Document ID: EPA-HQ-OW-2002-0033-0142
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2003-04-14T04:00Z

Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000)
United
States
Office
of
Water
EPA­
822­
B­
00­
004
Environmental
Protection
Office
of
Science
and
Technology
October
2000
Agency
4304
EPA­
822­
B­
00­
004
October
2000
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000)

Final
Office
of
Science
and
Technology
Office
of
Water
U.
S.
Environmental
Protection
Agency
Washington,
DC
20460
NOTICE
The
policies
and
procedures
set
forth
in
this
document
are
intended
solely
to
describe
EPA
methods
for
developing
or
revising
ambient
water
quality
criteria
to
protect
human
health,
pursuant
to
Section
304(
a)
of
the
Clean
Water
Act,
and
to
serve
as
guidance
to
States
and
authorized
Tribes
for
developing
their
own
water
quality
criteria.
This
guidance
does
not
substitute
for
the
Clean
Water
Act
or
EPA's
regulations;
nor
is
it
a
regulation
itself.
Thus,
it
does
not
impose
legally­
binding
requirements
on
EPA,
States,
Tribes
or
the
regulated
community,
and
may
not
apply
to
a
particular
situation
based
upon
the
circumstances.

This
document
has
been
reviewed
in
accordance
with
U.
S.
Environmental
Protection
Agency
policy
and
approved
for
publication.
Mention
of
trade
names
or
commercial
products
does
not
constitute
endorsement
or
recommendation
for
use.

ii
iii
FOREWORD
This
document
presents
EPA's
recommended
Methodology
for
developing
ambient
water
quality
criteria
as
required
under
Section
304(
a)
of
the
Clean
Water
Act
(
CWA).
The
Methodology
is
guidance
for
scientific
human
health
assessments
used
by
EPA
to
develop,
publish,
and
from
time
to
time
revise,
recommended
criteria
for
water
quality
accurately
reflecting
the
latest
scientific
knowledge.
The
recommended
criteria
serve
States
and
Tribes'
needs
in
their
development
of
water
quality
standards
under
Section
303(
c)
of
the
CWA.

The
term
"
water
quality
criteria"
is
used
in
two
sections
of
the
Clean
Water
Act,
Section
304(
a)(
1)
and
Section
303(
c)(
2).
The
term
has
a
different
program
impact
in
each
section.
In
Section
304,
the
term
represents
a
scientific
assessment
of
ecological
and
human
health
effects
that
EPA
recommends
to
States
and
authorized
Tribes
for
establishing
water
quality
standards
that
ultimately
provide
a
basis
for
controlling
discharges
or
releases
of
pollutants.
Ambient
water
quality
criteria
associated
with
specific
stream
uses
when
adopted
as
State
or
Tribal
water
quality
standards
under
Section
303
define
the
maximum
levels
of
a
pollutant
necessary
to
protect
designated
uses
in
ambient
waters.
The
water
quality
criteria
adopted
in
the
State
or
Tribal
water
quality
standards
could
have
the
same
numerical
limits
as
the
criteria
developed
under
Section
304.
However,
in
many
situations
States
and
authorized
Tribes
may
want
to
adjust
water
quality
criteria
developed
under
Section
304
to
reflect
local
environmental
conditions
and
human
exposure
patterns
before
incorporation
into
water
quality
standards.
When
adopting
their
water
quality
criteria,
States
and
authorized
Tribes
have
four
options:
(
1)
adopt
EPA's
304(
a)
recommendations;
(
2)
adopt
304(
a)
criteria
modified
to
reflect
site­
specific
conditions;
(
3)
develop
criteria
based
on
other
scientifically
defensible
methods;
or
(
4)
establish
narrative
criteria
where
numeric
criteria
cannot
be
determined.

EPA
will
use
this
Methodology
to
develop
new
ambient
water
quality
criteria
and
to
revise
existing
recommended
water
quality
criteria.
It
also
provides
States
and
authorized
Tribes
the
necessary
guidance
to
adjust
water
quality
criteria
developed
under
Section
304
to
reflect
local
conditions
or
to
develop
their
own
water
quality
criteria
using
scientifically
defensible
methods
consistent
with
this
Methodology.
EPA
encourages
States
and
authorized
Tribes
to
use
this
Methodology
to
develop
or
revise
water
quality
criteria
to
appropriately
reflect
local
conditions.
EPA
believes
that
ambient
water
quality
criteria
inherently
require
several
risk
management
decisions
that
are,
in
many
cases,
better
made
at
the
State,
Tribal,
or
regional
level.
Additional
guidance
to
assist
States
and
authorized
Tribes
in
the
modification
of
criteria
based
on
the
Methodology
will
accompany
this
document
in
the
form
of
three
companion
Technical
Support
Documents
on
Risk
Assessment,
Exposure
Assessment,
and
Bioaccumulation
Assessment.

Geoffrey
H.
Grubbs
Director
Office
of
Science
and
Technology
v
ACKNOWLEDGMENTS
Project
Leader
Denis
Borum
U.
S.
EPA
Office
of
Science
and
Technology
Coauthors
Risk
Assessment
Joyce
M.
Donohue,
Ph.
D.*
U.
S.
EPA
Office
of
Science
and
Technology
Julie
T.
Du,
Ph.
D.*
U.
S.
EPA
Office
of
Science
and
Technology
Charles
O.
Abernathy,
Ph.
D.
U.
S.
EPA
Office
of
Science
and
Technology
Exposure
Denis
Borum
*
U.
S.
EPA
Office
of
Science
and
Technology
Helen
Jacobs,
M.
S.
U.
S.
EPA
Office
of
Science
and
Technology
Henry
Kahn,
D.
Sc.
U.
S.
EPA
Office
of
Science
and
Technology
Bioaccumulation
Keith
G.
Sappington,
M.
S.*
U.
S.
EPA
Office
of
Science
and
Technology
Lawrence
P.
Burkhard,
Ph.
D.
U.
S.
EPA
Office
of
Research
and
Development
Philip
M.
Cook,
Ph.
D.
U.
S.
EPA
Office
of
Research
and
Development
Erik
L.
Winchester,
M.
S.
U.
S.
EPA
Office
of
Science
and
Technology
U.
S.
EPA
Technical
Reviewers
William
Beckwith
U.
S.
EPA
Region
1
Jeff
Bigler
U.
S.
EPA
Office
of
Science
and
Technology
Sally
Brough
U.
S.
EPA
Region
10
Karen
Clark
U.
S.
EPA
Office
of
General
Counsel
Gregory
Currey
U.
S.
EPA
Office
of
Wastewater
Management
Vicki
Dellarco
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
Charles
Delos
U.
S.
EPA
Office
of
Science
and
Technology
Arnold
Den
U.
S.
EPA
Region
9
Catherine
Eiden
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
Michael
Firestone
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
Steven
Galson
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
Sue
Gilbertson
U.
S.
EPA
Office
of
Science
and
Technology
Denise
Hakowski
U.
S.
EPA
Region
3
Joel
Hansel
U.
S.
EPA
Region
4
Wayne
Jackson
U.
S.
EPA
Region
2
Annie
Jarabek
U.
S.
EPA
Office
of
Research
and
Development
William
Jordan
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
vi
Margaret
Kelly
U.
S.
EPA
Office
of
Children's
Health
Protection
Henry
Lee
U.
S.
EPA
Office
of
Research
and
Development
Sharon
Lin
U.
S.
EPA
Office
of
Wetlands,
Oceans,
and
Watersheds
Roseanne
Lorenzana
U.
S.
EPA
Region
10
Gregory
McCabe
U.
S.
EPA
Region
7
Jennifer
Mclain
U.
S.
EPA
Office
of
Ground
Water
and
Drinking
Water
Bruce
Mintz
U.
S.
EPA
Office
of
Research
and
Development
Dave
Moon
U.
S.
EPA
Region
8
William
Morrow
U.
S.
EPA
Office
of
Science
and
Technology
Jacqueline
Moya
U.
S.
EPA
Office
of
Research
and
Development
Deirdre
Murphy
U.
S.
EPA
Office
of
Air
Quality
Planning
and
Standards
Joseph
Nabholz
U.
S.
EPA
Office
of
Prevention,
Pesticides,
and
Toxic
Substances
Russell
Nelson
U.
S.
EPA
Region
6
Jennifer
Orme­
Zavaleta
U.
S.
EPA
Office
of
Research
and
Development
Lynn
Papa
U.
S.
EPA
Office
of
Research
and
Development
Robert
Pepin
U.
S.
EPA
Region
5
David
Pfeifer
U.
S.
EPA
Region
5
Rita
Schoeny
U.
S.
EPA
Office
of
Science
and
Technology
Charles
Stephan
U.
S.
EPA
Office
of
Research
and
Development
Linda
Teuschler
U.
S.
EPA
Office
of
Research
and
Development
David
Tomey
U.
S.
EPA
Region
1
Fritz
Wagener
U.
S.
EPA
Region
4
Jennifer
Wigal
U.
S.
EPA
Office
of
Science
and
Technology
Jeanette
Wiltse
U.
S.
EPA
Office
of
Scence
and
Technology
Gary
Wolinsky
U.
S.
EPA
Region
9
Philip
Woods
U.
S.
EPA
Region
9
William
Wuerthele
U.
S.
EPA
Region
8
*
Principal
U.
S.
EPA
Author
and
Contact
vii
EXTERNAL
PEER
REVIEW
WORKGROUP
The
following
professionals
were
part
of
the
External
Peer
Review
Workgroup
that
provided
technical
and
scientific
review
regarding
the
content
and
technical
approach
in
the
July
1998
Draft
Ambient
Water
Quality
Criteria
Derivation
Methodology:
Human
Health.
Their
comments
were
reviewed
and
incorporated
where
appropriate
to
develop
this
final
document.

Kenneth
T.
Bogen,
Ph.
D.
Lawrence
Livermore
National
Laboratory
Paul
E.
Brubaker,
Ph.
D.
P.
E.
Brubaker
Associates
Peter
L.
DeFur,
Ph.
D.
Virginia
Commonwealth
University
Karen
Erstfeld,
Ph.
D.
Rutgers
University
Bob
Fares,
Ph.
D.
Environmental
Standards,
Inc.
Laura
Green,
Ph.
D.
Cambridge
Environmental,
Inc.
Robert
Hales,
Ph.
D.
Virginia
Institute
of
Marine
Science
Brendan
Hickie,
Ph.
D.
Trent
University
Ernest
Hodgson,
Ph.
D.
North
Carolina
State
University
Paul
Locke,
Ph.
D.
Johns
Hopkins
University
Lynn
S.
McCarty,
Ph.
D.
LS
McCarty
Scientific
Research
and
Consulting
Erik
Rifkin,
Ph.
D.
Rifkin
and
Associates,
Inc.
Damian
Shea,
Ph.
D.
North
Carolina
State
University
Nga
Tran,
Ph.
D.
Johns
Hopkins
University
Curtis
Travis,
Ph.
D.
Project
Performance
Corp.

