&
70­
100%
benthic
inverts
(
burrowing
mayfly,
scud,
chironomid
types).
some
consume
small
fish
as
well.
Channel
catfish
Ictalurus
punctatus
36­
54cm
­­­
2.8­
4
Changes
with
age;
can
grow
up
to
50
cm
or
larger.
Two
studies
indicated
they
consume
plants,
one
other
did
not
4
5­
30cm
3.1
­­­
5­
30
cm;
consumes
largely
benthic
inverts.
(
60­
80%),
detritus
(
10­
15%)
and
zooplankton
(
10­
25%)
Catfish
(
cont'd)
30­
35cm
3.3
3­
3.5
30­
35cm;
consumes
fish
(
32%),
benthic
inverts.
(
40%),
zoopl.
(
12%),
and
detritus
(
15%).
Some
populations
consume
up
to
25%
algae.
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Channel
catfish
Ictalurus
punctatus
35­
45cm
3.8
3.5­
3.9
35­
45cm;
consumes
fish
(
67%),
benthic
inverts.
(
25%),
and
detritus
(
8%).
Some
populations
consume
up
to
25%
algae
>
45cm
4
4.0­
4.2
>
45cm;
consumes
fish
(
100%)
Flat
bullhead
Ictalurus
platycephalus
 
3.2
3­
4
Can
grow
to
large
sizes;
feeds
on
molluscs
(
primarily
clams),
bryozoans,
&
worms
4
Flathead
catfish
Pylodictis
olivaris
­­­
3.8
­­­
Diet
consists
primarily
of
fish
with
some
crayfish,
&
molluscs.
4
Yellow
bullhead
Ictalurus
natalis
30­
46
cm
2.6
­­­
Scavengers,
often
consumes
minnows,
crayfish,
insect
larvae,
worms
and
algae
3
Clam
Clams
(
general)
­­­
­­­
2.2
2.1­
2.4
Filter
feeders
on
plankton,
detritus.
Includes
zooplankton.
2
2
Amethyst
gemclam
 
 
2.2
2.1­
2.4
Filter
feeder.
2
Atlantic
rangia
Rangia
cuneata
­­­
2.2
2.1­
2.4
Filter
feeder.
2
Baltic
macoma
Macoma
balthica
 
2.2
2.1­
2.4
Filter
feeder.
2
Dwarf
surf
clam
Mulina
lateralis
­­­
2.2
2.1­
2.4
Filter
feeder.
2
Crab
Blue
crab
Callinectes
sapidus
­­­
3.2
2.8­
3.4
Feed
primarily
on
molluscs
(
39%),
organic
debris
(
20%),
fish
(
20%),
crustaceans
(
15%),
plants
(
4%)
worms
(
2%)
3
3
Croaker
Atlantic
croaker
Micropogonias
undulatus
­­­
3
­­­
Opportunistic
bottom
feeders;
mostly
on
polychaetes,
copepods,
mysids,
small
clams
(
From:
Mercer,
1989)
3
3
Estuarine
Flatfish
Flounder
(
general)
­­­
­­­
3.2
3.0­
3.4
Feeds
on
benthic
organisms
(
e.
g.,
sea
urchins,
sand
dollars,
brittle
stars,
shrimp,
molluscs,
&
worms)
most
of
which
are
detritivores.
 
3
Lefteye
flounders
(
Bothidae)
­­­
­­­
3.2
3.0­
3.4
Assumed
equal
to
general
flounder
diet.
3
European
flounder
Platichthys
flesus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Righteye
flounders
­­­
­­­
3.2
3.0­
3.4
Assumed
equal
to
general
flounder
diet.
3
Starry
flounder
Platichthys
stellatus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Winter
flounder
Pseudopleuronectes
americanus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Smelt
(
general)
­­­
­­­
3
­­­
Assumed
same
TL
as
other
smelt,
except
for
adult
smelt
in
Great
Lakes
3
American
(
Rainbow)
smelt
Osmerus
mordax
­­­
3.1
­­­
Feeds
on
zoopl.;
some
surface
insects
4
­­­
3.5
­­­
In
Great
Lakes,
>
1yr
old
feed
on
smaller
fish
and
on
Mysis
(
TL3);
and
other
inverts
&
zooplankton
(
TL2)
­­­
3.1
­­­
During
first
year,
feed
on
zoopl.
3
Juvenile
night
smelt
Spirinchus
starksi
­­­
3
­­­
Assumed
same
as
other
smelt
3
Juvenile
top
smelt
(
Osmeridae)
­­­
­­­
3
­­­
Assumption
3
surf
smelt
Hypomesus
pretiosus
­­­
3
­­­
Assumed
same
TL
as
other
smelt.
3
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Flounder
Flounder
(
general)
­­­
­­­
3.2
3.0­
3.4
Feeds
on
benthic
organisms
(
e.
g.,
sea
urchins,
sand
dollars,
brittle
stars,
shrimp,
molluscs,
&
worms)
most
of
which
are
detritivores.
­­­
3
Lefteye
flounders
(
Bothidae)
­­­
­­­
3.2
3.0­
3.4
Assumed
equal
to
general
flounder
diet.
3
European
flounder
Platichthys
flesus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Righteye
flounders
­­­
­­­
3.2
3.0­
3.4
Assumed
equal
to
general
flounder
diet.
3
Starry
flounder
Platichthys
stellatus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Winter
flounder
Pseudopleuronectes
americanus
­­­
3.2
3.0­
3.4
Assumed
same
as
general
flounder
diet.
3
Freshwater
Salmon
Kokanee
salmon
Oncorhynchus
nerka
4
­­­
Larger
specimens.
4
4
Herring
Herring
(
general)
­­­
­­­
3.2
3.1­
3.4
Feeds
primarily
on
copepods
and
krill.
­­­
3
Atlantic
herring
Clupea
harengus
­­­
3.2
3.1­
3.4
Feeds
primarily
on
copepods
and
krill.
3
Pacific
herring
Clupea
pallasi
 
3.2
3.1­
3.4
Feeds
primarily
on
copepods
and
krill.
3
Mullet
Striped
mullet
Mugil
cephalus
­­­
2.2
­­­
Feeds
on
plant
material,
detritus,
&
plankton.
2
2
Oyster
Molluscs
(
general)
­­­
­­­
2.1
2.0­
2.2
Bivalves
feed
on
plankton
­­­
2
Mussels
(
general)
­­­
­­­
2.2
2.1­
2.4
Filter
feeders
on
plankton,
detritus,
zooplankton.
­­­

Perch
Yellow
perch
Perca
flavescens
20­
30cm
3.4
3.1­
3.8
20­
30cm;
consumes
10%
zoopl.,
50%
benthic
inverts,
34%
fish
(
some
pops.);
nearly
100%
fish
in
other
populations.
4
4
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Pike
Northern
pike
Esox
lucius
>
10cm
4
­­­
>
10cm;
diet
primarily
all
fish.
4
4
Pickerel
(
redfin
&
grass)
Esox
americanus
larger
specimens
4
­­­
Larger
specimens
consume
small
fish
4
Scallop
Molluscs
(
general)
­­­
­­­
2.1
2.0­
2.2
Bivalves
feed
on
plankton
2
2
Scup
Scup
Stenotomus
chrysops
­­­
3
­­­
Bottom
feeder,
primarily
on
molluscs,
worms,
and
small
crustaceans
(
Jordan
and
Evermann,
1969)
3
Shrimp
Shrimp
(
general)
(
Palaemonidae)
­­­
3.0
­­­
Filter
feeding
on
zoopl.
3
3
Grass
shrimp
Palaemonetes
sp.
­­­
2.1
­­­
Detritivore,
primarily
on
plant
material
2
Mysis
Mysis
relicta
­­­
3.5
3­
4
Cold
water
forms;
during
warm
months,
restricted
to
hypolimnion
4
Smelt
Smelt
(
general)
­­­
­­­
3
­­­
Assumed
same
TL
as
other
smelt,
except
for
adult
smelt
in
Great
Lakes
­­­
3
American
(
Rainbow)
smelt
Osmerus
mordax
­­­
3.1
­­­
Feeds
on
zoopl.;
some
surface
insects
4
­­­
3.5
­­­
In
Great
Lakes,
>
1yr
old
feed
on
smaller
fish
and
on
Mysis
(
TL3);
and
other
inverts
&
zooplankton
(
TL2)
American
(
Rainbow)
smelt
Osmerus
mordax
­­­
3.1
­­­
During
first
year,
feed
on
zoopl.

Juvenile
night
smelt
Spirinchus
starksi
­­­
3
­­­
Assumed
same
as
other
smelt
3
Juvenile
top
smelt
(
Osmeridae)
­­­
­­­
3
­­­
Assumption
3
surf
smelt
Hypomesus
pretiosus
­­­
3
­­­
Assumed
same
TL
as
other
smelt.
3
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Sturgeon
White
sturgeon
Acipenser
transmontanus
­­­
­­­
3­
4
Estimated
range
based
on
following
account
by
Jordan
&
Evermann
(
1969):
Can
grow
to
several
hundred
pounds,
diet
reportedly
consists
of
small
plants
&
small
animals,
including
small
fish.
One
young
specimen
(
25
in.)
had
11
minnows
in
its
stomach,
larger
specimens
had
several
suckers
about
12
in.
long.
(
Jordan
&
Evermann,
1969).
4
4
Lake
sturgeon
Acipenser
rubicundus
­­­
 
3­
4
Estimated
range
based
on
following
account
by
Jordan
&
Evermann
(
1969):
Can
grow
up
to
100
pounds,
averages
about
40­
50
pounds
for
adults;
primarily
a
bottom
feeder,
reportedly
feeding
on
small
gastropods,
crustaceans,
insect
larvae
and
small
fishes
4
Trout
Brook
trout
Salvelinus
fontinalis
10­
40cm
3.2
­­­
10­
40cm;
at
most,
7­
8%
fish
in
diet;
remainder
primarily
benthic
inverts.
but
also
some
zoopl.
in
some
populations
3
4
Cutthroat
trout
Salmo
clarki
<
40cm
3
­­­
<
40cm;
consumes
invertebrates
4
>
40cm
3.2
­­­
>
40cm;
becomes
piscivorous
Table
2.4.9:
Trophic
Level
Assignment
of
Aquatic
Species
Corresponding
to
CSFII
Consumption
Categories
CSFII
Consumption
Category
Common
Name
Scientific
Name
Size
Trophic
Level
(
a)

(
Mean)
Trophic
Level
(
a)

(
Range)
Notes
(
a)
Species
Assigned
Trophic
Level
(
b)
CSFII
Assigned
Trophic
Level
(
c)

Trout
(
cont'd)
old
adults
4
­­­
Assumption
for
oldest
specimens
Dolly
Varden
trout
Salvelinus
malma
 
­
­­
3­
4
TL
changes
with
age
4
10­
30cm
3
­­­
10­
30cm;
diet
100%
benthic
inverts.
Dolly
Varden
trout
Salvelinus
malma
30­
40cm
3.75
­­­
30­
40cm;
diet
75%
fish,
17%
benthic
inverts.
>
40cm
4
­­­
>
40cm;
diet
consists
of
100%
fish
Lake
trout
Salvelinus
namaycush
20­
30cm
3.7
3.5­
4.0
20­
30cm;
feeds
primarily
on
small
fish
(
70%)
and
benthic
inverts.
(
30%)
4
30­
40cm
3.9
3.7­
4.1
30­
40cm;
feeds
primarily
on
fish
(
90%)
and
some
benthic
inverts.
(
10%)
>
40cm
4.2
4.0­
4.5
>
40cm;
feeds
entirely
on
fish;
in
L.
Michigan,
feed
on
alewives,
which
feed
on
Mysis,
which
feed
on
zoopl.
Rainbow
trout
Oncorhynchus
mykiss
<
30cm
3
­­­
<
30cm;
diet
completely
of
benthic
inverts.
or
both
inverts.
&
zoopl.
4
30­
50cm
3.6
­­­
30­
50cm;
diet
35­
90%
fish,
25­
75%
benthic
inverts,
zoopl.,
terrestrial
insects
>
50cm
4
­­­
>
50cm;
diet
of
100%
fish
Footnotes:
(
a)
Unless
otherwise
specified,
information
on
trophic
status
was
obtained
from
U.
S.
EPA,
(
1995d,
1995e,
1995f).
Game
fish
data
were
limited
to
specimens
considered
to
be
representative
of
the
edible
size
range
(
i.
e.,
sizes
ranges
of
20cm
or
larger).
(
b)
In
determining
species
trophic
level
assignments,
preference
was
given
to
data
on
larger
specimens.
Trophic
level
4
was
assigned
to
a
species
with
data
indicating
TL
3.5
or
higher;
trophic
level
3
was
assigned
to
a
species
with
data
indicating
TL
2.5
to
3.4;
trophic
level
2
assigned
for
TL
1.5­
2.4.
(
c)
In
determining
CSFII
category
trophic
level
assignments,
best
professional
judgement
was
used.
For
example,
the
CSFII
category
for
catfish
includes
4
species
that
are
assigned
TL3
and
3
species
assigned
as
TL4.
Thus,
it
is
assumed
that
half
(
50%)
of
consumption
in
the
catfish
CSFII
category
is
from
TL3
and
half
from
TL4.
Except
for
shrimp,
all
other
CSFII
categories
included
species
that
either
were
exclusively
or
predominately
one
trophic
level
(
e.
g.,
trout,
estuarine
flatfish,
smelt).
255
f
R
'
j
CR
i
CR
tot
C
f
R
,
i
(
Equation
2.4.26)
Calculation
of
Consumption­
Weighted
Lipid
Content,
by
Trophic
Level.
Using
consumption
rate
data
from
CSFII
(
daily
average
per
capita
estimates
of
individuals
18
years
and
older
 
Table
2.4.7),
the
mean
lipid
content
estimated
for
organisms
assigned
to
each
CSFII
category
(
Table
2.4.8),
and
the
trophic
level
assignments
of
each
CSFII
consumption
category
(
Table
2.4.9),
consumption
weighted
mean
lipid
content
determined
within
each
trophic
level
according
to
the
following
equation.

where:

f
R
=
Lipid
fraction
representative
of
aquatic
species
eaten
by
the
target
population
that
correspond
to
a
given
trophic
level
CR
i
=
Consumption
rate
of
species
"
i"
of
a
given
trophic
level
eaten
by
the
target
population
CR
tot
=
Consumption
rate
of
all
species
at
that
same
trophic
level
eaten
by
the
target
population
f
R
,
i
=
Lipid
fraction
of
species
"
i"
eaten
by
the
target
population
Calculation
of
the
consumption­
weighted
lipid
content
values
is
shown
in
Table
2.4.10.
The
mean,
consumption
weighted
percent
lipid
values
were
calculated
as
(
expressed
here
to
2
significant
figures
for
convenience):

Trophic
Level
Two:
2.3%
Trophic
Level
Three:
1.5%
Trophic
Level
Four:
3.1%

Because
of
limitations
in
the
availability
and
precision
of
the
used
to
estimate
consumption
rates,
lipid
content,
and
trophic
level
status,
uncertainty
exists
in
the
estimation
of
national
default,
consumption­
weighted
lipid
content.
To
illustrate
some
of
this
uncertainty,
"
high"
and
"
low"
estimates
of
the
consumption
weighted
lipid
content
values
were
determined
using
the
species
with
the
highest
and
lowest
species
mean
lipid
value,
respectively,
within
each
CSFII
category.
"
High"
and
"
low"
estimates
of
percent
lipid
content
values
within
each
trophic
level
are:

"
High"
Estimate
for
Trophic
Level
Two:
3.0%
"
High"
Estimate
for
Trophic
Level
Three:
2.2%
"
High"
Estimate
for
Trophic
Level
Four:
6.2%

"
Low"
Estimate
for
Trophic
Level
Two:
1.5%
"
Low"
Estimate
for
Trophic
Level
Three:
0.77%
"
Low"
Estimate
for
Trophic
Level
Four:
0.91%
256
The
reason
that
there
is
not
a
greater
difference
between
the
mean
lipid
content
values
(
where
each
species
within
a
CSFII
category
was
given
equal
weighting)
and
the
"
high"
and
"
low"
estimates
is
likely
because
the
mean
consumption
rates
in
the
CSFII
survey
are
weighted
heavily
by
relatively
lean
aquatic
organisms
such
as
shrimp,
crab,
perch,
and
flounder.
Therefore,
because
the
consumption
of
aquatic
organisms
may
differ
on
a
local
or
regional
basis
from
that
reflected
in
the
CSFII
survey,
EPA
recommends
that
States
and
Tribes
give
preference
to
using
local
and
regional
data
on
consumption
patterns
over
national
default
estimates,
when
available.

2.4.5.4
Freely
Dissolved
Fraction
The
next
step
in
calculating
a
BAF
used
in
deriving
an
AWQC
involves
adjusting
the
baseline
BAF
to
account
for
the
freely
fraction
of
the
chemical
at
the
site(
s)
to
which
the
AWQC
will
apply.
The
same
equation
used
to
estimate
the
freely
dissolved
fraction
for
determining
a
baseline
BAF
(
Equation
2.4.11)
is
used
to
estimate
the
freely
dissolved
fraction
for
determining
the
AWQC
BAF.
However,
in
this
case,
however,
the
POC
and
DOC
values
should
be
based
on
the
site(
s)
where
the
BAF
and
the
criterion
will
be
applied
and
not
where
the
samples
were
collected
for
determining
the
BAF.
If
POC
and
DOC
data
are
not
available
for
the
site(
s)
to
which
the
AWQC
will
apply,
then
data
from
sites
closely
related
to
those
to
which
the
AWQC
sites
can
be
used.
Care
should
be
taken
to
ensure
that
conditions
affecting
the
POC
and
DOC
concentrations
at
the
surrogate
sites
are
representative
of
conditions
at
the
AWQC
sites.
States
and
tribes
are
encouraged
to
use
local
or
regional
data
when
appropriate
and
scientifically
defensible.
If
such
data
are
unavailable,
then
the
default
values
for
POC
and
DOC
can
be
used.
EPA
has
developed
national
default
values
of
0.48
mg/
L
(
4.8
x
10­
7
kg/
L)
for
POC
and
2.9
mg/
L
(
2.9x
10­
6
kg/
L)
for
DOC.
Both
of
these
values
are
50th
percentile
values
(
medians)
based
on
an
analysis
of
over
132,000
DOC
values
and
81,000
POC
values
contained
in
EPA's
STORET
data
base.
These
default
values
reflect
the
combination
of
values
for
streams,
lakes
and
estuaries
across
the
United
States.
Further
delineation
of
the
POC
and
DOC
concentrations
in
different
water
body
types
is
provided
in
Table
2.4.11.
These
default
values,
which
are
derived
at
a
more
disaggregated
level
may
provide
more
appropriate
estimates
of
POC
and
DOC
concentrations
associated
with
the
field
BAF
study
compared
to
the
national
default
medians
listed
above.
The
K
ow
value
for
the
chemical
is
the
same
as
used
for
deriving
the
baseline
BAF
for
the
chemical.
257
Table
2.4.10:
Calculation
of
National
Default
Consumption­
Weighted
Mean
Lipid
Content
of
Consumed
Aquatic
Organisms
Habitat
CSFII
Category
(
1)
Assigned
Trophic
Level(
2)
Trophic
Level
Weighting
Factor(
3)
Average
Percent
Lipids
(
4)
Mean
Consumption
Rate
(
g/
person/
day)
Trophic
Level
Weighted
Mean
Consumption
rate
(
g/
person/
day)
CSFII
Category
Weights
Consumption­
Weighted
Percent
Lipid
Values
Estuarine
clam
2
1.0
1.68
0.03146
0.03146
0.09046
0.15219
Estuarine
mullet
2
1.0
4.49
0.08756
0.08756
0.25176
1.13069
Estuarine
oyster
2
1.0
1.62
0.22555
0.22555
0.64852
1.05162
Estuarine
scallop
2
1.0
0.70
0.00322
0.00322
0.00926
0.00648
Estuarine
anchovy
3
1.0
7.25
0.00292
0.00292
0.00078
0.00568
Freshwater
carp
3
1.0
4.45
0.05727
0.05727
0.01538
0.06842
Freshwater
catfish
3
0.5
2.36
1.18227
0.59114
0.15874
0.37486
Estuarine
crab
3
1.0
1.40
0.37126
0.37126
0.09970
0.13918
Estuarine
croaker
3
1.0
4.77
0.06749
0.06749
0.01812
0.08648
Estuarine
estuarine
flatfish
3
1.0
1.48
0.52735
0.52735
0.14161
0.20927
Estuarine
flounder
3
1.0
0.87
0.29941
0.29941
0.08040
0.07022
Estuarine
herring
3
1.0
10.34
0.03925
0.03925
0.01054
0.10897
Estuarine
scup
3
1.0
3.70
0.00068
0.00068
0.00018
0.00068
Estuarine
shrimp
3
1.0
0.75
1.72959
1.72959
0.46446
0.34821
Estuarine
smelt
3
1.0
4.50
0.03753
0.03753
0.01008
0.04535
Freshwater
catfish
4
0.5
2.36
1.18227
0.59114
0.35205
0.83133
Freshwater
freshwater
salmon
4
1.0
2.28
0.01096
0.01096
0.00653
0.01486
Estuarine
perch
4
1.0
3.00
0.60368
0.60368
0.35952
1.07956
Freshwater
pike
4
1.0
0.84
0.02337
0.02337
0.01392
0.01168
Estuarine
sturgeon
4
1.0
1.09
0.00054
0.00054
0.00032
0.00035
Freshwater
trout
4
1.0
4.29
0.44946
0.44946
0.26767
1.14837
Trophic
Level
Sum
of
Consumption
Rates
(
g/
pers./
day)
Sum
of
Weights
Consumption­
Weighted
Mean
Percent
Lipid
Trophic
Level
2
0.34779
1.00
2.34%

Trophic
Level
3
3.72389
1.00
1.46%

Trophic
Level
4
1.67915
1.00
3.09%

Total:
5.75082
(
1)
Source
of
consumption
data:
USDA's
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII)
combined
from
1989,
1990
&
1991
for
individuals
18
years
and
older
(
USEPA,
1998b
and
Table
2.4.7).
(
2)
Trophic
level
designation
of
organisms
corresponding
to
CSFII
consumption
categories,
as
described
in
the
text
and
Table
2.4.9.
(
3)
Trophic
level
weighing
factor
used
to
apportion
consumption
rates
to
multiple
trophic
levels
for
catfish
only
(
see
text).
(
4)
Mean
lipid
content
for
species
assigned
to
each
CSFII
category
as
described
in
the
text
and
Table
2.4.8.
258
Table
2.4.11:
National
Default
Values
for
POC
and
DOC
in
U.
S.
Water
Bodies
WATER
BODY
TYPE
DOC
(
mg/
L)
POC
(
mg/
L)

50th%
(
Median)
Mean
50th%
(
Median)
Mean
Stream/
River
4.0
6.2
0.70
1.3
Lake
2.1
3.0
0.31
0.43
Estuary
2.7
3.4
0.90
1.1
All
Types
2.9
4.9
0.48
0.83
Source:
USEPA
STORET
data
base,
data
retrieval
February,
1996.
Sample
sizes
for
DOC
are:
77,637
(
stream/
river);
40,472
(
lake);
14,376
(
estuary);
132,485
(
all
water
bodies)
Sample
sizes
for
POC
are:
30,236
(
stream/
river);
39,931
(
lake);
10,920
(
estuary);
81,087
(
all
water
bodies)

2.4.6
Determining
BAFs
for
Inorganic
Substances
Unlike
organic
chemicals,
the
lipid­
BAF
relationship
does
not
generally
apply
to
the
determination
of
BAFs
for
inorganic
chemicals.
Thus,
BAFs
and
BCFs
for
inorganics
should
not
be
expressed
on
a
lipid­
normalized
basis,
and
are
not
as
transferable
from
one
species
to
another,
or
one
tissue
to
another,
as
with
organic
chemicals.
Bioaccumulation
of
some
trace
metals
is
substantially
greater
in
internal
organs
than
muscle
tissue.
For
example,
BCFs
for
various
tissues
of
the
rainbow
trout
after
exposure
to
cadmium
for
178
days
are
as
follows
(
Giles,
1988):

liver
325
kidney
75
gut
and
skin
7
muscle
1
Merlini
and
Pozzi
(
1977)
reported
that
lead
bioconcentrated
30
times
more
in
bluegill
liver
than
in
bluegill
muscle
tissue
after
eight
days.
Because
of
the
differential
uptake
to
different
tissues
and
species,
the
BAFs
should
be
measured
in
edible
tissues.

BAFs
or
BCFs
measured
in
plants
or
invertebrate
animals
may
be
available.
However,
these
factors
might
be
one
or
more
orders
of
magnitude
greater
than
BAFs
or
BCFs
for
the
edible
tissue
of
fish
as
noted
in
Table
5
in
each
of
the
EPA
criteria
documents
for
cadmium,
copper,
lead
and
nickel
(
USEPA,
1985a;
USEPA,
1985b;
USEPA,
1985c;
and
USEPA,
1986).
For
this
reason,
invertebrate
BAFs
and
BCFs
should
only
be
used
in
the
derivation
of
human
health
criteria
when
they
are
considered
to
be
a
significant
component
of
the
diet
of
the
target
consumers.
259
Although
bioaccumulation
of
many
inorganic
chemicals
is
similar
to
their
bioconcentration,
mercury
and
certain
other
metals
are
subject
to
methylation
through
microbial
action
in
nature,
and
may
biomagnify
through
the
food
chain.
For
example,
research
demonstrates
that
methyl
mercury
is
bioaccumulative
in
fish
and
biomagnifies
in
aquatic
food
webs
(
Grieb
et
al.
1990;
Gardner,
1978).

The
following
two
procedures,
in
order
of
priority,
should
be
used
to
estimate
BAFs
for
inorganic
chemicals.
EPA
is
not
aware
of
any
other
generic
procedures
for
predicting
the
BAF
for
these
substances.

Field­
measured
BAFs
are
the
most
preferred
BAFs
for
inorganic
chemicals.
Section
2.4.4.1
(
Field­
Measured
BAFs)
describes
data
requirements
for
measuring
BAF
values
using
field
data
for
nonpolar
organic
chemicals.
These
requirements
are
applicable
to
field­
measured
BAFs
for
inorganic
chemicals
as
well,
except
that
inorganic
BAFs
should
not
be
lipid­
normalized
because
bioaccumulation
of
inorganics
is
not
proportional
to
lipid
content.
However,
as
noted
above,
inorganic
bioaccumulation
can
differ
dramatically
between
tissues.
Thus,
BAFs
based
on
uptake
into
edible
tissue
should
be
used
to
calculate
human
health
criteria.

If
field­
measured
BAFs
are
not
available
for
inorganic
chemicals,
a
laboratory
BCF
may
be
used
to
estimate
bioaccumulation
of
inorganic
substances
from
water.
The
BCF
may
be
used
because
for
most
inorganic
substances,
bioaccumulation
and
bioconcentration
are
similar.
Section
2.4.4.3
(
Predicted
BAF
Based
on
Laboratory­
Measured
BCF
and
a
Food­
Chain
Multiplier)
describes
acceptable
data
for
measuring
BCFs
for
organic
chemicals
in
the
laboratory,
which
are
applicable
to
BCFs
measured
for
inorganic
chemicals.
For
inorganic
chemicals
where
dietary
exposure
forms
a
significant
portion
of
the
exposure
to
target
organisms,
(
e.
g.
mercury,
selenium)
BCFs
should
be
used
in
conjunction
with
field­
derived
food
chain
multipliers.

2.4.7
Example
Calculations
The
two
examples
below
illustrate
how
BAFs
are
developed
using
two
of
the
four
methods
recommended
for
deriving
BAFs
for
nonpolar
organic
chemicals
for
use
in
establishing
AWQC
for
human
health.
The
first
example
illustrates
the
development
of
a
BAF
when
field­
measured
BAFs
are
available.
The
second
example
illustrates
the
development
of
BAFs
using
a
laboratory­
measured
BCF
and
a
food­
chain
multiplier.