Potential
areas
for
conflict
of
interest
were
investigated
via
direct
inquiry
with
the
peer
reviews
and
review
of
their
current
affiliations.
No
conflicts
of
interest
were
identified.
ix
TABLE
OF
CONTENTS
Page
NOTICE
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ii
FOREWORD
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iii
ACKNOWLEDGMENTS
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v
EXTERNAL
PEER
REVIEW
WORKGROUP
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vii
CONTENTS
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ix
TABLES
AND
FIGURES
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xiv
LIST
OF
ACRONYMS
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xv
1.
INTRODUCTION
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1­
1
1.1
Water
Quality
Criteria
and
Standards
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1­
1
1.2
Purpose
of
This
Document
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1­
1
1.3
History
of
the
Ambient
Water
Quality
Criteria
(
AWQC)
Methodology
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1­
2
1.4
Relationship
of
Water
Quality
Standards
to
AWQC
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1­
4
1.5
Need
for
the
AWQC
Methodology
Revisions
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1­
4
1.5.1
Group
C
Chemicals
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1­
6
1.5.2
Consideration
of
Non­
Water
Sources
of
Exposure
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1­
7
1.5.3
Cancer
Risk
Ranges
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1­
8
1.6
Overview
of
the
AWQC
Methodology
Revisions
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1­
9
1.7
References
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1­
13
2.
CLARIFICATIONS
ON
THE
METHODOLOGY,
RISK
CHARACTERIZATION,
AND
OTHER
ISSUES
FOR
DEVELOPING
CRITERIA
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2­
1
2.1
Identifying
the
Population
Subgroup
that
the
AWQC
Should
Protect
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2­
1
2.2
Science,
Science
Policy,
and
Risk
Management
.
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2­
3
2.3
Setting
Criteria
to
Protect
Against
Multiple
Exposures
From
Multiple
Chemicals
(
Cumulative
Risk)
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2­
4
2.4
Cancer
Risk
Range
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2­
6
2.5
Microbiological
Ambient
Water
Quality
Criteria
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2­
7
2.6
Risk
Characterization
Considerations
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2­
9
2.7
Discussion
of
Uncertainty
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.
2­
11
2.7.1
Observed
Range
of
Toxicity
Versus
Range
of
Environmental
Exposure
.
.
.
.
2­
11
2.7.2
Continuum
of
Preferred
Data/
Use
of
Defaults
.
.
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2­
11
2.7.3
Significant
Figures
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2­
11
2.8
Other
Considerations
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2­
13
2.8.1
Minimum
Data
Considerations
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2­
13
2.8.2
Site­
Specific
Criterion
Calculation
.
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2­
13
2.8.3
Organoleptic
Criteria
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2­
14
2.8.4
Criteria
for
Chemical
Classes
.
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2­
15
2.8.5
Criteria
for
Essential
Elements
.
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2­
16
2.9
References
.
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2­
16
x
3.
RISK
ASSESSMENT
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3­
1
3.1
Cancer
Effects
.
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.
3­
1
3.1.1
Background
on
EPA
Cancer
Risk
Assessment
Guidelines
.
.
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.
.
3­
1
3.1.2
EPA's
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
and
the
Subsequent
July,
1999
Draft
Revised
Cancer
Guidelines
.
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.
3­
2
3.1.3
Methodology
for
Deriving
AWQC
by
the
1999
Draft
Revised
Cancer
Guidelines
.
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3­
4
3.1.3.1
Weight­
of­
Evidence
Narrative
.
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3­
5
3.1.3.2
Mode
of
Action­
General
Considerations
and
Framework
for
Analysis
.
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3­
6
3.1.3.3
Dose
Estimation
.
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.
3­
7
A.
Determining
the
Human
Equivalent
Dose
.
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.
3­
7
B.
Dose­
Response
Analysis
.
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.
.
3­
7
3.1.3.4
Characterizing
Dose­
Response
Relationships
in
the
Range
of
Observation
and
at
Low
Environmentally
Relevant
Doses
.
.
.
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.
.
3­
8
A.
Extrapolation
to
Low,
Environmentally
Relevant
Doses
.
.
.
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.
.
.
.
3­
9
B.
Biologically­
Based
Modeling
Approaches
.
.
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.
3­
9
C.
Default
Linear
Extrapolation
Approach
.
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.
3­
10
D.
Default
Nonlinear
Approach
.
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.
3­
11
E.
Both
Linear
and
Nonlinear
Approaches
.
.
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.
.
3­
13
3.1.3.5
AWQC
Calculation
.
.
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.
3­
13
A.
Linear
Approach
.
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.
3­
13
B.
Nonlinear
Approach
.
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.
.
3­
14
3.1.3.6
Risk
Characterization
.
.
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.
.
3­
14
3.1.3.7
Use
of
Toxicity
Equivalence
Factors
(
TEF)
and
Relative
Potency
Estimates
.
.
.
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.
.
3­
15
3.1.4
References
for
Cancer
Section
.
.
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.
.
3­
16
3.2
Noncancer
Effects
.
.
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.
3­
17
3.2.1
1980
AWQC
National
Guidelines
for
Noncancer
Effects
.
.
.
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.
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.
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.
.
.
3­
17
3.2.2
Noncancer
Risk
Assessment
Developments
Since
1980
.
.
.
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.
.
.
3­
18
3.2.3
Issues
and
Recommendations
Concerning
the
Derivation
of
AWQC
for
Noncarcinogens
.
.
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.
.
3­
20
3.2.3.1
Using
the
Current
NOAEL/
UF­
Based
RfD
Approach
or
Adopting
More
Quantitative
Approaches
for
Noncancer
Risk
Assessment
.
.
.
.
.
.
.
.
3­
20
A.
The
Benchmark
Dose
.
.
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.
.
3­
22
B.
Categorical
Regression
.
.
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.
3­
24
C.
Summary
.
.
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.
.
.
3­
25
3.2.3.2
Presenting
the
RfD
as
a
Single
Point
or
as
a
Range
for
Deriving
AWQC
.
.
.
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.
.
3­
25
3.2.3.3
Guidelines
to
be
Adopted
for
Derivation
of
Noncancer
Health
Effects
Values
.
.
.
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.
.
3­
27
3.2.3.4
Treatment
of
Uncertainty
Factors/
Severity
of
Effects
During
the
RfD
Derivation
and
Verification
Process
.
.
.
.
.
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.
.
.
.
3­
27
xi
3.2.3.5
Use
of
Less­
Than­
90­
Day
Studies
to
Derive
RfDs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3­
27
3.2.3.6
Use
of
Reproductive/
Developmental,
Immunotoxicity,
and
Neurotoxicity
Data
as
the
Basis
for
Deriving
RfDs
.
.
.
.
.
.
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.
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.
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.
.
.
.
.
.
.
3­
28
3.2.3.7
Applicability
of
Toxicokinetic
Data
in
Risk
Assessment
.
.
.
.
.
.
.
.
.
.
3­
28
3.2.3.8
Consideration
of
Linearity
(
or
Lack
of
a
Threshold)
for
Noncarcinogenic
Chemicals
.
.
.
.
.
.
.
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.
.
.
3­
29
3.2.3.9
Minimum
Data
Guidance
.
.
.
.
.
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.
.
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.
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.
.
.
.
.
.
.
.
.
.
3­
29
3.2.4
References
for
Noncancer
Effects
.
.
.
.
.
.
.
.
.
.
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.
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.
.
3­
30
4.
EXPOSURE
.
.
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.
.
4­
1
4.1.
Exposure
Policy
Issues
.
.
.
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.
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.
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.
.
.
4­
1
4.1.1
Sources
of
Exposure
Associated
with
Ambient
Water
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
4­
2
4.1.1.1
Appropriateness
of
Including
the
Drinking
Water
Pathway
in
AWQC
.
.
.
.
.
.
.
.
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.
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.
.
.
4­
2
4.1.1.2
Setting
Separate
AWQC
for
Drinking
Water
and
Fish
Consumption
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
4­
2
4.1.1.3
Incidental
Ingestion
from
Ambient
Surface
Waters
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
4­
3
4.2.
Consideration
of
Non­
Water
Sources
of
Exposure
When
Setting
AWQC
.
.
.
.
.
.
.
.
.
.
4­
3
4.2.1
Policy
Background
.
.
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.
4­
3
4.2.2
The
Exposure
Decision
Tree
Approach
.
.
.
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.
4­
5
4.2.2.1
Problem
Formulation
.
.
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.
4­
9
4.2.2.2
Data
Adequacy
.
.
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4­
10
4.2.2.3
Regulatory
Actions
.
.
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4­
13
4.2.2.4
Apportionment
Decisions
.
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.
.
4­
13
4.2.3
Additional
Points
of
Clarification
on
the
Exposure
Decision
Tree
Approach
for
Setting
AWQC
.
.
.
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.
4­
15
4.2.4
Quantification
of
Exposure
.
.
.
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.
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.
.
.
4­
16
4.2.5
Inclusion
of
Inhalation
and
Dermal
Exposures
.
.
.
.
.
.
.
.
.
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.
.
.
.
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.
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.
.
.
.
4­
16
4.3
Exposure
Factors
Used
in
the
AWQC
Computation
.
.
.
.
.
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.
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.
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.
.
.
.
4­
17
4.3.1
Human
Body
Weight
Values
for
Dose
Calculations
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
4­
18
4.3.1.1
Rate
Protective
of
Human
Health
from
Chronic
Exposure
.
.
.
.
.
.
.
.
4­
19
4.3.1.2
Rates
Protective
of
Developmental
Human
Health
Effects
.
.
.
.
.
.
.
.
4­
20
4.3.2
Drinking
Water
Intake
Rates
.
.
.
.
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.
.
4­
21
4.3.2.1
Rate
Protective
of
Human
Health
from
Chronic
Exposure
.
.
.
.
.
.
.
.
4­
23
4.3.2.2
Rates
Protective
of
Developmental
Human
Health
Effects
.
.
.
.
.
.
.
.
4­
24
4.3.2.3
Rates
Based
on
Combining
Drinking
Water
Intake
and
Body
Weight
.
.
.
.
.
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.
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4­
24
4.3.3
Fish
Intake
Rates
.
.
.
.
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.
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.
.
.
.
4­
25
4.3.3.1
Rates
Protective
of
Human
Health
from
Chronic
Exposure
.
.
.
.
.
.
.
4­
25
4.3.3.2
Rates
Protective
of
Developmental
Human
Health
Effects
.
.
.
.
.
.
.
.
4­
29
4.3.3.3
Rates
Based
on
Combining
Fish
Intake
and
Body
Weight
.
.
.
.
.
.
.
.
4­
30
4.4
References
for
Exposure
.
.
.
.
.
.
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.
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.
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.
4­
30
xii
5.
BIOACCUMULATION
.
.
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.
.
5­
1
5.1
Introduction
.
.
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.
.
.
5­
1
5.1.1
Important
Bioaccumulation
and
Bioconcentration
Concepts
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
2
5.1.2
Goal
of
the
National
BAF
.
.
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.
.
5­
3
5.1.3
Changes
to
the
1980
Methodology
.
.
.
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.
.
5­
3
5.1.3.1
Overall
Approach
.
.
.
.
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.
.
5­
4
5.1.3.2
Lipid
Normalization
.
.
.
.
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.
.
.
5­
4
5.1.3.3
Bioavailability
.
.
.
.
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.
.
5­
5
5.1.3.4
Trophic
Level
Considerations
.
.
.
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.
.
.
5­
5
5.1.3.5
Site­
Specific
Adjustments
.
.
.
.
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.
.
5­
5
5.1.4
Organization
of
This
Section
.
.
.
.
.
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.
.
5­
6
5.2
Definitions
.
.
.
.
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.
.
.
.
.
.
5­
6
5.3
Framework
for
Determining
National
Bioaccumulation
Factors
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
10
5.3.1
Four
Different
Methods
.
.
.
.
.
.
.
.
.
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.
.
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.
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.
.
.
.
.
.
.
.
5­
10
5.3.2
Overview
of
BAF
Derivation
Framework
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
.
.
.
5­
12
5.3.3
Defining
the
Chemical
of
Concern
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
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.
.
.
.
.
.
5­
14
5.3.4
Collecting
and
Reviewing
Data
.
.
.
.
.
.
.
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.
.
.
.
.
.
5­
14
5.3.5
Classifying
the
Chemical
of
Concern
.
.
.
.
.
.
.
.
.
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.
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.
.
.
.
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.
.
.
.
.
.
.
.
5­
15
5.4
National
Bioaccumulation
Factors
for
Nonionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
16
5.4.1
Overview
.
.
.
.
.
.
.
.
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.
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.
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.
.
.
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.
.
.
.
.
.
.
.
5­
16
5.4.2
Selecting
the
BAF
Derivation
Procedure
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
18
5.4.2.1
Chemicals
with
Moderate
to
High
Hydrophobicity
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
18
5.4.2.2
Chemicals
with
Low
Hydrophobicity
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
19
5.4.2.3
Assessing
Metabolism
.
.
.
.
.
.
.
.
.
.
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.
.
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.
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.
.
.
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.
.
.
.
.
.
.
.
.
.
.
5­
20
5.4.3
Deriving
National
BAFs
Using
Procedure
#
1
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
22
5.4.3.1
Calculating
Individual
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
23
A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
23
B.
Baseline
BAF
R
f
d
Derived
from
BSAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
28
C.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCFtT
and
FCM
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
32
D.
Baseline
BAF
R
f
d
from
a
K
ow
and
FCM
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
38
5.4.3.2
Selecting
Final
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
39
5.4.3.3
Calculating
National
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
41
5.4.4
Deriving
National
BAFs
Using
Procedure
#
2
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
44
5.4.4.1
Calculating
Individual
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
45
A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
45
B.
Baseline
BAF
R
f
d
Derived
from
Field­
Measured
BSAFs
.
.
.
.
.
.
.
.
5­
46
C.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
.
.
.
.
.
.
.
.
.
.
5­
46
5.4.4.2
Selecting
Final
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
46
5.4.4.3
Calculating
the
National
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
47
5.4.5
Deriving
National
BAFs
Using
Procedure
#
3
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
47
5.4.5.1
Calculating
Individual
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
47
A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
48
xiii
B.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
.
.
.
.
.
.
.
.
.
.
5­
48
C.
Baseline
BAF
R
f
d
from
a
K
ow
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
49
5.4.5.2
Selecting
Final
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
49
5.4.5.3
Calculating
the
National
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
50
5.4.6
Deriving
National
BAFs
Using
Procedure
#
4
.
.
.
.
.
.
.
.
.
.
.
.
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.
5­
51
5.4.6.1
Calculating
Individual
Baseline
BAF
R
f
ds
.
.
.
.
.
.
.
.
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.
.
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.
.
.
5­
52
A.
Baseline
BAF
R
f
d
from
Field­
measured
BAFs
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
52
B.
Baseline
BAF
R
f
d
from
a
Laboratory­
measured
BCF
.
.
.
.
.
.
.
.
.
.
.
5­
53
5.4.6.2
Selecting
Final
Baseline
BAF
R
f
ds
.
.
.
.
.
.
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.
5­
53
5.4.6.3
Calculating
National
BAFs
.
.
.
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.
5­
54
5.5
National
Bioaccumulation
Factors
for
Ionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
55
5.6
National
Bioaccumulation
Factors
for
Inorganic
and
Organometallic
Chemicals
.
.
.
.
5­
57
5.6.1
Selecting
the
BAF
Derivation
Procedure
.
.
.
.
.
.
.
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.
5­
57
5.6.2
Bioavailability
.
.
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.
5­
58
5.6.3
Deriving
BAFs
Using
Procedure
#
5
.
.
.
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.
5­
58
5.6.3.1
Determining
Field­
Measured
BAFs
.
.
.
.
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.
5­
59
5.6.3.2
Determining
Laboratory­
Measured
BCFs
.
.
.
.
.
.
.
.
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.
5­
60
5.6.3.3
Determining
the
National
BAFs
.
.
.
.
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.
.
.
.
.
.
.
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.
5­
60
5.6.4
Deriving
BAFs
Using
Procedure
#
6
.
.
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.
5­
61
5.6.4.1
Determining
Field­
Measured
BAFs
.
.
.
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.
.
5­
62
5.6.4.2
Determining
Laboratory­
Measured
BCFs
.
.
.
.
.
.
.
.
.
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.
.
.
5­
62
5.6.4.3
Determining
the
National
BAF
.
.
.
.
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5­
62
5.7
References
.
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.
.
5­
63
xiv
TABLES
AND
FIGURES
Page
Table
3­
1.
Uncertainty
Factors
and
the
Modifying
Factor
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
3­
19
Figure
4­
1.
Exposure
Decision
Tree
for
Defining
Proposed
RfD
(
or
POD/
UF)
Apportionment
.
.
.
.
.
.
.
.
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.
.
4­
8
Figure
5­
1
Framework
for
Deriving
a
National
BAF
.
.
.
.
.
.
.
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.
.
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.
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.
.
.
.
5­
13
Figure
5­
2
BAF
Derivation
for
Nonionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
17
Table
5­
1
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3
and
4
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
36
xv
LIST
OF
ACRONYMS
ADI
Acceptable
Daily
Intake
ARAR
Applicable
or
Relevant
and
Appropriate
Requirements
ASTM
American
Society
of
Testing
and
Materials
AWQC
Ambient
Water
Quality
Criteria
BAF
Bioaccumulation
Factor
BAF
R
f
d
Baseline
Bioaccumulation
Factor
BCF
Bioconcentration
Factor
BCF
R
f
d
Baseline
Bioconcentration
Factor
BCFtT
Bioconcentration
Factor
Based
on
Total
Concentrations
in
Tissue
and
Water
BMD
Benchmark
Dose
BMDL
Lower­
Bound
Confidence
Limit
on
the
BMD
BMF
Biomagnification
Factor
BMR
Benchmark
Response
BSAF
Biota­
Sediment
Accumulation
Factors
BW
Body
Weight
C
R
Lipid­
normalized
Concentration
C
soc
Organic
Carbon­
normalized
Concentration
C
t
Concentration
of
the
Chemical
in
the
Specified
Wet
Tissue
C
w
Concentration
of
the
Chemical
in
Water
CDC
U.
S.
Centers
for
Disease
Control
and
Prevention
CSFII
Continuing
Survey
of
Food
Intake
by
Individuals
CWA
Clean
Water
Act
DDT
1,1,1­
trichloro­
2,2­
bis(
p­
chlorophenyl)
ethane
DDE
1,1­
dichloro­
2,2­
bis(
p­
chlorophenyl)
ethylene
DDD
1,1­
dichloro­
2,2­
bis(
p­
chlorophenyl)
ethane
DI
Drinking
Water
Intake
DNA
Deoxyribonucleic
Acid
DNOC
2,4­
dinitro­
o­
cresol
DOC
Dissolved
Organic
Carbon
ED
10
Dose
Associated
with
a
10
Percent
Extra
Risk
EPA
Environmental
Protection
Agency
f
fd
Fraction
Freely
Dissolved
f
R
Fraction
Lipid
FCM
Food
Chain
Multiplier
FEL
Frank
Effect
Level
FI
Fish
Intake
FIFRA
Federal
Insecticide,
Fungicide,
and
Rodenticide
Act
GLI
Great
Lakes
Water
Quality
Initiative
HCBD
Hexachlorobutadiene
IARC
International
Agency
for
Research
on
Cancer
II
Incidental
Ingestion
xvi
ILSI
International
Life
Sciences
Institute
IRIS
Integration
Risk
Information
System
kg
kilogram
K
ow
Octanol­
Water
Partition
Coefficient
L
Liter
LAS
Linear
Alkylbenzesulfonate
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
M
R
Mass
of
Lipid
in
Specified
Tissue
M
t
Mass
of
Specified
Tissue
(
Wet
Weight)
MCL
Maximum
Contaminant
Level
MCLG
Maximum
Contaminant
Level
Goal
MF
Modifying
Factor
mg
Milligrams
ml
Milliliters
MOA
Mode
of
Action
MOE
Margin
of
Exposure
NCHS
National
Center
for
Health
Statistics
NCI
National
Cancer
Institute
NFCS
Nationwide
Food
Consumption
Survey
NHANES
National
Health
and
Nutrition
Examination
Survey
NOAEL
No
Observed
Adverse
Effect
Level
NOEL
No
Observed
Effect
Level
NPDES
National
Pollutant
Discharge
Elimination
System
PAH
Polycyclic
Aromatic
Hydrocarbon
PCB
Polychlorinated
Biphenyls
POD
Point
of
Departure
POC
Particulate
Organic
Carbon
RDA
Recommended
Daily
Allowance
RfC
Reference
Concentration
RfD
Reference
Dose
RfD
DT
Reference
Dose
for
Developmental
Effects
RPF
Relative
Potency
Factor
RSC
Relative
Source
Contribution
RSD
Risk­
Specific
Dose
SAB
Science
Advisory
Board
SDWA
Safe
Drinking
Water
Act
SF
Safety
Factor
STORET
Storage
Retrieval
TEAM
Total
Exposure
Assessment
Methodology
TEF
Toxicity
Equivalency
Factor
TMDL
Total
Maximum
Daily
Load
xvii
TSD
Technical
Support
Document
USDA
United
States
Department
of
Agriculture
USEPA
United
States
Environmental
Protection
Agency
UF
Uncertainty
Factor
WQBEL
Water
Quality­
Based
Effluent
Limits
1­
1
1.
INTRODUCTION
1.1
WATER
QUALITY
CRITERIA
AND
STANDARDS
Pursuant
to
Section
304(
a)(
1)
of
the
Clean
Water
Act
(
CWA),
the
U.
S.
Environmental
Protection
Agency
(
EPA)
is
required
to
publish,
and
from
time
to
time
thereafter
revise,
criteria
for
water
quality
accurately
reflecting
the
latest
scientific
knowledge
on
the
kind
and
extent
of
all
identifiable
effects
on
human
health
which
may
be
expected
from
the
presence
of
pollutants
in
any
body
of
water.

Historically,
the
ambient
water
quality
criteria
(
AWQC
or
304(
a)
criteria)
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
criteria
in
ambient
water.
These
304(
a)
criteria
may
be
used
as
guidance
by
States
and
authorized
Tribes
to
establish
water
quality
standards,
which
ultimately
provide
a
basis
for
controlling
discharges
or
releases
of
pollutants
into
ambient
waters.

In
1980,
AWQC
were
derived
for
64
pollutants
using
guidelines
developed
by
the
Agency
for
calculating
the
impact
of
waterborne
pollutants
on
aquatic
organisms
and
on
human
health.
Those
guidelines
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.

1.2
PURPOSE
OF
THIS
DOCUMENT
The
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000)
(
hereafter
the
"
2000
Human
Health
Methodology")
addresses
the
development
of
AWQC
to
protect
human
health.
The
Agency
intends
to
use
the
2000
Human
Health
Methodology
both
to
develop
new
AWQC
for
additional
pollutants
and
to
revise
existing
AWQC.
Within
the
next
several
years,
EPA
intends
to
focus
on
deriving
AWQC
for
chemicals
of
high
priority
(
including,
but
not
limited
to,
mercury,
arsenic,
PCBs,
and
dioxin).
Furthermore,
EPA
anticipates
that
304(
a)
criteria
development
in
the
future
will
be
for
bioaccumulative
chemicals
and
pollutants
considered
highest
priority
by
the
Agency.
The
2000
Human
Health
Methodology
is
also
intended
to
provide
States
and
authorized
Tribes
flexibility
in
establishing
water
quality
standards
by
providing
scientifically
valid
options
for
developing
their
own
water
quality
criteria
that
consider
local
conditions.
States
and
authorized
Tribes
are
strongly
encouraged
to
use
this
Methodology
to
derive
their
own
AWQC.
However,
the
2000
Human
Health
Methodology
also
defines
the
default
factors
EPA
intends
to
use
in
evaluating
and
determining
consistency
of
State
water
quality
standards
with
the
requirements
of
the
CWA.
The
1­
2
Agency
intends
to
use
these
default
factors
to
calculate
national
water
quality
criteria
under
Section
304(
a)
of
the
Act.
EPA
will
also
use
this
Methodology
as
guidance
when
promulgating
water
quality
standards
for
a
State
or
Tribe
under
Section
303(
c)
of
the
CWA.

This
Methodology
does
not
substitute
for
the
CWA
or
EPA's
regulations;
nor
is
it
a
regulation
itself.
Thus,
the
2000
Human
Health
Methodology
cannot
impose
legally­
binding
requirements
on
EPA,
States,
Tribes
or
the
regulated
community,
and
may
not
apply
to
a
particular
situation
based
upon
the
circumstances.
EPA
and
State/
Tribal
decision­
makers
retain
the
discretion
to
use
different,
scientifically
defensible,
methodologies
to
develop
human
health
criteria
on
a
case­
by­
case
basis
that
differ
from
this
Methodology
where
appropriate.
EPA
may
change
the
Methodology
in
the
future
through
intermittent
refinements
as
advances
in
science
or
changes
in
Agency
policy
occur.

The
2000
Human
Health
Methodology
incorporates
scientific
advancements
made
over
the
past
two
decades.
The
use
of
this
Methodology
is
an
important
component
of
the
Agency's
efforts
to
improve
the
quality
of
the
Nation's
waters.
EPA
believes
the
Methodology
will
enhance
the
overall
scientific
basis
of
water
quality
criteria.
Further,
the
Methodology
should
help
States
and
Tribes
address
their
unique
water
quality
issues
and
risk
management
decisions,
and
afford
them
greater
flexibility
in
developing
their
water
quality
programs.

There
are
three
companion
Technical
Support
Document
(
TSD)
volumes
for
the
2000
Human
Health
Methodology:
a
Risk
Assessment
TSD;
an
Exposure
Assessment
TSD;
and
a
Bioaccumulation
TSD.
These
documents
are
intended
to
further
support
States
and
Tribes
in
developing
AWQC
to
reflect
local
conditions.
The
Risk
Assessment
TSD
(
USEPA,
2000)
is
being
published
concurrently
with
this
Methodology.
Publication
of
the
Exposure
Assessment
and
Bioaccumulation
TSDs
are
anticipated
in
2001.

1.3
HISTORY
OF
THE
AMBIENT
WATER
QUALITY
CRITERIA
(
AWQC)
METHODOLOGY
In
1980,
EPA
published
AWQC
for
64
pollutants/
pollutant
classes
identified
in
Section
307(
a)
of
the
CWA
and
provided
a
methodology
for
deriving
the
criteria
(
USEPA,
1980).
These
1980
AWQC
National
Guidelines
(
or
the
"
1980
Methodology")
for
developing
AWQC
for
the
protection
of
human
health
addressed
three
types
of
endpoints:
noncancer,
cancer,
and
organoleptic
(
taste
and
odor)
effects.
Criteria
for
protection
against
noncancer
and
cancer
effects
were
estimated
by
using
risk
assessment­
based
procedures,
including
extrapolation
from
animal
toxicity
or
human
epidemiological
studies.
Basic
human
exposure
assumptions
were
applied
to
the
criterion
equation.

The
risk
assessment­
based
procedures
used
to
derive
the
AWQC
to
protect
human
health
were
specific
to
whether
the
endpoint
was
cancer
or
noncancer.
When
using
cancer
as
the
critical
risk
assessment
endpoint
(
which
had
been
assumed
not
to
have
a
threshold),
the
AWQC
were
1Throughout
this
document,
the
term
"
risk
level"
regarding
a
cancer
assessment
using
linear
approach
refers
to
an
upper­
bound
estimate
of
excess
lifetime
cancer
risk.

1­
3
presented
as
a
range
of
concentrations
associated
with
specified
incremental
lifetime
risk
levels1.
When
using
noncancer
effects
as
the
critical
endpoint,
the
AWQC
reflected
an
assessment
of
a
"
no­
effect"
level,
since
noncancer
effects
were
assumed
to
have
a
threshold.
The
key
features
of
each
procedure
are
described
briefly
in
the
following
paragraphs.

Cancer
effects.
If
human
or
animal
studies
on
a
contaminant
indicated
that
it
induced
a
statistically
significant
carcinogenic
response,
the
1980
AWQC
National
Guidelines
treated
the
contaminant
as
a
carcinogen
and
derived
a
low­
dose
cancer
potency
factor
from
available
animal
data
using
the
linearized
multistage
model
(
LMS).
The
LMS,
which
uses
a
linear,
nonthreshold
assumption
for
low­
dose
risk,
was
used
by
the
Agency
as
a
science
policy
choice
in
protecting
public
health,
and
represented
a
plausible
upper
limit
for
low­
dose
risk.
The
cancer
potency
factor,
which
expresses
incremental,
lifetime
risk
as
a
function
of
the
rate
of
intake
of
the
contaminant,
was
then
combined
with
exposure
assumptions
to
express
that
risk
in
terms
of
an
ambient
water
concentration.
In
the
1980
AWQC
National
Guidelines,
the
Agency
presented
a
range
of
contaminant
concentrations
corresponding
to
incremental
cancer
risks
of
10­
7
to
10­
5
(
that
is,
a
risk
of
one
additional
case
of
cancer
in
a
population
of
ten
million
to
one
additional
cancer
case
in
a
population
of
one
hundred
thousand,
respectively).