2.4.7.1
Example
1:
Field­
Measured
BAF
for
Chemical
M
The
calculation
of
a
BAF
used
in
the
derivation
of
a
human
health
criteria
is
a
two­
step
process.
The
first
step
is
to
derive
baseline
BAFs
for
appropriate
trophic
levels.
The
second
step
is
to
derive
BAFs
that
can
be
used
in
deriving
human
health
AWQC.
Each
of
these
steps
are
illustrated
below.
260
Baseline
BAF
fd
R
'
Measured
BAF
t
T
f
fd
&
1
1
f
R
(
Equation
2.4.27)
Baseline
BAF
for
Chemical
M
This
example
illustrates
the
development
of
a
baseline
BAF
appropriate
for
trophic
level
four
for
a
lipophilic
chemical
M.
Data
are
available
from
Lake
Washington
(
a
hypothetical
lake)
on
the
total
concentration
of
chemical
M
in
fish
tissue
in
lake
trout
and
the
water
column.
A
review
of
the
dietary
preferences
of
lake
trout
indicates
that
this
organism
is
at
trophic
level
four
for
larger
size
ranges
commonly
consumed
by
the
target
population
(
USEPA,
1995d,
1995e,
1995f).
The
development
of
a
baseline
BAF
for
a
given
trophic
level
requires
information
on
a
field­
measured
BAF
(
Measured
BAFtT
),
the
fraction
of
the
chemical
that
is
freely
dissolved
in
the
ambient
water
(
f
fd),
and
the
fraction
lipid
content
of
the
species
sampled
(
f
R
)
.
The
equation
for
calculating
a
baseline
BAF
using
a
field­
measured
BAF
is:

To
determine
a
field­
measured
BAF,
information
is
needed
on
the
total
concentration
of
chemical
M
in
fish
tissue
and
in
ambient
water
at
the
site
of
sampling.
For
this
example,
the
mean
total
concentration
for
chemical
M
in
fish
tissue
is
100
ng/
g
and
the
mean
total
water
column
concentration
160
pg/
L.
Data
from
the
field
studies
indicates
that
the
mean
water
column
concentration
reflects
adequate
temporal
and
spatial
averaging
based
on
the
K
ow
of
this
chemical
and
is
representative
of
the
average
exposure
of
fish
to
chemical
M.
The
field­
measured
BAF
tT
for
chemical
M
is
625,000
L/
kg,
as
demonstrated
below.

Field­
measured
BAFtT
=
Total
concentration
of
chemical
M
in
fish
tissue
Total
concentration
of
chemical
M
in
the
water
column
(
Equation
2.4.28)

Field­
measured
BAFtT
=
(
100
ng/
g)(
1,000
pg/
ng)(
1,000
g/
kg)
=
625,000
L/
kg­
tissue
160
pg/
L
(
Equation
2.4.29)

To
determine
the
fraction
of
chemical
M
that
is
freely
dissolved
in
the
ambient
water
requires
information
on
the
particulate
organic
carbon
(
POC)
and
dissolved
organic
carbon
(
DOC)
in
the
ambient
water
where
the
samples
were
collected
and
the
K
ow
of
chemical
M.
For
this
example,
the
median
POC
concentration
from
Lake
Washington,
where
the
samples
were
collected,
is
0.6
mg/
L
261
f
fd
'
1
[
1
%
(
POC
@
K
ow)
%
(
DOC
@
K
ow
10
)]

(
Equation
2.4.30)

f
fd
'
1
[
1
%
(
6.0
x
10
&
7kg/
L
@
100,000
L/
kg)
%
(
8.0
x
10
&
6
kg/
L
@
100,000
10
L/
kg)]
'
0.8772
(
Equation
2.4.31)

Baseline
BAF
fd
R
'
625,000
0.8772
&
1
1
0.08
'
8,906,166
L/
kg
&
lipid
(
Equation
2.4.32)
(
6.0
x
10­
7
kg/
L)
and
the
median
DOC
concentration
is
8.0
mg/
L
(
8.0
x
10­
6
kg/
L).
Importantly,
the
POC
and
DOC
concentrations
used
in
calculating
the
freely
dissolved
fraction
for
baseline
BAFs
is
from
the
waterbody
used
in
the
BAF
study.
Use
of
default
POC
and
DOC
concentrations
in
the
derivation
of
baseline
BAFs
is
not
appropriate.
The
K
ow
for
chemical
M
is
100,000
or
a
log
K
ow
of
5.0.
The
fraction
freely
dissolved
for
chemical
M
is
0.8722,
as
shown
below.

The
freely
dissolved
fraction
has
been
expressed
to
four
significant
digits
for
convenience.
The
scientific
basis
supporting
this
equation
for
estimating
the
freely
dissolved
fraction
is
described
in
Section
2.4.4.1
and
Appendix
D.

Finally,
the
mean
fraction
lipid
content
of
the
fish
species
sampled
in
Lake
Washington
was
8
percent.
Using
the
baseline
BAF
equation
and
information
on
the
field­
measured
BAF,
the
fraction
freely
dissolved,
and
the
fraction
lipid
content
provides
a
baseline
BAF
for
lake
trout
of
8,906,166,
which
is
illustrated
below.

For
the
purposes
of
this
example,
it
has
been
assumed
that
only
one
acceptable
BAF
value
is
available
for
trophic
level
four
organisms.
Thus,
the
baseline
BAF
for
trophic
level
four
is
equal
to
this
baseline
BAF.
Had
other
acceptable
field­
measured
BAFs
been
available
for
trophic­
level
four
262
BAF
for
AWQC(
TL
n)
'
[
(
Baseline
BAF
fd
R
)
TL
n
@
(
f
R
)
TL
n
%
1]
@
(
f
fd)

(
Equation
2.4.33)
organisms,
then
the
baseline
BAF
for
trophic
level
four
would
have
been
calculated
as
the
geometric
mean
of
the
acceptable
baseline
BAFs
at
trophic
level
four.

BAF
for
Chemical
M
to
Be
Used
in
Deriving
AWQC
After
the
derivation
of
trophic
level­
specific
baseline
BAFs
for
chemical
M
(
described
in
the
previous
section),
the
next
step
is
to
calculate
BAFs
that
will
be
used
in
the
derivation
of
AWQC.
This
step
is
necessary
to
adjust
the
baseline
BAFs
to
conditions
that
are
expected
to
affect
the
bioavailability
of
chemical
M
at
the
sites
applicable
to
the
AWQC.
Derivation
of
AWQC
BAFs
requires
information
on:
(
1)
the
baseline
BAF
at
appropriate
trophic
levels,
(
2)
the
percent
lipid
of
the
aquatic
organisms
consumed
by
humans
at
the
site(
s)
of
interest
(
trophic
level
specific),
and
(
3)
the
freely
dissolved
fraction
of
the
chemical
in
ambient
water
at
the
site(
s)
of
interest.
For
each
trophic
level,
the
equation
for
deriving
a
BAF
to
used
in
deriving
AWQC
is:

where:
BAF
for
AWQC
(
TL
n)
=
BAF
at
trophic
level
"
n"
used
to
derive
AWQC
based
on
site
conditions
for
lipid
content
of
consumed
aquatic
organisms
for
trophic
level
"
n"
and
the
freely
dissolved
fraction
in
the
site
water
Baseline
BAF
R
f
d
(
TL
n)
=
BAF
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis
for
trophic
level
"
n"
f
R
(
TL
n)
=
Fraction
lipid
of
aquatic
species
consumed
at
trophic
level
"
n"
f
fd
=
Fraction
of
the
total
chemical
in
water
that
is
freely
dissolved
For
the
purposes
of
this
example,
an
AWQC
BAF
is
being
calculated
only
for
aquatic
organisms
at
one
trophic
level
(
trophic
level
four).
If
fish
consumption
data
indicates
the
target
population
consumes
significant
quantities
from
multiple
trophic
levels,
then
AWQC
BAFs
should
be
derived
for
each
of
the
appropriate
trophic
levels.

For
chemical
M,
the
baseline
BAF
at
trophic
level
four
is
calculated
to
be
8,906,166
L/
kg­
lipid
as
described
above.
The
fraction
lipid
content
of
aquatic
species
consumed
by
the
target
population
at
trophic
level
four
is
assumed
to
be
3.1%
based
on
the
national
default
lipid
content
for
trophic
level
four
derived
in
Section
2.4.5.3.
The
freely
dissolved
fraction
of
chemical
M
that
is
expected
in
the
sites
applicable
to
the
AWQC
was
determined
to
be
0.9615.
This
value
is
calculated
as
shown
below
using
hypothetical
expected
POC
and
DOC
concentrations
at
the
sites
applicable
to
the
AWQC
of
263
f
fd
'
1
[
1
%
(
POC
@
K
ow)
%
(
DOC
@
K
ow
10
)]

(
Equation
2.4.34)

f
fd
'
1
[
1
%
(
3.0
x
10
!
7
kg/
L
@
100,000
L/
kg)
%
(
1.0
x
10
!
6
kg/
L
@
100,000
10
L/
kg)]

(
Equation
2.4.35)
0.3
mg/
L
(
3.0
x
10­
7
kg/
L)
and
1.0
mg/
L
(
1.0
x
10­
6
kg/
L),
respectively,
and
the
same
K
ow
of
100,000
for
chemical
M.

f
fd
=
0.9615
Using
the
AWQC
BAF
equation
described
previously,
the
AWQC
BAF
for
trophic
level
four
organisms
is
calculated
to
be
265,463
L/
kg
as
shown
below.

AWQC
BAF
for
Trophic
Level
Four
=
[(
8,906,166
L/
kg­
lipid)°(
0.031)
+
1]
°
(
0.9615)
=
265,463
L/
kg­
tissue
This
AWQC
BAF
relates
the
total
concentration
in
water
to
the
total
concentration
in
tissue
of
trophic
level
four
organisms,
based
on
the
expected
conditions
that
would
affect
the
bioavailability
of
chemical
M
(
i.
e.,
freely
dissolved
fraction
at
AWQC
sites
and
lipid
content
of
consumed
aquatic
organisms).

2.4.7.2
Example
2:
Laboratory­
Measured
BCF
for
Chemical
R
When
a
field­
measured
BAFtT
or
field­
measured
BSAF
are
not
available,
a
laboratorymeasured
BAFtT
along
with
a
food­
chain
multiplier
should
be
used
to
derive
a
baseline
BAF
and
then
an
AWQC
BAF
for
use
in
deriving
human
health
criteria.

Baseline
BAF
for
Chemical
R
The
development
of
a
baseline
BAF
R
f
d
for
chemical
R
specific
to
a
given
trophic
level
requires
information
on
a
laboratory
measured
BCF
(
measured
BCF
tT
),
the
fraction
of
the
chemical
that
is
freely
dissolved
in
the
test
water
(
f
fd),
the
fraction
lipid
content
of
the
species
sampled
(
f
R
)
,
and
the
food­
chain
multiplier
for
the
chemical
(
FCM).
For
a
given
trophic
level,
the
equation
for
calculating
a
baseline
BAF
R
f
d
using
a
laboratory
BCF
tT
and
food­
chain
multiplier
is:
264
f
fd
'
1
[
1
%
(
POC
@
K
ow)
%
(
DOC
@
K
ow
10
)]

(
Equation
2.4.39)
Baseline
BAF
fd
R
'
(
FCM)
Measured
BCF
t
T
f
fd
&
1
1
f
R
(
Equation
2.4.36)

The
basis
of
this
equation
is
described
in
Section
2.4.4.3.

The
laboratory­
measured
BCF
requires
information
on
the
total
concentration
of
chemical
R
in
fish
tissue
and
the
total
concentration
of
chemical
R
in
the
test
water.
For
this
example,
the
mean
total
fish
tissue
concentration
for
chemical
R
is
10
ng/
g
and
the
mean
total
test
water
concentration
is
3
ng/
L.
The
laboratory­
measured
BCF
is
3333
L/
kg.

Laboratory
measured
BCF
tT
=
Total
concentration
of
chemical
R
in
fish
tissue
Total
concentration
of
chemical
R
in
test
water
(
Equation
2.4.37)

Laboratory
measured
BCF
tT
=
(
10
ng/
g)(
1,000
g/
Kg)
=
3333
L/
kg
­
tissue
3
ng/
L
(
Equation
2.4.38)

To
determine
the
fraction
of
chemical
R
that
is
freely
dissolved
in
the
test
water
requires
information
on
the
particulate
organic
carbon
(
POC)
and
dissolved
organic
carbon
(
DOC)
in
the
test
water
and
the
K
ow
of
chemical
R.
For
this
example,
the
median
POC
concentration
in
the
test
water
is
0.6
mg/
L
(
6.0
x
10­
7
kg/
L)
and
the
median
DOC
concentration
is
8.0
mg/
L
(
8.0
x
10­
6
kg/
L).
The
K
ow
for
chemical
R
is
10,000
or
a
log
K
ow
of
4.0.
The
fraction
freely
dissolved
for
chemical
R
is
0.9860,
as
shown
below.
265
f
fd
'
1
[
1
%
(
6.0
x
10
&
7
kg/
L
@
10,000
L/
kg)
%
(
8.0
x
10
&
6
kg/
L
@
10,000
10
L/
kg)]
'
0.9862
(
Equation
2.4.40)

BAF
for
AWQC(
TL
n)
'
[
(
Baseline
BAF
fd
R
)
TL
n
@
(
f
R
)
TL
n
%
1]
@
(
f
fd)
(
Equation
2.4.42)
The
freely
dissolved
fraction
has
been
expressed
to
four
significant
digits
for
convenience.
The
scientific
basis
supporting
this
equation
is
explained
in
Section
2.4.4.1
and
Appendix
D.

The
fraction
lipid
content
of
the
fish
species
sampled
in
the
laboratory
is
8
percent.
The
foodchain
multiplier
based
on
a
log
K
ow
of
4
is
1.072,
as
indicated
in
Table
2.4.4
(
assuming
mixed
benthic
and
pelagic
food
web
structure
and
trophic
level
four
for
the
tested
species).
Using
the
baseline
BAF
R
f
d
equation
and
the
information
on
the
laboratory­
measured
BCF
tT
,
the
fraction
freely
dissolved,
the
fraction
lipid
content,
and
the
FCM
provides
a
baseline
BAF
R
f
d
of
45,274
L/
kg
­
lipid,
which
is
used
in
the
derivation
of
the
BAF
as
described
in
the
next
section.

Baseline
BAF
fd
R
'
(
1.072)
3333
0.9862
&
1
1
0.08
'
45,274
L/
kg
&
lipid
(
Equation
2.4.41)

For
the
purposes
of
this
example,
it
has
been
assumed
that
only
one
acceptable
baseline
BAF
value
could
be
derived
for
trophic
level
four
organisms.
Thus,
the
baseline
BAF
for
trophic
level
four
is
equal
to
this
baseline
BAF.
Had
other
acceptable
BCFs
been
available
for
trophic­
level
four
organisms,
then
the
trophic
level
four
baseline
BAF
would
have
been
calculated
as
the
geometric
mean
of
the
acceptable
BCF­
predicted
baseline
BAFs
for
trophic
level
four.

AWQC
BAF
for
Chemical
R
After
the
derivation
of
trophic
level­
specific
baseline
BAFs
for
chemical
R
(
described
in
the
previous
section),
the
next
step
is
to
calculate
BAFs
that
will
be
used
in
the
derivation
of
AWQC.
This
step
is
necessary
to
adjust
the
baseline
BAFs
to
conditions
that
are
expected
to
affect
the
bioavailability
of
chemical
R
at
the
sites
applicable
to
the
AWQC.
Derivation
of
AWQC
BAFs
requires
information
on:
(
1)
the
baseline
BAF
at
appropriate
trophic
levels,
(
2)
the
percent
lipid
of
the
aquatic
organisms
consumed
by
humans
at
the
site(
s)
of
interest
(
trophic
level
specific),
and
(
3)
the
freely
dissolved
fraction
of
the
chemical
in
ambient
water
at
the
site(
s)
of
interest.
For
each
trophic
level,
the
equation
for
deriving
a
BAF
to
used
in
deriving
AWQC
is:
266
f
fd
'
1
[
1
%
(
POC
@
K
ow)
%
(
DOC
@
K
ow
10
)]

(
Equation
2.4.43)

f
fd
'
1
[
1
%
(
4.8
x
10
!
7
kg/
L
@
10,000
L/
kg)
%
(
2.9
x
10
!
6
kg/
L
@
10,000
10
L/
kg)

(
Equation
2.4.44)

f
fd
'
0.9924
where:

BAF
for
AWQC
(
TL
n)
=
BAF
at
trophic
level
"
n"
used
to
derive
AWQC
based
on
site
conditions
for
lipid
content
of
consumed
aquatic
organisms
for
trophic
level
"
n"
and
the
freely
dissolved
fraction
in
the
site
water
Baseline
BAF
R
f
d
(
TL
n)
=
BAF
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis
for
trophic
level
"
n"

f
R
(
TL
n)
=
Fraction
lipid
of
aquatic
species
consumed
at
trophic
level
"
n"

f
fd
=
Fraction
of
the
total
chemical
in
water
that
is
freely
dissolved
For
the
purposes
of
this
example,
an
AWQC
BAF
for
chemical
R
is
being
calculated
only
for
aquatic
organisms
at
one
trophic
level
(
trophic
level
four).
If
fish
consumption
data
indicates
the
target
population
consumes
significant
quantities
from
multiple
trophic
levels,
then
AWQC
BAFs
should
be
derived
for
each
of
the
appropriate
trophic
levels.

For
chemical
R,
the
baseline
BAF
at
trophic
level
four
is
calculated
to
be
45,274
L/
kg­
lipid
as
described
above.
The
fraction
lipid
content
of
aquatic
species
consumed
at
trophic
level
four
is
assumed
to
be
3.1%
based
on
the
national
default
lipid
content
for
trophic
level
four
derived
in
Section
2.4.5.3.
The
freely
dissolved
fraction
of
chemical
R
that
is
expected
in
the
sites
applicable
to
the
AWQC
was
determined
to
be
0.9924.
This
value
is
calculated
as
shown
below
using
hypothetical
expected
POC
and
DOC
concentrations
at
the
sites
applicable
to
the
AWQC
of
0.48
mg/
L
(
4.8
x
10­
7
kg/
L)
and
2.9
mg/
L
(
2.9
x
10­
6
kg/
L),
respectively,
and
the
same
K
ow
of
10,000
for
chemical
R.

Using
the
AWQC
BAF
equation
described
previously,
the
AWQC
BAF
for
trophic
level
four
organisms
is
calculated
to
be
1,394
L/
kg
as
shown
below.
267
AWQC
BAF
for
Trophic
Level
Four
=
[
45,274
L/
kg­
lipid)°(
0.031)
+
1]
°
(
0.9924)
=
1,394
L/
kg­
tissue
This
AWQC
BAF
relates
the
total
concentration
in
water
to
the
total
concentration
in
tissue
of
trophic
level
four
organisms,
based
on
the
expected
conditions
that
would
affect
the
bioavailability
of
chemical
R
(
i.
e.,
freely
dissolved
fraction
at
AWQC
sites
and
lipid
content
of
consumed
aquatic
organisms).

2.4.8
Trophic
Level­
Specific
Fish
Consumption
Rates
When
local
or
regional
data
are
unavailable
for
calculating
fish
and
shellfish
consumption
rates,
EPA
has
derived
national
default
consumption
rates
of
17.8
g/
person/
d,
39
g/
person/
d,
and
86.3
g/
person/
d
based
on
the
90th,
95th
and
99th
percentile
of
average
per
capita
fish
consumption
from
the
adult
U.
S.
population
(
see
Section
2.3
on
exposure).
These
default
consumption
figures
reflect
total
consumption
of
aquatic
organisms
across
all
trophic
levels.
However,
as
described
in
above,
EPA
recommends
that
BAFs
be
determined
separately
for
specific
trophic
levels
because
accumulation
of
chemicals
is
often
related
to
the
trophic
position
of
the
aquatic
organism,
particularly
for
highly
persistent,
lipophilic
organic
chemicals.
The
question
then
becomes
how
to
best
relate
available
fish
consumption
rates
to
the
trophic
level­
specific
BAFs
in
the
calculation
of
AWQC.

When
calculating
AWQC,
EPA
recommends
that
if
possible,
fish
consumption
rates
be
determined
for
individual
trophic
levels
for
which
BAFs
have
been
derived.
For
example,
if
available
fish
and
shellfish
consumption
survey
data
indicate
that
the
target
population
is
consuming
significant
portions
of
aquatic
organisms
at
trophic
levels
two,
three,
and
four,
then
both
BAFs
and
fish
consumption
rates
should
be
determined
for
each
of
these
trophic
levels
to
provide
the
most
accurate
representation
of
contaminant
exposure
via
the
consumption
of
aquatic
organisms.
In
this
example,
applying
the
total
consumption
rate
from
all
three
trophic
levels
to
a
BAF
that
is
derived
for
a
single
trophic
level
may
not
accurately
reflect
likely
exposure
to
the
target
population,
if
BAFs
differ
greatly
by
trophic
level.

Calculating
fish
consumption
rates
for
individual
trophic
levels
requires
information
on
the
trophic
status
of
consumed
species
from
the
consumption
survey.
Determination
of
trophic
status
of
aquatic
organisms
is
best
determined
on
a
local
or
regional
basis
and
should
involve
consideration
of
the
size
(
age)
of
aquatic
organisms
in
addition
to
their
dietary
preferences.
If
local
or
regional
information
is
not
available,
then
EPA
recommends
the
use
of
the
most
recent
version
of
the
document:
Trophic
Level
and
Exposure
Analysis
for
Selected
Piscivorous
Birds
and
Mammals
(
USEPA
1995d,
1995e,
1995f),
which
contains
information
on
the
dietary
composition
of
numerous
aquatic
species.
This
draft
document
is
currently
being
revised
based
on
peer
review
comments
and
is
expected
to
be
made
final
in
1998.
Described
below
is
the
derivation
of
trophic
level­
specific
fish
consumption
rates
for
EPA's
CSFII­
based,
national
default
fish
consumption
rates
listed
above.
268
f(
FI,
TL
n)
'
FI(
TL
n)

FI(
all
TL)
In
estimating
trophic
level­
specific
consumption
rates
appropriate
to
the
national
default
consumption
rates
from
the
CSFII
consumption
survey
(
USEPA,
1998b),
EPA
first
estimated
the
trophic
level
of
aquatic
organisms
corresponding
to
each
of
the
CSFII
consumption
categories
for
mean
per
capita
consumption
of
adults
(
see
Table
2.4.9
and
associated
text
for
trophic
level
determinations
of
the
various
CSFII
consumption
categories;
see
Table
2.4.7
for
mean
per
capita
consumption
rates
from
the
CSFII
survey).
Since
the
national
default
consumption
rates
of
17.8,
39.0,
and
86.3
g/
person/
day
reflect
consumption
at
the
90th,
95th
and
99th
percentiles,
respectively,
trophic
level
assignments
would
have
ideally
been
made
according
to
consumption
patterns
corresponding
to
these
higher
percentiles
because
consumption
patterns
might
differ
at
higher
percentiles.
However,
inherent
limitations
in
the
data
from
the
CSFII
survey
prevented
a
meaningful
assessment
of
consumption
patterns
at
these
upper
percentiles.
Therefore,
the
consumption
pattern
reflective
of
mean
per
capita
consumption
rates
was
assumed
to
adequately
reflect
consumption
at
the
higher
consumption
rate
percentiles.

The
second
step
involved
calculating
the
fraction
of
total
fish
and
shellfish
consumption
at
trophic
level
two,
three
and
four
using
the
mean
per
capita
consumption
rates
of
adults
from
CSFII
survey
(
Table
2.4.7).
The
fractions
of
total
fish
consumption
at
specific
trophic
levels
is
shown
below:

where:

f
(
FI,
TL
n)
=
Fraction
of
total
mean
per
capita
fish
consumption
at
trophic
level
"
n"
FI
(
TL
n)
=
Mean
per
capita
fish
consumption
rate
at
trophic
level
"
n"
(
g/
person/
day)
FI
(
TL
all)
=
Total
mean
per
capita
fish
consumption
rate
for
all
trophic
levels
(
g/
person/
day)

Fraction
total
fish
consumption
at:

Trophic
Level
Two:
0.06048
=
0.34779
(
g/
pers./
day)
/
5.75082
(
g/
pers./
day)
Trophic
Level
Three:
0.64754
=
3.72389
(
g/
pers./
day)
/
5.75082
(
g/
pers./
day)
Trophic
Level
Four:
0.29198
=
1.67915
(
g/
pers./
day)
/
5.75082
(
g/
pers./
day)

These
fractions
indicate
that
on
a
national,
per
capita
average
basis,
the
majority
of
fish
and
shellfish
consumption
is
occurring
at
trophic
level
three,
followed
by
trophic
level
trophic
level
four.
This
is
corroborated
by
the
comparatively
greater
consumption
of
shrimp,
catfish,
perch,
estuarine
flatfish,
trout,
crab,
and
flounder
(
Table
2.4.7).
Finally,
the
trophic
level­
specific
consumption
rates
269
FI
i
th
percentile
(
TL
n)
'
FI
i
th
percentile
(
all
TL)
@
f
(
FI,
TL
n)
applicable
to
90th,
95th,
and
99th
percentile
national
default
consumption
values
were
calculated
using
the
following
equation:

where:

FI
ith
percentile
(
TL
n)
=
Estimated
fish
consumption
rate
at
trophic
level
"
n"
for
the
ith
percentile
FI
ith
percentile
(
TL
all)
=
Total
fish
consumption
rate
for
all
trophic
levels
for
the
ith
percentile
f
(
FI,
TL
n)
=
Fraction
of
total
mean
per
capita
fish
consumption
at
trophic
level
"
n"
(
determined
above)

Trophic
level­
specific
consumption
rates
(
in
g/
person/
day)
corresponding
to
the
national
default
consumption
rates
of
17.8g/
d,
39.0g/
d,
and
86.3g/
d
(
i.
e.,
the
90th,
95th
and
99th
percentile
of
mean
per
capita
consumption
of
adults
from
the
CSFII
survey)
are
shown
below.

90th
95th
99th
TL2
1.1
2.4
5.2
TL3
11.5
25.2
55.9
TL4
5.2
11.4
25.2
All
TL
17.8
39.0
86.3
EPA
recognizes
that
in
some
situations,
States
and
Tribes
may
lack
sufficient
data
to
determine
trophic
level­
specific
fish
consumption
rates
that
are
applicable
to
their
target
populations
and
site(
s)
of
concern.
In
these
situations,
EPA
recommends
that
States
and
Tribes
assign
the
total
fish
consumption
rates
to
the
highest
BAF
determined
across
the
relevant
trophic
levels.
In
most
cases,
this
will
be
the
BAF
corresponding
to
trophic
level
four,
but
in
some
cases,
it
may
be
trophic
level
three.
This
approach
reflects
a
assumption
that
may
be
conservative,
the
degree
to
which
will
depend
on
the
actual
consumption
pattern
of
the
target
population.

2.4.9
References
American
Fisheries
Society.
1991.
Common
and
Scientific
Names
of
Fishes
from
the
United
States
and
Canada.
Fifth
Edition.
Bethesda,
MD:
American
Fisheries
Society.
AFS
Spec.
Publ.
No.
20.
270
Armstrong,
R.
W.
and
R.
J.
Sloan.
1988.
PCB
Patterns
in
Hudson
River
Fish:
I.
Resident
Freshwater
Species.
In:
C.
L
Smith
(
ed).
Fisheries
Research
in
the
Hudson
River:
The
Hudson
River
Environmental
Society.
Albany,
NY:
State
University
of
New
York
Press.
pp.
304­
324.

Baumann,
T.
and
L.
H.
Grimme.
1981.
Determination
of
Hydrophobic
Parameters
for
Pyridazinone
Herbicides
by
Liquid­
Liquid
Partition
and
Reversed­
Phase
High­
Performance
Liquid
Chromatography.
J.
Chromatog.
206:
7­
15.

Bligh,
E.
G.
and
W.
J.
Dyner.
1959.
A
Rapid
Method
of
Total
Lipid
Extraction
and
Purification.
Canadian
J.
Biochem.
and
Physiol.
37:
911­
917.

Brooke,
D.
N.,
A.
J.
Dobbs
and
N.
Williams.
1986.
Octanol:
Water
Partition
Coefficients
(
P):
Measurement,
Estimation,
and
Interpretation,
Particularly
for
Chemicals
with
P
>
105.
Ecotoxicol.
Environ.
Safety.
11:
251­
260.

Brooke,
D.,
I.
Nielsen,
J.
de
Bruijn
and
J.
Hermens.
1990.
An
Interlaboratory
Evaluation
of
the
Stir­
Flask
Method
for
the
Determination
of
Octanol­
Water
Partition
Coefficient
(
log
P
OW).
Chemosphere.
21:
119­
133.

Burkhard,
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Doucette
and
A.
W.
Andren.
1984.
Generator
Column
Determination
of
Octanol
/
Water
Partition
Coefficients
for
Selected
Polychlorinated
Biphenyl
Congeners.
Environ.
Sci.
Technol.
18:
457­
459.

Yin,
C.
and
J.
P.
Hassett.
1986.
Gas­
Partitioning
Approach
for
Laboratory
and
Field
Studies
of
Mirex
Fugacity
in
Water.
Environ.
Sci.
Technol.
20:
1213­
1217.

Yin,
C.
and
J.
P.
Hassett.
1989.
Fugacity
and
Phase
Distribution
of
Mirex
in
Oswego
River
and
Lake
Ontario
Waters.
Chemosphere.
19:
1289­
1296.

3.
MINIMUM
DATA
CONSIDERATIONS
3.1
Background
The
1980
AWQC
National
Guidelines
did
not
present
specific
minimum
data
requirements.
However,
the
following
minimum
data
requirements
were
implied
from
the
text:

3.1.1
Threshold
Effects
Guidelines
Animal
dose­
response
toxicity
data
were
used
in
developing
guidelines
for
deriving
criteria
based
on
noncarcinogenic
responses.
The
following
guidelines
for
deriving
criteria
were
adopted:
277
°
A
free­
standing
Frank
Effect
Level
(
FEL)
is
unsuitable
for
the
derivation
of
criteria.

°
A
free­
standing
No
Observed
Effect
Level
(
NOEL)
is
unsuitable
for
the
derivation
of
criteria.
If
multiple
NOELs
are
available,
with
or
without
additional
data
on
Lowest
Observed
Effect
Levels
(
LOELs),
No
Observed
Adverse
Effect
Levels
data
(
NOAELs),
or
Lowest
Observed
Adverse
Effect
Levels
(
LOAELs).
The
highest
NOEL
should
be
used
to
derive
a
criterion.

°
A
NOAEL,
LOEL,
or
LOAEL
can
be
suitable
for
criteria
derivation.
A
well­
defined
NOAEL
from
a
chronic
study
(
90­
day
study
was
considered
minimum)
may
be
used
directly,
applying
the
appropriate
uncertainty
factor.
For
a
LOEL,
a
judgment
needs
to
be
made
as
to
whether
it
actually
corresponds
to
a
NOAEL
or
a
LOAEL.
In
the
case
of
a
LOAEL,
an
additional
uncertainty
factor
is
applied;
the
magnitude
of
the
additional
uncertainty
factor
is
judgmental
and
should
lie
in
the
range
of
1
to
10.
Caution
must
be
exercised
not
to
substitute
FELs
for
LOAELs.

3.1.2
Non­
Threshold
Effects
This
section
discusses
lifetime
animal
studies
or
human
studies
where
excess
cancer
risk
has
been
associated
with
exposure
to
the
agent.

3.1.2.1
Animal
Studies
°
For
some
chemicals,
several
studies
conducted
at
several
doses
and
different
routes
of
exposure
are
available
for
different
animal
species,
strains,
and
sexes.
A
choice
must
be
made
as
to
which
of
the
data
sets
from
several
studies
are
to
be
used
in
the
model.
The
procedures
listed
below,
used
in
evaluating
these
data,
are
consistent
with
the
estimate
of
a
maximum­
likely­
risk.

°
The
data
(
i.
e.,
dose
and
tumor
incidence)
used
in
the
model
are
data
sets
where
the
incidence
is
statistically
significantly
higher
than
the
control
for
at
least
one
test
dose
level
and/
or
where
the
tumor
incidence
rate
shows
a
statistically
significantly
trend
with
respect
to
dose
level.
The
data
set
which
gives
the
highest
estimate
of
lifetime
carcinogenic
risk,
q1*,
estimated
from
each
of
these
data
sets,
is
used
for
risk
assessment.

°
If
sufficient
data
exist
for
two
or
more
significant
tumor
sites
in
the
same
study,
the
number
of
animals
with
at
least
one
of
the
specific
tumor
sites
under
consideration
was
used
as
incidence
data
in
the
model.

°
Since
to
a
close
approximation,
the
surface
area
is
proportional
to
the
2/
3
power
of
the
weight
as
would
be
the
case
for
a
perfect
sphere,
the
exposure
in
mg/(
body
weight)
2/
3/
day
is
similarly
considered
to
be
an
equivalent
exposure.
278
°
Use
data
from
organ
sites
which
are
statistically
higher
than
the
control
data.

3.1.3
Exposure
Assumptions
The
three
exposure­
related
parameters
listed
below
were
provided
in
the
1980
AWQC
National
Guidelines.
Although
the
concept
of
accounting
for
dietary
and
inhalation
exposures
was
included,
no
parameters
were
provided.

°
2
L/
day
drinking
water
consumption;

°
6.5
g/
day
consumption
of
fish;
and
°
Lipid­
normalized
bioconcentration
factor
(
BCF).

3.2
Minimum
Data
Considerations
in
the
Federal
Register
Notice
Many
sections
of
the
Federal
Register
notice
which
accompanies
this
Technical
Support
Document
(
TSD)
include
discussions
of
data
quality.
While
many
of
these
discussions
are
qualitative
in
nature,
they
may
help
direct
the
reader
to
the
kinds
of
data
which
meet
a
minimally
successful
risk
assessment.
For
example,
in
the
exposure
section,
there
is
a
discussion
of
what
constitutes
acceptable
data
for
conducting
a
relative
source
contribution
assessment;
in
addition,
there
is
a
discussion
regarding
minimally
acceptable
fish
consumption
surveys
and
data
collection.
In
developing
bioaccumulation
factors,
there
is
a
discussion
in
the
Federal
Register
of
what
is
regarded
as
a
minimally
acceptable
BCF
and
K
ow.
That
section
also
cites
a
field
guidance
document
which
will
contain
minimum
data
requirements
for
assessing
field­
measured
BAFs
and
field­
measured
lipid
levels
and
POC/
DOC.
Once
this
document
is
finalized
in
1998,
the
results
will
be
cited
and
incorporated
into
the
final
TSD.

On
the
toxicological
side
of
the
human
health
methodology,
the
following
minimum
data
is
suggested
for
RfD
development:

3.2.1
Noncancer
­
Data
Suggestions
3.2.1.1
RfD
Development
(
Minimal
Data)

°
One
well­
conducted
subchronic
(
90
days)
mammalian
bioassay
by
the
oral
route
of
exposure
in
which
a
NOAEL
or
LOAEL
can
be
derived.

°
If
the
most
critical
endpoint
is
an
acute
effect,
which
occurs
short­
term,
it
should
be
used
as
the
basis
of
the
RfD.

°
One
short­
term
developmental
study,
if
it
can
be
shown
that
the
developmental
toxicity
endpoint
is
the
critical
effect
given
other
subchronic
or
chronic
studies.
279
°
One
developmental
study
cannot
be
used
as
the
basis
of
an
RfD
on
its
own
unless
other
studies
exist
to
support
its
use.

Of
course,
a
more
ideal
data
set
is
preferred
but
not
always
available.
The
following
data
set
is
considered
complete
and
likely
to
have
much
less
uncertainty
associated
with
the
resulting
RfD:

3.2.1.2
RfD
Development
(
Ideal
Situation)

°
One
well­
conducted
epidemiological
study;
or
°
Two
or
more
adequate
chronic
studies
in
two
animal
species,
one
of
which
must
be
with
rodents,
by
the
oral
route
of
exposure
in
which
one
can
identify
a
NOAEL
and
LOAEL;
and
°
One
adequate
mammalian
multi­
generation
reproductive
toxicity
study
by
the
oral
route
of
exposure;
and
°
Two
adequate
mammalian
developmental
toxicity
studies
by
the
oral
route
of
exposure
in
different
species;
and
°
Mechanistic,
pharmacokinetic
and
target
organ
toxicity
data;
and
°
If
the
most
critical
endpoint
is
an
acute
effect,
which
occurs
short­
term,
it
should
be
used
as
the
basis
of
the
RfD.

°
The
species
most
biologically
relevant
to
humans
is
known;
in
the
absence
of
the
most
biologically
relevant
species,
the
most
sensitive
species
is
chosen
as
the
basis
for
RfD
development.
For
example,
study
results
from
an
animal
whose
pharmacokinetics
and
toxicokinetics
match
those
of
a
human
would
be
considered
the
most
biologically
relevant.

Minimum
data
suggestions
for
benchmark
and
categorical
regression
analyses
are
currently
evolving.
However,
the
examples
and
text
provided
in
this
TSD
under
the
noncancer
section
do
provide
some
information
of
the
data
needs
of
each
of
these
analyses.

3.2.2
Cancer
­
Data
Suggestions
3.2.2.1
Minimum
Data
A
minimally
acceptable
data
base
for
cancer
assessment
is
one
similar
to
the
weight
of
evidence
established
by
the
1986
Guidelines
for
Carcinogen
Risk
Assessment
(
e.
g.,
A,
B,
and
C
classifications)
fully
described
at
51
FR
33992
and
in
the
Federal
Register
notice
which
accompanies
280
this
TSD.
However,
such
a
data
base
may
be
lacking
information
on
mode
of
action,
which
is
important
for
making
judgments
using
the
new
Cancer
Guidelines
of
1996
(
61
FR
17960).
At
a
minimum,
some
information
is
needed
to
determine
the
mode
of
action;
otherwise,
the
chemical
must
be
treated
as
a
linear
compound.

3.2.2.2
Ideal
Situation
The
goal
is
to
establish
a
complete
data
base
which
includes
not
only
adequate
tumor
data
from
chronic
studies,
as
described
above,
but
data
on
mode
of
action,
metabolism,
pharmacokinetics,
and
target
toxicity.
The
ultimate
goal
in
any
cancer
assessment
is
to
establish
the
mechanism
by
which
the
cancer
develops.
Since
the
number
of
studies
for
a
weight­
of­
evidence
is
yet
to
be
established,
there
is
no
quantitative
guidance
being
presented
today.
These
minimum
data
requirements
may
be
established
in
time
to
incorporate
them
into
the
final
TSD.
However,
as
with
all
determinations
based
on
a
weight­
of­
evidence,
the
number
of
studies
which
demonstrate
(
1)
a
carcinogenic
effect
in
a
number
of
animal
species
and
sexes,
and
(
2)
a
particular
mode
of
action,
determines
the
confidence
in
the
overall
weight
of
evidence.

3.2.3
Exposure
­
Data
Suggestions
Numerous
suggestions
are
made
at
the
beginning
of
the
exposure
analyses
section
of
this
TSD
regarding
the
factors
used
in
the
AWQC
derivation.
These
factors
include
(
1)
body
weight
of
the
individuals
exposed;
(
2)
drinking
water
ingestion
rates;
(
3)
fish
consumption
rates;
(
4)
incidental
ingestion
of
water;
and
(
5)
the
relative
source
contribution
factor
to
account
for
other
exposures.
Body
weights
and
fish
intake
assumptions
are
used
in
each
criterion.
The
suggestions
made
are
to
help
the
exposure
assessor
locate
sources
of
information
for
conducting
exposure
analyses
and
do
not
prescribe
minimum
data
considerations.
However,
the
specific
approach
to
estimating
non­
water
sources
of
exposure
when
setting
AWQC
(
i.
e.,
the
Exposure
Decision
Tree
Approach)
discusses
data
adequacy
considerations.
This
discussion
is
not
repeated
here;
the
reader
is
referred
to
the
data
adequacy
subsection
in
Section
2.3.4.1
of
this
TSD.

3.3
Site­
Specific
Criterion
Calculation
The
1980
AWQC
National
Guidelines
allowed
for
site­
specific
modifications
to
reflect
local
environmental
conditions
and
human
exposure
patterns.
The
methodology
stated
that
"
local"
may
refer
to
any
appropriate
geographic
area
where
common
aquatic
environmental
or
exposure
patterns
exist.
Thus
"
local"
may
signify
a
Statewide,
regional,
river
reach
or
entire
river.

In
today's
proposal,
site­
specific
criteria
may
be
developed
as
long
as
the
site­
specific
data,
either
toxicological
or
exposure­
related,
is
justifiable.
For
example,
a
State
should
use
a
site­
specific
fish
consumption
rate
that
represents
at
least
the
central
tendency
(
median
or
mean)
of
the
population
surveyed
(
either
sport
or
subsistence,
or
both).
If
a
site­
specific
fish
consumption
rate
for
sport
anglers
or
subsistence
anglers
is
lower
than
an
EPA
default
value,
it
may
be
used
in
calculating
AWQC.
To
justify
such
a
level
(
either
higher
or
lower
than
EPA
defaults)
the
State
should
present
281
survey
data
it
used
in
arriving
at
the
site­
specific
fish
consumption
rate.
The
same
conditions
apply
to
site­
specific
calculations
of
BAF,
percent
fish
lipid,
or
the
RSC.
In
the
case
of
deviations
from
toxicological
values
(
IRIS
values:
verified
noncancer
and
cancer
assessments),
EPA
recommends
that
the
data
upon
which
the
deviation
is
based
be
presented
to
and
approved
by
the
Agency
before
a
criterion
is
developed.

3.4
Organoleptic
Criteria
The
1980
AWQC
National
Guidelines
provided
for
the
development
of
organoleptic
criteria
if
organoleptic
data
were
available
for
a
specific
contaminant.
The
methodology
also
made
a
clear
distinction
that
organoleptic
criteria
and
toxicity­
based
criteria
are
derived
from
completely
different
endpoints
and
that
organoleptic
criteria
have
no
demonstrated
relationship
to
potential
adverse
human
health
effects.
The
1992
National
Experts
Workshop
participants
and
the
Great
Lakes
Committees
of
the
Initiative
both
recommended
that
EPA
place
highest
priority
on
setting
toxicity­
based
criteria,
rather
then
using
limited
resources
to
set
organoleptic
criteria.
Both
efforts,
the
GLI
and
the
National
Experts
Workshop,
concluded
that
organoleptic
effects,
while
significant
from
an
aesthetic
standpoint,
were
not
a
significant
health
concern
and
did
not
merit
significant
expenditures
of
time
and
effort.
While
it
can
be
argued
that
organoleptic
properties
indirectly
affect
human
health
(
people
may
drink
less
water
or
eat
less
fish
due
to
objectionable
taste
or
odor),
they
have
not
been
demonstrated
to
result
in
direct
adverse
effects,
such
as
cancer
or
other
types
of
toxicity.

3.5
Criteria
for
Chemical
Classes
The
1980
AWQC
National
Guidelines
allowed
for
the
development
of
criteria
for
chemical
classes.
A
chemical
class
was
defined
as
any
group
of
chemical
compounds
which
were
reviewed
in
a
single
risk
assessment
document.
The
Guidelines
also
stated
that
in
criterion
development,
isomers
should
be
regarded
as
part
of
a
chemical
class
rather
than
as
a
single
compound.
A
class
criterion,
therefore,
was
an
estimate
of
risk/
safety
which
applied
to
more
than
one
member
of
a
class.
It
involved
the
use
of
available
data
on
one
or
more
chemicals
of
a
class
to
derive
criteria
for
other
compounds
of
the
same
class
in
the
event
that
insufficient
data
were
available
to
derive
compoundspecific
criteria.
The
criterion
applied
to
each
member
of
the
class,
rather
than
to
the
sum
of
the
compounds
within
the
class.
The
1980
methodology
also
acknowledged
that,
since
relatively
minor
structural
changes
within
the
class
of
compounds
can
have
pronounced
effects
on
their
biological
activities,
reliance
on
class
criteria
should
be
minimized.

The
1980
methodology
prescribed
the
following
analysis
when
developing
a
class
criterion:

°
A
detailed
review
of
the
chemical
and
physical
properties
of
the
chemicals
within
the
group
should
be
made.
A
close
relationship
within
the
class
with
respect
to
chemical
activity
would
suggest
a
similar
potential
to
reach
common
biological
sites
within
tissues.
Likewise,
similar
lipid
solubilities
would
suggest
the
possibility
of
comparable
absorption
and
distribution.
282
°
Qualitative
and
quantitative
data
for
chemicals
within
the
group
are
examined.
Adequate
toxicological
data
on
a
number
of
compounds
with
a
group
provide
a
more
reasonable
basis
for
extrapolation
to
other
chemicals
of
the
same
class
than
minimal
data
on
one
chemical
or
a
few
chemicals
within
the
group.

°
Similarities
in
the
nature
of
the
toxicological
response
to
chemicals
in
the
class
provide
additional
support
for
the
prediction
that
the
response
to
other
members
of
the
class
may
be
similar.
In
contrast,
where
the
biological
response
has
been
shown
to
differ
markedly
on
a
qualitative
and
quantitative
basis
for
chemicals
within
a
class,
the
extrapolation
of
a
criterion
to
other
members
is
not
appropriate.

°
Additional
support
for
the
validity
of
extrapolation
of
a
criterion
to
other
members
of
a
class
could
be
provided
by
evidence
of
similar
metabolic
and
pharmacokinetic
data
for
some
members
of
the
class.

The
proposal
in
the
Federal
Register
allows
for
the
development
of
a
criterion
for
classes
of
chemicals,
as
long
as
the
1980
methodology
guidance
is
followed
and
a
justification
is
provided
through
the
analysis
of
mechanistic
data,
pharmacokinetic
data,
structure­
activity
relationship
data,
and
limited
acute
and
chronic
toxicity
data.
When
potency
differences
between
members
of
a
class
are
great
(
such
as
in
the
case
of
chlorinated
dioxins
and
furans),
toxicity
equivalency
factors
(
TEFs)
may
be
more
appropriately
developed
than
one
class
criterion.

3.6
Criteria
for
Essential
Elements
The
1980
AWQC
National
Guidelines
acknowledged
that
developing
criteria
for
essential
elements,
particularly
metals,
must
be
a
balancing
act
between
toxicity
and
essentiality.
The
1980
guidelines
state:

that
the
criteria
must
consider
essentiality
and
cannot
be
established
at
levels
which
would
result
in
deficiency
of
the
element
in
the
human
population.
The
difference
between
the
RDA
and
the
daily
doses
causing
a
specified
risk
level
for
carcinogens
or
the
ADIs
(
now
RfDs)
for
noncarcinogens
defines
the
spread
of
daily
doses
from
which
the
criterion
may
be
derived.
Because
errors
are
inherent
in
defining
both
essential
and
maximum­
tolerable
levels,
the
criterion
is
derived
from
the
dose
levels
near
the
center
of
such
dose
ranges.

In
the
current
proposal,
EPA
endorses
the
guidance
from
the
1980
methodology
and
adds
that
the
process
for
developing
criteria
for
essential
elements
should
be
similar
to
that
used
for
any
other
chemical
with
minor
modifications.
The
RfD
represents
concern
for
one
end
of
the
exposure
spectrum
(
toxicity),
whereas
the
RDA
represents
the
other
end
(
minimum
essentiality).
Where
the
RDA
and
RfD
values
might
occasionally
appear
to
be
similar
in
magnitude
to
one
another,
it
does
not
imply
incompatibility
of
the
two
methodological
approaches,
nor
does
it
imply
inaccuracy
or
error
in
either
calculation.
Appendices
A­
1
Appendix
A
TABLE
A.
1
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
5.59
4.91
6.28
50th
%
0.00
0.00
0.00
90th
%
17.80
14.89
20.63
95th
%
39.04
36.13
42.16
99th
%
86.30
81.99
96.67
Marine
Mean
12.42
11.55
13.29
50th
%
0.00
0.00
0.00
90th
%
45.98
44.48
48.34
95th
%
64.08
61.61
68.05
99th
%
111.38
101.94
120.49
All
Fish
Mean
18.01
16.85
19.17
50th
%
0.00
0.00
0.00
90th
%
60.64
57.06
64.63
95th
%
86.25
80.29
91.00
99th
%
142.96
134.23
154.15
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
8,478
individuals
to
the
U.
S.
population
of
177,807,000
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
2
TABLE
A.
2
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
75.56
66.37
84.75
50th
%
0.00
0.00
0.00
90th
%
242.49
205.05
277.26
95th
%
547.61
493.47
587.37
99th
%
1,171.84
1,123.52
1,252.78
Marine
Mean
172.86
160.73
184.99
50th
%
0.00
0.00
0.00
90th
%
624.83
598.84
670.34
95th
%
911.05
877.29
952.66
99th
%
1,573.20
1,468.43
1,713.17
All
Fish
Mean
248.42
232.19
264.64
50th
%
0.00
0.00
0.00
90th
%
829.02
791.06
872.61
95th
%
1,197.36
1,133.18
1,264.74
99th
%
2,014.67
1,839.55
2,180.87
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
8,478
individuals
to
the
U.
S.
population
of
177,807,000
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
3
TABLE
A.
3
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
of
Age
14
and
Younger
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Grams/
person/
day
Habitat
Statistic
Estimate
Fresh/
Estuarine
Mean
45.73
n
=
295
50th
%
28.35
N
=
6,267,000
90th
%
108.36
95th
%
136.24
99th
%
214.62
Marine
Mean
73.62
n
=
663
50th
%
56.00
N
=
13,190,000
90th
%
153.20
95th
%
176.90
99th
%
337.24
All
Fish
Mean
74.80
n
=
807
50th
%
56.49
N
=
16,159,000
90th
%
153.70
95th
%
178.08
99th
%
337.46
+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
of
acute
consumers
of
age
14
and
younger
to
the
population
of
acute
consumers
of
age
14
and
younger
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
4
TABLE
A.
4
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
of
Age
14
and
Younger
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
Habitat
Statistic
Estimate
Fresh/
Estuarine
Mean
1,721.99
n
=
295
50th
%
1,271.12
N
=
6,267,000
90th
%
3,760.67
95th
%
4,208.18
99th
%
9,789.49
Marine
Mean
2,532.95
n
=
663
50th
%
2,107.05
N
=
13,190,000
90th
%
5,068.69
95th
%
6,376.47
99th
%
8,749.02
All
Fish
Mean
2,624.35
n
=
807
50th
%
2,172.61
N
=
16,159,000
90th
%
5,020.14
95th
%
6,904.83
99th
%
10,384.82
+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
of
acute
consumers
of
age
14
and
younger
to
the
population
of
acute
consumers
of
age
14
and
younger
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
5
TABLE
A.
5
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Grams/
person/
day
Habitat
Statistic
Estimate
Fresh/
Estuarine
Mean
61.40
n
=
445
50th
%
35.22
N
=
10,853,000
90th
%
148.83
95th
%
185.44
99th
%
363.56
Marine
Mean
76.53
n
=
774
50th
%
62.96
N
=
17,967,000
90th
%
149.78
95th
%
178.74
99th
%
271.06
All
Fish
Mean
88.80
n
=
952
50th
%
69.95
N
=
21,924,000
90th
%
170.01
95th
%
212.56
99th
%
361.04
+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
of
female
acute
consumers
of
age
15
to
44
to
the
population
of
female
acute
consumers
of
age
15
to
44
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
6
TABLE
A.
6
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
Habitat
Statistic
Estimate
Fresh/
Estuarine
Mean
961.58
n
=
445
50th
%
533.18
N
=
10,853,000
90th
%
2,578.81
95th
%
3,403.75
99th
%
6,167.24
Marine
Mean
1,227.41
n
=
774
50th
%
986.25
N
=
17,967,000
90th
%
2,469.67
95th
%
3,007.98
99th
%
4,800.68
All
Fish
Mean
1,414.54
n
=
952
50th
%
1,100.44
N
=
21,924,000
90th
%
2,726.46
95th
%
3,740.83
99th
%
6,703.25
+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
acute
consumers
of
females
of
age
14
and
younger
to
the
population
of
acute
consumers
of
females
of
age
14
and
younger
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
7
TABLE
A.
7
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
70.91
64.16
77.65
n
=
1,541
50th
%
42.45
37.24
46.91
N
=
37,166,000
90th
%
176.58
165.08
193.26
95th
%
230.41
224.00
255.55
99th
%
402.56
358.58
518.41
Marine
Mean
91.49
87.35
95.64
n
=
2,432
50th
%
77.56
74.89
78.52
N
=
57,830,000
90th
%
172.29
168.00
182.00
95th
%
215.62
201.99
225.63
99th
%
313.05
292.80
324.81
All
Fish
Mean
106.39
102.37
110.41
n
=
3,007
50th
%
85.36
84.00
87.36
N
=
70,949,000
90th
%
206.76
197.84
213.00
95th
%
258.22
241.00
266.86
99th
%
399.26
336.50
423.56
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
of
acute
consumers
18
years
of
age
or
older
to
the
population
of
acute
consumers
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
8
TABLE
A.
8
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
Acute
Consumers+
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
959.15
867.58
1,050.72
n
=
1,541
50th
%
601.88
532.31
656.86
N
=
37,166,000
90th
%
2,442.97
2,233.16
2,606.66
95th
%
3,116.28
2,839.90
3,303.96
99th
%
5,151.98
4,432.30
6,931.61
Marine
Mean
1,270.78
1,214.65
1,326.90
n
=
2,432
50th
%
1,062.93
1,019.60
1,087.06
N
=
57,830,000
90th
%
2,467.68
2,331.88
2,585.09
95th
%
3,116.74
2,906.16
3,264.98
99th
%
4,250.22
4,037.74
4,387.96
All
Fish
Mean
1,461.71
1,406.34
1,517.09
n
=
3,007
50th
%
1,189.29
1,156.77
1,225.43
N
=
70,949,000
90th
%
2,802.28
2,685.81
2,868.73
95th
%
3,588.11
3,308.93
3,798.54
99th
%
5,355.90
5,095.58
5,766.99
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
a
sample
of
acute
consumers
18
years
of
age
or
older
to
the
population
of
acute
consumers
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
9
TABLE
A.
9
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
4.71
4.17
5.25
50th
%
0.00
0.00
0.00
90th
%
12.62
10.91
13.98
95th
%
32.16
29.81
35.15
99th
%
82.45
77.17
86.40
Marine
Mean
10.94
10.14
11.73
50th
%
0.00
0.00
0.00
90th
%
39.51
37.29
42.91
95th
%
59.62
57.03
61.84
99th
%
106.84
104.59
114.55
All
Fish
Mean
15.65
14.67
16.63
50th
%
0.00
0.00
0.00
90th
%
55.02
51.38
56.00
95th
%
78.34
75.21
80.56
99th
%
133.46
125.27
140.21
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
11,912
individuals
to
the
U.
S.
population
of
242,707,000
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
10
TABLE
A.
10
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
74.16
65.74
82.57
50th
%
0.00
0.00
0.00
90th
%
204.00
177.97
225.16
95th
%
547.64
505.10
565.37
99th
%
1,274.55
1,197.29
1,324.90
Marine
Mean
186.06
170.81
201.31
50th
%
0.00
0.00
0.00
90th
%
663.00
627.39
717.18
95th
%
991.96
960.40
1,044.69
99th
%
1,942.17
1,815.48
2,042.99
All
Fish
Mean
260.22
242.60
277.83
50th
%
0.00
0.00
0.00
90th
%
880.47
844.35
918.79
95th
%
1,308.54
1,267.15
1,346.71
99th
%
2,356.54
2,224.54
2,556.68
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
11,912
individuals
to
the
U.
S.
population
of
242,707,000
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
11
TABLE
A.
11
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population,
Acute
Consumers+
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
68.00
61.92
74.07
n
=
1,892
50th
%
39.52
36.16
44.68
N
=
44,946,000
90th
%
170.84
158.74
181.79
95th
%
224.78
212.91
245.98
99th
%
374.74
336.50
431.34
Marine
Mean
87.77
83.74
91.80
n
=
3,184
50th
%
71.77
69.73
74.23
N
=
73,100,000
90th
%
169.39
167.00
173.65
95th
%
209.50
198.11
221.73
99th
%
320.41
292.80
341.88
All
Fish
Mean
100.63
96.66
104.60
n
=
3,927
50th
%
80.79
79.29
83.90
N
=
89,800,000
90th
%
197.44
188.74
205.12
95th
%
253.38
231.51
264.45
99th
%
371.59
359.29
401.61
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
the
sample
to
the
population
of
acute
consumers
in
the
48
conterminous
states,
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
12
TABLE
A.
12
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population,
Acute
Consumers+
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*

Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
1,076.80
980.00
1,173.61
n
=
1,892
50th
%
656.62
588.84
709.37
N
=
44,946,000
90th
%
2,695.81
2,546.77
2,819.33
95th
%
3,399.46
3,132.65
3,839.47
99th
%
6,526.10
5,270.61
6,931.61
Marine
Mean
1,495.37
1,422.63
1,568.12
n
=
3,184
50th
%
1,151.58
1,120.00
1,181.14
N
=
73,100,000
90th
%
2,956.38
2,838.46
3,083.70
95th
%
3,887.52
3,770.65
4,113.22
99th
%
6,510.73
5,772.57
6,852.01
All
Fish
Mean
1,674.31
1,606.79
1,741.83
n
=
3,927
50th
%
1,307.30
1,267.12
1,339.46
N
=
89,800,000
90th
%
3,299.54
3,133.69
3,462.35
95th
%
4,258.69
4,065.32
4,483.83
99th
%
7,126.90
6,644.11
7,794.41
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

+
Note:
Acute
consumer
=
Individual
who
consumed
fish
at
least
once
during
the
3­
day
reporting
period.
n
=
sample
size
N
=
population
size
Estimates
are
projected
from
the
sample
to
the
population
of
acute
consumers
in
the
48
conterminous
states,
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
13
TABLE
A.
13
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
New
England
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
4.94
3.87
6.01
50th
%
0.00
0.00
0.00
90th
%
17.58
6.92
25.76
95th
%
30.11
25.76
57.86
99th
%
72.04
64.61
74.67
Marine
Mean
16.96
15.57
18.35
50th
%
0.00
0.00
0.00
90th
%
56.95
49.00
66.28
95th
%
74.59
73.91
83.81
99th
%
124.64
99.13
178.05
All
Fish
Mean
21.90
19.95
23.85
50th
%
0.00
0.00
0.00
90th
%
73.56
65.07
74.68
95th
%
91.33
83.81
96.30
99th
%
145.86
138.94
178.05
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
595
individuals
to
the
regional
population
of
12,769,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
14
TABLE
A.
14
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Middle
Atlantic
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
3.79
2.62
4.96
50th
%
0.00
0.00
0.00
90th
%
12.13
9.28
13.71
95th
%
25.29
19.32
31.42
99th
%
61.72
48.00
84.21
Marine
Mean
14.89
12.76
17.02
50th
%
0.00
0.00
0.00
90th
%
52.01
46.27
55.67
95th
%
67.21
60.67
74.63
99th
%
148.91
113.21
154.15
All
Fish
Mean
18.68
15.59
21.77
50th
%
0.00
0.00
0.00
90th
%
61.26
55.67
64.74
95th
%
80.54
69.72
94.85
99th
%
153.23
149.28
171.37
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,585
individuals
to
the
regional
population
of
37,330,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
15
TABLE
A.
15
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
South
Atlantic
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
4.92
3.93
5.91
50th
%
0.00
0.00
0.00
90th
%
16.72
8.63
20.77
95th
%
30.45
28.10
39.25
99th
%
77.54
72.35
86.41
Marine
Mean
11.41
9.72
13.11
50th
%
0.00
0.00
0.00
90th
%
44.56
36.43
49.55
95th
%
63.37
57.88
65.33
99th
%
102.78
91.51
107.98
All
Fish
Mean
16.33
15.10
17.57
50th
%
0.00
0.00
0.00
90th
%
57.62
54.65
65.74
95th
%
83.39
77.30
92.88
99th
%
130.78
122.02
139.45
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
2,245
individuals
to
the
regional
population
of
42,307,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
16
TABLE
A.
16
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
East
North
Central
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
2.88
1.99
3.77
50th
%
0.00
0.00
0.00
90th
%
5.10
1.71
5.94
95th
%
18.24
14.49
26.02
99th
%
58.24
56.50
66.00
Marine
Mean
10.33
8.33
12.33
50th
%
0.00
0.00
0.00
90th
%
38.82
37.33
42.96
95th
%
56.88
52.63
65.03
99th
%
113.83
104.59
132.39
All
Fish
Mean
13.21
11.07
15.35
50th
%
0.00
0.00
0.00
90th
%
47.50
43.87
51.55
95th
%
72.05
65.87
80.51
99th
%
114.31
106.60
132.39
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
2,222
individuals
to
the
regional
population
of
41,565,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
17
TABLE
A.
17
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
East
South
Central
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
10.66
5.84
15.49
50th
%
0.00
0.00
0.00
90th
%
37.64
28.11
49.93
95th
%
58.41
51.53
72.36
99th
%
165.12
86.40
224.33
Marine
Mean
5.97
4.41
7.54
50th
%
0.00
0.00
0.00
90th
%
23.87
18.54
32.27
95th
%
37.29
32.27
47.13
99th
%
61.16
56.00
76.45
All
Fish
Mean
16.63
12.03
21.24
50th
%
0.00
0.00
0.00
90th
%
52.26
45.44
60.67
95th
%
69.94
66.77
86.40
99th
%
165.12
119.53
224.33
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
671
individuals
to
the
regional
population
of
15,113,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
18
TABLE
A.
18
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
West
North
Central
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
4.48
2.22
6.74
50th
%
0.00
0.00
0.00
90th
%
4.41
0.31
9.19
95th
%
25.84
14.42
47.73
99th
%
104.32
58.05
178.33
Marine
Mean
8.37
5.71
11.03
50th
%
0.00
0.00
0.00
90th
%
31.50
27.83
39.88
95th
%
49.00
40.36
56.00
99th
%
99.62
66.20
103.24
All
Fish
Mean
12.85
8.06
17.64
50th
%
0.00
0.00
0.00
90th
%
42.99
36.39
54.78
95th
%
63.05
55.03
86.40
99th
%
141.07
112.00
178.33
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
785
individuals
to
the
regional
population
of
17,720,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
19
TABLE
A.
19
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
West
South
Central
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
7.04
4.82
9.26
50th
%
0.00
0.00
0.00
90th
%
23.85
17.18
37.48
95th
%
55.46
47.71
74.95
99th
%
112.68
77.17
115.75
Marine
Mean
6.02
4.71
7.33
50th
%
0.00
0.00
0.00
90th
%
22.23
18.72
27.67
95th
%
33.75
28.38
41.99
99th
%
92.12
55.67
100.20
All
Fish
Mean
13.06
10.04
16.08
50th
%
0.00
0.00
0.00
90th
%
49.69
37.48
55.92
95th
%
74.77
57.88
85.33
99th
%
114.22
100.74
115.75
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,287
individuals
to
the
regional
population
of
26,321,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
20
TABLE
A.
20
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Mountain
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
3.23
1.86
4.60
50th
%
0.00
0.00
0.00
90th
%
0.48
0.00
5.25
95th
%
20.90
10.51
36.00
99th
%
78.60
55.50
96.67
Marine
Mean
7.97
5.64
10.29
50th
%
0.00
0.00
0.00
90th
%
30.96
27.83
34.86
95th
%
52.68
37.80
58.73
99th
%
89.62
63.71
91.00
All
Fish
Mean
11.20
8.26
14.13
50th
%
0.00
0.00
0.00
90th
%
39.32
36.89
48.20
95th
%
58.55
55.67
68.37
99th
%
95.84
91.00
136.75
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
889
individuals
to
the
regional
population
of
13,385,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
21
TABLE
A.
21
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Pacific
Region
­
Finfish
and
Shellfish
Grams/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
3.93
2.78
5.07
50th
%
0.00
0.00
0.00
90th
%
10.16
8.53
12.08
95th
%
26.46
22.72
29.79
99th
%
68.74
51.53
91.02
Marine
Mean
12.88
10.18
15.58
50th
%
0.00
0.00
0.00
90th
%
50.78
43.68
54.65
95th
%
69.74
60.67
79.57
99th
%
111.49
106.93
121.52
All
Fish
Mean
16.81
14.32
19.29
50th
%
0.00
0.00
0.00
90th
%
55.87
51.53
59.76
95th
%
83.44
71.74
95.95
99th
%
122.64
116.93
169.54
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,633
individuals
to
the
regional
population
of
36,197,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
22
TABLE
A.
22
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
New
England
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
72.50
59.10
85.89
50th
%
0.00
0.00
0.00
90th
%
271.02
103.78
363.88
95th
%
503.29
366.45
723.60
99th
%
1,031.43
755.19
1,240.52
Marine
Mean
283.20
251.27
315.14
50th
%
0.00
0.00
0.00
90th
%
964.92
852.34
1,041.69
95th
%
1,321.16
1,163.41
1,547.33
99th
%
2,083.17
1,789.78
2,631.23
All
Fish
Mean
355.70
315.28
396.12
50th
%
0.00
0.00
0.00
90th
%
1,059.55
1,019.19
1,269.56
95th
%
1,536.32
1,323.53
1,568.72
99th
%
2,362.63
2,120.35
3,014.33
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
595
individuals
to
the
regional
population
of
12,769,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
23
TABLE
A.
23
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Middle
Atlantic
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
63.05
45.79
80.31
50th
%
0.00
0.00
0.00
90th
%
204.00
130.89
260.42
95th
%
413.18
320.99
597.44
99th
%
1,223.45
759.78
1,324.90
Marine
Mean
239.72
211.16
268.29
50th
%
0.00
0.00
0.00
90th
%
768.83
706.68
834.11
95th
%
1,128.64
930.83
1,365.60
99th
%
2,047.28
1,697.69
2,352.51
All
Fish
Mean
302.77
262.82
342.72
50th
%
0.00
0.00
0.00
90th
%
941.32
803.12
1,160.53
95th
%
1,416.61
1,209.29
1,561.31
99th
%
2,510.55
2,302.98
2,673.55
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,585
individuals
to
the
regional
population
of
37,330,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
24
TABLE
A.
24
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
South
Atlantic
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
73.39
60.04
86.73
50th
%
0.00
0.00
0.00
90th
%
256.50
150.60
290.43
95th
%
509.78
451.81
596.27
99th
%
1,216.10
1,092.18
1,353.36
Marine
Mean
178.44
154.11
202.77
50th
%
0.00
0.00
0.00
90th
%
641.43
604.63
690.25
95th
%
977.52
913.81
1,071.87
99th
%
1,690.98
1,331.69
1,943.82
All
Fish
Mean
251.83
234.98
268.67
50th
%
0.00
0.00
0.00
90th
%
858.43
795.11
943.87
95th
%
1,297.53
1,192.41
1,411.80
99th
%
2,175.84
1,943.82
2,323.75
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
2,245
individuals
to
the
regional
population
of
42,307,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
25
TABLE
A.
25
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
East
North
Central
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
44.34
29.23
59.46
50th
%
0.00
0.00
0.00
90th
%
75.18
25.97
100.90
95th
%
316.64
222.48
408.50
99th
%
858.02
808.03
1,001.91
Marine
Mean
197.24
155.33
239.14
50th
%
0.00
0.00
0.00
90th
%
682.31
624.84
747.86
95th
%
1,114.31
943.16
1,333.60
99th
%
2,128.19
1,923.34
3,430.60
All
Fish
Mean
241.58
198.48
284.68
50th
%
0.00
0.00
0.00
90th
%
827.02
731.33
943.16
95th
%
1,388.72
1,241.40
1,513.97
99th
%
2,376.91
1,930.85
3,430.60
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
2,222
individuals
to
the
regional
population
of
41,565,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
26
TABLE
A.
26
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
East
South
Central
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
199.53
106.27
292.79
50th
%
0.00
0.00
0.00
90th
%
807.10
573.22
1,021.39
95th
%
1,086.94
1,021.39
1,355.11
99th
%
2,552.93
2,257.50
3,044.97
Marine
Mean
94.04
70.81
117.28
50th
%
0.00
0.00
0.00
90th
%
340.21
258.49
425.71
95th
%
569.69
435.77
690.12
99th
%
1,370.45
880.38
1,644.42
All
Fish
Mean
293.57
209.10
378.05
50th
%
0.00
0.00
0.00
90th
%
977.86
898.05
1,023.86
95th
%
1,342.17
1,035.12
1,585.92
99th
%
2,786.16
2,257.50
3,044.97
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
671
individuals
to
the
regional
population
of
15,113,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
27
TABLE
A.
27
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
West
North
Central
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
68.68
31.08
106.29
50th
%
0.00
0.00
0.00
90th
%
44.73
5.23
99.14
95th
%
428.01
129.96
904.27
99th
%
1,819.78
929.24
2,310.54
Marine
Mean
165.41
101.17
229.64
50th
%
0.00
0.00
0.00
90th
%
546.17
438.28
711.48
95th
%
839.85
762.30
981.18
99th
%
2,354.93
1,809.91
2,890.40
All
Fish
Mean
234.09
137.32
330.86
50th
%
0.00
0.00
0.00
90th
%
756.81
578.06
829.11
95th
%
1,222.04
941.54
1,653.86
99th
%
2,988.90
2,224.38
3,733.22
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
785
individuals
to
the
regional
population
of
17,720,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
28
TABLE
A.
28
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
West
South
Central
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
106.38
74.14
138.63
50th
%
0.00
0.00
0.00
90th
%
428.38
297.04
565.37
95th
%
863.50
717.62
1,038.97
99th
%
1,398.61
1,112.73
1,416.47
Marine
Mean
96.30
71.85
120.75
50th
%
0.00
0.00
0.00
90th
%
354.59
291.60
396.92
95th
%
655.36
548.14
747.56
99th
%
1,189.90
1,088.33
1,697.71
All
Fish
Mean
202.68
154.13
251.23
50th
%
0.00
0.00
0.00
90th
%
840.80
698.95
887.76
95th
%
1,085.60
920.62
1,133.18
99th
%
1,725.49
1,416.47
1,755.07
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,287
individuals
to
the
regional
population
of
26,321,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
29
TABLE
A.
29
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Mountain
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
43.34
27.12
59.55
50th
%
0.00
0.00
0.00
90th
%
7.32
0.00
64.26
95th
%
295.74
145.18
458.98
99th
%
1,028.60
750.63
1,115.58
Marine
Mean
132.52
88.63
176.42
50th
%
0.00
0.00
0.00
90th
%
458.07
421.93
583.88
95th
%
751.69
680.90
902.64
99th
%
1,535.18
1,344.97
1,713.17
All
Fish
Mean
175.86
128.71
223.01
50th
%
0.00
0.00
0.00
90th
%
641.64
542.17
729.72
95th
%
957.82
817.21
1,075.34
99th
%
1,702.77
1,421.80
1,786.53
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
889
individuals
to
the
regional
population
of
13,385,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
30
TABLE
A.
30
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Pacific
Region
­
Finfish
and
Shellfish
Milligrams/
kilogram/
person/
day
90%
Interval*
Habitat
Statistic
Estimate
Lower
Bound
Upper
Bound
Fresh/
Estuarine
Mean
59.58
44.67
74.50
50th
%
0.00
0.00
0.00
90th
%
182.83
125.87
221.07
95th
%
416.82
345.98
472.91
99th
%
1,018.21
861.13
1,114.52
Marine
Mean
226.16
162.61
289.71
50th
%
0.00
0.00
0.00
90th
%
799.42
760.71
945.97
95th
%
1,159.26
1,096.99
1,369.73
99th
%
2,173.89
1,995.09
2,598.14
All
Fish
Mean
285.75
222.88
348.61
50th
%
0.00
0.00
0.00
90th
%
946.55
873.95
1,054.94
95th
%
1,413.34
1,295.67
1,489.91
99th
%
2,589.08
2,181.37
2,661.40
*
Percentile
intervals
were
estimated
using
the
percentile
bootstrap
method
with
1,000
bootstrap
replications.

Note:
Estimates
are
projected
from
a
sample
of
1,633
individuals
to
the
regional
population
of
36,197,000
using
survey
weights.

Source
of
individual
consumption
data:
Combined
1989,
1990,
and
1991
USDA
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
31
TABLE
A.
31
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Estuarine
Shrimp
1.72959
Perch
0.60368
Flatfish
(
Estuarine)
0.52735
Crab
(
Estuarine)
0.37126
Flounder
0.29941
Oyster
0.22555
Mullet
0.08756
Croaker
0.06749
Herring
0.03925
Smelts
0.03753
Clam
(
Estuarine)
0.03146
Scallop
(
Estuarine)
0.00322
Anchovy
0.00292
Scup
0.00068
Sturgeon
0.00054
Freshwater
Catfish
1.18227
Trout
0.44946
Carp
0.05727
Pike
0.02337
Salmon
(
Freshwater)
0.01096
Marine
Tuna
4.71788
Flatfish
(
Marine)
1.28921
Cod
1.26813
Salmon
(
Marine)
0.91786
Haddock
0.61729
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
32
TABLE
A.
31
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Marine
(
Con't.)
Crab
(
Marine)
0.43234
Pollock
0.37254
Clam
(
Marine)
0.35788
Ocean
Perch
0.30679
Porgy
0.30502
Scallop
(
Marine)
0.28389
Sea
Bass
0.25467
Lobster
0.25446
Swordfish
0.17743
Sardine
0.13812
Squid
0.12760
Pompano
0.10485
Sole
0.10096
Mackerel
0.07188
Whiting
0.06481
Shark
0.02596
Halibut
0.02396
Mussels
0.01911
Whitefish
0.00888
Snapper
0.00735
Octopus
0.00512
Barracuda
0.00151
Abalone
0.00103
Seafood
0.00057
Unknown
Fish
0.00077
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
33
TABLE
A.
31
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

All
Species
Tuna
4.71788
Shrimp
1.72959
Flatfish
(
Marine)
1.28921
Cod
1.26813
Catfish
1.18227
Salmon
(
Marine)
0.91786
Haddock
0.61729
Perch
0.60368
Flatfish
(
Estuarine)
0.52735
Trout
0.44946
Crab
(
Marine)
0.43234
Pollock
0.37254
Crab
(
Estuarine)
0.37126
Clam
(
Marine)
0.35788
Ocean
Perch
0.30679
Porgy
0.30502
Flounder
0.29941
Scallop
(
Marine)
0.28389
Sea
Bass
0.25467
Lobster
0.25446
Oyster
0.22555
Swordfish
0.17743
Sardine
0.13812
Squid
0.12760
Pompano
0.10485
Sole
0.10096
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
34
TABLE
A.
31
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
18
Years
of
Age
or
Older
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

All
Species
(
Con't.)
Mullet
0.08756
Mackerel
0.07188
Croaker
0.06749
Whiting
0.06481
Carp
0.05727
Herring
0.03925
Smelts
0.03753
Clam
(
Estuarine)
0.03146
Shark
0.02596
Halibut
0.02396
Pike
0.02337
Mussels
0.01911
Salmon
(
Freshwater)
0.01096
Whitefish
0.00888
Snapper
0.00735
Octopus
0.00512
Scallop
(
Estuarine)
0.00322
Anchovy
0.00292
Barracuda
0.00151
Abalone
0.00103
Fish
0.00077
Scup
0.00068
Seafood
0.00057
Sturgeon
0.00054
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
35
TABLE
A.
32
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
14
Years
of
Age
and
Younger
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Estuarine
Shrimp
0.36872
Perch
0.28899
Flatfish
(
Estuarine)
0.17291
Flounder
0.08613
Oyster
0.03172
Crab
(
Estuarine)
0.02880
Mullet
0.02396
Clam
(
Estuarine)
0.01273
Croaker
0.00335
Smelts
0.00080
Anchovy
0.00061
Scollop
(
Estuarine)
0.00034
Freshwater
Catfish
0.47501
Trout
0.34732
Carp
0.02862
Pike
0.01170
Salmon
(
Freshwater)
0.00321
Marine
Tuna
2.82208
Cod
1.13423
Pollock
0.64386
Flatfish
(
Marine)
0.42271
Ocean
Perch
0.38098
Porgy
0.32992
Salmon
(
Marine)
0.26894
Notes:
Estimates
are
projected
from
a
sample
of
2,977
individuals
of
age
14
and
younger
to
the
population
of
55,163,000
individuals
of
age
14
and
younger
using
3
years
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
36
TABLE
A.
32
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
14
Years
of
Age
and
Younger
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Marine
(
Con't.)
Haddock
0.24788
Clam
(
Marine)
0.14478
Squid
0.12532
Sea
Bass
0.07716
Pompano
0.06221
Lobster
0.05980
Mackerel
0.04899
Mussels
0.03597
Crab
(
Marine)
0.03354
Scallop
(
Marine)
0.02981
Whiting
0.02808
Whitefish
0.01170
Halibut
0.00995
Sardine
0.00765
Seafood
0.00005
Unknown
Fish
0.00568
All
Species
Tuna
2.82208
Cod
1.13423
Pollock
0.64386
Catfish
0.47501
Flatfish
(
Marine)
0.42271
Ocean
Perch
0.38098
Shrimp
0.36872
Notes:
Estimates
are
projected
from
a
sample
of
2,977
individuals
of
age
14
and
younger
to
the
population
of
55,163,000
individuals
of
age
14
and
younger
using
3
years
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
37
TABLE
A.
32
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
14
Years
of
Age
and
Younger
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

All
Species
(
Con't.)
Haddock
0.24788
Trout
0.34732
Porgy
0.32992
Perch
0.28899
Salmon
(
Marine)
0.26894
Flatfish
(
Estuarine)
0.17291
Clam
(
Marine)
0.14478
Squid
0.12532
Flounder
0.08613
Sea
Bass
0.07716
Pompano
0.06221
Lobster
0.05980
Mackerel
0.04899
Mussels
0.03597
Crab
(
Marine)
0.03354
Oyster
0.03172
Scallop
(
Marine)
0.02981
Crab
(
Estuarine)
0.02880
Carp
0.02862
Whiting
0.02808
Mullet
0.02396
Clam
(
Estuarine)
0.01273
Pike
0.01170
Whitefish
0.01170
Halibut
0.00995
Sardine
0.00765
Fish
0.00568
Croaker
0.00335
Notes:
Estimates
are
projected
from
a
sample
of
2,977
individuals
of
age
14
and
younger
to
the
population
of
55,163,000
individuals
of
age
14
and
younger
using
3
years
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
TABLE
A.
32
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Individuals
14
Years
of
Age
and
Younger
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

A­
38
Salmon
(
Freshwater)
0.00321
Smelts
0.00080
Anchovy
0.00061
Scallop
(
Estuarine)
0.00034
Seafood
0.00005
Notes:
Estimates
are
projected
from
a
sample
of
2,977
individuals
of
age
14
and
younger
to
the
population
of
55,163,000
individuals
of
age
14
and
younger
using
3
years
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
39
TABLE
A.
33
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Estuarine
Shrimp
1.52145
Perch
0.55348
Flatfish
(
Estuarine)
0.45313
Flounder
0.23224
Crab
(
Estuarine)
0.22766
Mullet
0.05635
Oyster
0.02736
Croaker
0.02672
Clam
(
Estuarine)
0.01925
Herring
0.01112
Scallop
(
Estuarine)
0.00225
Anchovy
0.00018
Freshwater
Catfish
0.81492
Trout
0.34703
Carp
0.07291
Pike
0.00756
Salmon
(
Freshwater)
0.00479
Marine
Tuna
4.41949
Flatfish
(
Marine)
1.10778
Cod
1.04468
Pollock
0.48699
Haddock
0.43548
Salmon
(
Marine)
0.40098
Crab
(
Marine)
0.26511
Notes:
Estimates
are
projected
from
a
sample
of
2,891
females
of
age
15
to
44
to
the
population
of
58,750,000
females
of
age
15
to
44
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
40
TABLE
A.
33
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Marine
(
Con't.)
Porgy
0.23300
Clam
(
Marine)
0.21896
Lobster
0.20638
Scallop
(
Marine)
0.19840
Sea
Bass
0.18125
Ocean
Perch
0.17925
Swordfish
0.17758
Squid
0.06367
Pompano
0.06076
Mackerel
0.04620
Sole
0.03611
Halibut
0.03495
Sardine
0.03436
Whiting
0.02711
Whitefish
0.00756
Octopus
0.00318
Abalone
0.00312
Mussels
0.00280
Seafood
0.00046
Shark
0.00034
All
Species
Tuna
4.41949
Shrimp
1.52145
Flatfish
(
Marine)
1.10778
Cod
1.04468
Catfish
0.81492
Perch
0.55348
Pollock
0.48699
Notes:
Estimates
are
projected
from
a
sample
of
2,891
females
of
age
15
to
44
to
the
population
of
58,750,000
females
of
age
15
to
44
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
41
TABLE
A.
33
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

All
Species
(
Con't.)
Flatfish
(
Estuarine)
0.45313
Haddock
0.43548
Salmon
(
Marine)
0.40098
Trout
0.34703
Crab
(
Marine)
0.26511
Porgy
0.23300
Flounder
0.23224
Crab
(
Estuarine)
0.22766
Clam
(
Marine)
0.21896
Lobster
0.20638
Scallop
(
Marine)
0.19840
Sea
Bass
0.18125
Ocean
Perch
0.17925
Swordfish
0.17758
Carp
0.07291
Squid
0.06367
Pompano
0.06076
Mullet
0.05635
Mackerel
0.04620
Sole
0.03611
Halibut
0.03495
Sardine
0.03436
Oyster
0.02736
Whiting
0.02711
Croaker
0.02672
Clam
(
Estuarine)
0.01925
Herring
0.01112
Notes:
Estimates
are
projected
from
a
sample
of
2,891
females
of
age
15
to
44
to
the
population
of
58,750,000
females
of
age
15
to
44
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
42
TABLE
A.
33
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
Females
of
Age
15
to
44
in
the
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

All
Species
(
Con't.)
Pike
0.00756
Whitefish
0.00756
Salmon
(
Freshwater)
0.00479
Octopus
0.00318
Abalone
0.00312
Mussels
0.00280
Scallop
(
Estuarine)
0.00225
Seafood
0.00046
Shark
0.00034
Anchovy
0.00018
Notes:
Estimates
are
projected
from
a
sample
of
2,891
females
of
age
15
to
44
to
the
population
of
58,750,000
females
of
age
15
to
44
using
3­
year
combined
survey
weights.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
43
TABLE
A.
34
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Estuarine
Shrimp
1.37241
Perch
0.52580
Flatfish
(
Estuarine)
0.43485
Crab
(
Estuarine)
0.29086
Flounder
0.24590
Oyster
0.17419
Mullet
0.07089
Croaker
0.05021
Herring
0.02937
Smelts
0.02768
Clam
(
Estuarine)
0.02691
Scallop
(
Estuarine)
0.00247
Anchovy
0.00228
Scup
0.00050
Sturgeon
0.00040
Freshwater
Catfish
1.06776
Trout
0.43050
Carp
0.04846
Pike
0.01978
Salmon
(
Freshwater)
0.00881
Marine
Tuna
4.19998
Cod
1.22827
Flatfish
(
Marine)
1.06307
Salmon
(
Marine)
0.73778
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
A­
44
TABLE
A.
34
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

Marine
(
Con't.)
Haddock
0.51533
Pollock
0.44970
Crab
(
Marine)
0.33870
Ocean
Perch
0.31878
Clam
(
Marine)
0.30617
Porgy
0.29844
Scallop
(
Marine)
0.21805
Sea
Bass
0.20794
Lobster
0.20001
Swordfish
0.13879
Squid
0.12196
Sardine
0.10313
Pompano
0.09131
Sole
0.07396
Mackerel
0.06379
Whiting
0.05498
Halibut
0.02463
Mussels
0.02217
Shark
0.01901
Whitefish
0.00916
Snapper
0.00539
Octopus
0.00375
Barracuda
0.00111
Abalone
0.00075
Seafood
0.00043
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
TABLE
A.
34
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

A­
45
Unknown
Fish
0.00186
All
Species
Tuna
4.19998
Shrimp
1.37241
Cod
1.22827
Catfish
1.06776
Flatfish
(
Marine)
1.06307
Salmon
(
Marine)
0.73778
Perch
0.52580
Haddock
0.51533
Pollock
0.44970
Flatfish
(
Estuarine)
0.43485
Trout
0.43050
Crab
(
Marine)
0.33870
Ocean
Perch
0.31878
Clam
(
Marine)
0.30617
Porgy
0.29844
Crab
(
Estuarine)
0.29086
Flounder
0.24590
Scallop
(
Marine)
0.21805
Sea
Bass
0.20794
Lobster
0.20001
Oyster
0.17419
Swordfish
0.13879
Squid
0.12196
Sardine
0.10313
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
TABLE
A.
34
(
continued)
DAILY
AVERAGE
PER
CAPITA
ESTIMATES
OF
FISH
CONSUMPTION
As
Consumed
Fish
U.
S.
Population
­
Mean
Consumption
by
Species
within
Habitat
Estimated
Mean
Habitat
Species
(
grams/
person/
day)

A­
46
Pompano
0.09131
Sole
0.07396
Mullet
0.07089
Mackerel
0.06379
Whiting
0.05498
Croaker
0.05021
Carp
0.04846
Herring
0.02937
Smelts
0.02768
Clam
(
Estuarine)
0.02691
Halibut
0.02463
Mussels
0.02217
Pike
0.01978
Shark
0.01901
Whitefish
0.00916
Salmon
(
Freshwater)
0.00881
Snapper
0.00539
Octopus
0.00375
Scallop
(
Estuarine)
0.00247
Anchovy
0.00228
Fish
0.00186
Barracuda
0.00111
Abalone
0.00075
Scup
0.00050
Seafood
0.00043
Sturgeon
0.00040
Notes:
Estimates
are
projected
from
a
sample
of
8,478
individuals
18
years
of
age
or
older
to
the
population
of
177,807,000
individuals
18
years
of
age
or
older
using
3­
year
combined
survey
weights.
The
population
for
this
survey
consisted
of
individuals
in
the
48
conterminous
states.