Noncancer
effects.
If
the
pollutant
was
not
considered
to
have
the
potential
for
causing
cancer
in
humans
(
later
defined
as
a
known,
probable,
or
possible
human
carcinogen
by
the
1986
Guidelines
for
Carcinogen
Risk
Assessment,
USEPA,
1986d),
the
1980
AWQC
National
Guidelines
treated
the
contaminant
as
a
noncarcinogen;
a
criterion
was
derived
using
a
threshold
concentration
for
noncancer
adverse
effects.
The
criteria
derived
from
noncancer
data
were
based
on
the
Acceptable
Daily
Intake
(
ADI)
(
now
termed
the
reference
dose
[
RfD]).
ADI
values
were
generally
derived
using
a
no­
observed­
adverse­
effect
level
(
NOAEL)
from
animal
studies,
although
human
data
were
used
whenever
available.
The
ADI
was
calculated
by
dividing
the
NOAEL
by
an
uncertainty
factor
to
account
for
uncertainties
inherent
in
extrapolating
limited
toxicological
data
to
humans.
In
accordance
with
the
National
Research
Council
recommendations
of
1977
(
NRC,
1977),
safety
factors
(
SFs)
(
later
redefined
as
uncertainty
factors)
of
10,
100,
or
1,000
were
used,
depending
on
the
quality
of
the
data.

Organoleptic
effects.
Organoleptic
characteristics
were
also
used
in
developing
criteria
for
some
contaminants
to
control
undesirable
taste
and/
or
odor
imparted
by
them
to
ambient
water.
In
some
cases,
a
water
quality
criterion
based
on
organoleptic
effects
would
be
more
stringent
than
a
criterion
based
on
toxicologic
endpoints.
The
1980
AWQC
National
Guidelines
emphasized
that
criteria
derived
for
organoleptic
endpoints
are
not
based
on
toxicological
information,
have
no
direct
relationship
to
adverse
human
health
effects
and,
therefore,
do
not
necessarily
represent
approximations
of
acceptable
risk
levels
for
humans.
1­
4
1.4
RELATIONSHIP
OF
WATER
QUALITY
STANDARDS
TO
AWQC
Under
Section
303(
c)
of
the
CWA,
States
have
the
primary
responsibility
for
establishing
water
quality
standards,
defined
under
the
Act
as
designated
beneficial
uses
of
a
water
segment
and
the
water
quality
criteria
necessary
to
support
those
uses.
Additionally,
Native
American
Tribes
authorized
to
administer
the
water
quality
standards
program
under
40
CFR
131.8
establish
water
quality
standards
for
waters
within
their
jurisdictions.
This
statutory
framework
allows
States
and
authorized
Tribes
to
work
with
local
communities
to
adopt
appropriate
designated
uses
and
to
adopt
criteria
to
protect
those
designated
uses.
Section
303(
c)
provides
for
EPA
review
of
water
quality
standards
and
for
promulgation
of
a
superseding
Federal
rule
in
cases
where
State
or
Tribal
standards
are
not
consistent
with
the
applicable
requirements
of
the
CWA
and
the
implementing
Federal
regulations,
or
where
the
Agency
determines
Federal
standards
are
necessary
to
meet
the
requirements
of
the
Act.
Section
303(
c)(
2)(
B)
specifically
requires
States
and
authorized
Tribes
to
adopt
water
quality
criteria
for
toxics
for
which
EPA
has
published
criteria
under
Section
304(
a)
and
for
which
the
discharge
or
presence
could
reasonably
be
expected
to
interfere
with
the
designated
use
adopted
by
the
State
or
Tribe.
In
adopting
such
criteria,
States
and
authorized
Tribes
must
establish
numerical
values
based
on
one
of
the
following:
(
1)
304(
a)
criteria;
(
2)
304(
a)
criteria
modified
to
reflect
site­
specific
conditions;
or,
(
3)
other
scientifically
defensible
methods.
In
addition,
States
and
authorized
Tribes
can
establish
narrative
criteria
where
numeric
criteria
cannot
be
determined.

It
must
be
recognized
that
the
Act
uses
the
term
"
criteria"
in
two
different
ways.
In
Section
303(
c),
the
term
is
part
of
the
definition
of
a
water
quality
standard.
Specifically,
a
water
quality
standard
is
composed
of
designated
uses
and
the
criteria
necessary
to
protect
those
uses.
Thus,
States
and
authorized
Tribes
are
required
to
adopt
regulations
which
contain
legally
enforceable
criteria.
However,
in
Section
304(
a)
the
term
criteria
is
used
to
describe
the
scientific
information
that
EPA
develops
to
be
used
as
guidance
by
States,
authorized
Tribes
and
EPA
when
establishing
water
quality
standards
pursuant
to
303(
c).
Thus,
two
distinct
purposes
are
served
by
the
304(
a)
criteria.
The
first
is
as
guidance
to
the
States
and
authorized
Tribes
in
the
development
and
adoption
of
water
quality
criteria
which
will
protect
designated
uses,
and
the
second
is
as
the
basis
for
promulgation
of
a
superseding
Federal
rule
when
such
action
is
necessary.

1.5
NEED
FOR
THE
AWQC
METHODOLOGY
REVISIONS
Since
1980,
EPA
risk
assessment
practices
have
evolved
significantly
in
all
of
the
major
Methodology
areas:
that
is,
cancer
and
noncancer
risk
assessments,
exposure
assessments,
and
bioaccumulation.
When
the
1980
Methodology
was
developed,
EPA
had
not
yet
developed
formal
cancer
or
noncancer
risk
assessment
guidelines.
Since
then,
EPA
has
published
several
risk
assessment
guidelines.
In
cancer
risk
assessment,
there
have
been
advances
in
the
use
of
mode
of
action
(
MOA)
information
to
support
both
the
identification
of
potential
human
carcinogens
and
the
selection
of
procedures
to
characterize
risk
at
low,
environmentally
relevant
exposure
levels.
EPA
published
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
1996a,
hereafter
the
"
1996
proposed
cancer
guidelines").
These
guidelines
presented
revised
1­
5
procedures
to
quantify
cancer
risk
at
low
doses,
replacing
the
current
default
use
of
the
LMS
model.
Following
review
by
the
Agency's
Science
Advisory
Board
(
SAB),
EPA
published
the
revised
Guidelines
for
Carcinogen
Risk
Assessment
 
Review
Draft
in
July
1999
(
USEPA,
1999a,
hereafter
the
"
1999
draft
revised
cancer
guidelines").
In
noncancer
risk
assessment,
the
Agency
is
moving
toward
the
use
of
the
benchmark
dose
(
BMD)
and
other
dose­
response
approaches
in
place
of
the
traditional
NOAEL
approach
to
estimate
an
RfD
or
Reference
Concentration
(
RfC).
Guidelines
for
Mutagenicity
Risk
Assessment
were
published
in
1986
(
USEPA,
1986b).
In
1991,
the
Agency
published
Guidelines
for
Developmental
Toxicity
Risk
Assessment
(
USEPA,
1991),
and
it
issued
Guidelines
for
Reproductive
Toxicity
Risk
Assessment
in
1996
(
USEPA,
1996b).
In
1998,
EPA
published
final
Guidelines
for
Neurotoxicity
Risk
Assessment
(
USEPA,
1998),
and
in
1999
it
issued
the
draft
Guidance
for
Conducting
Health
Risk
Assessment
of
Chemical
Mixtures
(
USEPA,
1999b).

In
1986,
the
Agency
made
available
to
the
public
the
Integrated
Risk
Information
System
(
IRIS).
IRIS
is
a
database
that
contains
risk
information
on
the
cancer
and
noncancer
effects
of
chemicals.
The
IRIS
assessments
are
peer
reviewed
and
represent
EPA
consensus
positions
across
the
Agency's
program
and
regional
offices.

New
studies
have
addressed
water
consumption
and
fish
tissue
consumption.
These
studies
provide
a
more
current
and
comprehensive
description
of
national,
regional,
and
specialpopulation
consumption
patterns
that
EPA
has
reflected
in
the
2000
Human
Health
Methodology.
In
addition,
more
formalized
procedures
are
now
available
to
account
for
human
exposure
from
multiple
sources
when
setting
health
goals
such
as
AWQC
that
address
only
one
exposure
source.
In
1986,
the
Agency
published
the
Total
Exposure
Assessment
Methodology
(
TEAM)
Study:
Summary
and
Analysis,
Volume
I,
Final
Report
(
USEPA,
1986c),
which
presents
a
process
for
conducting
comprehensive
evaluation
of
human
exposures.
In
1992,
EPA
published
the
revised
Guidelines
for
Exposure
Assessment
(
USEPA,
1992),
which
describe
general
concepts
of
exposure
assessment,
including
definitions
and
associated
units,
and
provide
guidance
on
planning
and
conducting
an
exposure
assessment.
The
Exposure
Factors
Handbook
was
updated
in
1997
(
USEPA,
1997a).
Also
in
1997,
EPA
developed
Guiding
Principles
for
Monte
Carlo
Analysis
(
USEPA,
1997b)
and
published
its
Policy
for
Use
of
Probabilistic
Analysis
in
Risk
Assessment
(
see
http://
www.
epa.
gov/
ncea/
mcpolicy.
htm).
The
Monte
Carlo
guidance
can
be
applied
to
exposure
assessments
and
risk
assessments.
The
Agency
has
recently
developed
the
Relative
Source
Contribution
(
RSC)
Policy
for
assessing
total
human
exposure
to
a
contaminant
and
apportioning
the
RfD
among
the
media
of
concern,
published
for
the
first
time
in
this
Methodology.

The
Agency
has
moved
toward
the
use
of
a
bioaccumulation
factor
(
BAF)
to
reflect
the
uptake
of
a
contaminant
from
all
sources
(
e.
g.,
ingestion,
sediment)
by
fish
and
shellfish,
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
6
Another
reason
for
the
2000
Human
Health
Methodology
is
the
need
to
bridge
the
gap
between
the
differences
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
CWA
and
Maximum
Contaminant
Level
Goals
(
MCLGs)
under
the
Safe
Drinking
Water
Act
(
SDWA).
Three
notable
differences
are
the
treatment
of
chemicals
designated
as
Group
C,
possible
human
carcinogens
under
the
1996
proposed
cancer
guidelines,
the
consideration
of
non­
water
sources
of
exposure
when
setting
an
AWQC
or
MCLG
for
a
noncarcinogen,
and
cancer
risk
ranges.
Those
three
differences
are
described
in
the
three
subsections
below,
respectively.

1.5.1
Group
C
Chemicals
Chemicals
were
typically
classified
as
Group
C
 
i.
e.,
possible
human
carcinogens
 
under
the
existing
(
1986)
EPA
cancer
classification
scheme
for
any
of
the
following
reasons:

1)
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."

2)
Tumor
response
is
of
marginal
statistical
significance
due
to
inadequate
design
or
reporting.

3)
Benign,
but
not
malignant,
tumors
occur
with
an
agent
showing
no
response
in
a
variety
of
short­
term
tests
for
mutagenicity.

4)
There
are
responses
of
marginal
statistical
significance
in
a
tissue
known
to
have
a
high
or
variable
background
rate.

The
1986
Guidelines
for
Carcinogen
Risk
Assessment
(
hereafter
the
"
1986
cancer
guidelines")
specifically
recognized
the
need
for
flexibility
with
respect
to
quantifying
the
risk
of
Group
C,
possible
human
carcinogens.
The
1986
cancer
guidelines
noted
that
agents
judged
to
be
in
Group
C,
possible
human
carcinogens,
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
(
USEPA,
1984)
and
finalized
in
1986
(
USEPA,
1986a).
The
1980
AWQC
National
Guidelines
did
not
explicitly
differentiate
among
agents
with
respect
to
the
weight
of
evidence
for
characterizing
them
as
likely
to
be
carcinogenic
to
humans.
For
all
pollutants
judged
as
having
adequate
data
for
quantifying
carcinogenic
risk
 
including
those
now
classified
as
Group
C
 
AWQC
were
derived
based
on
data
on
cancer
incidence.
In
the
1980
AWQC
National
Guidelines,
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
1­
7
published
for
specific
carcinogens
presented
water
concentrations
for
these
pollutants
corresponding
to
individual
lifetime
excess
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
agents,
the
MCLG
was
usually
obtained
using
an
RfD
based
on
the
pollutant's
noncancer
effects
with
the
application
of
an
additional
uncertainty
factor
of
1
to
10
to
account
for
carcinogenic
potential
of
the
chemical.
If
valid
noncancer
data
for
a
Group
C
agent
were
not
available
to
establish
an
RfD
but
adequate
data
are
available
to
quantify
the
cancer
risk,
then
the
MCLG
was
based
upon
a
nominal
lifetime
excess
cancer
risk
in
the
range
of
10­
6
to10­
5
(
ranging
from
one
case
in
a
population
of
one
million
to
one
case
in
a
population
of
one
hundred
thousand).
Even
in
those
cases
where
the
RfD
approach
has
been
used
for
the
derivation
of
the
MCLG
for
a
Group
C
agent,
the
drinking
water
concentrations
associated
with
excess
cancer
risks
in
the
range
of
10­
6
to
10­
5
were
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
caseby
case
basis.
Unlike
the
drinking
water
program,
however,
the
pesticides
program
does
not
add
an
extra
uncertainty
factor
to
account
for
potential
carcinogenicity
when
using
the
RfD
approach.

In
the
1999
draft
revised
cancer
guidelines,
there
are
no
more
alphanumeric
categories.
Instead,
there
will
be
longer
narratives
for
hazard
characterization
that
will
use
consistent
descriptive
terms
when
assessing
cancer
risk.

1.5.2
Consideration
of
Non­
water
Sources
of
Exposure
The
1980
AWQC
National
Guidelines
recommended
that
contributions
from
non­
water
sources,
namely
air
and
non­
fish
dietary
intake,
be
subtracted
from
the
Acceptable
Daily
Intake
(
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
used
in
the
derivation
of
MCLGs
proposed
and
promulgated
in
drinking
water
regulations
through
the
mid­
1980s.
More
recently,
the
drinking
water
program
has
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
"
apportioned"
to
drinking
water
reflected
by
the
MCLG
value.
In
using
this
percentage
procedure,
the
drinking
water
program
1­
8
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
to
public
health
by
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.

In
the
2000
Human
Health
Methodology,
guidance
for
the
routine
consideration
of
nonwater
sources
of
exposure
[
both
ingestion
exposures
(
e.
g.,
food)
and
exposures
other
than
the
oral
route
(
e.
g.,
inhalation)]
is
presented.
The
approach
is
called
the
Exposure
Decision
Tree.
Relative
source
contribution
estimates
will
be
made
by
EPA
using
this
approach,
which
allows
for
use
of
either
the
subtraction
or
percentage
methods,
depending
on
chemical­
specific
circumstances,
within
the
20
to
80
percent
range
described
above.

1.5.3
Cancer
Risk
Ranges
In
addition
to
the
different
risk
assessment
approaches
discussed
above
for
deriving
AWQC
and
MCLGs
for
Group
C
agents,
there
have
been
different
risk
management
approaches
by
the
drinking
water
and
surface
water
programs
on
lifetime
excess
risk
values
when
setting
health­
based
criteria
for
carcinogens.
The
surface
water
program
has
derived
AWQC
for
carcinogens
that
generally
corresponded
to
lifetime
excess
cancer
risk
levels
of
10­
7
to
10­
5.
The
drinking
water
program
has
set
MCLGs
for
Group
C
agents
based
on
a
slightly
less
stringent
risk
range
of
10­
6
to
10­
5,
while
MCLGs
for
chemicals
with
strong
evidence
of
carcinogenicity
(
that
is,
classified
as
Group
A,
known,
or
B
probable,
human
carcinogen)
are
set
at
zero.
The
drinking
water
program
is
now
following
the
principles
of
the
1999
draft
revised
cancer
guidelines
to
determine
the
type
of
low­
dose
extrapolation
based
on
mode
of
action.

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.

With
the
2000
Human
Health
Methodology,
EPA
will
publish
its
national
304(
a)
water
quality
criteria
at
a
10­
6
risk
level,
which
EPA
considers
appropriate
for
the
general
population.
EPA
is
increasing
the
degree
of
consistency
between
the
drinking
water
and
ambient
water
programs,
given
the
somewhat
different
requirements
of
the
CWA
and
SDWA.
2
Although
appearing
in
this
equation
as
a
factor
to
be
multiplied,
the
RSC
can
also
be
an
amount
subtracted.
Refer
to
the
explanation
key
below
the
equations.

1­
9
(
Equation
1­
1)

(
Equation
1­
2)
1.6
OVERVIEW
OF
THE
AWQC
METHODOLOGY
REVISIONS
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,
values
for
parameters
such
as
a
field­
measured
BAF
or
a
point
of
departure
from
an
animal
study
[
in
the
form
of
a
lowest­
observed­
adverse­
effect
level
(
LOAEL)/
no­
observed
­
adverse­
effect
level
(
NOAEL)/
lower
95
percent
confidence
limit
on
a
dose
associated
with
a
10
percent
extra
risk
(
LED
10)]
are
empirically
measured
using
scientific
methods.
By
contrast,
the
decision
to
use
animal
effects
as
surrogates
for
human
effects
involves
judgment
on
the
part
of
the
EPA
(
and
similarly,
by
other
agencies)
as
to
the
best
practice
to
follow
when
human
data
are
lacking.
Such
a
decision
is,
therefore,
a
matter
of
science
policy.
The
choice
of
default
fish
consumption
rates
for
protection
of
a
certain
percentage
(
i.
e.,
the
90th
percentile)
of
the
general
population
is
clearly
a
risk
management
decision.
In
many
cases,
the
Agency
has
selected
parameter
values
using
its
best
judgment
regarding
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
Section
2
of
this
document.
Section
2
also
provides
further
details
with
regard
to
risk
characterization
for
this
Methodology,
with
emphasis
placed
on
explaining
the
uncertainties
in
the
overall
risk
assessment.

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

Noncancer
Effects2
Cancer
Effects:
Nonlinear
Low­
Dose
Extrapolation
1­
10
(
Equation
1­
3)
Cancer
Effects:
Linear
Low­
Dose
Extrapolation
where:

AWQC
=
Ambient
Water
Quality
Criterion
(
mg/
L)
RfD
=
Reference
dose
for
noncancer
effects
(
mg/
kg­
day)
POD
=
Point
of
departure
for
carcinogens
based
on
a
nonlinear
low­
dose
extrapolation
(
mg/
kg­
day),
usually
a
LOAEL,
NOAEL,
or
LED
10
UF
=
Uncertainty
Factor
for
carcinogens
based
on
a
nonlinear
low­
dose
extrapolation
(
unitless)
RSD
=
Risk­
specific
dose
for
carcinogens
based
on
a
linear
low­
dose
extrapolation
(
mg/
kg­
day)
(
dose
associated
with
a
target
risk,
such
as
10­
6)
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
(
default
=
70
kg
for
adults)
DI
=
Drinking
water
intake
(
default
=
2
L/
day
for
adults)
FI
i
=
Fish
intake
at
trophic
level
(
TL)
I
(
I
=
2,
3,
and
4)
(
defaults
for
total
intake
=
0.0175
kg/
day
for
general
adult
population
and
sport
anglers,
and
0.1424
kg/
day
for
subsistence
fishers).
Trophic
level
breakouts
for
the
general
adult
population
and
sport
anglers
are:
TL2
=
0.0038
kg/
day;
TL3
=
0.0080
kg/
day;
and
TL4
=
0.0057
kg/
day.
BAF
i
=
Bioaccumulation
factor
at
trophic
level
I
(
I=
2,
3
and
4),
lipid
normalized
(
L/
kg)

For
highly
bioaccumulative
chemicals
where
ingestion
from
water
might
be
considered
negligible,
EPA
is
currently
evaluating
the
feasibility
of
developing
and
implementing
AWQCs
that
are
expressed
in
terms
of
concentrations
in
tissues
of
aquatic
organisms.
Such
tissue
residue
criteria
might
be
used
as
an
alternative
to
AWQCs
which
are
expressed
as
concentrations
in
water,
particularly
in
situations
where
AWQCs
are
at
or
below
the
practical
limits
for
quantifying
a
chemical
in
water.
Even
though
tissue
residue
criteria
would
not
require
the
use
of
a
BAF
in
their
derivation,
implementing
such
criteria
would
still
require
a
mechanism
for
relating
chemical
loads
and
concentrations
in
water
and
sediment
to
concentrations
in
tissues
of
appropriate
fish
and
shellfish
(
e.
g.,
a
BAF
or
bioaccumulation
model).
At
this
time,
no
revisions
are
planned
to
the
Methodology
to
provide
specific
guidance
on
developing
fish
tissue­
based
water
quality
criteria.
1­
11
However,
guidance
may
be
provided
in
the
future
either
as
a
separate
document
or
integrated
in
a
specific
304(
a)
water
quality
criteria
document
for
a
chemical
that
warrants
such
an
approach.