Source
of
individual
consumption
data:
USDA
Combined
1989,
1990,
and
1991
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII).

The
fish
component
of
foods
containing
fish
was
calculated
using
data
from
the
recipe
file
for
release
7
of
the
USDA's
Nutrient
Data
Base
for
Individual
Food
Intake
Surveys.
B­
1
Appendix
B
Evaluation
of
the
Quality
of
Data
Set(
s)
for
Use
in
Deriving
an
RfD
The
derivation
of
RfDs
begins
with
a
thorough
review
and
assessment
of
the
toxicological
data
base
to
identify
the
type
and
magnitude
of
possible
adverse
health
effects
associated
with
a
chemical.
This
evaluation
should
include
an
examination
of
the
full
range
of
possible
health
effects,
including
acute,
short­
term
(
14
to
28
days),
subchronic,
reproductive/
developmental,
and
chronic
effects.

To
be
useful
for
supporting
the
derivation
of
an
RfD,
a
study
must
meet
certain
standards
with
regard
to
experimental
design,
conduct
and
data
reporting.
This
appendix
provides
general
guidance
on
criteria
for
appropriate
study
design
for
a
variety
of
types
of
toxicity
studies.
These
guidelines
provide
the
assessor
with
a
means
to
evaluate
the
quality
and
adequacy
of
data.
Appropriate
studies
are
used
both
for
the
evaluation
of
potential
hazard
of
the
chemical
and
for
the
derivation
of
the
RfD.

Acute
Toxicity
Determination
Studies
of
acute
exposure
(
one
dose
or
multiple
dose
exposure
occurring
within
a
short
time
(
e.
g.
less
than
24
hours))
are
widely
available
for
many
chemicals.
Acute
toxicity
[
often
expressed
in
terms
of
the
lethal
dose
(
or
concentration)
to
50
percent
of
the
population
(
LD
50
or
LC
50)]
is
usually
the
initial
step
in
experimental
assessment
and
evaluation
of
a
chemical's
toxic
characteristics.
Such
studies
are
used
in
establishing
a
dosage
regimen
in
subchronic
and
other
studies
and
may
provide
initial
information
on
the
mode
of
toxic
action
of
a
substance.
Because
LD
50
or
LC
50
studies
are
of
short
duration,
inexpensive
and
easy
to
conduct,
they
are
commonly
used
in
hazard
classification
systems.

Acute
lethality
studies
are
of
limited
use,
however,
in
the
derivation
of
chronic
criteria,
since
the
establishment
of
chronic
criteria
should
never
be
based
on
exposures
that
approach
acutely
lethal
levels.
However,
the
data
from
such
studies
do
provide
information
on
health
hazards
likely
to
arise
from
individual
short­
term
exposures.
Such
studies
provide
high
dose
effects
data
from
which
to
evaluate
potential
effects
from
exposures
which
may
temporarily
exceed
the
acceptable
chronic
exposure
level.
An
evaluation
of
the
data
should
include
the
incidence
and
severity
of
all
abnormalities,
the
reversibility
of
abnormalities
observed
other
than
lethality,
gross
lesions,
body
weight
changes,
effects
on
mortality,
and
any
other
toxic
effects.

In
recent
years
guidelines
have
been
established
to
improve
quality
and
provide
uniformity
in
test
conditions.
Unfortunately,
many
published
LD
50
or
LC
50
tests
were
not
conducted
in
accordance
with
current
EPA
or
OECD
guidelines
(
USEPA,
1985;
OECD,
1987)
since
they
were
conducted
prior
to
establishment
of
those
guidelines.
For
this
reason,
it
becomes
necessary
to
examine
each
test
or
study
to
determine
if
the
study
was
conducted
in
an
adequate
manner.

The
following
is
a
list
of
ideal
conditions
compiled
from
various
testing
guidelines
which
may
be
used
for
determination
of
adequacy
of
acute
toxicity
data.
Many
published
studies
do
not
report
B­
2
details
of
test
conditions
making
such
determinations
difficult.
However,
test
conditions
guidelines
that
might
be
considered
ideal
may
include:

General:

C
Animal
age
and
species
identified.

C
Minimum
of
5
animals
per
sex
per
dose
group
(
both
sexes
should
be
used).

C
14­
day
or
longer
observation
period
following
dosing.

C
Minimum
of
3
dose
levels
appropriately
spaced
(
most
statistical
methods
require
at
least
3
dose
levels).

C
Identification
of
purity
or
grade
of
test
material
used
(
particularly
important
in
older
studies).

C
If
a
vehicle
used,
the
selected
vehicle
is
known
to
be
non­
toxic.

C
Gross
necropsy
results
for
test
animals.

C
Acclimation
period
for
test
animals
before
initiating
study.

Specific
conditions
for
oral
LD
50:

C
Dosing
by
gavage
or
capsule.

C
Total
volume
of
vehicle
plus
test
material
remain
constant
for
all
dose
levels.

C
Animals
were
fasted
before
dosing.

Specific
conditions
for
dermal
LD
50:

C
Exposure
on
intact,
clipped
skin
and
involve
approximately
10
percent
of
body
surface.

C
Animals
prevented
from
oral
access
to
test
material
by
restraining
or
covering
test
site.

Specific
conditions
for
inhalation
LC
50:

C
Duration
of
exposure
at
least
4
hours.

C
If
an
aerosol
(
mist
or
particulate),
the
particle
size
(
median
diameter
and
deviation)
should
be
reported.
B­
3
Although
the
above
listed
conditions
would
be
included
in
an
ideally
conducted
study,
not
all
of
these
conditions
need
to
be
included
in
an
adequately
conducted
study.
Therefore,
some
discretion
is
required
on
the
part
of
the
individual
reviewing
these
studies
(
USEPA,
1985;
OECD,
1987).

Short­
Term
Toxicity
Studies
(
14­
Day
or
28­
Day
Repeated
Dose
Toxicity)

Short­
term
exposure
generally
refers
to
multiple
or
continuous
exposure
usually
occurring
over
a
14­
day
to
28­
day
time
period.
The
purpose
of
short­
term
repeated
dose
studies
is
to
provide
information
on
possible
adverse
health
effects
from
repeated
exposures
over
a
limited
time
period.

The
following
guidelines
were
derived
using
the
OECD
Guidelines
for
Testing
of
Chemicals
(
OECD,
1987)
for
determining
the
design
and
quality
of
a
repeated
dose
short­
term
toxicity
study:

C
Minimum
of
3
dose
levels
administered
and
an
adequate
control
group
used.

C
Minimum
of
10
animals
per
sex,
per
dose
group
(
both
sexes
should
be
used).

C
The
highest
dose
level
should
ideally
elicit
some
signs
of
toxicity
without
inducing
excessive
lethality
and
the
lowest
dose
should
ideally
produce
no
signs
of
toxicity.

C
Ideal
dosing
regimes
include
7
days
per
week
for
a
period
of
14
days
or
28
days.

C
All
animals
should
be
dosed
by
the
same
method
during
the
entire
experiment
period.

C
Animals
should
be
observed
daily
for
signs
of
toxicity
during
the
treatment
period
(
i.
e.,
14
or
28
days).
Animals
that
die
during
the
study
are
necropsied
and
all
survivors
in
the
treatment
groups
are
sacrificed
and
necropsied
at
the
end
of
the
study
period.

C
All
observed
results,
quantitative
and
incidental,
should
be
evaluated
by
an
appropriate
statistical
method.

C
Clinical
examinations
should
include
hematology
and
clinical
biochemistry,
urinalysis
may
be
required
when
expected
to
provide
an
indication
of
toxicity.
Pathological
examination
should
include
gross
necropsy
and
histopathology.

The
findings
of
short­
term
repeated
dose
toxicity
studies
should
be
considered
in
terms
of
the
observed
toxic
effects
and
the
necropsy
and
histopathological
findings.
The
evaluation
will
include
the
incidence
and
severity
of
abnormalities,
gross
lesions,
body
weight
changes,
effects
on
mortality,
and
other
general
or
specific
toxic
effects
(
OECD,
1987).

These
guidelines
represent
ideal
conditions
and
studies
will
not
be
expected
to
meet
all
standards
in
order
to
be
considered
to
be
adequate.
For
example,
the
National
Toxicology
Program's
cancer
bioassay
program
has
generated
a
substantial
data
base
of
short­
term
repeated
dose
studies.
The
study
periods
for
these
range
from
14
days
to
20
days
with
12
to
15
doses
administered
generally
B­
4
for
5
dose
levels
and
a
control.
Since
the
quality
of
this
data
is
good,
it
is
desirable
to
consider
these
study
results
even
though
they
do
not
always
identically
follow
the
protocol.

Subchronic
and
Chronic
Toxicity
Studies
involving
subchronic
exposure
(
occurring
usually
over
3
months)
and
chronic
exposure
(
those
involving
an
extended
period
of
time,
or
a
significant
fraction
of
the
subject's
lifetime)
are
designed
to
permit
a
determination
of
no­
observed­
effect
levels
(
NOEL)
and
toxic
effects
associated
with
continuous
or
repeated
exposure
to
a
chemical.
Subchronic
studies
provide
information
on
health
hazards
likely
to
arise
from
repeated
exposure
over
a
limited
period
of
time.
They
provide
information
on
target
organs,
the
possibilities
of
accumulation,
and,
with
the
appropriate
uncertainty
factors,
may
be
used
in
establishing
water
quality
criteria
for
human
health.
Chronic
studies
provide
information
on
potential
effects
following
prolonged
and
repeated
exposure.
Such
effects
might
require
a
long
latency
period
or
are
cumulative
in
nature
before
manifesting
disease.
The
design
and
conduct
of
such
tests
should
allow
for
detection
of
general
toxic
effects
including
neurological,
physiological,
biochemical,
and
hematological
effects
and
exposure­
related
pathological
effects.

The
following
guidelines
were
derived
using
the
EPA
Health
Effects
Testing
Guidelines
(
USEPA,
1985),
for
determining
the
quality
of
a
subchronic
or
chronic
(
long
term)
study.
Additional
detailed
guidance
may
be
found
in
that
document.
These
guidelines
represent
ideal
conditions
and
studies
will
not
be
expected
to
meet
all
standards
in
order
to
be
considered
for
use
as
the
basis
for
RfD
derivation.
Ideally,
a
subchronic/
chronic
study
should
include:

C
Minimum
of
3
dose
levels
administered
and
an
adequate
control
group
used.

C
Minimum
of
10
animals
for
subchronic,
20
animals
for
chronic
studies
per
sex,
per
dose
group
(
both
sexes
should
be
used).

C
The
highest
dose
level
should
elicit
some
signs
of
toxicity
without
inducing
excessive
lethality
and
the
lowest
dose
should
ideally
produce
no
signs
of
toxicity.

C
Ideal
dosing
regimes
include
dosing
for
5­
7
days
per
week
for
13
weeks
or
greater
(
90
days
or
greater)
for
subchronic,
and
at
least
12
months
or
greater
for
chronic
studies
in
rodents.
For
other
species,
repeated
dosing
should
ideally
occur
over
10
percent
or
greater
of
animal's
lifespan
for
subchronic
studies
and
50
percent
or
greater
of
the
animal's
lifespan
for
chronic
studies.

C
All
animals
should
be
dosed
by
the
same
method
during
the
entire
experimental
period.

C
Animals
should
be
observed
daily
during
the
treatment
period
(
i.
e.,
90
days
or
greater).
B­
5
C
Animals
that
die
during
the
study
are
necropsied
and,
at
the
conclusion
of
the
study,
surviving
animals
are
sacrificed
and
necropsied
and
appropriate
histopathological
examinations
carried
out.

C
Results
should
be
evaluated
by
an
appropriate
statistical
method
selected
during
experimental
design.

C
Such
toxicity
tests
should
evaluate
the
relationship
between
the
dose
of
the
test
substance
and
the
presence,
incidence
and
severity
of
abnormalities
(
including
behavioral
and
clinical
abnormalities),
gross
lesions,
identified
target
organs,
body
weight
changes,
effects
on
mortality,
and
any
other
toxic
effects
noted
in
USEPA
(
1985).

Developmental
Toxicity
Guidelines
for
reproductive
and
developmental
toxicity
studies
have
been
developed
by
EPA
(
USEPA,
1985
and
OECD,
1987).
Developmental
toxicity
can
be
evaluated
via
a
relatively
shortterm
study
in
which
the
compound
is
administered
during
the
period
of
organogenesis.
Based
on
the
EPA
Health
Effects
Testing
Guidelines
(
USEPA,
1985),
ideal
studies
should
include:

C
Minimum
of
20
young,
adult,
pregnant
rats,
mice,
or
hamsters
or
12
young,
adult,
pregnant
rabbits
recommended
per
dose
group.

C
Minimum
of
3
dose
levels
with
an
adequate
control
group
used.

C
The
highest
dose
should
induce
some
slight
maternal
toxicity
but
no
more
than
10
percent
mortality.
The
lowest
dose
should
not
produce
grossly
observable
effects
in
dams
or
fetuses.
The
middle
dose
level,
in
an
ideal
situation,
will
produce
minimal
observable
toxic
effects.

C
Dose
period
should
cover
the
major
period
of
organogenesis
(
days
6
to
15
gestation
for
rat
and
mouse,
6
to
14
for
hamster,
and
6
to
18
for
rabbit).

C
Dams
should
be
observed
daily;
weekly
food
consumption
and
body
weight
measurements
should
be
taken.

C
Necropsy
should
include
both
gross
and
microscopic
examination
of
the
dams;
the
uterus
should
be
examined
so
that
the
number
of
embryonic
or
fetal
deaths
and
the
number
of
viable
fetuses
can
be
counted;
fetuses
should
be
weighted.

C
One­
third
to
one­
half
of
each
litter
should
be
prepared
and
examined
for
skeletal
anomalies
and
the
remaining
animals
prepared
and
examined
for
soft
tissue
anomalies.

As
with
any
other
type
of
study,
the
appropriate
statistical
analyses
must
be
performed
on
the
data
for
a
study
to
qualify
as
a
good
quality
study.
In
addition,
developmental
studies
are
unique
in
B­
6
the
sense
that
they
yield
two
potential
experimental
units
for
statistical
analysis,
the
litter
and
the
individual
fetus.
The
EPA
testing
guidelines
do
not
provide
any
recommendation
on
which
unit
to
use,
but
the
Guidelines
for
the
Developmental
Toxicity
Risk
Assessment
(
USEPA,
1991)
states
that
"
since
the
litter
is
generally
considered
the
experimental
unit
in
most
developmental
toxicity
studies
.
.
.,
the
statistical
analyses
should
be
designed
to
analyze
the
relevant
data
based
on
incidence
per
litter
or
on
the
number
of
litters
with
a
particular
endpoint."
Others
have
also
identified
the
litter
as
the
preferred
experimental
unit
(
Palmer,
1981
and
Madson
et
al.,
1982).

Information
on
maternal
toxicity
is
very
important
when
evaluating
developmental
effects
because
it
helps
determine
if
differential
susceptibility
exists
for
the
offspring
and
mothers.
Since
the
conceptus
relies
on
its
mother
for
certain
physiological
processes,
interruption
of
maternal
homeostasis
could
result
in
abnormal
prenatal
development.
Substances
which
affect
prenatal
development
without
compromising
the
dam
are
considered
to
be
a
greater
developmental
hazard
than
chemicals
which
cause
developmental
effects
at
maternally
toxic
doses.
Unfortunately,
maternal
toxicity
information
has
not
been
routinely
presented
in
earlier
studies
and
has
become
a
standard
practice
in
studies
only
recently.
In
an
attempt
to
use
whatever
data
are
available,
maternal
toxicity
information
may
not
be
required
if
developmental
effects
are
serious
enough
to
warrant
consideration
regardless
of
the
presence
of
maternal
toxicity.

Reproductive
Toxicity
The
EPA
Health
Effects
Testing
Guidelines
(
USEPA,
1985)
include
guidelines
for
both
reproduction
and
fertility
studies
and
developmental
studies.
These
EPA
guidelines
can
serve
as
the
ideal
experimental
situation
with
which
to
compare
study
quality.
Studies
being
evaluated
do
not
need
to
match
precisely
but
rather
should
be
similar
enough
that
one
can
be
assured
that
the
chemical
was
adequately
tested
and
that
the
results
are
a
reliable
estimate
of
the
true
reproductive
or
developmental
toxicity
of
the
chemical.

These
guidelines
also
recommend
a
two­
generation
reproduction
study
to
provide
information
on
the
ability
of
a
chemical
to
impact
gonadal
function,
conception,
parturition
and
the
growth
and
development
of
the
offspring.
Additional
information
concerning
the
effects
of
a
test
compound
on
neonatal
morbidity,
mortality,
and
developmental
toxicity
may
also
be
provided.
The
recommendations
for
reproductive
testing
are
lengthy
and
quite
detailed
and
may
be
reviewed
further
in
the
EPA
Health
Effects
Testing
Guidelines.
In
general,
the
test
compound
is
administered
to
the
parental
(
P)
animals
(
at
least
20
males
and
enough
females
to
yield
20
pregnant
females)
at
least
10
weeks
before
mating,
through
the
resulting
pregnancies
and
through
weaning
of
their
offspring
(
F1
or
first
generation).
The
compound
is
then
administered
to
the
F1
generation
similarly
through
the
production
of
the
second
generation
(
or
F2)
offspring
until
weaning.
Recommendations
for
numbers
of
dose
groups
and
dose
levels
are
similar
to
those
reported
for
developmental
studies.
Details
should
also
be
provided
on
mating
procedures,
standardization
of
litter
sizes
(
if
possible,
4
males
and
4
females
from
each
litter
are
randomly
selected),
observation,
gross
necropsy
and
histopathology.
Full
histopathology
is
recommended
on
the
following
organs
of
all
high
dose
and
control
P
and
F1
animals
used
in
mating:
vagina,
uterus,
testes,
epididymides,
seminal
vesicles,
prostate,
pituitary
gland,
and
target
organs.
Organs
of
animals
from
other
dose
groups
should
be
examined
when
pathology
has
been
demonstrated
in
high
dose
animals
(
USEPA,
1985).
B­
7
References
Interagency
Regulatory
Liaison
Group
(
IRLG)

Madson,
J.
M.
et
al.
1982.
Teratology
Test
Methods
for
Laboratory
Animals.
In:
Principles
and
Methods
of
Toxicology.
Hayes,
A.
W.
(
ed).
New
York:
Raven
Press.

Organization
for
Economic
Cooperation
and
Development
(
OECD),
1987.
Guidelines
for
Testing
of
Chemicals.
Paris,
France.

Palmer,
A.
K.,
1981.
Regulatory
Requirements
for
Reproductive
Toxicology:
Theory
and
Practice.
In:
Developmental
Toxicology.
Kimmel,
C.
A.
and
J.
Buelke­
Sam
(
eds).
New
York:
Raven
Press.

USEPA.
1985.
Health
Effects
Testing
Guidelines.
40
CFR
Part
798.
Federal
Register
Vol.
50.
September
27.

USEPA.
1991.
Final
Guidelines
for
Development
Toxicity
Risk
Assessment.
Federal
Register
56:
63798­
63826.
December
5.
C­
1
Appendix
C
Derivation
of
Basic
Equations
Concerning
Bioconcentration
and
Bioaccumulation
of
Organic
Chemicals
Introduction
Most
work
dealing
with
the
bioconcentration
and
bioaccumulation
of
organic
chemicals
has
concerned
chemicals
whose
log
K
ow
s
are
greater
than
3.
The
purpose
of
this
appendix
is
to
explain
why
modifications
of
the
equations
generally
used
with
such
chemicals
are
necessary
so
that
the
equations
also
are
appropriate
for
chemicals
whose
K
ow
s,
BCFs,
or
BAFs
are
less
than
1,000,
and
to
derive
all
of
the
appropriate
equations
that
are
used
in
the
calculation
of
BAFs
for
the
final
Guidance.

Background
BCFs
were
originally
defined
as:

BCF
t
T
'
C
t
B
C
t
W
(
1)
where:

=
Total
BCF
(
i.
e.,
a
BCF
that
is
based
on
the
total
concentrations
of
the
chemical
in
BCF
t
T
the
water
and
in
the
aquatic
biota)

=
Total
concentration
of
the
chemical
in
the
aquatic
biota,
based
on
the
wet
weight
C
t
B
of
the
aquatic
biota
=
Total
concentration
of
the
chemical
in
the
water
around
the
aquatic
biota
C
t
W
This
is
not
the
nomenclature
that
was
used
originally,
but
it
is
used
here
for
clarity.

It
was
subsequently
realized
that
extrapolation
of
BCFs
for
organic
chemicals
from
one
species
to
another
would
be
more
accurate
if
the
BCFs
were
normalized
on
the
basis
of
the
amount
of
lipid
in
the
aquatic
biota.
It
was
also
realized
that
extrapolation
of
BCFs
for
organic
chemicals
from
one
water
to
another
would
be
more
accurate
if
the
BCFs
were
calculated
on
the
basis
of
the
freely
dissolved
concentration
of
the
organic
chemical
in
the
water
around
the
aquatic
biota.
Thus,
two
additional
BCFs
were
defined
and
used:

BCF
t
R
'
C
R
C
t
W
(
2)
C­
2
BCF
fd
R
'
C
R
C
fd
W
(
3)
where:

=
Lipid­
normalized
total
BCF
(
i.
e.,
normalized
to
100
percent
lipid
and
based
on
BCF
t
R
the
total
concentration
of
the
chemical
in
the
water
around
the
biota)

C
R
=
Lipid­
normalized
concentration
of
the
chemical
in
the
aquatic
biota
=
Lipid­
normalized,
freely
dissolved
BCF
BCF
fd
R
=
Freely
dissolved
concentration
of
chemical
in
the
water
around
the
aquatic
biota
C
fd
W
The
experimental
definition
of
C
R
is:

C
R
=
the
total
amount
of
chemical
in
the
aquatic
biota
the
amount
of
lipid
in
the
aquatic
biota
=
(
4)
(
B)(
C
t
B
)

L
'
(
B)(
C
t
B
)

(
f
R
)
(
B)
'
C
t
B
f
R
where:

B
=
Wet
weight
of
the
aquatic
biota.

L
=
Weight
of
the
lipid
in
the
aquatic
biota.

f
R
=
Fraction
of
the
aquatic
biota
that
is
lipid
=
L/
B
Using
Equation
4
to
substitute
for
C
R
in
Equation
2
and
then
using
Equation
1:

BCF
t
R
'
C
t
B
(
C
t
W)
(
f
R
)
'
BCF
t
T
f
R
(
5)

If
f
fd
=
the
fraction
of
the
chemical
in
the
water
around
the
aquatic
biota
that
is
freely
dissolved,
then:
C­
3
f
fd
'
C
fd
W
C
t
W
(
6)

Using
Equations
4
and
6
to
substitute
for
C
R
and
in
Equation
3
and
then
using
Equation
1:
C
fd
W
BCF
fd
R
'
C
t
B
(
f
R
)
(
C
t
W)
(
f
fd
)
'
BCF
t
T
(
f
R
)
(
f
fd
)

(
7)

Equations
1,
5,
and
7
show
the
relationships
between
the
three
different
BCFs.

Theoretical
justification
for
use
of
both
lipid­
normalization
and
the
freely
dissolved
concentration
of
the
organic
chemical
in
the
ambient
water
is
based
on
the
concept
of
equilibrium
partitioning,
whereas
practical
justification
is
provided
by
the
general
similarity
of
the
value
of
BCF
fd
R
for
an
organic
chemical
across
both
species
and
waters.
It
will
be
demonstrated,
however,
that
a
more
complete
application
of
equilibrium
partition
theory
shows
that
extrapolates
well
only
BCF
fd
R
for
chemicals
whose
K
ow
s
are
greater
than
1,000,
whereas
a
different
BCF
extrapolates
well
for
organic
chemicals
whose
K
ow
s
are
greater
than
1,000
as
well
as
for
chemicals
whose
K
ow
s
are
less
than
1,000.

Partition
Theory
and
Bioconcentration
Equilibrium
partition
theory
provides
the
understanding
necessary
to
ensure
proper
use
of
K
ow
s,
BCFs,
and
BAFs
in
the
derivation
of
water
quality
criteria
for
organic
chemicals.
For
the
purpose
of
applying
partition
theory,
aquatic
biota
can
be
modeled
as
consisting
of
water,
lipid,
and
non­
lipid
organic
matter
(
Barber
et
al.,
1991).
In
this
model,
an
organic
chemical
in
aquatic
biota
exists
in
three
forms:

1.
Chemical
that
is
freely
dissolved
in
the
water
that
is
in
the
biota.

2.
Chemical
that
is
partitioned
to
the
lipid
that
is
in
the
biota.

3.
Chemical
that
is
partitioned
to
non­
lipid
organic
matter
in
the
biota.
The
total
concentration
of
chemical
in
the
water
inside
the
biota
includes
chemical
that
is
partitioned
to
lipid
and
non­
lipid
organic
matter
in
the
water.

According
to
this
model:

C
t
B
'
(
f
W)(
C
fd
WB)
%
(
f
R
)
(
C
L)
%
(
f
N)(
C
N
)

(
8)
C­
4
where:

f
W
=
Fraction
of
the
aquatic
biota
that
is
water
=
Freely
dissolved
concentration
of
the
organic
chemical
in
the
water
in
the
C
fd
WB
aquatic
biota
f
R
=
Fraction
of
the
aquatic
biota
that
is
lipid
C
L
=
Concentration
of
the
organic
chemical
in
the
lipid
f
N
=
Fraction
of
the
aquatic
biota
that
is
non­
lipid
organic
matter
C
N
=
Concentration
of
the
organic
chemical
in
the
non­
lipid
organic
matter
in
the
aquatic
biota
The
most
important
partitioning
of
the
organic
chemical
within
the
aquatic
biota
is
between
the
lipid
and
the
water,
which
is
described
by
the
following
equation:

K
LW
'
C
L
C
fd
WB
(
9)
where:

K
LW
=
the
lipid­
water
partition
coefficient.

"
K
LW"
(
Gobas
1993)
is
used
herein
because
it
is
more
descriptive
than
"
K
L,"
which
is
used
by
DiToro
et
al.,
(
1991).
This
partition
coefficient
is
central
to
the
equilibrium
partition
approach
that
is
used
to
derive
sediment
quality
criteria
(
DiToro
et
al.,
1991),
the
Gobas
model
that
is
used
to
derive
Food­
Chain
Multipliers
for
the
final
Guidance,
and
the
equations
given
here
that
are
used
to
derive
BCFs
and
BAFs
for
the
final
Guidance.

In
order
for
Equations
8
and
9
to
be
correct,
partition
theory
requires
that
the
concentration
of
the
organic
chemical
in
the
lipid,
C
L,
be
defined
as:

C
L
'
the
amount
of
chemical
partitioned
to
lipid
in
aquatic
biota
the
amount
of
lipid
in
the
aquatic
biota
It
is
difficult
to
determine
C
L
experimentally
because
it
is
not
easy
to
measure
only
the
chemical
that
is
partitioned
to
the
lipid
(
i.
e.,
it
is
not
easy
to
separate
the
three
different
kinds
of
chemical
that,
according
to
the
model,
exist
in
aquatic
biota).
Because
all
of
the
organic
chemical
in
the
biota
is
measured
when
C
R
is
determined,
C
R
can
be
determined
easily,
and
C
R
is
higher
than
C
L.

It
is
useful
to
define
another
BCF
as:
C­
5
BCF
fd
L
'
C
L
C
fd
W
(
10)

Because
C
L
is
lower
than
C
R
,
<
.
BCF
fd
L
BCF
fd
R
The
only
difference
between
K
LW
and
is
that
the
denominator
in
K
LW
is
,
whereas
BCF
fd
L
C
fd
WB
the
denominator
in
is
.
When
partition
theory
applies,
however,
all
phases
are
in
BCF
fd
L
C
fd
W
equilibrium
and
so:

C
fd
W
'
C
fd
WB
(
11)

Therefore,
when
the
organic
chemical
is
not
metabolized
by
the
aquatic
biota
and
when
growth
dilution
is
negligible:

BCF
fd
L
'
K
LW
(
12)

Because
octanol
is
a
useful
surrogate
for
lipid,
a
reasonable
approximation
is
that:

K
LW
'
K
ow
(
13)
where:

K
ow
=
the
octanol­
water
partition
coefficient.

Thus:

predicted
BCF
fd
L
'
K
LW
'
K
ow
(
14)

By
using
Equations
9
and
11
to
substitute
for
C
L
and
in
Equation
8:
C
fd
WB
C
t
B
'
(
f
W)(
C
fd
W
)
%
(
f
R
)
(
BCF
fd
L
)(
C
fd
W
)
%
(
f
N
)(
C
N)
(
15)

By
using
Equation
6
to
substitute
for
in
Equation
15:
C
fd
W
C
t
B
'
(
f
W)(
f
fd
)(
C
t
W)
%
(
f
R
)
(
BCF
fd
L
)(
f
fd
)(
C
t
W)
%
(
f
N
)(
C
N
)

(
16)
C­
6
Dividing
by
gives:
C
t
W
C
t
B
C
t
W
'
(
f
W)(
f
fd
)
%
(
f
R
)
(
BCF
fd
L
)(
f
fd
)
%
(
f
N
)(
C
N)

C
t
W
(
17)

Using
Equation
1
and
rearranging
gives:

BCF
t
T
'
(
f
fd
)
[
f
W
%
(
f
R
)
(
BCF
fd
L
)
%
(
f
N
)(
C
N
)

(
f
fd
)(
C
t
W)
]

(
18)

Using
Equation
6:

BCF
t
T
'
(
f
fd
)
[
f
W
%
(
f
R
)
(
BCF
fd
L
)
%
(
f
N
)(
C
N
)

C
fd
W
]

(
19)

Substituting
and
rearranging
gives:
x
'
f
W
%
(
f
N)(
C
N
C
fd
W
)

BCF
t
T
'
(
f
fd
)
[
x
%
(
f
R
)
(
BCF
fd
L
)
]

(
20)

The
term
"
"
accounts
for
the
amount
of
organic
chemical
that
is
partitioned
to
the
lipid
(
f
R
)
(
BCF
fd
L
)

in
the
biota,
whereas
in
"
x,"
the
term
"
f
W"
accounts
for
the
amount
of
organic
chemical
that
is
freely
dissolved
in
the
water
in
the
biota
and
the
term
"
"
accounts
for
the
amount
of
organic
(
f
N)(
C
N
C
fd
W
)

chemical
that
is
partitioned
to
non­
lipid
organic
matter
in
the
biota.
The
relative
magnitudes
of
these
three
terms
depend
on
the
following:

C
Because
of
bones
and
other
inorganic
matter,
the
sum
of
f
W
+
f
R
+
f
N
must
be
less
than
1.

C
f
W
is
usually
about
0.7
to
0.9.

C
Because
f
R
must
be
measured
if
the
BAF
or
BCF
is
to
be
useful,
f
R
is
known
for
the
aquatic
biota;
it
is
usually
between
0.03
and
0.15.
C­
7
(
BCF
t
T
f
fd
&
1
)
(
1
f
R
)
C
The
term
"
"
is
similar
to
(
see
Equation
10)
and
is
therefore
probably
related
(
C
N
C
fd
W
)
BCF
fd
L
to
K
ow
(
see
Equation
14),
although
the
affinity
of
the
chemical
for
non­
lipid
organic
matter
is
probably
much
less
than
its
affinity
for
lipid.

Although
such
considerations
aid
in
understanding
"
x,"
the
magnitude
of
"
x"
in
Equation
20
is
important
only
for
chemicals
whose
log
K
ow
s
are
in
the
range
of
1
to
3.
For
organic
chemicals
whose
log
K
ow
s
are
about
1,
f
fd
is
about
1.
In
addition,
such
chemicals
distribute
themselves
so
as
to
have
similar
concentrations
in
water
and
in
the
different
organic
phases
in
the
aquatic
biota,
which
means
that
will
be
approximately
1
if
both
metabolism
and
growth
dilution
are
negligible.
An
organic
BCF
t
T
chemical
whose
log
K
ow
is
less
than
1
will
also
have
a
on
the
order
of
1
because
water
is
the
BCF
t
T
predominant
component
in
aquatic
biota.
Setting
"
x"
equal
to
1
is
about
right
in
the
range
of
log
K
ow
s
in
which
it
is
not
negligible
(
see
also
McCarty
et
al.,
1992).

Substituting
x
=
1
into
Equation
20:

BCF
t
T
'
(
f
fd
)
[
1
%
(
f
R
)
(
BCF
fd
L
)
]

(
21)

Rearranging
gives:

BCF
fd
L
'
(
BCF
t
T
f
fd
&
1
)
(
1
f
R
)

(
22)

can
be
called
the
"
baseline
BCF"
because
it
is
the
most
useful
BCF
for
extrapolating
from
one
BCF
fd
L
species
to
another
and
from
one
water
to
another
for
organic
chemicals
with
both
high
and
low
K
ow
s.
The
baseline
BCF
is
intended
to
reference
bioconcentration
of
organic
chemicals
to
partitioning
between
lipid
and
water.

Equations
12,
13,
and
22
demonstrate
that
both
K
ow
and
are
useful
approximations
of
the
baseline
BCFs.
It
will
probably
be
possible
to
improve
both
approximations
within
a
few
years,
but
such
improvements
might
not
affect
the
BCFs
substantially
and
probably
will
not
require
changes
in
the
rest
of
the
equations
or
the
terminology.
C­
8
When
is
greater
than
1,000,
the
"­
1"
in
Equation
22
is
negligible
and
so
this
equation
BCF
t
T
becomes
equivalent
to
Equation
7
(
i.
e.,
when
is
large,
is
a
useful
approximation
of
the
BCF
t
T
BCF
fd
R
baseline
BCF).

Bioaccumulation
By
analogy
with
Equations
21
and
22:

BAF
t
T
'
(
f
fd
)
[
1
%
(
f
R
)
(
BAF
fd
L
)
]

(
23)

BAF
fd
L
'
(
BAF
t
T
f
fd
&
1
)
(
1
f
R
)

(
24)

can
be
called
the
"
baseline
BAF"
because
it
is
the
most
useful
BAF
for
extrapolating
from
one
BAF
fd
L
species
to
another
and
from
one
water
to
another
for
chemicals
with
both
high
and
low
K
ow
s.

It
is
convenient
to
define
a
food­
chain
multiplier
(
FCM)
as:

FCM
'
baseline
BAF
baseline
BCF
'
BAF
fd
L
BCF
fd
L
(
25)

Some
of
the
consequences
of
Equation
25
are:

1.
Substituting
Equations
22
and
24
into
Equation
25:

FCM
'
BAF
t
T
&
f
fd
BCF
t
T
&
f
fd
(
26)

Therefore,
only
when
f
fd
is
much
less
than
and
.
BAF
t
T
'
(
FCM)(
BCF
t
T
)
BAF
t
T
BCF
t
T
2.
When
FCM
=
1
(
as
for
trophic
level
2
in
the
Gobas
model):

baseline
BAF
'
baseline
BCF
(
27)
C­
9
3.
Predicted
baseline
BAFs
can
be
obtained
using
FCMs
and
the
following
rearrangement
of
Equation
25:

predicted
baseline
BAF
=
(
FCM)(
baseline
BCF)
(
28)

a.
Using
a
laboratory­
measured
BCF
in
Equation
22:

predicted
baseline
BAF
(
29)
'
(
FCM)
(
measured
BCF
fd
L
)

(
30)
'
(
FCM)
(
BCF
t
T
f
fd
&
1
)
(
1
f
R
)

b.
Using
a
predicted
BCF
in
Equation
14:

predicted
baseline
BAF
(
31)
'
(
FCM)
(
predicted
BCF
fd
L
)

(
32)
'
(
FCM)
(
K
OW)

The
FCMs
used
to
calculate
predicted
baseline
BAFs
must
be
appropriate
for
the
trophic
level
of
the
aquatic
biota
for
which
the
predicted
baseline
BAF
is
intended
to
apply.

Although
BAFs
can
be
related
to
BCFs
using
FCMs,
BAFs,
and
BCFs
can
also
be
related
using
Biomagnification
Factors
(
BMFs).
The
two
systems
are
entirely
compatible,
but
confusion
can
result
if
the
terms
are
not
used
consistently
and
clearly.
Because
both
systems
are
used
in
the
final
Guidance
and
elsewhere,
it
is
appropriate
to
explain
the
relation
between
the
two
here.
The
basic
difference
is
that
FCMs
always
relate
back
to
trophic
level
one,
whereas
BMFs
always
relate
back
to
the
next
trophic
level.
In
the
FCM
system:

BAF
TL1
=
BCF
BAF
TL2
=
(
FCM
TL2)(
BAF
TL1)

BAF
TL3
=
(
FCM
TL3)(
BAF
TL1)

BAF
TL4
=
(
FCM
TL4)(
BAF
TL1)

In
the
BMF
system:

BAF
TL1
=
BCF
BAF
TL2
=
(
BMF
TL2)(
BAF
TL1)

BAF
TL3
=
(
BMF
TL3)(
BAF
TL2)
C­
10
BAF
TL4
=
(
BMF
TL4)(
BAF
TL3)

Therefore:

BMF
TL2
=
FCM
TL2
BMF
TL3
=
(
FCM
TL3)/(
FCM
TL2)

BMF
TL4
=
(
FCM
TL4)/(
FCM
TL3)

Both
metabolism
and
growth
dilution
can
cause
BMFs
to
be
less
than
1.

Calculation
of
Criteria
Baseline
BCFs
and
BAFs
can
be
extrapolated
between
species
and
waters,
but
they
cannot
be
used
directly
in
the
calculation
of
criteria
that
are
based
on
the
total
concentration
of
the
chemical
in
the
water.
The
BCFs
and
BAFs
that
are
needed
to
calculate
such
criteria
can
be
calculated
from
measured
and
predicted
baseline
BCFs
and
BAFs
using
the
following
equations,
which
are
derived
from
Equations
21
and
23:

BCF
t
T
'
[
1
%
(
baseline
BCF)(
f
R
)
]
(
f
fd
)

(
33)

BAF
t
T
'
[
1
%
(
baseline
BAF)(
f
R
)
]
(
f
fd
)

(
34)

References
Barber,
M.
C.,
L.
A.
Suarez,
and
R.
R.
Lassiter.
1991.
Modeling
Bioaccumulation
of
Organic
Pollutants
in
Fish
with
an
Application
to
PCBs
in
Lake
Ontario
Salmonids.
Can.
J.
Fish.
Aquat.
Sci.
48:
318­
337.

DiToro,
D.
M.,
C.
S.
Zarba,
D.
J.
Hansen,
W.
J.
Berry,
R.
C.
Swartz,
C.
E.
Cowan,
S.
P.
Pavlou,
H.
E.
Allen,
N.
A.
Thomas,
and
P.
R.
Paquin.
1991.
Technical
Basis
for
Establishing
Sediment
Quality
Criteria
for
Nonionic
Organic
Chemicals
Using
Equilibrium
Partitioning.
Environ.
Toxicol.
Chem.
10:
1541­
1583.

Gobas,
F.
A.
P.
C.
1993.
A
Model
for
Predicting
the
Bioaccumulation
of
Hydrophobic
Organic
Chemicals
in
Aquatic
Food­
Webs:
Application
to
Lake
Ontario.
Ecological
Modeling
69:
1­
17.

McCarty,
L.
S.,
D.
Mackay,
A.
D.
Smith,
G.
W.
Ozburn,
and
D.
G.
Dixon.
1992.
Residue­
Based
Interpretation
of
Toxicity
and
Bioconcentration
QSARs
from
Aquatic
Bioassays:
Neutral
Narcotic
Organics.
Environ.
Toxicol.
Chem.
11:
917­
930.
C­
11
D­
1
C
t
w
'
C
fd
w
%
POC
@
C
poc
%
DOC
@
C
doc
(
1)

C
t
w
'
C
fd
w
@
(
1
%
POC
@
K
poc
%
DOC
@
K
doc)

(
2)
Appendix
D
Derivation
of
the
Equation
Defining
ffd
Experimental
investigations
have
shown
that
hydrophobic
organic
chemicals
exist
in
water
in
three
phases,
(
1)
the
freely
dissolved
phase;
(
2)
sorbed
to
suspended
solids
(
particulate
organic
carbon);
and
(
3)
sorbed
to
dissolved
organic
matter
(
Hassett
and
Anderson,
1979;
Carter
and
Suffet,
1982;
Landrum
et
al.,
1984;
Gschwend
and
Wu,
1985;
McCarthy
and
Jimenez,
1985;
Eadie
et
al.,
1990,
1992).
The
total
concentration
of
the
chemical
in
water
is
the
sum
of
the
concentrations
of
the
sorbed
chemical
and
the
freely
dissolved
chemical
(
Gschwend
and
Wu,
1985;
Cook
et
al.,
1993):

where:

Cfw
d
=
Concentration
of
freely
dissolved
chemical
in
the
ambient
water
(
kg
of
chemical/
L
of
water)

Ctw
=
Total
concentration
of
the
chemical
in
the
ambient
water
(
kg
of
chemical/
L
of
water)

C
poc
=
Concentration
of
chemical
sorbed
to
the
particulate
organic
carbon
in
the
ambient
water
(
kg
of
chemical/
kg
of
organic
carbon)

C
doc
=
Concentration
of
chemical
sorbed
to
the
dissolved
organic
carbon
in
the
water
(
kg
of
chemical/
kg
of
organic
carbon)

POC
=
Concentration
of
particulate
organic
carbon
in
the
ambient
water
(
kg
of
organic
carbon/
L
of
water)

DOC
=
Concentration
of
dissolved
organic
carbon
in
the
ambient
water
(
kg
of
organic
carbon/
L
of
water)

The
above
equation
can
also
be
expressed
using
partitioning
relationships
as:

where:
D­
2
f
fd
'
C
fd
w
C
t
w
(
3)

f
fd
'
1
(
1
%
POC
@
K
poc
%
DOC
@
K
doc)

(
4)

K
doc
.
K
ow
10
(
5)
K
poc
=
C
poc
/
C
fw
d
and
K
doc
=
C
doc
/
C
fw
d
K
poc
=
equilibrium
partition
coefficient
of
the
chemical
between
POC
and
the
freely
dissolved
phase
in
the
ambient
water
K
doc
=
equilibrium
partition
coefficient
of
the
chemical
between
DOC
and
the
freely
dissolved
phase
in
the
ambient
water
From
Equation
2,
the
fraction
of
the
chemical
which
is
freely
dissolved
in
the
water
can
be
calculated
using
the
following
equations:

Experimental
investigations
by
Eadie
et
al.
(
1990,
1992),
Landrum
et
al.
(
1984),
Yin
and
Hassett
(
1986,
1989),
Chin
and
Gschwend
(
1992),
and
Herbert
et
al.
(
1993)
have
shown
that
K
doc
is
directly
proportional
to
the
K
ow
of
the
chemical
and
is
less
than
the
K
ow.
The
K
doc
can
be
estimated
using
the
following
equation:

The
above
equation
is
based
upon
the
results
of
Yin
and
Hassett
(
1986,
1989),
Chin
and
Gschwend
(
1992),
and
Herbert
et
al.
(
1993).
These
investigations
were
done
using
unbiased
methods,
such
as
the
dynamic
headspace
gas­
partitioning
(
sparging)
and
the
fluorescence
methods,
for
determining
the
K
doc.

Experimental
investigations
by
Eadie
at
al.
(
1990,
1992)
and
Dean
et
al.
(
1993)
have
shown
that
K
poc
is
approximately
equal
to
the
K
ow
of
the
chemical.
The
K
poc
can
be
estimated
using
the
following
equation:
D­
3
K
poc
.
K
ow
(
6)

f
fd
'
1
(
1
%
POC
@
K
ow
%
(
DOC
@
K
ow
10
))

(
7)
By
substituting
Equations
5
and
6
into
Equation
4,
the
following
equation
is
obtained:

The
utility
in
using
the
freely
dissolved
equation
described
above
to
derive
baseline
BAFs
applicable
to
multiple
sites
has
been
evaluated
recently
in
a
study
conducted
by
Burkhard
et
al.
(
1997).
In
their
study,
Burkhard
et
al.
measured
BAFs
for
various
chlorinated
butadienes,
chlorinated
benzenes
and
hexachloroethane
for
three
species
of
forage
fish
and
blue
crab
in
Bayou
d'Inde
of
the
Calcasieu
River
system,
Louisiana.
Using
the
freely
dissolved
equation,
Burkhard
et
al.
adjusted
their
field­
measured
BAFs
to
baseline
BAFs
(
BAF
R
f
d)
and
compared
these
to
baseline
BAFs
determined
for
other
trophic
level
three
species
in
two
other
field
studies
(
Pereria
et
al.,
1988;
Oliver
and
Niimi,
1988).
The
field
study
by
Pereria
et
al.
(
1988)
was
conducted
in
different
sites
within
the
Calcasieu
River
system
and
that
of
Oliver
and
Niimi
(
1988)
in
Lake
Ontario.
Burkhard
et
al.
found
no
significant
difference
between
BAF
R
f
d
determined
in
their
study
and
those
determined
by
Pereria
et
al.
(
1988)
(
Tukey's,
"
=
.05).
However,
for
one
chemical
(
HCBD)
about
an
order
of
magnitude
difference
was
observed
in
the
measured
BAF
R
f
d
between
the
two
studies.
Burkhard
et
al.
further
noted
their
baseline
BAFs
were
not
substantially
different
than
those
derived
for
Lake
Ontario,
suggesting
broader
applicability
of
properly
derived
baseline
BAFs.

References
Carter,
C.
W.
and
I.
H.
Suffet.
1982.
Binding
of
DDT
to
Dissolved
Humic
Materials.
Environ.
Sci.
Technol.
16:
735­
740.

Chin,
Y.,
and
P.
M.
Gschwend.
1992.
Partitioning
of
Polycyclic
Aromatic
Hydrocarbons
to
Marine
Porewater
Organic
Colloids.
Environ.
Sci.
Technol.
26:
1621­
1626.

Cook,
P.
M.,
R.
J.
Erickson.
R.
L.
Spehar,
S.
P.
Bradbury
and
G.
T.
Ankley.
1993.
Interim
Report
on
Data
and
Methods
for
Assessment
of
2,3,7,8­
tetrachlorodibenzo­
p­
dioxin
Risks
to
Aquatic
Life
and
Associated
Wildlife.
Duluth,
MN:
USEPA,
Environmental
Research
Laboratory.
EPA/
600/
R­
93/
055.
D­
4
Dean,
K.
E.,
M.
M.
Shafer,
and
D.
E.
Armstrong.
1993.
Particle­
Mediated
Transport
and
Fate
of
a
Hydrophobic
Organic
Contaminant
in
Southern
Lake
Michigan:
the
Role
of
Major
Water
Column
Particle
Species.
J.
Great
Lakes
Res.
19:
480­
496.

Eadie,
B.
J.,
N.
R.
Morehead
and
P.
F.
Landrum.
1990.
Three­
Phase
Partitioning
of
Hydrophobic
Organic
Compounds
in
Great
Lake
Waters.
Chemosphere.
20:
161­
178.

Eadie,
B.
J.,
N.
R.
Morehead,
J.
Val
Klump
and
P.
F.
Landrum.
1992.
Distribution
of
Hydrophobic
Organic
Compounds
between
Dissolved
and
Particulate
Organic
Matter
in
Green
Bay
Waters.
J.
Great
Lakes
Res.
18:
91­
97.

Gschwend.
P.
M.
and
S.
Wu.
1985.
On
the
Constancy
of
Sediment­
Water
Partition
Coefficients
of
Hydrophobic
Organic
Pollutants.
Environ.
Sci.
Technol.
19:
90­
96.

Hassett,
J.
P.
and
M.
A.
Anderson.
1979.
Association
of
Hydrophobic
Organic
Compounds
with
Dissolved
Organic
Matter
in
Aquatic
Systems.
Environ.
Sci.
Technol.
13:
1526­
1529.

Herbert,
B.
E.,
P.
M.
Bertsch
and
J.
M.
Novak.
1993.
Pyrene
Sorption
by
Water­
Soluble
Organic
Carbon.
Environ.
Sci.
Technol.
27:
398­
403.

Landrum,
P.
F.,
S.
R.
Nihart,
B.
J.
Eadie
and
W.
S.
Gardner.
1984.
Reverse­
Phase
Separation
Method
for
Determining
Pollutant
Binding
to
Aldrich
Humic
and
Dissolved
Organic
Carbon
of
Natural
Waters.
Environ.
Sci.
Technol.
18:
187­
192.

McCarthy,
J.
F.
and
B.
D.
Jimenez.
1985.
Interaction
Between
Polycyclic
Aromatic
Hydrocarbons
and
Dissolved
Humic
Material:
Binding
and
Dissociation.
Environ.
Sci.
Technol.
19:
1072­
1076.

Yin,
C.
and
J.
P.
Hassett.
1986.
Gas­
Partitioning
Approach
for
Laboratory
and
Field
Studies
of
Mirex
Fugacity
in
Water.
Environ.
Sci.
Technol.
20:
1213­
1217.

Yin,
C.
and
J.
P.
Hassett.
1989.
Fugacity
and
Phase
Distribution
of
Mirex
in
Oswego
River
and
Lake
Ontario
Waters.
Chemosphere.
19:
1289­
1296.
E­
1
(
A
soc)
i
'
(
C
soc)
i
(
C
fd
w
)
i
(
2)

(
BAF
fd
R
)
i
(
BAF
fd
R
)
r
'
(
BSAF)
i
(
A
soc)
i
(
BSAF)
r
(
A
soc)
r
(
3)

(
BAF
fd
R
)
i
'
(
BAF
fd
R
)
r
·
(
BSAF)
i
(
K
ow)
i
(
BSAF)
r
(
K
ow)
r
(
5)
Appendix
E
Derivation
of
the
Equation
to
Predict
BAF
from
the
BSAF
Several
steps
are
involved
in
the
derivation
of
the
equation
to
predict
the
BAF
for
a
chemical
from
the
BSAF.
First,
in
the
basic
equation
for
BAF
for
a
given
chemical,
BSAF
and
C
soc
can
be
substituted
for
C
R
for
a
given
chemical
i
as
follows:

(
BAF
fd
R
)
i
'
(
BSAF)
i
@
(
C
soc)
i
(
C
fd
w
)
i
(
1)

The
chemical
concentration
quotient
between
sediment
organic
carbon
and
a
freely
dissolved
state
in
overlying
water
may
be
symbolized
by
A
soc,
as
follows:

Thus
the
ratio
of
BAF
R
f
ds
for
chemical
i
and
a
reference
chemical
r
may
be
expressed
as:

If
both
chemicals
have
similar
fugacity
ratios
between
water
and
sediment,
the
following
assumption
can
be
made:
(
A
soc)
i
(
A
soc)
r
'
(
K
ow)
i
(
K
ow)
r
(
4)

therefore:

The
assumption
of
equal
or
similar
fugacity
ratios
between
water
and
sediment
for
each
chemical
is
equivalent
to
assuming
that
for
all
chemicals
used
in
BAF
R
f
d
calculations:
(
1)
the
concentration
ratios
between
sediment
and
suspended
solids
in
the
water,
and
(
2)
the
degree
of
equilibrium
between
E­
2
suspended
solids
and
C
w
f
d
are
the
same.
Thus,
errors
could
be
introduced
by
inclusion
of
chemicals
with
non­
steady­
state
external
loading
rates
or
chemicals
with
strongly
reduced
C
w
f
d
due
to
rapid
volatilization
from
the
water.
F­
1
Appendix
F
EPA
New
Draft
Protocol
for
Determining
Octanol­
Water
Partition
Coefficients
(
K
ow)
For
Compounds
with
Log
Kow
Values
>
5
1.
Introduction
The
octanol­
water
partition
coefficient
(
K
ow)
is
one
of
the
most
widely
used
chemical
parameters.
The
K
ow
of
a
chemical
has
been
found
to
be
representative
of
a
chemical's
propensity
to
partition
into
biotic
and
abiotic
components
of
the
environment
as
well
as
a
chemical's
propensity
to
accumulate
in
living
organisms.
Because
of
these
associations,
the
K
ow
is
widely
used
to
predict
a
chemical's
behavior
in
the
environment
and
to
evaluate
a
chemical's
impact
on
human
health.

The
octanol­
water
partition
coefficient
(
K
ow)
is
a
unitless
measure
and
is
defined
as
the
ratio
of
the
equilibrium
concentrations,
C,
of
a
chemical
in
the
two
phases
of
a
system
consisting
of
n
­
octanol
and
water
at
standard
temperature
and
pressure
(
STP,
25o
C,
1
atm):

K
ow
=
C
oct/
C
w
where
C
oct
represents
the
concentration
in
the
n
­
octanol
phase,
and
C
w
represents
the
concentration
in
the
water.
The
concentrations
in
the
respective
phases
are
expressed
in
the
same
volume­
referenced
units
(
i.
e.,
mg/
ml,
mole/
L,
etc.),
therefore,
the
K
ow
is
a
unitless
property.
Since
the
value
of
the
partition
coefficient
spans
orders
of
magnitude,
it
is
frequently
expressed
on
a
log
scale
(
base
ten)
such
that
a
given
chemical
has
a
log
K
ow
value
which
may
range
from
1
to
>
8.
This
parameter
is
also
called
the
log
P
value.

Some
specific
applications
of
the
K
ow
within
the
U.
S.
EPA
include:
evaluation
of
a
chemical's
potential
to
bioaccumulate
in
aquatic
life,
wildlife
and
humans;
modeling
the
fate,
transport
and
distribution
of
a
chemical
in
the
environment;
prediction
of
the
distribution
of
a
contaminant
in
a
living
organism;
classification
of
persistent
bioaccumulators
for
regulatory
actions;
derivation
of
soil
screening
levels;
calculation
of
water
quality
benchmarks;
and
derivation
of
Sediment
Quality
Criteria.

Although
a
seemingly
simple
experimental
determination,
K
ow
measurement
is
beset
with
difficulties.
The
appropriateness
and
accuracy
of
laboratory
methods
to
directly
measure
a
K
ow
are
influenced
by
a
number
of
factors
which
include
the
magnitude
of
the
value
itself.
For
chemicals
with
log
K
ow
values
at
or
exceeding
5,
common
sources
of
measurement
error
include:
(
1)
failure
to
achieve
equilibrium;
(
2)
incomplete
phase
separation
or
interphase
mixing
during
sampling;
(
3)
emulsion
effects
derived
from
?
excessive"
mixing
or
induced
by
contaminants;
(
4)
propensity
of
the
chemical
to
self­
associate,
tautomerize
or
form
hydrates;
and
(
5)
the
presence
of
small
quantities
of
contaminants
with
a
lower
K
ow
value.
These
errors
tend
not
to
be
random,
but
to
give
measured
numbers
lower
than
the
true
value,
frequently
by
an
order
of
magnitude
or
more.
The
likelihood
and
degree
of
error
increases
with
increasing
K
ow
and
also
seems
to
be
more
prevalent
for
certain
classes
of
chemicals
(
such
as
halogenated
compounds
or
phthalate
esters).
F­
2
As
a
result,
in
addition
to
direct
experimental
measurement
methods,
techniques
to
indirectly
experimentally
measure
or
estimate
K
ow
values
have
been
developed.