AWQC
for
the
protection
of
human
health
are
designed
to
minimize
the
risk
of
adverse
effects
occurring
to
humans
from
chronic
(
lifetime)
exposure
to
substances
through
the
ingestion
of
drinking
water
and
consumption
of
fish
obtained
from
surface
waters.
The
Agency
is
not
recommending
the
development
of
additional
water
quality
criteria
similar
to
the
"
drinking
water
health
advisories"
that
focus
on
acute
or
short­
term
effects;
these
are
not
seen
as
routinely
having
a
meaningful
role
in
the
water
quality
criteria
and
standards
program.
However,
as
discussed
below,
there
may
be
some
instances
where
the
consideration
of
acute
or
short­
term
toxicity
and
exposure
in
the
derivation
of
AWQC
is
warranted.

Although
the
AWQC
are
based
on
chronic
health
effects
data
(
both
cancer
and
noncancer
effects),
the
criteria
are
intended
to
also
be
protective
against
adverse
effects
that
may
reasonably
be
expected
to
occur
as
a
result
of
elevated
acute
or
short­
term
exposures.
That
is,
through
the
use
of
conservative
assumptions
with
respect
to
both
toxicity
and
exposure
parameters,
the
resulting
AWQC
should
provide
adequate
protection
not
only
for
the
general
population
over
a
lifetime
of
exposure,
but
also
for
special
subpopulations
who,
because
of
high
water­
or
fishintake
rates,
or
because
of
biological
sensitivities,
have
an
increased
risk
of
receiving
a
dose
that
would
elicit
adverse
effects.
The
Agency
recognizes
that
there
may
be
some
cases
where
the
AWQC
based
on
chronic
toxicity
may
not
provide
adequate
protection
for
a
subpopulation
at
special
risk
from
shorter­
term
exposures.
The
Agency
encourages
States,
Tribes,
and
others
employing
the
2000
Human
Health
Methodology
to
give
consideration
to
such
circumstances
in
deriving
criteria
to
ensure
that
adequate
protection
is
afforded
to
all
identifiable
subpopulations.
(
See
Section
4.3,
Factors
Used
in
the
AWQC
Computation,
for
additional
discussion
of
these
subpopulations.)

The
EPA
is
in
the
process
of
revising
its
cancer
guidelines,
including
its
descriptions
of
human
carcinogenic
potential.
Once
final
guidelines
are
published,
they
will
be
the
basis
for
assessment
under
this
methodology.
In
the
meanwhile,
the
1986
guidelines
are
used
and
extended
with
principles
discussed
in
EPA's
1999
Guidelines
for
Carcinogen
Risk
Assessment
­
Review
Draft
(
hereafter
"
1999
draft
revised
cancer
guidelines").
These
principles
arise
from
new
science
about
cancer
discovered
in
the
last
15
years
and
from
EPA
policy
of
recent
years
supporting
full
characterization
of
hazard
and
risk
both
for
the
general
population
and
potentially
sensitive
groups
such
as
children.
These
principles
are
incorporated
in
recent
and
ongoing
assessments
such
as
the
reassessment
of
dioxin,
consistent
with
the
1986
guidelines.
Until
final
guidelines
are
published,
information
is
presented
to
describe
risk
under
both
the
old
guidelines
and
draft
revisions.
Dose­
response
assessment
under
the
1986
guidelines
employs
a
linearized
multistage
model
to
extrapolate
tumor
dose­
response
observed
in
animal
or
human
studies
down
to
zero
dose,
zero
extra
risk.
The
dose­
response
assessment
under
EPA's
1999
draft
revised
cancer
guidelines
is
a
two­
step
process.
In
the
first
step,
the
response
data
are
modeled
in
the
range
of
empirical
observation.
Modeling
in
the
observed
range
is
done
with
biologically
based
or
appropriate
curve­
fitting
modeling.
In
the
second
step,
extrapolation
below
the
range
of
observation
is
accomplished
by
biologically
based
modeling
if
there
are
sufficient
data
or
by
a
1­
12
default
procedure
(
linear,
nonlinear,
or
both).
A
point
of
departure
(
POD)
for
extrapolation
is
estimated
from
modeling
observed
data.
The
lower
95
percent
confidence
limit
on
a
dose
associated
with
10
percent
extra
risk
(
LED
10)
is
the
standard
POD
for
low­
dose
extrapolation.
The
linear
default
procedure
is
a
straight
line
extrapolation
to
the
origin
(
i.
e.,
zero
dose,
zero
extra
risk)
from
the
POD,
which
is
the
LED
10
identified
in
the
observable
response
range.
The
result
of
this
procedure
is
generally
comparable
(
within
2­
fold)
to
that
of
using
a
linearized
multistage
model
under
existing,
1986
guidelines.
The
linear
low­
dose
extrapolation
applies
to
agents
that
are
best
characterized
by
the
assumption
of
linearity
(
e.
g.,
direct
DNA
reactive
mutagens)
for
their
MOA.
A
linear
approach
would
also
be
applied
when
inadequate
or
no
information
is
available
to
explain
the
carcinogenic
MOA;
this
is
a
science
policy
choice
in
the
interest
of
public
health.
If
it
is
determined
that
the
MOA
understanding
fully
supports
a
nonlinear
extrapolation,
the
AWQC
is
derived
using
the
nonlinear
default
which
is
based
on
a
margin
of
exposure
(
MOE)
analysis
using
the
LED
10
as
the
POD
and
applying
uncertainty
factors
(
UFs)
to
arrive
at
an
acceptable
MOE.
There
may
be
situations
where
it
is
appropriate
to
apply
both
the
linear
and
nonlinear
default
procedures
(
e.
g.,
for
an
agent
that
is
both
DNA
reactive
and
active
as
a
promoter
at
higher
doses).

For
substances
that
are
carcinogenic,
particularly
those
for
which
the
MOA
suggests
nonlinearity
at
low
doses,
the
Agency
recommends
that
an
integrated
approach
be
taken
in
looking
at
cancer
and
noncancer
effects.
If
one
effect
does
not
predominate,
AWQC
values
should
be
determined
for
both
carcinogenic
and
noncarcinogenic
endpoints.
The
lower
of
the
resulting
values
should
be
used
for
the
AWQC.

When
deriving
AWQC
for
noncarcinogens
and
carcinogens
based
on
a
nonlinear
low­
dose
extrapolation,
a
factor
is
included
to
account
for
other
non­
water
exposure
sources
[
both
ingestion
exposures
(
e.
g.,
food)
and
exposures
other
than
the
oral
route
(
e.
g.,
inhalation)]
so
that
the
entire
RfD,
or
POD/
UF,
is
not
apportioned
to
drinking
water
and
fish
consumption
alone.
Guidance
is
provided
in
the
2000
Human
Health
Methodology
for
determining
the
factor
(
i.
e.,
the
RSC)
to
be
used
for
a
particular
chemical.
The
Agency
is
recommending
the
use
of
an
Exposure
Decision
Tree
procedure
to
support
the
determination
of
the
appropriate
RSC
value
for
a
given
water
contaminant.
In
the
absence
of
data,
the
Agency
intends
to
use
20
percent
of
the
RfD
(
or
POD/
UF)
as
the
default
RSC
in
calculating
304(
a)
criteria
or
promulgating
State
or
Tribal
water
quality
standards
under
Section
303(
c).

With
AWQC
derived
for
carcinogens
based
on
a
linear
low­
dose
extrapolation,
the
Agency
will
publish
recommended
criteria
values
at
a
10­
6
risk
level.
States
and
authorized
Tribes
can
always
choose
a
more
stringent
risk
level,
such
as
10­
7.
EPA
also
believes
that
criteria
based
on
a
10­
5
risk
level
are
acceptable
for
the
general
population
as
long
as
States
and
authorized
Tribes
ensure
that
the
risk
to
more
highly
exposed
subgroups
(
sportfishers
or
subsistence
fishers)
does
not
exceed
the
10­
4
level.
Clarification
on
this
risk
management
decision
is
provided
in
Section
2
of
this
document.

The
default
fish
consumption
value
for
the
general
adult
population
in
the
2000
Human
Health
Methodology
is
17.5
grams/
day,
which
represents
an
estimate
of
the
90th
percentile
1­
13
consumption
rate
for
the
U.
S.
adult
population
based
on
the
U.
S.
Department
of
Agriculture's
(
USDA's)
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII)
1994­
96
data
(
USDA,
1998).
EPA
will
use
this
default
intake
rate
with
future
national
304(
a)
criteria
derivations
or
revisions.
This
default
value
is
chosen
to
be
protective
of
the
majority
of
the
general
population.
However,
States
and
authorized
Tribes
are
urged
to
use
a
fish
intake
level
derived
from
local
data
on
fish
consumption
in
place
of
this
default
value
when
deriving
AWQC,
ensuring
that
the
fish
intake
level
chosen
is
protective
of
highly
exposed
individuals
in
the
population.
EPA
has
provided
default
values
for
States
and
authorized
Tribes
that
do
not
have
adequate
information
on
local
or
regional
consumption
patterns,
based
on
numerous
studies
that
EPA
has
reviewed
on
sport
anglers
and
subsistence
fishers.
EPA's
defaults
for
these
population
groups
are
estimates
of
their
average
consumption.
EPA
recommends
a
default
of
17.5
grams/
day
for
sport
anglers
as
an
approximation
of
their
average
consumption
and
142.4
grams/
day
for
subsistence
fishers,
which
falls
within
the
range
of
averages
for
this
group.
Consumption
rates
for
women
of
childbearing
age
and
children
younger
than
14
are
also
provided
to
maximize
protection
in
those
cases
where
these
subpopulations
may
be
at
greatest
risk.

In
the
2000
Human
Health
Methodology,
criteria
are
derived
using
a
BAF
rather
than
a
BCF.
To
derive
the
BAF,
States
and
authorized
Tribes
may
use
EPA's
Methodology
or
any
method
consistent
with
this
Methodology.
EPA's
highest
preference
in
developing
BAFs
are
BAFs
based
on
field­
measured
data
from
local/
regional
fish.

1.7
REFERENCES
NRC
(
National
Research
Council).
1977.
Drinking
Water
and
Health.
Safe
Drinking
Water
Committee.
National
Academy
of
Sciences,
National
Academy
Press.
Washington,
DC.

USDA.
1998.
U.
S.
Department
of
Agriculture.
1994
 
1996
Continuing
Survey
of
Food
Intakes
by
Individuals
and
1994
 
1996
Diet
and
Health
Knowledge
Survey.
Agricultural
Research
Service,
USDA.
NTIS
CD
 
ROM,
accession
number
PB98
 
500457.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1980.
Guidelines
and
methodology
used
in
the
preparation
of
health
effect
assessment
chapters
of
the
consent
decree
water
criteria
documents.
Federal
Register
45:
79347,
Appendix
3.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1984.
Proposed
guidelines
for
carcinogen
risk
assessment.
Federal
Register
49:
46294.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986a.
Guidelines
for
carcinogen
risk
assessment.
Federal
Register
51:
33992­
34003.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986b.
Guidelines
for
mutagenicity
risk
assessment.
Federal
Register
51:
34006­
34012.
1­
14
USEPA
(
U.
S.
Environmental
Protection
Agency).
1986c.
Total
Exposure
Assessment
Model
(
TEAM)
Study:
Summary
and
Analysis,
Volume
I.
Final
Report.
EPA/
600/
6­
87/
002a.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986d.
Guidelines
for
exposure
assessment.
Federal
Register
51:
34042­
34054.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1991.
Guidelines
for
developmental
toxicity
risk
assessment.
Federal
Register
56:
63789­
63826.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1992.
Guidelines
for
exposure
assessment.
Federal
Register
57:
22888­
22938.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996a.
Proposed
guidelines
for
carcinogen
risk
assessment.
Federal
Register
61:
17960­
18011.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996b.
Guidelines
for
reproductive
toxicity
risk
assessment.
Federal
Register
61:
6274­
56322.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1997a.
Exposure
Factors
Handbook.
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
P­
95/
002Fa..

USEPA
(
U.
S.
Environmental
Protection
Agency).
1997b.
Guiding
Principles
for
Monte
Carlo
Analysis.
Risk
Assessment
Forum.
Washington,
DC.
EPA/
630/
R­
97/
001.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1998.
Guidelines
for
neurotoxicity
risk
assessment.
Federal
Register
63:
26926.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999a.
1999
Guidelines
for
Carcinogen
Risk
Assessment.
Review
Draft.
Office
of
Research
and
Development.
Washington,
DC.
NCEA­
F­
0644.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999b.
Guidance
for
Conducting
Health
Risk
Assessment
of
Chemical
Mixtures.
Final
Draft.
Risk
Assessment
Forum
Technical
Panel.
Washington,
DC.
EPA/
NCEA­
C­
0148.
September.
Website:
http://
www.
epa.
gov/
ncea/
raf/
rafpub.
htm
USEPA
(
U.
S.
Environmental
Protection
Agency).
2000.
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000).
Technical
Support
Document
Volume
1:
Risk
Assessment.
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
EPA­
822­
B­
00­
005.
August.
2­
1
2.
CLARIFICATIONS
ON
THE
METHODOLOGY,
RISK
CHARACTERIZATION,
AND
OTHER
ISSUES
FOR
DEVELOPING
CRITERIA
2.1
IDENTIFYING
THE
POPULATION
SUBGROUP
THAT
THE
AWQC
SHOULD
PROTECT
Water
quality
criteria
are
derived
to
establish
ambient
concentrations
of
pollutants
which,
if
not
exceeded,
will
protect
the
general
population
from
adverse
health
impacts
from
those
pollutants
due
to
consumption
of
aquatic
organisms
and
water,
including
incidental
water
consumption
related
to
recreational
activities.
For
each
pollutant,
chronic
criteria
are
derived
to
reflect
long­
term
consumption
of
food
and
water.
An
important
decision
to
make
when
setting
AWQC
is
the
choice
of
the
particular
population
to
protect.
For
instance,
criteria
could
be
set
to
protect
those
individuals
who
have
average
or
"
typical"
exposures,
or
the
criteria
could
be
set
so
that
they
offer
greater
protection
to
those
individuals
who
are
more
highly
exposed.
EPA
has
selected
default
parameter
values
that
are
representative
of
several
defined
populations:
adults
in
the
general
population;
sport
(
recreational)
fishers;
subsistence
fishers;
women
of
childbearing
age
(
defined
as
ages
15­
44);
and
children
(
up
to
the
age
of
14).
In
deciding
on
default
parameter
values,
EPA
is
aware
that
multiple
parameters
are
used
in
combination
when
calculating
AWQC
(
e.
g.,
intake
rates
and
body
weight).
EPA
describes
the
estimated
population
percentiles
that
are
represented
by
each
of
the
default
exposure
parameter
values
in
Section
4.

EPA's
national
304(
a)
criteria
are
usually
derived
to
protect
the
majority
of
the
general
population
from
chronic
adverse
health
effects.
EPA
has
used
a
combination
of
median
values,
mean
values,
and
percentile
estimates
for
the
parameter
value
defaults
to
calculate
its
national
304(
a)
criteria.
EPA
believes
that
its
assumptions
afford
an
overall
level
of
protection
targeted
at
the
high
end
of
the
general
population
(
i.
e.,
the
target
population
or
the
criteria­
basis
population).
EPA
also
believes
that
this
is
reasonably
conservative
and
appropriate
to
meet
the
goals
of
the
CWA
and
the
304(
a)
criteria
program.
EPA
considers
that
its
target
protection
goal
is
satisfied
if
the
population
as
a
whole
will
be
adequately
protected
by
the
human
health
criteria
when
the
criteria
are
met
in
ambient
water.
However,
associating
the
derived
criteria
with
a
specific
population
percentile
is
far
more
difficult,
and
such
a
quantitative
descriptor
typically
requires
detailed
distributional
exposure
and
dose
information.
EPA's
Guidelines
For
Exposure
Assessment
(
USEPA,
1992)
describes
the
extreme
difficulty
in
making
accurate
estimates
of
exposures
and
indicates
that
uncertainties
at
the
more
extreme
ends
of
the
distribution
increase
greatly.
On
quantifying
population
exposures/
risks,
the
guidelines
specifically
state:

In
practice,
it
is
difficult
even
to
establish
an
accurate
mean
health
effect
risk
for
a
population.
This
is
due
to
many
complications,
including
uncertainties
in
using
animal
data
for
human
dose­
response
relationships,
nonlinearities
in
the
doseresponse
curve,
projecting
incidence
data
from
one
group
to
another
dissimilar
group,
etc.
Although
it
has
been
common
practice
to
estimate
the
number
of
cases
of
disease,
especially
cancer,
for
populations
exposed
to
chemicals,
it
should
be
understood
that
these
estimates
are
not
meant
to
be
accurate
estimates
of
real
(
or
actuarial)
cases
of
disease.
The
estimate's
value
lies
in
framing
2­
2
hypothetical
risk
in
an
understandable
way
rather
than
in
any
literal
interpretation
of
the
term
"
cases."

Although
it
is
not
possible
to
subject
the
estimates
to
such
a
rigorous
analysis
(
say,
for
example,
to
determine
what
criterion
value
provides
protection
of
exactly
the
90th
percentile
of
the
population),
EPA
believes
that
the
combination
of
parameter
value
assumptions
achieves
its
target
goal,
without
being
inordinately
conservative.
The
standard
assumptions
made
for
the
national
304(
a)
criteria
are
as
follows.
The
assumed
body
weight
value
used
is
an
arithmetic
mean,
as
are
the
RSC
intake
estimates
of
other
exposures
(
e.
g.,
non­
fish
dietary),
when
data
are
available.
The
BAF
component
data
(
e.
g.,
for
lipid
values,
for
particulate
and
dissolved
organic
carbon)
are
based
on
median
(
i.
e.,
50th
percentile)
values.
The
drinking
water
intake
values
are
approximately
90th
percentile
estimates
and
fish
intake
values
are
90th
percentile
estimates.
EPA
believes
the
use
of
these
values
will
result
in
304(
a)
criteria
that
are
protective
of
a
majority
of
the
population;
this
is
EPA's
goal.

However,
EPA
also
strongly
believes
that
States
and
authorized
Tribes
should
have
the
flexibility
to
develop
criteria,
on
a
site­
specific
basis,
that
provide
additional
protection
appropriate
for
highly
exposed
populations.
EPA
is
aware
that
exposure
patterns
in
general,
and
fish
consumption
in
particular,
vary
substantially.
EPA
understands
that
highly
exposed
populations
may
be
widely
distributed
geographically
throughout
a
given
State
or
Tribal
area.
EPA
recommends
that
priority
be
given
to
identifying
and
adequately
protecting
the
most
highly
exposed
population.
Thus,
if
the
State
or
Tribe
determines
that
a
highly
exposed
population
is
at
greater
risk
and
would
not
be
adequately
protected
by
criteria
based
on
the
general
population,
and
by
the
national
304(
a)
criteria
in
particular,
EPA
recommends
that
the
State
or
Tribe
adopt
more
stringent
criteria
using
alternative
exposure
assumptions.

EPA
has
provided
recommended
default
intake
rates
for
various
population
groups
for
State
and
Tribal
consideration.
EPA
does
not
intend
for
these
alternative
default
values
to
be
prescriptive.
EPA
strongly
emphasizes
its
preference
that
States
and
Tribes
use
local
or
regional
data
over
EPA's
defaults,
if
they
so
choose,
as
being
more
representative
of
their
population
groups
of
concern.

In
the
course
of
updating
the
2000
Human
Health
Methodology,
EPA
received
some
questions
regarding
the
population
groups
for
which
the
criteria
would
be
developed.
EPA
does
not
intend
to
derive
multiple
304(
a)
criteria
for
all
subpopulation
groups
for
every
chemical.
As
stated
above,
criteria
that
address
chronic
adverse
health
effects
are
most
applicable
to
the
CWA
Section
304(
a)
criteria
program
and
the
chemicals
evaluated
for
this
program.
If
EPA
determined
that
pregnant
women/
fetuses
or
young
children
were
the
target
population
(
or
criteria
basis
population)
of
a
chemical's
RfD
or
POD/
UF,
then
the
304(
a)
criteria
would
be
developed
using
exposure
parameters
for
that
subgroup.
This
would
only
be
relevant
for
acute
or
subchronic
toxicity
situations.
This
does
not
conflict
with
the
fact
that
chronic
health
effects
potentially
reflect
a
person's
exposure
during
both
childhood
and
adult
years.
2­
3
For
RfD­
based
and
POD/
UF­
based
chemicals,
EPA's
policy
is
that,
in
general,
the
RfD
(
or
POD/
UF)
should
not
be
exceeded
and
the
exposure
assumptions
used
should
reflect
the
population
of
concern.
It
is
recommended
that
when
a
State
or
authorized
Tribe
sets
a
waterbody­
specific
AWQC,
they
consider
the
populations
most
exposed
via
water
and
fish.
EPA's
policy
on
cancer
risk
management
goals
is
discussed
in
Section
2.4.

Health
Risks
to
Children
In
recognition
that
children
have
a
special
vulnerability
to
many
toxic
substances,
EPA's
Administrator
directed
the
Agency
in
1995
to
explicitly
and
consistently
take
into
account
environmental
health
risks
to
infants
and
children
in
all
risk
assessments,
risk
characterizations,
and
public
health
standards
set
for
the
United
States.
In
April
1997,
President
Clinton
signed
Executive
Order
13045
on
the
protection
of
children
from
environmental
health
risks,
which
assigned
a
high
priority
to
addressing
risks
to
children.
In
May
1997,
EPA
established
the
Office
of
Children's
Health
Protection
to
ensure
the
implementation
of
the
President's
Executive
Order.
EPA
has
increased
efforts
to
ensure
its
guidance
and
regulations
take
into
account
risks
to
children.
Circumstances
where
risks
to
children
should
be
considered
in
the
context
of
the
2000
Human
Health
Methodology
are
discussed
in
the
Section
3.2,
Noncancer
Effects
(
in
terms
of
developmental
and
reproductive
toxicity)
and
in
Section
4,
Exposure
(
for
appropriate
exposure
intake
parameters).

Details
on
risk
characterization
and
the
guiding
principles
stated
above
are
included
in
EPA's
March
21,
1995
policy
statement
and
the
discussion
of
risk
characterization
(
USEPA,
1995)
and
the
1999
Guidelines
for
Carcinogen
Risk
Assessment.
Review
Draft
(
USEPA,
1999a)
and
the
Reproductive
and
Toxicity
Risk
Assessment
Guidelines
of
1996
(
USEPA,
1996b).

2.2
SCIENCE,
SCIENCE
POLICY,
AND
RISK
MANAGEMENT
An
important
part
of
risk
characterization,
as
described
later
in
Section
2.7,
is
to
make
risk
assessments
transparent.
This
means
that
conclusions
drawn
from
the
science
are
identified
separately
from
policy
judgments
and
risk
management
decisions,
and
that
the
use
of
default
values
or
methods,
as
well
as
the
use
of
assumptions
in
risk
assessments,
are
clearly
articulated.
In
this
Methodology,
EPA
has
attempted
to
separate
scientific
analysis
from
science
policy
and
risk
management
decisions
for
clarity.
This
should
allow
States
and
Tribes
(
who
are
also
prospective
users
of
this
Methodology)
to
understand
the
elements
of
the
Methodology
accurately
and
clearly,
and
to
easily
separate
out
the
scientific
decisions
from
the
science
policy
and
risk
management
decisions.
This
is
important
so
that
when
questions
are
asked
regarding
the
scientific
merit,
validity,
or
apparent
stringency
or
leniency
of
AWQC,
the
implementer
of
the
criteria
can
clearly
explain
what
judgments
were
made
to
develop
the
criterion
in
question
and
to
what
degree
these
judgments
were
based
on
science,
science
policy,
or
risk
management.
To
some
extent
this
process
will
also
be
displayed
in
future
AWQC
documents.

When
EPA
speaks
of
science
or
scientific
analysis,
it
is
referring
to
the
extraction
of
data
from
toxicological
or
exposure
studies
and
surveys
with
a
minimum
of
judgment
being
used
to
2­
4
make
inferences
from
the
available
evidence.
For
example,
if
EPA
is
describing
a
POD
from
an
animal
study
(
e.
g.,
a
LOAEL),
this
is
usually
determined
as
a
lowest
dose
that
produces
an
observable
adverse
effect.
This
would
constitute
a
scientific
determination.
Judgments
applying
science
policy,
however,
may
enter
this
determination.
For
example,
several
scientists
may
differ
in
their
opinion
of
what
is
adverse,
and
this
in
turn
can
influence
the
selection
of
a
LOAEL
in
a
given
study.
The
use
of
an
animal
study
to
predict
effects
in
a
human
in
the
absence
of
human
data
is
an
inherent
science
policy
decision.
The
selection
of
specific
UFs
when
developing
an
RfD
is
another
example
of
science
policy.
In
any
risk
assessment,
a
number
of
decision
points
occur
where
risk
to
humans
can
only
be
inferred
from
the
available
evidence.
Both
scientific
judgments
and
policy
choices
may
be
involved
in
selecting
from
among
several
possible
inferences
when
conducting
a
risk
assessment.

Risk
management
is
the
process
of
selecting
the
most
appropriate
guidance
or
regulatory
actions
by
integrating
the
results
of
risk
assessment
with
engineering
data
and
with
social,
economic,
and
political
concerns
to
reach
a
decision.
In
this
Methodology,
the
choice
of
a
default
fish
consumption
rate
which
is
protective
of
90
percent
of
the
general
population
is
a
risk
management
decision.
The
choice
of
an
acceptable
cancer
risk
by
a
State
or
Tribe
is
a
risk
management
decision.

Many
of
the
components
in
the
2000
Human
Health
Methodology
are
an
amalgam
of
science,
science
policy,
and/
or
risk
management.
For
example,
most
of
the
default
values
chosen
by
EPA
are
based
on
examination
of
scientific
data
and
application
of
either
science
policy
or
risk
management.
This
includes
the
default
assumption
of
2
liters
a
day
of
drinking
water;
the
assumption
of
70
kilograms
for
an
adult
body
weight;
the
use
of
default
percent
lipid
and
particulate
organic
carbon/
dissolved
organic
carbon
(
POC/
DOC)
for
developing
national
BAFs;
the
default
fish
consumption
rates
for
the
general
population
and
sport
and
subsistence
anglers;
and
the
choice
of
a
default
cancer
risk
level.
Some
decisions
are
more
grounded
in
science
and
science
policy
(
such
as
the
choice
of
default
BAFs)
and
others
are
more
obviously
risk
management
decisions
(
such
as
the
determination
of
default
fish
consumption
rates
and
cancer
risk
levels).
Throughout
the
2000
Human
Health
Methodology,
EPA
has
identified
the
kind
of
decision
necessary
to
develop
defaults
and
what
the
basis
for
the
decision
was.
More
details
on
the
concepts
of
science
analysis,
science
policy,
risk
management,
and
how
they
are
introduced
into
risk
assessments
are
included
in
Risk
Assessment
in
the
Federal
Government:
Managing
the
Process
(
NRC,
1983).

2.3
SETTING
CRITERIA
TO
PROTECT
AGAINST
MULTIPLE
EXPOSURES
FROM
MULTIPLE
CHEMICALS
(
CUMULATIVE
RISK)

EPA
is
very
much
aware
of
the
complex
issues
and
implications
of
cumulative
risk
and
has
endeavored
to
begin
developing
an
overall
approach
at
the
Agency­
wide
level.
Assuming
that
multiple
exposures
to
multiple
chemicals
are
additive
is
scientifically
sound
if
they
exhibit
the
same
toxic
endpoints
and
modes
of
action.
There
are
numerous
publications
relevant
to
cumulative
risk
that
can
assist
States
and
Tribes
in
understanding
the
complex
issues
associated
with
cumulative
risk.
These
include
the
following:
2­
5
<
Durkin,
P.
R.,
R.
C.
Hertzberg,
W.
Stiteler,
and
M.
Mumtaz.
1995.
The
identification
and
testing
of
interaction
patterns.
Toxicol.
Letters
79:
251­
264.

<
Hertzberg,
R.
C.,
G.
Rice,
and
L.
K.
Teuschler.
1999.
Methods
for
health
risk
assessment
of
combustion
mixtures.
In:
Hazardous
Waste
Incineration:
Evaluating
the
Human
Health
and
Environmental
Risks.
S.
Roberts,
C.
Teaf
and
J.
Bean,
(
eds).
CRC
Press
LLC,
Boca
Raton,
FL.
Pp.
105­
148.

<
Rice,
G.,
J.
Swartout,
E.
Brady­
Roberts,
D.
Reisman,
K.
Mahaffey,
and
B.
Lyon.
1999.
Characterization
of
risks
posed
by
combustor
emissions.
Drug
and
Chem.
Tox.
22:
221­
240.

<
USEPA.
1999.
Guidance
for
Conducting
Health
Risk
Assessment
of
Chemical
Mixtures.
Final
Draft.
Risk
Assessment
Forum
Technical
Panel.
Washington,
DC.
NCEA­
C­
0148.
September.
Web
site:
http://
www.
epa.
gov/
ncea/
raf/
rafpub.
htm
<
USEPA.
1998.
Methodology
for
Assessing
Health
Risks
Associated
with
Multiple
Pathways
of
Exposure
to
Combustor
Emissions.
(
Update
to
EPA/
600/
6­
90/
003
Methodology
for
Assessing
Health
Risks
Associated
with
Indirect
Exposure
to
Combustor
Emissions).
National
Center
for
Environmental
Assessment.
Washington,
DC.
EPA­
600­
R­
98­
137.
Website
http://
www.
epa.
gov/
ncea/
combust.
htm
<
USEPA.
1996.
PCBs:
Cancer
Dose­
Response
Assessment
and
Application
to
Environmental
Mixtures.
National
Center
for
Environmental
Assessment.
Washington,
DC.
EPA/
600/
P­
96/
001F.

<
USEPA.
1993.
Review
Draft
Addendum
to
the
Methodology
for
Assessing
Health
Risks
Associated
with
Indirect
Exposure
to
Combustor
Emissions.
Office
of
Health
and
Environmental
Assessment,
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
AP­
93/
003.
November.

<
USEPA.
1993.
Provisional
Guidance
for
Quantitative
Risk
Assessment
of
Polycyclic
Aromatic
Hydrocarbons.
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
R­
93/
089.
July.

<
USEPA.
1990.
Technical
Support
Document
on
Health
Risk
Assessment
of
Chemical
Mixtures.
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
8/
90/
064.
August.

<
USEPA.
1989a.
Risk
Assessment
Guidance
for
Superfund.
Vol.
1.
Human
Health
Evaluation
Manual
(
Part
A).
Office
of
Emergency
and
Remedial
Response.
Washington,
DC.
EPA/
540/
1­
89/
002.
2­
6
<
USEPA.
1989b.
Interim
Procedures
for
Estimating
Risks
Associated
with
Exposures
to
Mixtures
of
Chlorinated
Dibenzo­
p­
Dioxins
and
­
Dibenzofurans
(
CDDs
and
CDFs)
and
1989
Update.
Risk
Assessment
Forum.
Washington,
DC.
EPA/
625/
3­
89/
016.
March.

The
Agency's
program
offices
are
also
engaged
in
on­
going
discussions
of
the
great
complexities,
methodological
challenges,
data
adequacy
needs
and
other
information
gaps,
as
well
as
the
science
policy
and
risk
management
decisions
that
will
need
to
be
made,
as
they
pursue
developing
a
sound
strategy
and,
eventually,
specific
guidance
for
addressing
cumulative
risks.
As
a
matter
of
internal
policy,
EPA
is
committed
to
refining
the
Methodology
as
advances
in
relevant
aspects
of
the
science
improve,
as
part
of
the
water
quality
criteria
program.

2.4
CANCER
RISK
RANGE
For
deriving
304(
a)
criteria
or
promulgating
water
quality
criteria
for
States
and
Tribes
under
Section
303(
c)
based
on
the
2000
Human
Health
Methodology,
EPA
intends
to
use
the
10­
6
risk
level,
which
the
Agency
believes
reflects
an
appropriate
risk
for
the
general
population.
EPA's
program
office
guidance
and
regulatory
actions
have
evolved
in
recent
years
to
target
a
10
!
6
risk
level
as
an
appropriate
risk
for
the
general
population.
EPA
has
recently
reviewed
the
policies
and
regulatory
language
of
other
Agency
mandates
(
e.
g.,
the
Clean
Air
Act
Amendments
of
1990,
the
Food
Quality
Protection
Act)
and
believes
the
target
of
a
10
!
6
risk
level
is
consistent
with
Agency­
wide
practice.

EPA
believes
that
both
10
!
6
and
10
!
5
may
be
acceptable
for
the
general
population
and
that
highly
exposed
populations
should
not
exceed
a
10
!
4
risk
level.
States
or
Tribes
that
have
adopted
standards
based
on
criteria
at
the
10
!
5
risk
level
can
continue
to
do
so,
if
the
highly
exposed
groups
would
at
least
be
protected
at
the
10
!
4
risk
level.
However,
EPA
is
not
automatically
assuming
that
10
!
5
will
protect
"
the
highest
consumers"
at
the
10
!
4
risk
level.
Nor
is
EPA
advocating
that
States
and
Tribes
automatically
set
criteria
based
on
assumptions
for
highly
exposed
population
groups
at
the
10
!
4
risk
level.
The
Agency
is
simply
endeavoring
to
add
that
a
specific
determination
should
be
made
to
ensure
that
highly
exposed
groups
do
not
exceed
a
10
!
4
risk
level.
EPA
understands
that
fish
consumption
rates
vary
considerably,
especially
among
subsistence
populations,
and
it
is
such
great
variation
among
these
population
groups
that
may
make
either
10
!
6
or
10
!
5
protective
of
those
groups
at
a
10
!
4
risk
level.
Therefore,
depending
on
the
consumption
patterns
in
a
given
State
or
Tribal
jurisdiction,
a
10
!
6
or
10
!
5
risk
level
could
be
appropriate.
In
cases
where
fish
consumption
among
highly
exposed
population
groups
is
of
a
magnitude
that
a
10
!
4
risk
level
would
be
exceeded,
a
more
protective
risk
level
should
be
chosen.
Such
determinations
should
be
made
by
the
State
or
Tribal
authorities
and
are
subject
to
EPA's
review
and
approval
or
disapproval
under
Section
303(
c)
of
the
CWA.

Adoption
of
a
10
!
6
or
10
!
5
risk
level,
both
of
which
States
and
authorized
Tribes
have
chosen
in
adopting
water
quality
standards
to
date,
represents
a
generally
acceptable
risk
management
decision,
and
EPA
intends
to
continue
providing
this
flexibility
to
States
and
Tribes.
EPA
believes
that
such
State
or
Tribal
decisions
are
consistent
with
Section
303(
c)
if
the
State
or
authorized
Tribe
has
identified
the
most
highly
exposed
subpopulation,
has
demonstrated
that
the
2­
7
chosen
risk
level
is
adequately
protective
of
the
most
highly
exposed
subpopulation,
and
has
completed
all
necessary
public
participation.
States
and
authorized
Tribes
also
have
flexibility
in
how
they
demonstrate
this
protectiveness
and
obtain
such
information.
A
State
or
authorized
Tribe
may
use
existing
information
as
well
as
collect
new
information
in
making
this
determination.
In
addition,
if
a
State
or
authorized
Tribe
does
not
believe
that
the
10
!
6
risk
level
adequately
protects
the
exposed
subpopulations,
water
quality
criteria
based
on
a
more
stringent
risk
level
may
be
adopted.
This
discretion
includes
combining
the
10
!
6
risk
level
with
fish
consumption
rates
for
highly
exposed
population
groups.

It
is
important
to
understand
that
criteria
for
carcinogens
are
based
on
chosen
risk
levels
that
inherently
reflect,
in
part,
the
exposure
parameters
used
to
derive
those
values.
Therefore,
changing
the
exposure
parameters
also
changes
the
risk.
Specifically,
the
incremental
cancer
risk
levels
are
relative,
meaning
that
any
given
criterion
associated
with
a
particular
cancer
risk
level
is
also
associated
with
specific
exposure
parameter
assumptions
(
e.
g.,
intake
rates,
body
weights).
When
these
exposure
parameter
values
change,
so
does
the
relative
risk.
For
a
criterion
derived
on
the
basis
of
a
cancer
risk
level
of
10
!
6,
individuals
consuming
up
to
10
times
the
assumed
fish
intake
rate
would
not
exceed
a
10
!
5
risk
level.
Similarly,
individuals
consuming
up
to
100
times
the
assumed
rate
would
not
exceed
a
10
!
4
risk
level.
Thus,
for
a
criterion
based
on
EPA's
default
fish
intake
rate
(
17.5
gm/
day)
and
a
risk
level
of
10
!
6,
those
consuming
a
pound
per
day
(
i.
e.,
454
grams/
day)
would
potentially
experience
between
a
10
!
5
and
a
10
!
4
risk
level
(
closer
to
a
10
!
5
risk
level).
(
Note:
Fish
consumers
of
up
to
1,750
gm/
day
would
not
exceed
the
10
!
4
risk
level.)
If
a
criterion
were
based
on
high­
end
intake
rates
and
the
relative
risk
of
10
!
6,
then
an
average
fish
consumer
would
be
protected
at
a
cancer
risk
level
of
approximately
10
!
8.
The
point
is
that
the
risks
for
different
population
groups
are
not
the
same.

2.5
MICROBIOLOGICAL
AMBIENT
WATER
QUALITY
CRITERIA
Guidance
for
deriving
microbiological
AWQC
is
not
a
part
of
this
Methodology.
In
1986,
EPA
published
Ambient
Water
Quality
Criteria
for
Bacteria
­
1986
(
USEPA,
1986a),
which
updated
and
revised
bacteriological
criteria
previously
published
in
1976
in
Quality
Criteria
for
Water
(
USEPA,
1976).
The
inclusion
of
guidance
for
deriving
microbiological
AWQC
was
considered
in
the
1992
national
workshop
that
initiated
the
effort
to
revise
the
1980
Methodology
and
was
recommended
by
the
SAB
in
1993.
Since
that
time,
however,
efforts
separate
from
these
Methodology
revisions
have
addressed
microbiological
AWQC
concerns.
The
purpose
of
this
section
is
to
briefly
describe
EPA's
current
recommendations
and
activities.

EPA's
Ambient
Water
Quality
Criteria
for
Bacteria
­
1986
recommends
the
use
of
Escherichia
coli
and
enterococci
rather
than
fecal
coliforms
(
USEPA,
1986a).
EPA's
criteria
recommendations
are:

°
Fresh
water:
E.
coli
not
to
exceed
126/
100
ml
or
enterococci
not
to
exceed
33/
100
ml;
and
°
Marine
water:
enterococci
not
to
exceed
35/
100
ml.
2­
8
These
criteria
should
be
calculated
as
the
geometric
mean
based
on
five
equally
spaced
samples
taken
over
a
30­
day
period.

In
addition,
EPA
recommends
that
States
adopt
a
single
sample
maximum,
based
on
the
expected
frequency
of
use.
No
sample
taken
should
exceed
this
value.
EPA
specifies
appropriate
single
sample
maximum
values
in
the
1986
criteria
document.

Current
Activities
and
Plans
for
Future
Work
EPA
has
identified
development
of
microbial
water
quality
criteria
as
part
of
its
strategy
to
control
waterborne
microbial
disease,
by
controlling
pathogens
in
waterbodies
and
by
protecting
designated
uses,
such
as
recreation
and
public
water
supplies.
The
program
fosters
an
integrated
approach
to
protect
both
ground­
water
and
surface
water
sources.
EPA
plans
to
conduct
additional
monitoring
for
Cryptosporidium
parvum
and
E.
coli,
and
determine
action
plans
in
accordance
with
the
results
of
this
monitoring.