Direct
experimental
measurement
techniques
include
the
shake­
flask
approach,
generator
column,
and
slow­
stir
methods.
The
shake­
flask
method
is
the
classical
approach
and
fairly
straight­
forward
for
chemicals
with
log
K
ow
values
below
5.
For
chemicals
with
higher
log
K
ow
values,
the
shake­
flask
approach
requires
large
volumes
of
water
and
formation
of
emulsions
becomes
a
significant
impediment
to
accurate
measurements.
The
generator­
column
approach
was
developed
to
measure
the
partition
coefficients
of
more
hydrophobic
chemicals
(
those
with
larger
log
K
ow
values).
This
is
a
laborious
method
which
results
in
more
reliable
data
than
the
shake­
flask
approach
for
chemicals
with
higher
log
K
ow
values,
but
some
discontinuities
in
the
data
for
higher­
chlorinated
PCB
congeners
have
been
observed.
A
third
direct
measurement
technique
is
the
slow­
stir
method.
In
this
method,
careful
stirring
and
close
temperature
control
can
prevent
or
limit
the
formation
of
emulsions
and
reliable
very
high
partition
coefficients
can
be
obtained
relatively
easily.

Because
of
the
difficulty
of
directly
and
accurately
measuring
K
ow
values,
estimation
methods
have
been
developed.
These
methods
can
be
divided
into
two
types:
those
requiring
a
training
set
of
chemicals
with
measured
K
ow
s
and
those
based
upon
fundamental
chemical
thermodynamics.
Those
methods
requiring
a
training
set
of
chemicals
use
Quantitative
Structure
Property
Relationships
(
QSPRs)
or
Quantitative
Structure
Activity
Relationships
(
QSARs)
to
derive
K
ow
s.
In
QSPRs,
K
ow
values
are
correlated
with
the
values
for
other
chemical
parameters­­
either
measured
or
calculated­­
using
data
available
from
the
training
set
of
chemicals.
In
QSARs,
K
ow
values
are
derived
from
fragment
constants
obtained
from
the
training
set
of
chemicals.

One
application
of
QSPRs
is
estimating
K
ow
s
indirectly
from
other
experimental
measurements.
In
this
approach,
the
K
ow
is
correlated
with
another
measured
property.
These
techniques
include
the
use
of
reversed­
phase
high
performance
liquid
chromatography
(
HPLC)
and
reversed­
phase
thin­
layer
chromatography
(
TLC).
In
applying
these
approaches,
K
ow
s
are
estimated
from
linear
equations
relating
retention
times
on
the
reversed­
phase
column
to
the
K
ow
values.
The
equations
are
developed
based
on
a
set
of
reference
chemicals
for
which
K
ow
values
are
well
established.
These
are
relatively
efficient
methods
because
they
do
not
require
quantification
of
concentrations,
but
the
linear
equations
can
not
be
extrapolated
beyond
the
K
ow
range
represented
by
the
reference
chemicals
from
which
the
equation
was
derived.
In
application,
values
for
the
reference
chemicals
are
usually
shake­
flask
values
obtained
from
the
literature,
resulting
in
unreliable
K
ow
estimates
for
chemicals
with
higher
log
K
ow
values.

In
addition
to
direct
and
indirect
measurement
methods,
QSPRs
are
also
used
to
establish
correlations
between
the
K
ow
and
calculated
properties.
For
example,
Hawker
and
Connell
(
1988)
developed
a
correlative
relationship
between
log
K
ow
and
molecular
surface
area
using
approximately
two
dozen
PCBs.
They
then
estimated
log
K
ow
s
for
the
remaining
PCBs
by
inputting
the
molecular
surface
area
of
each
PCB.
This
technique
is
limited
to
estimating
K
ow
s
for
chemicals
which
are
similar
to
the
chemicals
used
in
developing
the
relationship.
1CLOGP
is
a
molecular
fragment­
based
model
developed
at
Pomona
College
by
Albert
Loe,
Corwin
Hanch,
and
(
See
Hansch
and
Leo,
1995,
for
model
description
and
performance
data.)

2LOGKOW
is
essentially
an
expanded
CLOGP
with
more
recent
training
data
and
additional
fragment
constants.
Research
Corporation.
(
See
Meylan
and
Howard,
1994,
for
model
details
and
performance
information.)

3SPARC
(
SPARC
Performs
Automated
Reasoning
in
Chemistry)
is
a
mechanistic
model
developed
at
the
Ecosystems
Research
and
Development
of
the
U.
S.
Environmental
Protection
Agency
by
Sam
Karickhoff,
Lionel
Carreira,
and
co­
workers.
The
model
complements
the
aforementioned
models
because
development,
training,
and
testing
were
done
away
from
K
OW
data.
(
See
F­
3
In
QSARs,
hydrophobic
fragment
values
are
derived
from
a
large
data
base
of
measured
K
ow
s.
These
fragment
constants
are
used
to
estimate
K
ow
in
two
ways.
One
approach
is
to
estimate
the
K
ow
by
adding
up
the
values
for
all
the
fragments
composing
the
chemical,
either
by
atom
or
by
functional
group.
The
other
approach
is
to
start
with
a
measured
K
ow
value
for
a
structurally
similar
compound
and
add
or
subtract
the
fragment
constants
for
functional
groups
or
atoms
to
estimate
the
K
ow
for
the
specific
compound.
In
both
these
cases,
the
calculated
K
ow
value
must
also
be
corrected
for
proximity
effects
between
structurally
close
substituent
groups,
and
the
K
ow
value
derived
is
only
as
good
as
the
data
associated
with
the
training
set
of
chemicals.
This
method
is
also
limited
to
predicting
K
ow
s
for
chemicals
with
structures
similar
to
those
within
the
training
set.
Computer­
based
models
exist
which
apply
QSAR
approaches
to
estimate
K
ow
s.
CLOGP1
and
LOGKOW2
data
bases
are
both
applications
of
this
approach.

Other
computer
methods
are
based
on
fundamental
chemical
structure
theory
and
are
not
limited
by
nor
require
a
training
set
of
chemicals
with
measured
K
ow
s.
For
example,
the
SPARC3
model
consists
of
a
set
of
core
models
describing
intra­
and
inter­
molecular
interactions.
These
models
are
linked
by
appropriate
thermodynamic
relationships
to
provide
estimates
of
reactivity
parameters
under
desired
conditions
(
e.
g.,
temperature,
pressure,
solvent).

Given
the
numerous
techniques
available
to
determine
the
K
ow
and
its
numerous
and
important
applications
across
the
Agency,
the
U.
S.
EPA
has
formed
an
Agency
K
ow
Work
Group
to
draft
the
following
guidance
for
selecting
reliable
values
of
K
ow
and
ultimately
for
developing
a
data
base
of
reliable
K
ow
values.
In
determining
these
recommended
K
ow
values,
the
preferable
option
would
be
to
recommend
actual
measured
values.
For
chemicals
with
log
K
ow
values
below
5,
the
classical
shake­
flask
approach
is
adequate
to
obtain
these
measurements.
Although
recent
advances
in
measurement
technology
(
development
of
the
slow
stir
method
and
increased
awareness
of,
and
compensation
for,
determinate
errors)
have
significantly
improved
the
quality
of
data
available
for
chemicals
with
higher
log
K
ow
values,
there
remains
a
serious
shortage
of
reliable
measured
data
for
compounds
with
higher
log
K
ow
values
(
log
K
ow
>
5).
Unfortunately,
it
is
frequently
these
chemicals
that
exhibit
a
propensity
to
accumulate
in
living
tissues
or
bind
to
soils
and
sediments.

2.
Protocol
For
Determining
Recommended
Kow
Values
F­
4
Measured
data
are
preferable
for
determining
recommended
K
ow
values.
However,
the
absence
or
scarcity
of
reliable
data
necessitates
the
use
of
estimation
methods
in
evaluating
data
and
in
assigning
K
ow
s.
K
ow
estimates
used
in
this
exercise
include:
(
1)
QSARs
(
e.
g.,
CLOGP,
LOGKOW,
and
fragment
additions
or
subtractions);
(
2)
QSPRs
(
e.
g.,
HPLC
and
TLC
methods)
and
(
3)
estimation
methods
based
on
fundamental
chemical
structure
theory
(
e.
g.
SPARC).
All
of
these
approaches
except
the
last
one
listed
(
the
SPARC
model)
require
measured
K
ow
values
for
a
training
set
of
chemicals.

Assigning
a
K
ow
from
these
data
will
necessarily
involve
scientific
judgement
in
evaluating
not
only
the
reliability
of
all
data
inputs
but
also
the
accretion/
concretion
of
evidence
in
support
of
the
recommended
K
ow
value.
Supporting
rationale
will
be
provided
for
each
recommended
value.

2.1
Operational
Guidelines
for
Kow
Selection
Protocol
°
For
chemicals
with
log
K
ow
>
5,
it
is
highly
unlikely
to
find
multiple
"
high
quality"
measurements.
(
Note:
"
high
quality"
is
data
judged
to
be
reliable
based
on
the
guidelines
presented
in
Section
3)

°
"
High
quality"
measured
data
are
preferred
over
estimates,
but
due
to
the
scarcity
of
"
high
quality"
data,
the
use
of
estimates
is
important
in
assigning
K
ow
s.

°
K
ow
measurements
by
slow
stir
are
extendable
to
108.
Shake
flask
K
ow
measurements
are
extendable
to
106
with
sufficient
attention
to
micro
emulsion
effects;
for
classes
of
chemicals
that
are
not
highly
sensitive
to
emulsion
effects
(
i.
e.,
PNAs)
this
range
may
extend
to
106.5.

C
What
is
to
be
considered
reasonable
agreement
in
log
K
ow
data
(
measured
or
estimated)
depends
primarily
on
the
log
K
ow
magnitude.
The
following
standards
for
data
agreement
have
been
set
for
this
guidance:
0.5
for
log
K
ow
>
7;
0.4
for
6
#
log
K
ow
#
7
;
0.3
for
log
K
ow
<
6.

C
Statistical
methods
should
be
applied
to
data
as
appropriate
but
application
is
limited
due
to
the
scarcity
of
data,
and
the
determinate/
methodic
nature
of
most
measurement
error(
s).

2.2
Tiered
Procedure
for
Setting
KOW
Values
I.
Assemble/
evaluate
experimental
and
calculated
data
(
e.
g.,
CLOGP,
LOGKOW,
SPARC)

II.
If
calculated
log
K
ow
s
>
8,

A.
Develop
independent
estimates
of
K
ow
using:
1.
Liquid
Chromatography
(
LC)
methods
with
"
appropriate"
standards.
(
See
Section
3
for
guidelines
for
LC
application.)
F­
5
2.
Structure
Activity
Relationship
(
SAR)
estimates
extrapolated
from
similar
chemicals
where
"
high
quality"
measurements
are
available.
"
High
quality"
SARs
are
described
in
Section
3.
3.
Property
Reactivity
Correlation
(
PRC)
estimates
based
on
other
measured
properties
(
solubility,
etc.)

B.
If
calculated
data
are
in
"
reasonable"
agreement
and
are
supported
by
independent
estimates
described
above,
report
average
calculated
value.
What
is
to
be
considered
reasonable
agreement
in
log
K
ow
data
(
measured
or
estimated)
depends
primarily
on
the
log
K
ow
magnitude.
The
following
standards
for
data
agreement
have
been
set
for
this
guidance:
0.5
for
log
K
ow
>
7;
0.4
for
6
#
log
K
ow
#
7
;
0.3
for
log
K
ow
<
6.

C.
If
calculated/
estimated
data
do
not
agree,
use
professional
judgement
to
evaluate/
blend/
weight
calculated
and
estimated
data
to
assign
a
K
ow
value.

D.
Document
rationale
including
relevant
statistics.

III.
If
calculated
log
K
ow
s
range
from
6
­
8,

A.
Look
for
"
high
quality"
measurements.
These
will
generally
be
slow
stir
measurements,
the
exception
being
certain
classes
of
compounds
where
micro
emulsions
tend
to
be
less
of
a
problem
(
i.
e.,
PNAs,
shake
flask
measurements
are
good
to
6.5).

B.
If
measured
data
are
available
and
are
in
reasonable
agreement
(
both
measurements
and
calculations),
report
average
measured
value.

C.
If
measured
data
are
in
reasonable
agreement,
but
differ
from
calculated
values,
develop
independent
estimates
and
apply
professional
judgement
to
evaluate/
blend/
weight
measured,
calculated
and
estimated
data
to
assign
K
ow
value.

D.
If
measured
data
are
not
in
reasonable
agreement
(
or
if
only
one
measurement
is
available),
use
II
A,
B,
and
C
to
produce
a
`
best
estimate;'
use
this
value
to
evaluate/
screen
measured
data;
report
average
value
of
screened
data.
If
no
measurements
reasonably
agree
with
`
best
estimate,'
apply
professional
judgement
to
evaluate/
blend/
weight
measured,
calculated
and
estimated
data
to
assign
K
ow.

E.
If
measured
data
are
unavailable,
proceed
through
II
A,
B,
C
and
report
the
`
best
estimate.'

F.
Document
rationale
including
relevant
statistics.

IV.
If
calculated
log
K
ow
s
<
6,
F­
6
A.
Proceed
as
in
III.
Slow
stir
is
the
preferred
method
but
shake
flask
data
can
be
considered
for
all
chemicals
if
sufficient
attention
has
been
given
to
emulsion
problems
in
the
measurement.

3.
Guidelines
for
Evaluating
Measured
and
Liquid
Chromatography­
estimated
KOWs
3.1
Assessment
of
Measured
Kow
Values
3.1.1
Molecular
Speciation.
In
order
to
interpret
measured
data,
it
is
necessary
to
understand
the
molecular
species
present
in
both
the
octanol
and
water
phases
including
ionization,
self­
association,
tautomerization,
and
hydrate
formation.
For
these
reasons,
it
is
difficult
to
conduct
or
interpret
such
measurements
for
mixtures
of
unknown
composition
or
for
single
molecules
of
unknown
structure.
Solutes
composed
of
more
than
one
molecular
species
may
also
show
substantial
temperature
dependence
of
K
ow
reflecting
relative
change
in
speciation
in
the
octanol
and
water
phases.

°
Ionization.
For
weakly
ionizable
molecules,
shake
flask
measurements
are
conducted
in
solutions
of
a
stable,
non­
extractable
buffer
to
suppress
ionization.

°
Self­
Association
­
For
molecules
able
to
associate
through
hydrogen­
bonding
(
e.
g.,
amines,
carboxylic
acids,
phenols,
especially
if
cyclic
dimers
can
form),
measurement
needs
to
be
conducted
at
a
sufficiently
low
concentration
that
K
ow
reflects
only
unassociated
form
of
the
molecule
in
both
water
and
octanol
phases.
Measurements
at
several
concentrations
with
no
change
in
K
ow
provide
an
indication
that
this
is
the
case.

°
Tautomerization
­
If
the
molecule
is
likely
to
exist
in
more
than
one
tautomeric
form,
the
ratio
of
tautomers
may
be
different
in
the
octanol
and
water
phases.
If
that
is
the
case,
the
measured
K
ow
may
not
be
a
very
meaningful
number.
Sometimes
molecules
exhibit
both
ionization
and
tautomerization,
leading
to
further
complications.

°
Hydrate
Formation
­
Similar
to
the
case
of
tautomerization,
hydrates
may
exist
to
different
degrees
in
the
water
and
octanol
phases
thus
confounding
the
interpretation
of
the
measured
value.
A
comparison
of
estimated
and
measured
K
ow
for
chloral
hydrate
suggests
that
such
hydration
may
be
much
less
important
in
the
octanol
than
water
phases,
making
the
compound
more
lipophilic
than
would
be
expected
from
the
hydrate
structure
alone.

3.1.2
Shake
Flask
or
Slow­
Stirring
Considerations.
(
1)
Water
and
octanol
phases
should
be
free
of
impurities;
(
2)
mixing
should
be
of
sufficient
duration
(
e.
g.,
7
days
for
dioctyl
phthalate)
to
reach
steady
state
equilibrium,
particularly
for
very
hydrophobic
chemicals;
(
3)
analytical
measurement
of
both
phases
is
particularly
important
when
using
volatile
solutes;
(
4)
Avoid
formation
of
emulsions
during
mixing
and
centrifuge
before
measuring;
(
5)
experimental
protocol
should
be
particularly
scrutinized
for
K
ow
measurements
4­
6;
(
6)
F­
7
ratio
of
octanol
to
water
should
be
reduced
for
high
K
ow
chemicals;
and
(
7)
sorption
to
glass
(
e.
g.,
for
pyrethroids)
during
workup
can
be
a
problem.

3.1.3
General
Considerations.
Solute
should
be
stable
to
hydrolysis
during
the
course
of
the
experiment.
Solutes
should
be
of
high
purity
as
the
presence
of
a
less
lipophilic
impurity
exerts
a
dominant
effect
in
the
measured
K
ow
value.
Mixtures
such
as
chlorinated
paraffins
(
containing
thousands
of
isomers,
congeners,
and
degrees
of
chlorination)
therefore
cannot
be
determined
except
by
chromatographic
methods.

3.1.4
Indicators
of
Potential
Concern.
Inconsistency
with
other
measured
values,
with
estimated
value,
or
inconsistency
among
estimated
values.
The
importance
of
professional
judgement
and
knowledge
of
chemistry
cannot
be
overemphasized
in
making
the
best
K
ow
assignments.
For
example,
inconsistency
between
measured
and
predicted
may
reflect
only
problems
in
the
training
set
used
based
upon
poor
experimental
values
when
better
data
have
since
become
available.

3.2
Assessment
of
Kow
Values
Estimated
using
Liquid
Chromatographic
Techniques
An
estimated
K
ow
value
would
be
considered
"
appropriate"
provided
the
following
experimental
conditions
existed
during
its
determination:

3.2.1
K
ow
s
used
for
the
reference
compounds
consist
of
"
high
quality"
slow
stir
measurements.

°
Better
estimates
for
K
ow
s
are
obtained
when
reference
and
test
chemicals
are
similar.

3.2.2
A
minimum
of
five
chemicals
are
used
in
developing
the
log
capacity
factor
(
k')­
log
K
ow
calibration
relationship.
The
K
ow
s
of
the
reference
chemicals
should
be
evenly
distributed
and
should
span
3
to
4
orders
of
magnitude.

3.2.3
The
log
k'
­
log
K
ow
calibration
curve
is
linear
and
has
a
correlation
coefficient
greater
than
0.95.

3.2.4
The
K
ow
estimated
for
the
test
chemical
is
within
the
range
of
K
ow
s
for
the
reference
compounds
or
does
not
exceed
the
upper
end
of
the
range
of
K
ow
s
for
the
reference
compounds
beyond
0.5
log
units
without
adequate
justification.

3.2.5
Chemical
speciation
must
be
accounted
for
in
performing
the
measurements.
For
example,
with
ionizable
chemicals,
measurements
must
be
performed
on
the
unionized
form
by
using
an
appropriate
buffer
with
a
pH
below
the
pK
for
an
acid
and
above
the
pK
for
a
base.

3.2.6
Reference
and
test
chemicals
are
of
known
purity
and
structure.
Independent
confirmation
of
the
identity
and
purity
of
the
reference
and
test
chemicals
is
required
or
highly
desirable.
F­
8
 
(
C2
'
log
K
OW
(
pyrene)
log
K
OW
(
phenanthrene)
.
0.50
 
$
C3H
'
0.5
log
K
OW
(
pyrene)
log
K
OW
(
naphthalene)
.
0.85
 
"
C4H2
'
log
K
OW
(
anthracene)
log
K
OW
(
naphthalene)
.
1.20
A
S
'
log
K
OW
R
S
log
K
OW
R
H
3.2.7
Chemical
mixtures
can
be
used
as
the
source
of
test
chemicals
provided
accurate
identities
can
be
assigned
to
individual
chromatographic
components.

4.
Estimation
of
KOW
from
Molecular
Fragments
For
computing
thermodynamic
properties
it
is
often
useful
to
consider
a
molecule
as
a
collection
of
molecular
fragments,
each
making
a
distinct
contribution
to
the
property
of
interest,
which
is
relatively
independent
of
the
rest
of
the
molecule.
The
rationale
behind
the
method
is
that
a
large
number
of
structures
can
be
generated
from
a
relatively
small
number
of
fragments,
and
thus
a
large
number
of
estimates
can
be
derived
from
a
small
number
of
experimentally
determined
fragment
constants.
The
accuracy
of
the
estimation,
however,
necessarily
improves
as
the
specificity
of
the
fragment
environment
increases,
which
entails
an
increase
in
the
number
of
fragments
or
corrective
factors
that
must
be
considered.
This
approach
is
applied
at
different
levels
of
sophistication.
One
user
may
employ
a
few
fragment
constants
and
generate
'
first
order'
estimates
whereas
another
may
make
numerous
corrections
or
adjustments
reflecting
more
fragment
specificity
for
a
given
molecular
environment.
For
a
more
complete
discussion
of
group
fragment
methods
one
should
consult
Hansch
and
Leo,
1995.

4.1
Addition
of
Ring
Fragments
For
condensed
ring
aromatics,
the
addition
of
rings
is
given
by
where
are
the
fragment
addition
constants
for
"
,
$
,
and
(
condensation
 
"
C4H2
,
 
$
C3H
,
 
(
C2
respectively.

4.2
Addition
of
Substituents
The
addition
of
a
substituent,
S
(
replacing
a
H
atom)
is
a
primary
application
of
this
method.
In
this
case
F­
9
A
arom
Cl
'
log
K
OW
chlorobenzene
log
K
OW
benzene
.
0.71
log
K
OW
1,3,5
&
trichlorobenzene
.
log
K
benzene
OW
%
3
0.71
'
4.26
where
R
is
the
base
molecule
and
is
a
substituent
constant,
which
is
experimentally
A
S
determined.
Tables
for
common
substituents
are
readily
available
or
can
be
easily
determined
from
measured
data.
One
must
distinguish
(
i.
e.,
have
different
substituent
constants
for)
attachment
to
aliphatic,
ethylenic,
acetylenic,
and
aromatic
carbon
atoms
in
'
R'.
Also
corrections
must
be
made
for
multiple
substitution
if
attachment
is
to
the
same
or
adjacent
carbons.
The
following
are
examples
of
K
ow
estimation.
The
fragment
constant
for
Cl
attached
to
aromatic
carbon
can
be
derived
from:

With
this
constant,
one
can
derive
An
exhaustive
list
of
substituent
constants
is
included
in
the
aforementioned
Hansch
and
Leo
(
1995)
reference.

5.
Example
Application
of
EPA
Draft
Kow
Selection
Protocol
BENZO(
A)
ANTHRACENE
CAS
log
KOW
Method
Reference/
Comments
56­
55­
3
5.79
shake
flask
Medchem
5.61
shake
flask
Steen
&
Karickhoff
(
1979)
5.79
RP­
HPLC
Wang
et
al
(
1986)
4.00
RP­
HPLC­
E
Brooke
et
al
(
1986)
5.00
RP­
HPLC
Brooke
et
al
(
1986)
5.66
CLOGP
5.83
SPARC
5.52
LOGKOW
Calculated
values
<
6,
therefore
enter
Step
IV
of
protocol.
Go
to
step
III
of
protocol
with
inclusion
of
shake
flask
data.
III
A.
Two
shake­
flask
measurements
in
good
agreement
with
one
another
and
avg
=
5.70
B.
Calculators:
SPARC,
LOGKOW
and
CLOGP
in
good
agreement
with
range
from
5.52­
5.83
and
avg
=
5.67.
Shake­
flask
measurements
in
excellent
agreement
with
calculators,
therefore
recommended
value
is
5.70
 
average
of
the
two
shake­
flask
measurements.
F­
10
F­
11
BENZO(
K)
FLUORANTHENE
CAS
log
KOW
Method
Reference/
Comments
207­
08­
9
6.12
CLOGP
6.30
SPARC
6.11
LOGKOW
Calculated
values
in
range
6­
8,
therefore
enter
Step
III
of
protocol.
III
A.
No
measured
values
available.
E.
Calculators:
SPARC,
LOGKOW
and
CLOGP
in
excellent
agreement
with
range
from
6.11­
6.30
and
avg
=
6.18.
°
Estimate
Kow
from
Molecular
Fragment
Constants:
Fluoranthene
(
5.12)
+
fa(
C
4
H
2)
(
1.20)
=
6.32
This
is
in
good
agreement
with
calculated
values.

Recommended
value
is
average
of
three
calculators
=
6.18.

BENZO(
A)
PYRENE
CAS
log
KOW
Method
Reference/
Comments
50­
32­
8
5.98
gen.
col.
Miller
et
al
(
1985)
6.34
shake
flask
Steen
&
Karickhoff
(
1979)
6.04
shake
flask
Medchem
6.00
shake
flask
Mallon
&
Harrison
(
1984)
5.99
shake
flask
Mallon
&
Harrison
(
1984)
6.42
RP­
HPLC
Rappaport
&
Eisenreich
(
1984)
6.24
RP­
HPLC
Hanal
et
al(
1981)
(
60%
solvent
values
6.04
RP­
HPLC
Wang
et
al
(
1986)
5.97
unknown
Hansch
&
Leo
6.12
CLOGP
6.25
SPARC
6.11
LOGKOW
Calculated
values
in
range
6­
8,
therefore
enter
Step
III
of
protocol.
III
A.
There
are
no
slow­
stir
measurements.
There
are
four
shake
flask
measurements
in
range
5.99­
6.34
with
an
average
=
6.09
B.
Calculators:
SPARC,
LOGKOW
and
CLOGP
in
excellent
agreement
with
range
from
6.11­
6.25
and
average
=
6.16
°
Estimate
K
ow
from
Molecular
Fragment
Constants:
pyrene
(
5.05)
+
f
g(
C
4
H
2)
(
1.20)
=
6.25
This
is
in
good
agreement
with
calculated
and
measured
values.
°
Three
RP­
HPLC
values
(
range
6.04
­
6.42)
with
average
6.26.

This
compound
is
on
the
border
of
whether
shake
flask
data
acceptable
but
PNAs
are
less
susceptible
to
emulsification
using
the
shake
flask
approach
than
other
compounds.
Therefore
shake
flask
measurements
may
be
acceptable
up
to
6.5
rather
than
6.0.
Therefore,
we
will
assume
the
shake
flask
F­
12
measurements
for
this
compound
are
accurate
and
recommend
going
with
the
average
of
the
four
shake
flask
measurements
=
6.09
DI­
N­
OCTYL
PHTHALATE
CAS
log
KOW
Method
Reference/
Comments
117­
84­
0
8.06
RP­
HPLC
McDuffie
(
1981)
9.49
CLOGP
8.39
SPARC
8.54
LOGKOW
Calculated
values
are
>
8.0
therefore
enter
step
II
of
protocol.
II
A.
No
published
measured
values
were
available,
J.
Ellington
(
EPA,
Athens)
measured
8.1
by
slow­
stir.
D.
Calculators:
Not
in
good
agreement:
SPARC,
8.39,
CLOGP,
9.49,
&
LOGKOW,
8.54.
°
SPARC
and
LOGKOW
calculators
are
close
to
the
Ellington
value.
°
Can
use
branching
correction
and
estimate
from
diethylhexyl
phthalate
(
2
slow
stir
measurements,
average
7.3)
.
A
secondary
branch
contributes
approximately
`­
0.3
`
per
which
would
place
the
unbranched
dioctyl
phthalate
at
7.9
(
7.3
+
2(
0.3));
this
is
close
to
the
Ellington
value.
°
The
RP­
HPLC
estimate
is
8.06.