EPA
recommends
no
change
at
this
time
in
the
stringency
of
its
bacterial
criteria
for
recreational
waters;
existing
criteria
and
methodologies
from
1986
will
still
apply.
The
recommended
methods
for
E.
coli
and
enterococci
have
been
improved.
As
outlined
in
the
Action
Plan
for
Beaches
and
Recreational
Waters
(
Beach
Action
Plan,
see
below),
the
Agency
plans
to
conduct
national
studies
on
improving
indicators
together
with
epidemiology
studies
for
new
criteria
development
(
USEPA,
1999b).
The
Agency
is
also
planning
to
establish
improved
temporal
and
spatial
monitoring
protocols.

In
the
Beach
Action
Plan,
EPA
identifies
a
multi­
year
strategy
for
monitoring
recreational
water
quality
and
communicating
public
health
risks
associated
with
potentially
pathogencontaminated
recreational
rivers,
lakes,
and
ocean
beaches.
It
articulates
the
Agency's
rationale
and
goals
in
addressing
specific
problems
and
integrates
all
associated
program,
policy,
and
research
needs
and
directions.
The
Beach
Action
Plan
also
provides
information
on
timing,
products
and
lead
organization
for
each
activity.
These
include
activities
and
products
in
the
areas
of
program
development,
risk
communication,
water
quality
indicator
research,
modeling
and
monitoring
research,
and
exposure
and
health
effects
research.

Recently,
EPA
approved
new
24­
hour
E.
coli
and
enterococcus
tests
for
recreational
waters
that
may
be
used
as
an
alternative
to
the
48­
hour
test
(
USEPA,
1997).
EPA
anticipates
proposing
these
methods
for
inclusion
in
the
40
CRF
136
in
the
Fall
of
2000.
EPA
has
also
published
a
video
with
accompanying
manual
on
the
original
and
newer
methods
for
enterococci
and
E.
coli
(
USEPA,
2000).

As
part
of
the
Beach
Action
Plan,
EPA
made
the
following
recommendations
for
further
Agency
study:
2­
9
°
Future
criteria
development
should
consider
the
risk
of
diseases
other
than
gastroenteritis.
EPA
intends
to
consider
and
evaluate
such
water­
related
exposure
routes
as
inhalation
and
dermal
absorption
when
addressing
microbial
health
effects.
The
nature
and
significance
of
other
than
the
classical
waterborne
pathogens
are
to
some
degree
tied
to
the
particular
type
of
waste
sources.

°
A
new
set
of
indicator
organisms
may
need
to
be
developed
for
tropical
water
if
it
is
proven
that
the
current
fecal
indicators
can
maintain
viable
cell
populations
in
the
soil
and
water
for
significant
periods
of
time
in
uniform
tropical
conditions.
Some
potential
alternative
indicators
to
be
fully
explored
are
coliphage,
other
bacteriophage,
and
Clostridium
perfringens.

°
Because
animal
sources
of
pathogens
of
concern
for
human
infection
such
as
Giardia
lamblia,
Cryptosporidium
parvum,
and
Escherichia
coli
0157:
H7
may
be
waterborne
or
washed
into
water
and
thus
become
a
potential
source
for
infection,
they
should
not
be
ignored
in
risk
assessment.
A
likely
approach
would
be
phylogenetic
differentiation;
that
is,
indicators
that
are
specific
to,
or
can
discriminate
among,
animal
sources.

°
EPA
intends
to
develop
additional
data
on
secondary
infection
routes
and
infection
rates
from
prospective
epidemiology
studies
and
outbreaks
from
various
types
of
exposure
(
e.
g.,
shellfish
consumption,
drinking
water,
recreational
exposure).

°
EPA
needs
to
improve
sampling
strategies
for
recreational
water
monitoring
including
consideration
of
rainfall
and
pollution
events
to
trigger
sampling.

2.6
RISK
CHARACTERIZATION
CONSIDERATIONS
On
March
21,
1995,
EPA's
Administrator
issued
the
EPA
Risk
Characterization
Policy
and
Guidance
(
USEPA,
1995).
This
policy
and
guidance
is
intended
to
ensure
that
characterization
information
from
each
stage
of
a
risk
assessment
is
used
in
forming
conclusions
about
risk
and
that
this
information
is
communicated
from
risk
assessors
to
risk
managers,
and
from
EPA
to
the
public.
The
policy
also
provides
the
basis
for
greater
clarity,
transparency,
reasonableness,
and
consistency
in
risk
assessments
across
EPA
programs.
The
fundamental
principles
which
form
the
basis
for
a
risk
characterization
are
as
follows:

°
Risk
assessments
should
be
transparent,
in
that
the
conclusions
drawn
from
the
science
are
identified
separately
from
policy
judgments,
and
the
use
of
default
values
or
methods
and
the
use
of
assumptions
in
the
risk
assessment
are
clearly
articulated.

°
Risk
characterizations
should
include
a
summary
of
the
key
issues
and
conclusions
of
each
of
the
other
components
of
the
risk
assessments,
as
well
as
describe
the
likelihood
of
harm.
The
summary
should
include
a
description
of
the
overall
strengths
and
limitations
(
including
uncertainties)
of
the
assessment
and
conclusions.
2­
10
°
Risk
characterizations
should
be
consistent
in
general
format,
but
recognize
the
unique
characteristics
of
each
specific
situation.

°
Risk
characterizations
should
include,
at
least
in
a
qualitative
sense,
a
discussion
of
how
a
specific
risk
and
its
context
compares
with
similar
risks.
This
may
be
accomplished
by
comparisons
with
other
pollutants
or
situations
on
which
the
Agency
has
decided
to
act,
or
other
situations
with
which
the
public
may
be
familiar.
The
discussion
should
highlight
the
limitations
of
such
comparisons.

°
Risk
characterization
is
a
key
component
of
risk
communication,
which
is
an
interactive
process
involving
exchange
of
information
and
expert
opinion
among
individuals,
groups,
and
institutions.

Additional
guiding
principles
include:

°
The
risk
characterization
integrates
the
information
from
the
hazard
identification,
doseresponse
and
exposure
assessments,
using
a
combination
of
qualitative
information,
quantitative
information,
and
information
regarding
uncertainties.

°
The
risk
characterization
includes
a
discussion
of
uncertainty
and
variability
in
the
risk
assessment.

°
Well­
balanced
risk
characterizations
present
conclusions
and
information
regarding
the
strengths
and
limitations
of
the
assessment
for
other
risk
assessors,
EPA
decision­
makers,
and
the
public.

In
developing
the
methodology
presented
here,
EPA
has
closely
followed
the
risk
characterization
guiding
principles
listed
above.
As
States
and
Tribes
adopt
criteria
using
the
2000
Human
Health
Methodology,
they
are
strongly
encouraged
to
follow
EPA's
risk
characterization
guidance.
There
are
a
number
of
areas
within
the
Methodology
and
criteria
development
process
where
risk
characterization
principles
apply:

°
Integration
of
cancer
and
noncancer
assessments
with
exposure
assessments,
including
bioaccumulation
potential
determinations,
in
essence,
weighing
the
strengths
and
weaknesses
of
the
risk
assessment
as
a
whole
when
developing
a
criterion.

°
Selecting
a
fish
consumption
rate,
either
locally
derived
or
the
national
default
value,
within
the
context
of
a
target
population
(
e.
g.,
sensitive
subpopulations)
as
compared
to
the
general
population.

°
Presenting
cancer
and/
or
noncancer
risk
assessment
options.

°
Describing
the
uncertainty
and
variability
in
the
hazard
identification,
the
dose­
response,
and
the
exposure
assessment.
2­
11
2.7
DISCUSSION
OF
UNCERTAINTY
2.7.1
Observed
Range
of
Toxicity
Versus
Range
of
Environmental
Exposure
When
characterizing
a
risk
assessment,
an
important
distinction
to
make
is
between
the
observed
range
of
adverse
effects
(
from
an
epidemiology
or
animal
study)
and
the
environmentally
observed
range
of
exposure
(
or
anticipated
human
exposure)
to
the
contaminant.
In
many
cases,
EPA
intends
to
apply
default
factors
to
account
for
uncertainties
or
incomplete
knowledge
in
developing
RfDs
or
cancer
risk
assessments
using
nonlinear
low­
dose
extrapolation
to
provide
a
margin
of
protection.
In
reality,
the
actual
effect
level
and
the
environmental
exposure
levels
may
be
separated
by
several
orders
of
magnitude.
The
difference
between
the
dose
causing
some
observed
response
and
the
anticipated
human
exposure
should
be
described
by
risk
assessors
and
managers,
especially
when
comparing
criteria
to
environmental
levels
of
a
contaminant.

2.7.2
Continuum
of
Preferred
Data/
Use
of
Defaults
In
both
toxicological
and
exposure
assessments,
EPA
has
defined
a
continuum
of
preferred
data
for
toxicological
assessments
ranging
from
a
highest
preference
for
chronic
human
data
(
e.
g.,
studies
that
examine
a
long­
term
exposure
of
humans
to
a
chemical,
usually
from
occupational
and/
or
residential
exposure)
and
actual
field
data
for
many
of
the
exposure
parameter
values
(
e.
g.,
locally
derived
fish
consumption
rates,
waterbody­
specific
bioaccumulation
rates),
to
default
values
which
are
at
the
lower
end
of
the
preference
continuum.
EPA
has
supplied
default
values
for
all
of
the
risk
assessment
parameters
in
the
2000
Human
Health
Methodology;
however,
it
is
important
to
note
that
when
default
values
are
used,
the
uncertainty
in
the
final
risk
assessment
may
be
higher,
and
the
final
resulting
criterion
may
not
be
as
applicable
to
local
conditions,
than
is
a
risk
assessment
derived
from
human/
field
data.
Using
defaults
assumes
generalized
conditions
and
may
not
capture
the
actual
variability
in
the
population
(
e.
g.,
sensitive
subpopulations/
high­
end
consumers).
If
defaults
are
chosen
as
the
basis
for
criteria,
these
inherent
uncertainties
should
be
communicated
to
the
risk
manager
and
the
public.
While
this
continuum
is
an
expression
of
preference
on
the
part
of
EPA,
it
does
not
imply
in
any
way
that
any
of
the
choices
are
unacceptable
or
scientifically
indefensible.

2.7.3
Significant
Figures
The
number
of
significant
figures
in
a
numeric
value
is
the
number
of
certain
digits
plus
one
estimated
digit.
Digits
should
not
be
confused
with
decimal
places.
For
example,
15.1,
0.0151,
and
0.0150
all
have
3
significant
figures.
Decimal
places
may
have
been
used
to
maintain
the
correct
number
of
significant
figures,
but
in
themselves
they
do
not
indicate
significant
figures
(
Brinker,
1984).
Since
the
number
of
significant
figures
must
include
only
one
estimated
digit,
the
sources
of
input
parameters
(
e.
g.,
fish
consumption
and
water
consumption
rates)
should
be
checked
to
determine
the
number
of
significant
figures
associated
with
data
they
provide.
However,
the
original
measured
values
may
not
be
available
to
determine
the
number
of
significant
figures
in
the
input
parameters.
In
these
situations,
EPA
recommends
utilizing
the
data
as
presented.
2­
12
When
developing
criteria,
EPA
recommends
rounding
the
number
of
significant
figures
at
the
end
of
the
criterion
calculation
to
the
same
number
of
significant
figures
in
the
least
precise
parameter.
This
is
a
generally
accepted
practice
which
can
be
found
described
in
greater
detail
in
APHA
(
1992)
and
Brinker
(
1984).
The
general
rule
is
that
for
multiplication
or
division,
the
resulting
value
should
not
possess
any
more
significant
figures
than
is
associated
with
the
factor
in
the
calculation
with
the
least
precision.
When
numbers
are
added
or
subtracted,
the
number
that
has
the
fewest
decimal
places,
not
necessarily
the
fewest
significant
figures,
puts
the
limit
on
the
number
of
places
that
justifiably
may
be
carried
in
the
sum
or
difference.
Rounding
off
a
number
is
the
process
of
dropping
one
or
more
digits
so
that
the
value
contains
only
those
digits
that
are
significant
or
necessary
in
subsequent
computations
(
Brinker,
1984).
The
following
rounding
procedures
are
recommended:
(
1)
if
the
digit
6,
7,
8,
or
9
is
dropped,
increase
the
preceding
digit
by
one
unit;
(
2)
if
the
digit
0,
1,
2,
3,
or
4
is
dropped,
do
not
alter
the
preceding
digit;
and
(
3)
if
the
digit
5
is
dropped,
round
off
the
preceding
digit
to
the
nearest
even
number
(
e.
g.,
2.25
becomes
2.2
and
2.35
becomes
2.4)
(
APHA,
1992;
Brinker,
1984).

EPA
recommends
that
calculations
of
water
quality
criteria
be
performed
without
rounding
of
intermediate
step
values.
The
resulting
criterion
may
be
rounded
to
a
manageable
number
of
decimal
places.
However,
in
no
case
should
the
number
of
digits
presented
exceed
the
number
of
significant
figures
implied
in
the
data
and
calculations
performed
on
them.
The
term
"
intermediate
step
values"
refers
to
values
of
the
parameters
in
Equations
1­
1
through
1­
3.
The
final
step
is
considered
the
resulting
AWQC.
Although
AWQC
are,
in
turn,
used
for
purposes
of
establishing
water
quality­
based
effluent
limits
(
WQBELs)
in
National
Pollutant
Discharge
Elimination
System
(
NPDES)
permits,
calculating
total
maximum
daily
loads
(
TMDLs),
and
applicable
or
relevant
and
appropriate
requirements
(
ARARs)
for
Superfund,
they
are
considered
the
final
step
of
this
Methodology
and,
for
the
purpose
of
this
discussion,
where
the
rounding
should
occur.

The
determination
of
appropriate
significant
figures
inevitably
involves
some
judgment
given
that
some
of
the
equation
parameters
are
adopted
default
exposure
values.
Specifically,
the
default
drinking
water
intake
rate
of
2
L/
day
is
a
value
adopted
to
represent
a
majority
of
the
population
over
the
course
of
a
lifetime.
Although
supported
by
drinking
water
consumption
survey
data,
this
value
was
adopted
as
a
policy
decision
and,
as
such,
does
not
have
to
be
considered
in
determining
the
parameter
with
the
least
precision.
That
is,
the
resulting
AWQC
need
not
always
be
reduced
to
one
significant
digit.
Similarly,
the
70­
kg
adult
body
weight
has
been
adopted
Agency­
wide
and
represents
a
default
policy
decision.

The
following
example
with
a
simplified
AWQC
equation
illustrates
the
rule
described
above.
The
example
is
for
hexachlorobutadiene
(
HCBD),
which
EPA
used
to
demonstrate
the
1998
draft
Methodology
revisions
(
USEPA,
1998b).
The
parameters
that
were
calculated
(
i.
e.,
not
policy
adopted
values)
include
values
with
significant
figures
of
two
(
the
POD
and
RSC),
three
(
the
UF),
and
four
(
the
FI
and
BAF).
Based
on
the
2000
Human
Health
Methodology,
the
final
criterion
should
be
rounded
to
two
significant
figures.
The
bold
numbers
in
parentheses
indicate
the
number
of
significant
figures
and
those
with
asterisks
also
indicate
Agency
adopted
policy
values.
2­
13
(
Equation
2­
1)

Example
[
Refer
to
draft
HCBD
document
for
details
on
the
POD/
UF,
RSC
and
BAF
data
(
EPA
822­
R­
98­
004).
Also
note
that
the
fish
intake
rate
in
this
example
is
the
revised
value.]:

AWQC
=
7.3
×
10­
5
mg/
L
(
0.073
µ
g/
L,
rounded
from
7.285
×
10­
2
µ
g/
L)
*
represents
Agency
adopted
policy
value
A
number
of
the
values
used
in
the
equation
may
result
in
intermediate
step
values
that
have
more
than
four
figures
past
the
decimal
place
and
may
be
carried
throughout
the
calculation.
However,
carrying
more
than
four
figures
past
the
decimal
place
(
equivalent
to
the
most
precise
parameter)
is
unnecessary
as
it
has
no
effect
on
the
resulting
criterion
value.

2.8
OTHER
CONSIDERATIONS
2.8.1
Minimum
Data
Considerations
For
many
of
the
preceding
technical
areas,
considerations
have
been
presented
for
data
quality
in
developing
toxicological
and
exposure
assessments.
For
greater
detail
and
discussion
of
minimum
data
recommendations,
the
reader
is
referred
to
the
specific
sections
in
the
Methodology
on
cancer
and
noncancer
risk
assessments
(
and
especially
to
the
referenced
EPA
risk
assessment
guidelines
documents),
exposure
assessment,
and
bioaccumulation
assessment,
in
addition
to
the
TSD
volumes
for
each.

2.8.2
Site­
Specific
Criterion
Calculation
The
2000
Human
Health
Methodology
allows
for
site­
specific
modifications
by
States
and
Tribes
to
reflect
local
environmental
conditions
and
human
exposure
patterns.
"
Local"
may
refer
to
any
appropriate
geographic
area
where
common
aquatic
environmental
or
exposure
patterns
exist.
Thus
"
local"
may
signify
Statewide,
regional,
a
river
reach,
or
an
entire
river.

Such
site­
specific
criteria
may
be
developed
as
long
as
the
site­
specific
data,
either
toxicological
or
exposure­
related,
is
justifiable.
For
example,
when
using
a
site­
specific
fish
consumption
rate,
a
State
should
use
a
value
that
represents
at
least
the
central
tendency
of
the
2­
14
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.
However,
to
justify
such
a
level
(
either
higher
or
lower
than
EPA
defaults),
the
State
should
assemble
appropriate
survey
data
to
arrive
at
a
defensible
site­
specific
fish
consumption
rate.

Such
data
must
also
be
submitted
to
EPA
for
its
review
when
approving
or
disapproving
State
or
Tribal
water
quality
standards
under
Section
303(
c).
The
same
conditions
apply
to
sitespecific
calculations
of
BAF,
percent
fish
lipid,
or
the
RSC.
In
the
case
of
deviations
from
toxicological
values
(
i.
e.,
IRIS
values:
verified
noncancer
and
cancer
assessments),
EPA
strongly
recommends
that
the
data
upon
which
the
deviation
is
based
be
presented
to
and
approved
by
the
Agency
before
a
criterion
is
developed.

Additional
guidance
on
site­
specific
modifications
to
the
2000
Human
Health
Methodology
is
provided
in
each
of
the
three
TSD
volumes.

2.8.3
Organoleptic
Criteria
Organoleptic
criteria
define
concentrations
of
chemicals
or
materials
which
impart
undesirable
taste
and/
or
odor
to
water.
Organoleptic
effects,
while
significant
from
an
aesthetic
standpoint,
are
not
a
significant
health
concern.
In
developing
and
utilizing
such
criteria,
two
factors
must
be
appreciated:
(
1)
the
limitations
of
most
organoleptic
data;
and
(
2)
the
human
health
significance
of
organoleptic
properties.
In
the
past,
EPA
has
developed
organoleptic
criteria
if
organoleptic
data
were
available
for
a
specific
contaminant.
The
1980
AWQC
National
Guidelines
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
because
there
is
no
toxicological
basis.
EPA
acknowledges
that
if
organoleptic
effects
(
i.
e.,
objectionable
taste
and
odor)
cause
people
to
reject
the
water
and
its
designated
uses,
then
the
public
is
effectively
deprived
of
the
natural
resource.
It
is
also
possible
that
intense
organoleptic
characteristics
could
result
in
depressed
fluid
intake
which,
in
turn,
might
lead
to
an
indirect
human
health
effect
via
decreased
fluid
consumption.
Although
EPA
has
developed
organoleptic
criteria
in
the
past
and
may
potentially
do
so
in
the
future,
this
will
not
be
a
significant
part
of
the
water
quality
criteria
program.
EPA
encourages
the
development
of
organoleptic
criteria
when
States
and
Tribes
believe
they
are
needed.
However,
EPA
cautions
States
and
Tribes
that
the
quality
of
organoleptic
data
is
often
significantly
less
than
that
of
toxicologic
data
used
in
establishing
health­
based
criteria.
Therefore,
a
comprehensive
evaluation
of
available
organoleptic
data
should
be
made,
and
the
selection
of
the
most
appropriate
database
for
the
criterion
should
be
based
on
sound
scientific
judgment.

In
1980,
EPA
provided
recommended
criteria
summary
language
when
both
types
of
data
are
available.
The
following
format
was
used
and
is
repeated
here:
2­
15
For
comparison
purposes,
two
approaches
were
used
to
derive
criterion
levels
for
____.
Based
on
available
toxicity
data,
for
the
protection
of
public
health
the
derived
level
is
____.
Using
available
organoleptic
data,
for
controlling
undesirable
taste
and
odor
quality
of
ambient
water
the
estimated
level
is
____.
It
should
be
recognized
that
organoleptic
data
as
a
basis
for
establishing
a
water
quality
criteria
have
no
demonstrated
relationship
to
potential
adverse
human
health
effects.

Similarly,
the
1980
Methodology
recommended
that
in
those
instances
where
a
level
to
limit
toxicity
cannot
be
derived,
the
following
statement
should
be
provided:

Sufficient
data
are
not
available
for
____
to
derive
a
level
which
would
protect
against
the
potential
toxicity
of
this
compound.

2.8.4
Criteria
for
Chemical
Classes
The
2000
Human
Health
Methodology
also
allows
for
the
development
of
a
criterion
for
classes
of
chemicals,
as
long
as
a
justification
is
provided
through
the
analysis
of
mechanistic
data,
toxicokinetic
data,
structure­
activity
relationship
data,
and
limited
acute
and
chronic
toxicity
data.
When
potency
differences
between
members
of
a
class
is
great
(
such
as
in
the
case
of
chlorinated
dioxins
and
furans),
toxicity
equivalency
factors
(
TEFs)
may
be
more
appropriately
developed
than
one
class
criterion.

A
chemical
class
is
defined
as
any
group
of
chemical
compounds
which
are
similar
in
chemical
structure
and
biological
activity,
and
which
frequently
occur
together
in
the
environment
usually
because
they
are
generated
by
the
same
commercial
process.
In
criterion
development,
isomers
should
be
regarded
as
part
of
a
chemical
class
rather
than
as
a
single
compound.
A
class
criterion,
therefore,
is
an
estimate
of
risk/
safety
which
applies
to
more
than
one
member
of
a
class.
It
involves
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
there
are
insufficient
data
available
to
derive
compound­
specific
criteria.
The
health­
based
criterion
may
apply
to
the
water
concentration
of
each
member
of
the
class,
or
may
apply
to
the
sum
of
the
water
concentrations
of
the
compounds
within
the
class.
Because
relatively
minor
structural
changes
within
the
class
of
compounds
can
have
pronounced
effects
on
their
biological
activities,
reliance
on
class
criteria
should
be
minimized
depending
on
the
data
available.

The
following
guidance
should
also
be
followed
when
considering
the
development
of
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.
2­
16
°
Qualitative
and
quantitative
toxicological
data
for
chemicals
within
the
group
should
be
examined.
Adequate
toxicological
data
on
a
number
of
compounds
within
a
group
provides
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
provides
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
toxicokinetic
data
for
some
members
of
the
class.

Additional
guidance
is
described
in
the
Technical
Support
Document
on
Health
Risk
Assessment
of
Chemical
Mixtures
(
USEPA,
1990).

2.9.5
Criteria
for
Essential
Elements
Developing
criteria
for
essential
elements,
particularly
metals,
must
be
a
balancing
act
between
toxicity
and
the
requirement
for
good
health.
The
AWQC
must
consider
essentiality
and
cannot
be
established
at
levels
that
would
result
in
deficiency
of
the
element
in
the
human
population.
The
difference
between
the
recommended
daily
allowance
(
RDA)
and
the
daily
doses
causing
a
specified
risk
level
for
carcinogens
or
the
RfDs
for
noncarcinogens
defines
the
spread
of
daily
doses
within
which
the
criterion
may
be
derived.
Because
errors
are
inherent
in
defining
both
essential
and
adverse­
effect
levels,
the
criterion
is
derived
from
a
dose
level
near
the
center
of
such
dose
ranges.

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).
While
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.

2.9
REFERENCES
APHA.
American
Public
Health
Association.
1992.
Standard
Methods:
For
the
Examination
of
Water
and
Wastewater.
18th
Edition.
Prepared
and
published
jointly
by:
American
Public
Health
Association,
American
Water
Works
Association,
and
Water
Environment
Federation.
Washington,
DC.
2­
17
Brinker,
R.
C.
1984.
Elementary
Surveying.
7th
Edition.
Cliff
Robichaud
and
Robert
Greiner,
Eds.
Harper
and
Row
Publishers,
Inc.
New
York,
NY.

NRC
(
National
Research
Council).
1983.
Risk
Assessment
in
the
Federal
Government:
Managing
the
Process.
National
Academy
Press.
Washington,
DC.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1976.
Quality
Criteria
for
Water.
Office
of
Water
and
Hazardous
Materials.
Washington,
DC.
July.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986a.
Ambient
Water
Quality
Criteria
for
Bacteria
 
1986.
Office
of
Water
Regulations
and
Standards.
Washington,
DC.
EPA/
440/
5­
84/
002.
January.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986b.
Test
Methods
for
Escherichia
coli
and
Enterococci
in
Water
by
the
Membrane
Filter
Procedure.
Office
of
Research
and
Development.
Cincinnati,
OH.
EPA/
600/
4­
85/
076.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1990.
Technical
Support
Document
on
Health
Risk
Assessment
of
Chemical
Mixtures.
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
8­
90/
064.
August.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1992.
Guidelines
for
exposure
assessment.
Federal
Register
57:
22888­
22938.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1995.
Policy
for
Risk
Characterization.
Memorandum
of
Carol
M.
Browner,
Administrator.
March
21,
1995.
Washington,
DC.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996a.
Draft
revisions
to
guidelines
for
carcinogen
risk
assessment.
Federal
Register
61:
17960.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996b.
Guidelines
for
reproductive
toxicity
risk
assessment.
Federal
Register
61:
6274­
56322.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1997.
Method
1600:
Membrane
Filter
Test
Method
for
Enterococci
in
Water.
Office
of
Water.
Washington,
DC.
EPA/
821/
R­
97/
004.
May.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1998a.
Draft
Water
Quality
Criteria
Methodology:
Human
Health.
Office
of
Water.
Washington,
DC.
EPA­
822­
Z­
98­
001.
(
Federal
Register
63:
43756)

USEPA
(
U.
S.
Environmental
Protection
Agency).
1998b.
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Hexachlorobutadiene
(
HCBD).
Draft.
Office
of
Water.
Washington,
DC.
EPA
882­
R­
98­
004.
July.
2­
18
USEPA
(
U.
S.
Environmental
Protection
Agency).
1999a.
1999
Guidelines
for
Carcinogen
Risk
Assessment.
Review
Draft.
Office
of
Research
and
Development.
Washington,
DC.
NCEA­
F­
0644.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999b.
Action
Plan
for
Beaches
and
Recreational
Waters.
Reducing
Exposures
to
Waterborne
Pathogens.
Office
of
Research
and
Development
and
Office
of
Water.
Washington,
DC.
EPA­
600­
R­
98­
079.
March.

USEPA
(
U.
S.
Environmental
Protection
Agency).
2000.
Improved
Enumeration
Methods
for
the
Recreational
Water
Quality
Indicators:
Enterococci
and
Escherichia
coli.
Office
of
Water,
Office
of
Science
and
Technology.
Washington,
DC.
EPA­
821­
R­
97­
004.
March.
3­
1
3.
RISK
ASSESSMENT
This
section
describes
the
methods
used
to
estimate
ambient
water
quality
criteria
(
AWQC)
for
the
protection
of
human
health
for
carcinogenic
chemicals
(
Section
3.1)
and
for
noncarcinogenic
chemicals
(
Section
3.2).

3.1
CANCER
EFFECTS
3.1.1
Background
on
EPA
Cancer
Risk
Assessment
Guidelines
The
current
EPA
Guidelines
for
Carcinogen
Risk
Assessment
were
published
in
1986
(
USEPA,
1986a,
hereafter
the
"
1986
cancer
guidelines").
The
1986
cancer
guidelines
categorize
chemicals
into
alpha­
numerical
Groups:
A,
known
human
carcinogen
(
sufficient
evidence
from
epidemiological
studies
or
other
human
studies);
B,
probable
human
carcinogen
(
sufficient
evidence
in
animals
and
limited
or
inadequate
evidence
in
humans);
C,
possible
human
carcinogen
(
limited
evidence
of
carcinogenicity
in
animals
in
the
absence
of
human
data);
D,
not
classifiable
(
inadequate
or
no
animal
evidence
of
carcinogenicity);
and
E,
evidence
of
noncarcinogenicity
for
humans
(
no
evidence
of
carcinogenicity
in
at
least
two
adequate
animal
tests
in
different
species
or
in
both
adequate
epidemiological
and
animal
studies).
Within
Group
B
there
are
two
subgroups,
Groups
B1
and
B2.
Group
B1
is
reserved
for
agents
for
which
there
is
limited
evidence
of
carcinogenicity
from
epidemiological
studies.
Group
B2
is
generally
for
agents
for
which
there
is
sufficient
evidence
from
animal
studies
and
for
which
there
is
inadequate
evidence
or
no
data
from
epidemiological
studies
(
USEPA,
1986).
The
system
was
similar
to
that
used
by
the
International
Agency
for
Research
on
Cancer
(
IARC).

The
1986
cancer
guidelines
include
guidance
on
what
constitutes
sufficient,
limited,
or
inadequate
evidence.
In
epidemiological
studies,
sufficient
evidence
indicates
a
causal
relationship
between
the
agent
and
human
cancer;
limited
evidence
indicates
that
a
causal
relationship
is
credible,
but
that
alternative
explanations,
such
as
chance,
bias,
or
confounding,
could
not
adequately
be
excluded;
inadequate
evidence
indicates
either
lack
of
pertinent
data,
or
a
causal
interpretation
is
not
credible.
In
general,
although
a
single
study
may
be
indicative
of
a
causeeffect
relationship,
confidence
in
inferring
a
causal
association
is
increased
when
several
independent
studies
are
concordant
in
showing
the
association.
In
animal
studies,
sufficient
evidence
includes
an
increased
incidence
of
malignant
tumors
or
combined
malignant
and
benign
tumors:

°
In
multiple
species
or
strains;

°
In
multiple
experiments
(
e.
g.,
with
different
routes
of
administration
or
using
different
dose
levels);

°
To
an
unusual
degree
in
a
single
experiment
with
regard
to
high
incidence,
unusual
site
or
type
of
tumor,
or
early
age
at
onset;
3­
2
°
Additional
data
on
dose­
response,
short­
term
tests,
or
structural
activity
relationships.

In
the
1986
cancer
guidelines,
hazard
identification
and
the
weight­
of­
evidence
process
focus
on
tumor
findings.
The
weight­
of­
evidence
approach
for
making
judgments
about
cancer
hazard
analyzes
human
and
animal
tumor
data
separately,
then
combines
them
to
make
the
overall
conclusion
about
potential
human
carcinogenicity.
The
next
step
of
the
hazard
analysis
is
an
evaluation
of
supporting
evidence
(
e.
g.,
mutagenicity,
cell
transformation)
to
determine
whether
the
overall
weight­
of­
evidence
conclusion
should
be
modified.

For
cancer
risk
quantification,
the
1986
cancer
guidelines
recommend
the
use
of
linearized
multistage
model
(
LMS)
as
the
only
default
approach.
The
1986
cancer
guidelines
also
mention
that
a
low­
dose
extrapolation
model
other
than
the
LMS
might
be
considered
more
appropriate
based
on
biological
grounds.
However,
no
guidance
is
given
in
choosing
other
approaches.
The
1986
cancer
guidelines
recommended
the
use
of
body
weight
raised
to
the
2/
3
power
(
BW2/
3)
as
a
dose
scaling
factor
between
species.

3.1.2
EPA's
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
and
the
Subsequent
July,
1999
Draft
Revised
Cancer
Guidelines
In
1996,
EPA
published
Proposed
Guidelines
for
Carcinogen
Risk
Assessment
(
USEPA,
1996a,
hereafter
the
"
1996
proposed
cancer
guidelines").
After
the
publication
of
the
1996
proposed
cancer
guidelines
and
a
February,
1997
and
January,
1999
Science
Advisory
Board
(
SAB)
review,
a
revision
was
made
in
July,
1999
Guidelines
for
Carcinogen
Risk
Assessment
­
Review
Draft
(
hereafter
the
"
1999
draft
revised
cancer
guidelines";
USEPA,
1999a),
and
an
SAB
meeting
was
convened
to
review
this
revised
document.
When
final
guidelines
are
published,
they
will
replace
the
1986
cancer
guidelines.
These
revisions
are
designed
to
ensure
that
the
Agency's
cancer
risk
assessment
methods
reflect
the
most
current
scientific
information
and
advances
in
risk
assessment
methodology.

In
the
meanwhile,
the
1986
guidelines
are
used
and
extended
with
principles
discussed
in
the
1999
draft
revised
cancer
guidelines.
These
principles
arise
from
scientific
discoveries
concerning
cancer
made
in
the
last
15
years
and
from
EPA
policy
of
recent
years
supporting
full
characterization
of
hazard
and
risk
both
for
the
general
population
and
potentially
sensitive
groups
such
as
children.
These
principles
are
incorporated
in
recent
and
ongoing
assessments
such
as
the
reassessment
of
dioxin,
consistent
with
the
1986
guidelines.
Until
final
guidelines
are
published,
information
is
presented
to
describe
risk
under
both
the
1986
guidelines
and
1999
draft
revisions.

The
1999
draft
revised
cancer
guidelines
call
for
the
full
use
of
all
relevant
information
to
convey
the
circumstances
or
conditions
under
which
a
particular
hazard
is
expressed
(
e.
g.,
route,
duration,
pattern,
or
magnitude
of
exposure).
They
emphasize
understanding
the
mode
of
action
(
MOA)
whereby
the
agent
induces
tumors.
The
MOA
underlies
the
hazard
assessment
and
provides
the
rationale
for
dose­
response
assessments.
3­
3
The
key
principles
in
the
1999
draft
revised
cancer
guidelines
include:

a)
Hazard
assessment
is
based
on
the
analysis
of
all
biological
information
rather
than
just
tumor
findings.

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

c)
The
1999
draft
revised
cancer
guidelines
emphasize
the
conditions
under
which
the
hazard
may
be
expressed
(
e.
g.,
route,
pattern,
duration
and
magnitude
of
exposure).
Further,
the
guidelines
call
for
a
hazard
characterization
to
integrate
the
data
analysis
of
all
relevant
studies
into
a
weight­
of­
evidence
conclusion
of
hazard
and
to
develop
a
working
conclusion
regarding
the
agent's
mode
of
action
in
leading
to
tumor
development.

d)
A
weight­
of­
evidence
narrative
with
accompanying
descriptors
(
listed
in
Section
3.1.3.1
below)
would
replace
the
current
alphanumeric
classification
system.
The
narrative
summarizes
the
key
evidence
for
carcinogenicity,
describes
the
agent's
MOA,
characterizes
the
conditions
of
hazard
expression,
including
route
of
exposure,
describes
any
disproportionate
effects
on
subgroups
of
the
human
population
(
e.
g.,
children),
and
recommends
appropriate
dose­
response
approach(
es).
Significant
strengths,
weaknesses,
and
uncertainties
of
contributing
evidence
are
also
highlighted.

e)
Biologically
based
extrapolation
models
are
the
preferred
approach
for
quantifying
risk.
These
models
integrate
data
and
conclusions
about
events
in
the
carcinogenic
process
throughout
the
dose­
response
range
from
high
to
low
doses.
It
is
anticipated,
however,
that
the
necessary
data
for
the
parameters
used
in
such
models
will
not
be
available
for
most
chemicals.
The
1999
draft
revised
cancer
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
observable
range
of
data
and
a
determination
is
made
of
the
point
of
departure
(
POD)
from
the
observed
range
to
extrapolate
to
low
doses.
The
second
step
is
extrapolation
from
the
POD
to
estimate
dose­
response
at
lower
doses.
In
addition
to
modeling
tumor
data,
the
1999
draft
revised
cancer
guidelines
call
for
the
use
and
modeling
of
other
kinds
of
responses
if
they
are
considered
to
be
more
informed
measures
of
carcinogenic
risk.
Nominally,
these
responses
reflect
key
events
in
the
carcinogenic
process
integral
to
the
MOA
of
the
agent.
3
Use
of
the
LED10
as
the
point
of
departure
is
recommended
with
this
Methodology,
as
it
is
with
the
1999
draft
revised
cancer
guidelines.

4
Additional
information
regarding
the
revised
method
for
assessing
carcinogens
may
be
found
in
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000).
Technical
Support
Document
,
Volume
1:
Risk
Assessment
(
USEPA,
2000).

3­
4
g)
Three
default
approaches
are
provided
 
linear,
nonlinear,
or
both
when
adequate
data
are
unavailable
to
generate
a
biologically
based
model.
As
the
first
step
for
all
approaches,
curve
fitting
in
the
observed
range
is
used
to
determine
a
POD.
A
standard
POD
is
the
effective
dose
corresponding
to
the
lower
95
percent
limit
on
a
dose
associated
with
10
percent
extra
risk
(
LED
10).
3
Linear:
The
linear
default
is
a
straight
line
extrapolation
from
the
response
at
LED
10
to
the
origin
(
zero
dose,
zero
extra
risk).
Nonlinear:
The
nonlinear
default
begins
with
the
identified
POD
and
provides
a
margin
of
exposure
(
MOE)
analysis
rather
than
estimating
the
probability
of
effects
at
low
doses.
The
MOE
analysis
is
used
to
determine
the
appropriate
margin
between
the
POD
and
the
exposure
level
of
interest,
in
this
Methodology,
the
AWQC.
The
key
objective
of
the
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
(
i.
e.,
nature
of
the
response,
slope
of
the
dose­
response
curve,
human
sensitivity
compared
with
experimental
animals,
nature
and
extent
of
human
variability
in
sensitivity
and
human
exposure).
Linear
and
nonlinear:
Section
3.1.3.4E
describes
the
situations
when
both
linear
and
nonlinear
defaults
are
used.

h)
The
approach
used
to
calculate
an
oral
human
equivalent
dose
when
assessments
are
based
on
animal
bioassays
has
been
refined
and
includes
a
change
in
the
default
assumption
for
interspecies
dose
scaling.
The
1999
draft
revised
cancer
guidelines
use
body
weight
raised
to
the
3/
4
power.

EPA
health
risk
assessment
practices
for
both
cancer
and
noncancer
endpoints
are
beginning
to
come
together
with
recent
proposals
to
emphasizeMOA
understanding
in
risk
assessment
and
to
model
response
data
in
the
observable
range
to
derive
PODs
for
data
sets
and
benchmark
doses
(
BMDs)
for
individual
studies.
The
modeling
of
observed
response
data
to
identify
PODs
in
a
standard
way
will
help
to
harmonize
cancer
and
noncancer
dose­
response
approaches
and
permit
comparisons
of
cancer
and
noncancer
risk
estimates.

3.1.3
Methodology
for
Deriving
AWQC4
by
the
1999
Draft
Revised
Cancer
Guidelines
Following
the
publication
of
the
Draft
Water
Quality
Criteria
Methodology:
Human
Health
(
USEPA,
1998a)
and
the
accompanying
TSD
(
USEPA,
1998b),
EPA
received
comments
from
the
public.
EPA
also
held
an
external
peer
review
of
the
draft
Methodology.
Both
the
peer
reviewers
and
the
public
recommended
that
EPA
incorporate
the
new
approaches
into
the
AWQC
Methodology.
5The
weight­
of­
evidence
narrative
is
intended
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.,
Carcinogenic
to
humans;
Likely
to
be
carcinogenic
to
humans;
Suggestive
evidence
of
carcinogenicity
but
not
sufficient
to
assess
human
carcinogenic
potential;
Data
are
inadequate
for
an
assessment
of
human
carcinogenic
potential;
and
Not
likely
to
be
carcinogenic
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
weightof
evidence
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
MOA
understanding
(
USEPA,
1996a,
1999a).

3­
5
Until
new
guidelines
are
published,
the
1986
cancer
guidelines
will
be
used
along
with
principles
of
the
1999
draft
revised
cancer
guidelines.
The
1986
guidelines
are
the
basis
for
IRIS
risk
numbers
which
were
used
to
derive
the
current
AWQC.
Each
new
assessment
applying
the
principles
of
the
1999
draft
revised
cancer
guidelines
will
be
subject
to
peer
review
before
being
used
as
the
basis
of
AWQC.