Recommended
value
is
8.1
(
Ellington,
unpublished),
supported
by
two
of
the
calculators
and
two
other
estimates.
F­
13
PYRENE
CAS
log
KOW
Method
Reference/
Comments
129­
00­
0
5.07
slow
stir
Stancil
(
1994)
5.18
gen.
col.
Miller
et
al
(
1985)
5.18
shake
flask
Karickhoff
et
al
(
1979)
5.09
shake
flask
Means
et
al
(
1980);
Hassett
et
al
(
1980)
5.09
shake
flask
Wang
et
al
(
1986)
5.08
shake
flask
Medchem
5.05
shake
flask
Ellington
&
Stancil
(
1988)
4.88
shake
flask
Hansch
&
Leo
(
1979);
Medchem
4.93
RP­
HPLC­
E
Hammers
et
al
(
1982)
4.89
RP­
HPLC­
E
Tomlinson
&
Hafkenschield
(
1986)
4.76
RP­
HPLC­
E
Hafkenschield
&
Tomlinson
(
1983)
5.52
RP­
HPLC
Rurkhard
et
al
(
1985)
5.05
RP­
HPLC
McDuffie
(
1981)
4.97
RP­
HPLC
Chin
et
al
(
1986)
4.96
RP­
HPLC
Rapaport
&
Eisenreich
(
1984)
5.08
RP­
HPLC
Wang
et
al
(
1986)
4.89
RP­
HPLC
Hanal
et
al
(
1981)
(
50%
acetonitrile)
4.95
CLOGP
5.02
SPARC
4.93
LOGKOW
Calculated
values
<
6,
therefore
enter
Step
IV
of
protocol.
Go
to
step
III
of
protocol
which
permits
consideration
of
shake
flask
data.
III
A.
Seven
of
eight
measured
values
are
in
good
agreement
(
range
=
4.88
to
5.18);
shake
flask,
slow
stir,
generator
column
avg
5.08.
B.
Calculators:
All
three
calculators
are
in
excellent
agreement
with
avg
=
4.94,
in
agreement
with
the
measured
value.
Recommended
value
is
average
of
slow
stir,
shake
flask
and
generator
column
values
=
5.08
6.
References
Hansch,
C.
and
A.
Leo.
1995.
Exploring
QSAR.
American
Chemical
Society.

Hawker,
D.
W.
and
D.
W.
Connell.
1988.
Environ.
Sci.
Technol.
22:
382­
387.

Hilal,
S.
H.,
L.
A.
Carreira,
and
S.
W.
Karickhoff.
1994.
Quantitative
Treatments
of
Solute/
Solvent
Interactions.
Theoretical
and
Computational
Chemistry
1:
291­
353.

Meylan,
W.
M.
and
P.
H.
Howard.
1995.
J.
Pharm.
Sci.
84:
83­
92.
G­
1
Appendix
G
Amount
of
Commercial
Food
Items
Consumed
and
Intake
of
Chemical
X
from
Commercial
Food
Items
Food
Item
Amount
of
food
item
consumed*
(
g/
day)
Average
chemical
X
concentration
(
ug/
g)
Average
intake
of
chemical
X
(
mg/
kg­
day)
High­
end
chemical
X
concentration
(
ug/
g)
High­
end
intake
of
chemical
X
(
mg/
kg­
day)

cheddar
cheese
7.07
4.55E­
04
4.59E­
08
2.00E­
02
2.0E­
06
beef
roast
15.06
4.55E­
04
9.78E­
08
1.00E­
02
2.2E­
06
steak
1.93
5.00E­
04
1.38E­
08
1.50E­
02
4.1E­
07
beef
loin
28.09
5.00E­
04
2.01E­
07
2.20E­
02
8.8E­
06
pork
chop
8.07
8.41E­
04
9.70E­
08
2.10E­
02
2.4E­
06
pork
roast
3.98
1.18E­
03
4.70E­
08
3.40E­
02
1.9E­
06
lamb
chop
1.13
2.27E­
04
3.66E­
09
1.00E­
02
1.6E­
07
veal
cutlet
1.11
2.05E­
04
3.24E­
09
9.00E­
03
1.4E­
07
turkey
breast
3.42
7.50E­
04
3.67E­
08
2.30E­
02
1.1E­
06
bologna
7.90
1.82E­
04
2.05E­
08
8.00E­
03
9.0E­
07
cod/
haddock
8.58
5.23E­
04
6.40E­
08
2.30E­
02
1.72E­
06
fishsticks
1.70
2.05E­
03
5.00E­
09
9.00E­
03
2.18E­
07
corn
grits
4.03
3.64E­
04
2.09E­
08
1.60E­
02
9.2E­
07
popcorn
1.18
2.05E­
04
3.44E­
09
9.00E­
03
1.5E­
07
cornbread
7.41
2.50E­
04
2.65E­
08
1.10E­
02
1.2E­
06
biscuits
4.58
9.09E­
04
5.95E­
08
2.40E­
02
1.6E­
06
pancakes
5.51
5.45E­
04
4.30E­
08
2.40E­
02
1.9E­
06
cereal
2.23
2.45E­
03
7.81E­
08
6.40E­
02
2.0E­
06
raisins
0.34
4.77E­
04
2.34E­
09
2.10E­
02
1.0E­
07
prunes
0.34
2.73E­
04
1.32E­
09
1.20E­
02
5.8E­
08
tomato
18.26
4.55E­
04
1.19E­
07
2.00E­
02
5.2E­
06
squash
1.47
5.91E­
04
1.24E­
08
2.60E­
02
5.5E­
07
pizza
8.77
2.27E­
04
2.85E­
08
1.00E­
02
1.3E­
06
Food
Item
Amount
of
food
item
consumed*
(
g/
day)
Average
chemical
X
concentration
(
ug/
g)
Average
intake
of
chemical
X
(
mg/
kg­
day)
High­
end
chemical
X
concentration
(
ug/
g)
High­
end
intake
of
chemical
X
(
mg/
kg­
day)

G­
2
meatloaf
8.04
6.82E­
04
7.83E­
08
2.30E­
02
2.6E­
06
potpie
1.64
5.68E­
04
1.33E­
08
2.50E­
02
5.9E­
07
margarine
4.44
4.55E­
04
2.88E­
08
2.00E­
02
1.3E­
06
butter
2.84
5.45E­
04
2.21E­
08
2.40E­
02
9.7E­
07
carmel
candy
2.37
1.36E­
04
4.62E­
09
6.00E­
03
2.0E­
07
Intake
from
all
foods
containing
chemical
X
161.50
1.18E­
06
4.26E­
05
Total
daily
intake
of
all
foods*
=
2,582
grams/
day
Intake
of
Chemical
X
after
subtracting
intake
from
meat
[
Calculation
=
intake
from
all
foods
­
(
fraction
of
meat
that
is
fish*
chemical
intake
from
all
commercial
meat)]

general
population
1.13E­
06
4.10E­
05
sportfisher
1.13E­
06
4.10E­
05
subsistence
fisher
1.06E­
06
3.86E­
05
*
These
estimates
are
weighted
averages
of
intakes
for
males
and
females
in
two
age
groups:
25­
30
year
olds
and
60­
65
year
olds
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
acrylonitrile.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
AWQC
'
RSD
x
BW
DI
%
j
4
i
'
2
(
FI
i
x
BAF
i)
Appendix
H
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Acrylonitrile
Summary
T
his
criteria
document
updates
the
national
criteria
for
Acrylonitrile
using
new
methods
and
information
described
in
the
Federal
Register
Notice
(
USEPA,
1998a)
and
Technical
Support
Document
(
USEPA,
1998b)
to
calculate
ambient
water
quality
criteria.
These
new
methods
include
approaches
to
determine
dose­
response
relationships
for
both
carcinogenic
and
non­
carcinogenic
effects,
updated
information
for
determining
exposure
factors
(
e.
g.,
values
for
fish
consumption),
exposure
assumptions,
and
procedures
to
determine
bioaccumulation
factors.
For
more
detailed
information
please
refer
to
the
U.
S.
EPA
Ambient
Water
Quality
Criteria
(
AWQC)
document
for
Acrylonitrile
(
USEPA,
1998c).

Background
Information
The
AWQC
is
being
derived
for
acrylonitrile
(
CAS
No.
107­
13­
1).
The
chemical
formula
is
C3H3N2.
Acrylonitrile
occurrence
in
environmental
media
is
not
well­
documented.
Several
regional
and
local
drinking
water
surveys
were
found
and
one
limited
study
analyzed
ambient
air
samples.
Limited
information
is
also
available
on
acrylonitrile
migration
into
foods
from
packaging
materials.

Acrylonitrile
is
largely
used
in
the
manufacture
of
copolymers
for
the
production
of
acrylic
and
modacrylic
fibers.
Other
major
uses
include
the
manufacture
of
acrylonitrile­
butadiene­
styrene
(
ABS)
and
styrene
acrylonitrile
(
SAN)
(
used
in
production
of
plastics),
and
nitrile
elastomers
and
latexes.
It
is
also
used
in
the
synthesis
of
antioxidants,
pharmaceuticals,
dyes,
and
surface­
active
agents.

According
to
the
U.
S.
Environmental
Protection
Agency's
(
EPA)
Toxic
Release
Inventory,
the
total
release
of
acrylonitrile
into
the
environment
in
1990
by
manufacturers,
was
8,077,470
pounds.
The
two
largest
pathways
of
release
were
underground
injection,
which
accounted
for
61%
(
or
4,925,276
pounds)
of
the
total
release,
and
emissions
into
the
air,
which
accounted
for
39%
(
or
3,148,049
pounds)
of
the
total
release.
Release
of
acrylonitrile
into
water
bodies
was
reported
at
3,877
pounds
and
release
onto
land
was
reported
at
268
pounds.
A
baseline
BAF
of
1.5
was
calculated
for
acrylonitrile.
The
baseline
BAF
was
calculated
using
a
value
of
0.17
for
the
log
Kow
and
1.000
for
the
food­
chain
multiplier
(
FCM)
at
trophic
level
4.
A
value
of
0.17
was
selected
as
a
typical
value
of
the
log
Kow
for
acrylonitrile
(
USEPA
1998c).
A
value
of
1.000
was
selected
as
the
FCM
for
trophic
level
4,
reflective
of
top
predator
fish
based
on
a
log
Kow
of
2.0
from
USEPA
(
1998b).
Using
these
data,
the
baseline
BAF
was
calculated
as:
Kow
*
FCM
=
(
100.17
)*
1.000
=
1.5
(
rounded
to
two
significant
digits).

Based
upon
sufficient
evidence
from
animal
studies
(
multiple
tumor
types
in
several
strains
of
rats
by
several
routes)
and
limited
evidence
from
human
studies
(
lung
tumors
in
workers),
positive
mutagenicity,
acrylonitrile
is
considered
as
a
likely
human
carcinogen
by
any
route.
A
linear
approach
is
used
for
the
low
dose
extrapolation.

AWQC
Calculation
For
Ambient
Waters
Used
as
Drinking
Water
Sources
The
cancer­
based
AWQC
was
calculated
using
the
RSD
and
other
input
parameters
listed
below:

where:

RSD
=
Risk
specific
dose
(
1.6
x
10­
6
mg/
kg­
day
at
10­
6
lifetime
risk)
BW
=
Human
body
weight
assumed
to
be
70
kg
DI
=
Drinking
water
intake
assumed
to
be
2
L/
day
FI
=
Fish
intake
at
trophic
level
i,
i=
2,3,
and
4;
total
intake
assumed
to
be
0.01780
kg/
day
BAF
=
Bioaccumulation
factor
at
trophic
level
i
(
i=
2,3,
and
4)
equal
to
1.03,
1.02,
and
1.05
L/
kg­
tissue
for
trophic
levels
2,3,
and
4,
respectively.

This
yields
concentrations
of
5.5
x
10­
5
mg/
L
(
or
0.05
µ
g/
L),
for
a
10­
6
(
one
in
a
million)
lifetime
cancer
risk.
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
1,3­
dichloropropene.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
For
Ambient
Waters
Not
Used
as
Drinking
Water
Sources
When
the
water
body
is
to
be
used
for
recreational
purposes
and
not
as
a
source
of
drinking
water,
the
drinking
water
value
(
DI
above)
is
eliminated
from
the
equation
and
it
is
substituted
with
an
incidental
ingestion
value
(
II).
The
incidental
intake
is
assumed
to
occur
from
swimming
and
other
activities.
The
fish
intake
value
is
assumed
to
remain
the
same.
The
default
value
for
incidental
ingestion
is
0.01
L/
day.
When
the
above
equation
is
used
to
calculate
the
AWQC
with
the
substitution
of
an
incidental
ingestion
of
0.01
L/
day
an
AWQC
of
4.0
x
10­
3
mg/
L
(
or
4.0
µ
g/
L)
is
obtained
for
a
10­
6
lifetime
cancer
risk.

Site­
Specific
or
Regional
Adjustments
to
Criteria
Several
parameters
in
the
AWQC
equation
can
be
adjusted
on
a
site­
specific
or
regional
basis
to
reflect
regional
or
local
conditions
and/
or
specific
populations
of
concern.
These
include
fish
consumption,
incidental
water
consumption
as
related
to
regional/
local
recreational
activities,
BAF
(
including
factors
used
to
derive
BAFs,
percent
lipid
of
fish
consumed
by
target
population,
and
species
representative
of
given
trophic
levels),
and
the
relative
source
contribution.
States
are
encouraged
to
make
adjustments
using
the
information
and
instructions
provided
in
the
Technical
Support
Document
(
USEPA,
1998b).

REFERENCES
USEPA.
1998a.
Federal
Register
Notice:
Proposed
Water
Quality
Criteria
Methodology
Revisions;
Human
Health.
(
See
Attached
Copy).

USEPA.
1998b.
Ambient
Water
Quality
Criteria
Derivation
Methodology;
Human
Health.
Technical
Support
Document.
EPA/
822/
B­
98/
005.
July.
(
See
Attached
Copy).

USEPA.
1998c.
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Acrylonitrile.
EPA/
822/
R­
98/
006.
July.
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
1,3­
dichloropropene.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
AWQC
'
RSD
x
BW
DI
%
j
4
i
'
2
(
FI
i
x
BAF
i)
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
1,3­
Dichloropropene
(
1,3­
DCP)

Summary
T
his
criteria
document
updates
the
national
criteria
for
1,3­
DCP
using
new
methods
and
information
described
in
the
Federal
Register
Notice
(
USEPA,
1998a)
and
Technical
Support
Document
(
USEPA,
1998b)
to
calculate
ambient
water
quality
criteria.
These
new
methods
include
approaches
to
determine
dose­
response
relationships
for
both
carcinogenic
and
non­
carcinogenic
effects,
updated
information
for
determining
exposure
factors
(
e.
g.,
values
for
fish
consumption),
exposure
assumptions,
and
procedures
to
determine
bioaccumulation
factors.
For
more
detailed
information
please
refer
to
the
U.
S.
EPA
Ambient
Water
Quality
Criteria
(
AWQC)
document
for
1,3­
Dichloropropene
(
1,3­
DCP)
(
USEPA,
1998c).

Background
Information
The
AWQC
is
being
derived
for
1,3­
Dichloropropene
(
CAS
No.
542­
75­
6).
The
chemical
formula
is
C3H4Cl2
and
molecular
weight
is
110.98
(
pure
isomers).
At
25oC,
the
physical
state
of
1,3­
DCP
is
a
pale
yellow
to
yellow
liquid.
Dichloropropene
(
DCP)
is
used
as
soil
fumigant
in
the
United
States
to
control
soil
nematodes
on
crops
grown
in
sandy
soils.
The
EPAs
National
Toxics
Inventory
data
base
reported
air
emissions
of
18,820,000
pounds/
year
in
the
U.
S.
(
USEPA,
1996a).
Numerous
studies
have
sampled
for
DCP
(
and
isomers)
in
drinking
water,
groundwater
and
surface
waters
across
the
U.
S.
(
Hall
et
al.,
1987;
Miller
et
al.,
1990;
RIDEM,
1990;
Rutledge,
1987;
STORET,
1992).
All
of
these
studies
report
concentrations
of
1,3­
DCP
usually
at
or
below
the
detection
limits
(
USEPA,
1998c).

The
AWQC
Bioaccumulation
factor
(
BAF)
is
2.2
L/
kg
of
tissue
for
1,3­
DCP.
This
BAF
is
based
on
the
total
concentration
of
1,3­
DCP
in
trophic
level
four
biota
divided
by
the
total
concentration
in
water,
assuming
default
values
for
the
freely­
dissolved
fraction
and
lipid
content
of
consumed
aquatic
organisms.

The
cancer
risk
evaluation
of
1,3­
DCP
uses
the
new
methods
in
the
proposed
cancer
guidelines
(
USEPA,
1996),
which
are
described
in
the
Federal
Register
Notice
(
USEPA,
1998a)
and
in
the
Technical
Support
Document
(
USEPA,
1998b).
Based
upon
sufficient
evidence
from
animal
studies
(
multiple
tumor
types
in
several
species
by
oral,
inhalation,
and
dermal
routes),
positive
mutagenicity,
and
structural
analogues,
1,3­
DCP
is
considered
"
likely
to
be
carcinogenic
to
humans
by
all
routes
of
exposure."
Based
on
the
mutagenic
mode
of
action,
a
linear
low
dose
approach
is
recommended.

AWQC
Calculation
For
Ambient
Waters
Used
as
Drinking
Water
Sources
The
cancer­
based
AWQC
was
calculated
using
the
RSD
and
other
input
parameters
listed
below:

where:

RSD
=
Risk
specific
dose
1.0
x
10­
5
mg/
kg/
day
(
10­
6
risk)
BW
=
Human
body
weight
assumed
to
be
70
kg
DI
=
Drinking
water
intake
assumed
to
be
2
L/
day
FI
=
Fish
intake
at
trophic
level
i,
i=
2,3,
and
4
total
intake
assumed
to
be
0.01780
kg/
day
BAF
=
Bioaccumulation
factor
at
trophic
level
i
(
i=
2,3,
and
4),
equal
to
2.32,
1.86,
and
2.78
L/
kgtissue
for
trophic
levels
2,3,
and
4,
respectively.

This
yields
a
value
of
3.4
x
10­
4
mg/
L,
or
0.34
µ
g/
L
(
rounded
from
0.343
µ
g/
L).

For
Ambient
Waters
Not
Used
as
Drinking
Water
Sources
When
the
water
body
is
used
for
recreational
purposes
and
not
as
a
source
of
drinking
water,
the
drinking
water
value
is
eliminated
from
the
equation
and
it
is
substituted
with
an
incidental
ingestion
value.
The
incidental
intake
is
assumed
to
occur
from
swimming
and
other
activities.
The
fish
intake
value
is
assumed
to
remain
the
same.
The
default
value
for
incidental
ingestion
is
0.01
L/
day.
When
the
above
equation
is
used
to
calculate
the
AWQC
with
the
substitution
of
an
incidental
ingestion
of
0.01
L/
day
an
AWQC
of
1.4
x10­
2
mg/
L
(
14
µ
g/
L)
is
obtained.
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
1,3­
dichloropropene.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
Site­
Specific
or
Regional
Adjustments
to
Criteria
Several
parameters
in
the
AWQC
equation
can
be
adjusted
on
a
site­
specific
or
regional
basis
to
reflect
regional
or
local
conditions
and/
or
specific
populations
of
concern.
These
include
fish
consumption;
incidental
water
consumption
as
related
to
regional/
local
recreational
activities;
BAF
(
including
factors
used
to
derive
BAFs
,
percent
lipid
of
fish
consumed
by
the
target
population,
and
species
representative
of
given
trophic
levels);
and
the
relative
source
contribution.
States
are
encouraged
to
make
adjustments
using
the
information
and
instructions
provided
in
the
Technical
Support
Document
(
USEPA,
1998b).

REFERENCES
USEPA.
1998a.
Federal
Register
Notice:
Proposed
Water
Quality
Criteria
Methodology
Revisions;
Human
Health.
(
See
Attached
Copy).

USEPA.
1998b.
Ambient
Water
Quality
Criteria
Derivation
Methodology;
Human
Health.
Technical
Support
Document.
EPA/
822/
B­
98/
005.
July.
(
See
Attached
Copy).

USEPA.
1998c.
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
1,3­
Dichloropropene
(
1,3­
DCP).
EPA/
822/
R­
98/
005.
July.
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
HCBD.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
AWQC
'
RSD
x
BW
DI
%
j
4
i
'
2
(
FI
i
x
BAF
i)

AWQC
'
Pdp
SF
&
RSC
x
BW
DI
%
j
4
i
'
2
(
FI
i
x
BAF
i)
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Hexachlorobutadiene
(
HCBD)

Summary
T
his
criteria
document
updates
the
national
criteria
for
HCBD
using
new
methods
and
information
described
in
the
Federal
Register
Notice
(
USEPA,
1998a)
and
Technical
Support
Document
(
USEPA,
1998b)
to
calculate
ambient
water
quality
criteria.
These
new
methods
include
approaches
to
determine
dose­
response
relationships
for
both
carcinogenic
and
non­
carcinogenic
effects,
updated
information
for
determining
exposure
factors
(
e.
g.,
values
for
fish
consumption),
exposure
assumptions,
and
procedures
to
determine
bioaccumulation
factors.
For
more
detailed
information
please
refer
to
the
U.
S.
EPA
Ambient
Water
Quality
Criteria
(
AWQC)
document
for
hexachlorobutadiene
(
HCBD)(
USEPA,
1998c).

Background
Information
The
AWQC
is
being
derived
for
hexachlorobutadiene
(
CAS
No.
87­
68­
3).
The
chemical
formula
is
C4Cl6
and
molecular
weight
is
260.76.
At
25oC,
HCBD
is
a
colorless
liquid.
HCBD
is
used
as
a
solvent
in
chlorine
gas
production,
as
an
intermediate
in
the
manufacture
of
rubber
compounds
and
lubricants,
and
as
a
pesticide.
The
EPA's
National
Toxics
Release
Inventory
data
base
reported
total
emissions
to
the
environment
in
1990
of
5,591
pounds/
year
in
the
U.
S.,
of
which
4,906
pounds
was
to
air.
Numerous
studies
have
sampled
for
HCBD
in
drinking
water,
groundwater
and
surface
waters
across
the
U.
S.
(
see
USEPA
1998c
for
a
summary).
The
vast
majority
of
samples
are
at
trace
levels
or
below
the
detection
limits
(
DL
.
0.1
m
g/
L).

The
AWQC
Bioaccumulation
factor
(
BAF)
is
620
L/
kg
of
tissue
for
HCBD.
This
BAF
is
based
on
the
total
concentration
of
HCBD
in
trophic
level
four
biota
divided
by
the
total
concentration
in
water,
assuming
default
values
for
the
freely­
dissolved
fraction
and
lipid
content
of
consumed
aquatic
organisms.

The
cancer
risk
evaluation
of
HCBD
uses
the
new
methods
described
in
the
Federal
Register
Notice
(
USEPA,
1998a)
and
in
the
Technical
Support
Document
(
USEPA,
1998b).
Based
on
a
renal
tumor
finding
in
one
chronic
feeding
study
at
one
high
dose
in
one
species
(
both
sexes
of
Sprague­
Dawley
rats),
"
via
oral
route,
HCBD
is
considered
as
likely
to
be
carcinogenic
to
humans
only
at
very
high
exposure
conditions,
where
significant
renal
toxicity
occurs."
There
is
some
mutagenic
activity
in
the
presence
of
metabolic
activation.
Thus,
a
mutagenic
mode
of
action
cannot
be
ruled
out.
As
a
result,
both
the
cancer­
based,
linear
low
dose
approach
and
the
non­
linear
margin
of
exposure
approaches
are
used
for
deriving
the
AWQC.

AWQC
Calculation
For
Ambient
Waters
Used
as
Drinking
Water
Sources
The
cancer­
based
AWQC
was
calculated
using
the
RSD
and
other
input
parameters
listed
below:

where:

RSD
=
Risk
specific
dose
2.5
x
10­
5
mg/
kg/
day
(
10­
6
risk)
BW
=
Human
body
weight
assumed
to
be
70
kg
DI
=
Drinking
water
intake
assumed
to
be
2
L/
day
FI
=
Fish
intake
at
trophic
level
i,
i=
2,3,
and
4;
total
intake
assumed
to
be
0.01780
kg/
day
BAF
=
Bioaccumulation
factor
at
trophic
level
i
(
i=
2,3,
and
4)
equal
to
1,518,
2,389,
and
1,294
L/
kg­
tissue
for
trophic
levels
2,3,
and
4,
respectively.

This
yields
a
value
of
4.6
x
10­
5
mg/
L,
or
0.046
µ
g/
L
(
rounded
from
0.0462
µ
g/
L).

The
AWQC
using
the
margin
of
exposure
approach
was
calculated
using
the
following
equation
and
input
parameters
listed
below.
This
is
a
preliminary
summary
of
a
criteria
document
being
prepared
for
the
derivation
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
for
the
protection
of
human
health
from
exposure
to
HCBD.
The
calculated
AWQC
values
presented
in
this
draft
are
subject
to
revision
pending
inclusion
of
further
information
concerning
exposure
as
well
as
possible
changes
in
the
toxicological
information
used
to
derive
the
criterion.
where:

Pdp
=
Point
of
departure
(
0.054
mg/
kg/
day)
SF
=
Safety
factor
of
300
RSC
=
Relative
source
contribution
from
air
of
1.2
x
10­
4
mg/
kg­
day,
subtracted
in
this
case
BW
=
Human
body
weight
assumed
to
be
70
kg
DI
=
Drinking
water
intake
assumed
to
be
2
L/
day
FI
=
Fish
intake
at
trophic
level
i,
i=
2,3,
and
4;
total
intake
assumed
to
be
0.01780
kg/
day
BAF
=
Bioaccumulation
factor
at
trophic
level
i
(
i=
2,3,
and
4)
equal
to
1,518,
2,389,
and
1,294
L/
kg­
tissue
for
trophic
levels
2,3,
and
4,
respectively.

This
yields
an
AWQC
of
1.1
x
10­
4
mg/
L
(
0.11
F
g/
L).

For
Ambient
Waters
Not
Used
as
Drinking
Water
Sources
When
the
waterbody
is
used
for
recreational
purposes
and
not
as
a
source
of
drinking
water,
the
drinking
water
value
is
eliminated
from
the
equation
and
it
substituted
with
an
incidental
ingestion
value.
The
incidental
intake
is
assumed
to
occur
from
swimming
and
other
activities.
The
fish
intake
value
is
assumed
to
remain
the
same.
The
default
value
for
incidental
ingestion
is
0.01
L/
day.
When
the
linear
approach
is
used
to
calculate
the
AWQC
with
the
substitution
of
an
incidental
ingestion
of
0.01
L/
day
a
cancer­
based
AWQC
of
4.9
x
10­
5
mg/
L
(
or
0.049
µ
g/
L,
rounded
from
0.0487
µ
g/
L)
is
obtained.
When
the
non­
linear
margin
of
exposure
approach
is
used
with
the
substitution
of
an
incidental
ingestion
of
0.01
L/
day,
the
AWQC
is
1.2
x
10­
4
mg/
L
(
or
0.12
µ
g/
L,
rounded
from
0.117
µ
g/
L).

Site­
Specific
or
Regional
Adjustments
to
Criteria
Several
parameters
in
the
AWQC
equations
can
be
adjusted
on
a
site­
specific
or
regional
basis
to
reflect
regional
or
local
conditions
and/
or
specific
populations
of
concern.
These
include
fish
consumption;
incidental
water
consumption
as
related
to
regional/
local
recreational
activities;
BAF
(
including
factors
used
to
derive
BAFs,
percent
lipid
of
fish
consumed
by
the
target
population,
and
species
representative
of
given
trophic
levels);
and
the
relative
source
contribution.
States
are
encouraged
to
make
adjustments
using
the
information
and
instructions
provided
in
the
Technical
Support
Document
(
USEPA,
1998b).
REFERENCES
USEPA.
1998a.
Federal
Register
Notice:
Proposed
Water
Quality
Criteria
Methodology
Revisions;
Human
Health.
(
See
Attached
Copy).

USEPA.
1998b.
Ambient
Water
Quality
Criteria
Derivation
Methodology;
Human
Health.
Technical
Support
Document.
EPA/
822/
B­
98/
005.
July.
(
See
Attached
Copy).

USEPA.
1998c.
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Hexachlorobutadiene
(
HCBD).
EPA/
822/
R­
98/
004.
July.