The
remainder
of
Section
3
illustrates
the
methodology
for
deriving
numerical
AWQC
for
carcinogens
applying
the
1999
draft
revised
cancer
guidelines
(
USEPA,
1999a).
This
discussion
of
the
revised
methodology
for
carcinogens
focuses
primarily
on
the
quantitative
aspects
of
deriving
numerical
AWQC
values.
It
is
important
to
note
that
the
cancer
risk
assessment
process
outlined
in
the
1999
draft
revised
cancer
guidelines
is
not
limited
to
the
quantitative
aspects.
A
numerical
AWQC
value
derived
for
a
carcinogen
is
to
be
based
on
appropriate
hazard
characterization
and
accompanied
by
risk
characterization
information.

This
section
contains
a
discussion
of
the
weight­
of­
evidence
narrative,
that
describes
all
information
relevant
to
a
cancer
risk
evaluation,
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
or
human
epidemiological
study
provide
the
underlying
basis
for
deriving
the
AWQC
value.
The
discussion
focuses
on
the
following:
(
1)
the
weight­
ofevidence
narrative;
(
2)
general
considerations
and
framework
for
analysis
of
the
MOA;
(
3)
dose
estimation;
(
4)
characterizing
dose­
response
relationships
in
the
range
of
observation
and
at
low,
environmentally
relevant
doses;
(
5)
calculating
the
AWQC
value;
(
6)
risk
characterization;
and
(
7)
use
of
Toxicity
Equivalent
Factors
(
TEF)
and
Relative
Potency
Estimates.
The
first
three
topics
encompass
the
quantitative
aspects
of
deriving
AWQC
for
carcinogens.

3.1.3.1
Weight­
of­
Evidence
Narrative5
The
1999
draft
revised
cancer
guidelines
include
a
weight­
of­
evidence
narrative
that
is
based
on
an
overall
judgment
of
biological
and
chemical/
physical
considerations.
Hazard
assessment
information
accompanying
an
AWQC
value
for
a
carcinogen
in
the
form
of
a
weightof
evidence
narrative
is
described
in
the
footnote.
Of
particular
importance
is
that
the
weight­
ofevidence
narrative
explicitly
provides
adequate
support
based
on
human
studies,
animal
bioassays,
and
other
key
evidence
for
the
conclusion
whether
the
substance
is
or
is
likely
to
be
carcinogenic
to
humans
from
exposures
through
drinking
water
and/
or
fish
ingestion.
The
Agency
emphasizes
6A
"
key
event"
is
an
empirically
observable,
precursor
step
that
is
itself
a
necessary
element
of
the
mode
of
action,
or
is
a
marker
for
such
an
element.

3­
6
the
importance
of
providing
an
explicit
discussion
of
the
MOA
for
the
substance
in
the
weight­
ofevidence
narrative
if
data
are
available,
including
a
discussion
that
relates
the
MOA
to
the
quantitative
procedures
used
in
the
derivation
of
the
AWQC.

3.1.3.2
Mode
of
Action
­
General
Considerations
and
Framework
for
Analysis
An
MOA
is
composed
of
key
events
and
processes
starting
with
the
interaction
of
an
agent
with
a
cell,
through
operational
and
anatomical
changes,
resulting
in
cancer
formation.
"
Mode"
of
action
is
contrasted
with
"
mechanism"
of
action,
which
implies
a
more
detailed,
molecular
description
of
events
than
is
meant
by
MOA.

Mode
of
action
analysis
is
based
on
physical,
chemical,
and
biological
information
that
helps
to
explain
key
events6
in
an
agent's
influence
on
development
of
tumors.
Inputs
to
MOA
analysis
include
tumor
data
in
humans,
animals,
and
among
structural
analogues
as
well
as
the
other
key
data.

There
are
many
examples
of
possible
modes
of
carcinogenic
action,
such
as
mutagenicity,
mitogenesis,
inhibition
of
cell
death,
cytotoxicity
with
reparative
cell
proliferation,
and
immune
suppression.
All
pertinent
studies
are
reviewed
in
analyzing
an
MOA,
and
an
overall
weighing
of
evidence
is
performed,
laying
out
the
strengths,
weaknesses,
and
uncertainties
of
the
case
as
well
as
potential
alternative
positions
and
rationales.
Identifying
data
gaps
and
research
needs
is
also
part
of
the
assessment.

Mode
of
action
conclusions
are
used
to
address
the
question
of
human
relevance
of
animal
tumor
responses,
to
address
differences
in
anticipated
response
among
humans
such
as
between
children
and
adults
or
men
and
women,
and
as
the
basis
of
decisions
about
the
anticipated
shape
of
the
dose­
response
relationship.

In
reaching
conclusions,
the
question
of
"
general
acceptance"
of
an
MOA
will
be
tested
as
part
of
the
independent
peer
review
that
EPA
obtains
for
its
assessment
and
conclusions.

Framework
for
Evaluating
a
Postulated
Carcinogenic
Mode(
s)
of
Action
The
framework
is
intended
to
be
an
analytic
tool
for
judging
whether
available
data
support
a
mode
of
carcinogenic
action
postulated
for
an
agent
and
includes
nine
elements:

1.
Summary
description
of
postulated
MOA
2.
Identification
of
key
events
3.
Strength,
consistency,
specificity
of
association
4.
Dose­
response
relationship
5.
Temporal
relationship
3­
7
6.
Biological
plausibility
and
coherence
7.
Other
modes
of
action
8.
Conclusion
9.
Human
relevance,
including
subpopulations
3.1.3.3
Dose
Estimation
A.
Determining
the
Human
Equivalent
Dose
by
the
Oral
Route
An
important
objective
in
the
dose­
response
assessment
is
to
use
a
measure
of
internal
or
delivered
dose
at
the
target
site
where
possible.
This
is
particularly
important
in
those
cases
where
the
carcinogenic
response
information
is
being
extrapolated
to
humans
from
animal
studies.
Generally,
by
the
oral
exposure
route,
the
measure
of
a
dose
provided
in
the
underlying
human
studies
or
animal
bioassays
is
the
applied
dose,
typically
given
in
terms
of
unit
mass
per
unit
body
weight
per
unit
time,
(
e.
g.,
mg/
kg­
day).
When
animal
bioassay
data
are
used,
it
is
necessary
to
make
adjustments
to
the
applied
dose
values
to
account
for
differences
in
toxicokinetics
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
1999
draft
revised
cancer
guidelines
recommend
that
when
adequate
data
are
available,
the
doses
used
in
animal
studies
can
be
adjusted
to
equivalent
human
doses
using
toxicokinetic
information
on
the
particular
agent.
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.
To
derive
an
equivalent
human
oral
dose
from
animal
data,
the
default
procedure
in
the
1999
draft
revised
cancer
guidelines
is
to
scale
daily
applied
oral
doses
experienced
for
a
lifetime
in
proportion
to
body
weight
raised
to
the
3/
4
power
(
BW3/
4).
The
adjustment
factor
is
used
because
metabolic
rates,
as
well
as
most
rates
of
physiological
processes
that
determine
the
disposition
of
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
(
USEPA,
1992a,
1999a).

The
use
of
BW3/
4
is
a
departure
from
the
scaling
factor
of
BW2/
3
that
was
based
on
surface
area
adjustment
and
was
included
in
the
1980
AWQC
National
Guidelines
as
well
as
the
1986
cancer
guidelines.

B.
Dose­
Response
Analysis
If
data
on
the
agent
are
sufficient
to
support
the
parameters
of
a
biologically
based
model
and
the
purpose
of
the
assessment
is
such
as
to
justify
investing
resources
supporting
its
use,
this
is
the
preferred
approach
for
both
the
observed
tumor
and
related
response
data
and
for
extrapolation
below
the
range
of
observed
data
in
either
animal
or
human
studies.
3­
8
3.1.3.4
Characterizing
Dose­
Response
Relationships
in
the
Range
of
Observation
and
at
Low
Environmentally
Relevant
Doses
The
first
quantitative
component
in
the
derivation
of
AWQC
for
carcinogens
is
the
doseresponse
assessment
in
the
range
of
observation.
For
most
agents,
in
the
absence
of
adequate
data
to
generate
a
biologically
based
model,
dose­
response
relationships
in
the
observed
range
can
be
addressed
through
curve­
fitting
procedures
for
response
data.
It
should
be
noted
that
the
1999
draft
revised
cancer
guidelines
call
for
modeling
of
not
only
tumor
data
in
the
observable
range,
but
also
other
responses
thought
to
be
important
events
preceding
tumor
development
(
e.
g.,
DNA
adducts,
cellular
proliferation,
receptor
binding,
hormonal
changes).
The
modeling
of
these
data
is
intended
to
better
inform
the
dose­
response
assessment
by
providing
insights
into
the
relationships
of
exposure
(
or
dose)
below
the
observable
range
for
tumor
response.
These
non­
tumor
response
data
can
only
play
a
role
in
the
dose­
response
assessment
if
the
agent's
carcinogenic
mode
of
action
is
reasonably
understood,
as
well
as
the
role
of
that
precursor
event.

The
1999
draft
revised
cancer
guidelines
recommend
calculating
the
lower
95
percent
confidence
limit
on
a
dose
associated
with
an
estimated
10
percent
increased
tumor
or
relevant
non­
tumor
response
(
LED
10)
for
quantitative
modeling
of
dose­
response
relationships
in
the
observed
range.
The
estimate
of
the
LED
10
is
used
as
the
POD
for
low­
dose
extrapolations
discussed
below.
This
standard
point
of
departure
(
LED
10)
is
adopted
as
a
matter
of
science
policy
to
remain
as
consistent
and
comparable
from
case
to
case
as
possible.
It
is
also
a
convenient
comparison
point
for
noncancer
endpoints.
The
rationale
supporting
use
of
the
LED
10
is
that
a
10
percent
response
is
at
or
just
below
the
limit
of
sensitivity
for
discerning
a
statistically
significant
tumor
response
in
most
long­
term
rodent
studies
and
is
within
the
observed
range
for
other
toxicity
studies.
Use
of
lower
limit
takes
experimental
variability
and
sample
size
into
account.
The
ED
10
(
central
estimate)
is
also
presented
as
a
reference
for
comparison
uses,
especially
for
use
in
relative
hazard/
potency
ranking
among
agents
for
priority
setting.

For
some
data
sets,
a
choice
of
the
POD
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
 
determine
the
extrapolation
range.
Therefore,
if
the
observed
response
is
below
the
LED
10,
then
a
lower
point
may
be
a
better
choice
(
e.
g.,
LED
5).
Human
studies
more
often
support
a
lower
POD
than
animal
studies
because
of
greater
sample
size.

The
POD
may
be
a
NOAEL
when
a
margin
of
exposure
analysis
is
the
nonlinear
doseresponse
approach.
The
kinds
of
data
available
and
the
circumstances
of
the
assessment
both
contribute
to
deciding
to
use
a
NOAEL
or
LOAEL
which
is
not
as
rigorous
or
as
ideal
as
curve
fitting,
but
can
be
appropriate.
If
several
data
sets
for
key
events
and
tumor
response
are
available
for
an
agent,
and
they
are
a
mixture
of
continuous
and
incidence
data,
the
most
practicable
way
to
assess
them
together
is
often
through
a
NOAEL/
LOAEL
approach.
7
For
discussion
of
the
cancer
risk
range,
see
Section
2.4.

3­
9
When
an
LED
value
estimated
from
animal
data
is
used
as
the
POD,
it
is
adjusted
to
the
human
equivalent
dose
using
an
interspecies
dose
adjustment
or
a
toxicokinetic
analysis
as
described
in
Section
3.1.3.3.

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.

A.
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
study.
Various
approaches
are
used
to
extrapolate
risk
outside
the
range
of
observed
experimental
data.
In
the
1999
draft
revised
cancer
guidelines,
the
choice
of
extrapolation
method
is
largely
dependent
on
the
mode
of
action.
It
should
be
noted
that
the
term
"
mode
of
action"
(
MOA)
is
deliberately
chosen
in
the
1999
draft
revised
cancer
guidelines
in
lieu
of
the
term
"
mechanism"
to
indicate
using
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
1999
draft
revised
cancer
guidelines
favor
the
choice
of
a
biologically
based
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
chemicals.
Thus,
the
1999
draft
revised
cancer
guidelines
allow
for
several
default
extrapolation
approaches
(
low­
dose
linear,
nonlinear,
or
both).

B.
Biologically
Based
Modeling
Approaches
If
a
biologically
based
approach
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
to
environmentally
relevant
doses.
For
the
purposes
of
deriving
AWQC,
the
environmentally
relevant
dose
would
be
the
risk­
specific
dose
(
RSD)
associated
with
incremental
lifetime
cancer
risks
in
the
10­
6
to
10­
4
range
for
carcinogens
for
which
a
linear
extrapolation
approach
is
applied.
7
The
use
of
the
RSD
and
the
POD/
UF
to
compute
the
AWQC
is
presented
in
Section
3.1.3.5,
below.
Although
biologically­
based
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
the
use
of
such
approaches
for
most
substances.
In
the
absence
of
such
data,
the
default
linear
approach,
the
nonlinear
(
MOE)
approach,
or
both
linear
and
nonlinear
approaches
will
be
used.
3­
10
C.
Default
Linear
Extrapolation
Approach
The
default
linear
approach
replaces
the
LMS
approach
that
has
served
as
the
default
for
EPA
cancer
risk
assessments.
Any
of
the
following
conclusions
leads
to
selection
of
a
linear
dose­
response
assessment
approach:

°
There
is
an
absence
of
sufficient
tumor
MOA
information.

°
The
chemical
has
direct
DNA
mutagenic
reactivity
or
other
indications
of
DNA
effects
that
are
consistent
with
linearity.

°
Human
exposure
or
body
burden
is
high
and
near
doses
associated
with
key
events
in
the
carcinogenic
process
(
e.
g.,
2,3,7,8­
tetrachlorodibenzo­
p­
dioxin).

°
Mode
of
action
analysis
does
not
support
direct
DNA
effects,
but
the
doseresponse
relationship
is
expected
to
be
linear
(
e.
g.,
certain
receptor­
mediated
effects).

The
procedures
for
implementing
the
default
linear
approach
begin
with
the
estimation
of
a
POD
as
described
above.
The
point
of
departure,
LED
10,
reflects
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
POD
and
the
origin
(
i.
e.,
zero
dose,
zero
extra
risk).
This
is
mathematically
represented
as:

y
=
mx
+
b
(
Equation
3­
1)
b
=
0
where:

y
=
Response
or
incidence
m
=
Slope
of
the
line
(
cancer
potency
factor)
=
ª
y/
ª
x
x
=
Dose
b
=
Slope
intercept
The
slope
of
the
line,
"
m"
(
the
estimated
cancer
potency
factor
at
low
doses),
is
computed
as:

(
Equation
3­
2)

The
RSD
is
then
calculated
for
a
specific
incremental
targeted
lifetime
cancer
risk
(
in
the
range
of
10­
6
to
10­
4)
as:
8In
1980,
the
target
lifetime
cancer
risk
range
was
set
at
10­
7
to
10­
5.
However,
both
the
expert
panel
for
the
AWQC
workshop
(
USEPA,
1993)
and
the
peer
review
workshop
experts
(
USEPA,
1999c)
recommended
that
EPA
change
the
risk
range
to
10­
6
to
10­
4,
to
be
consistent
with
SDWA
program
decisions.
See
Section
2.4
for
more
details.

3­
11
(
Equation
3­
3)

where:

RSD
=
Risk­
specific
dose
(
mg/
kg­
day)
Target
Incremental
Cancer
Risk8
=
Value
in
the
range
of
10­
6
to10­
4
m
=
Cancer
potency
factor
(
mg/
kg­
day)­
1
The
use
of
the
RSD
to
compute
the
AWQC
is
described
in
Section
3.1.3.5
below.

D.
Default
Nonlinear
Approach
As
discussed
in
the
1999
draft
revised
cancer
guidelines,
any
of
the
following
conclusions
leads
to
a
selection
of
a
nonlinear
(
MOE)
approach
to
dose­
response
assessment:

°
A
tumor
MOA
supporting
nonlinearity
applies
(
e.
g.,
some
cytotoxic
and
hormonal
agents
such
as
disruptors
of
hormonal
homeostasis),
and
the
chemical
does
not
demonstrate
mutagenic
effects
consistent
with
linearity.

°
An
MOA
supporting
nonlinearity
has
been
demonstrated,
and
the
chemical
has
some
indication
of
mutagenic
activity,
but
it
is
judged
not
to
play
a
significant
role
in
tumor
causation.

Thus,
a
default
assumption
of
nonlinearity
is
appropriate
when
there
is
no
evidence
for
linearity
and
sufficient
evidence
to
support
an
assumption
of
nonlinearity.
The
MOA
may
lead
to
a
dose­
response
relationship
that
is
nonlinear,
with
response
falling
much
more
quickly
than
linearly
with
dose,
or
being
most
influenced
by
individual
differences
in
sensitivity.
Alternatively,
the
MOA
may
theoretically
have
a
threshold
(
e.
g.,
the
carcinogenicity
may
be
a
secondary
effect
of
toxicity
or
of
an
induced
physiological
change
that
is
itself
a
threshold
phenomenon).

The
nonlinear
approach
may
be
used,
for
instance,
in
the
case
of
a
bladder
tumor
inducer,
where
the
chemical
is
not
mutagenic
and
causes
only
stone
formation
in
male
rat
bladders
at
high
doses.
This
dynamic
leads
to
tumor
formation
only
at
the
high
doses.
Stone
and
subsequent
tumor
formation
are
not
expected
to
occur
at
doses
lower
than
those
that
induce
the
physiological
changes
that
lead
to
stone
formation.
(
More
detail
on
this
chemical
is
provided
in
the
cancer
section
of
the
Risk
Assessment
TSD;
USEPA,
2000).
EPA
does
not
generally
try
to
distinguish
between
modes
of
action
that
might
imply
a
"
true
threshold"
from
others
with
a
nonlinear
dose­
3­
12
response
relationship,
because
there
is
usually
not
sufficient
information
to
distinguish
between
those
possibilities
empirically.

The
nonlinear
MOE
approach
in
the
1986
proposed
cancer
guidelines
compares
an
observed
response
rate
such
as
the
LED
10,
NOAEL,
or
LOAEL
with
actual
or
nominal
environmental
exposures
of
interest
by
computing
the
ratio
between
the
two.
In
the
context
of
deriving
AWQC,
the
environmentally
relevant
exposures
are
nominal
targets
rather
than
actual
exposures.

If
the
evidence
for
an
agent
indicates
nonlinearity
(
e.
g.,
when
carcinogenicity
is
secondary
to
another
toxicity
for
which
there
is
a
threshold),
the
MOE
analysis
for
the
toxicity
is
similar
to
what
is
done
for
a
noncancer
endpoint,
and
an
RfD
or
RfC
for
that
toxicity
may
also
be
estimated
and
considered
in
the
cancer
assessment.
However,
a
threshold
of
carcinogenic
response
is
not
necessarily
assumed.
It
should
be
noted
that
for
cancer
assessment,
the
MOE
analysis
begins
from
a
POD
that
is
adjusted
for
toxicokinetic
differences
between
species
to
give
a
human
equivalent
dose.

To
support
the
use
of
the
MOE
approach,
risk
assessment
information
provides
evaluation
of
the
current
understanding
of
the
phenomena
that
may
be
occurring
as
dose
(
exposure)
decreases
substantially
below
the
observed
data.
This
gives
information
about
the
risk
reduction
that
is
expected
to
accompany
a
lowering
of
exposure.
The
various
factors
that
influence
the
selection
of
the
UF
in
an
MOE
approach
are
also
discussed
below.

There
are
two
main
steps
in
the
MOE
approach.
The
first
step
is
the
selection
of
a
POD.
The
POD
may
be
the
LED
10
for
tumor
incidence
or
a
precursor,
or
in
some
cases,
it
may
also
be
appropriate
to
use
a
NOAEL
or
LOAEL
value.
When
animal
data
are
used,
the
POD
is
a
human
equivalent
dose
or
concentration
arrived
at
by
interspecies
dose
adjustment
(
as
discussed
in
Section
3.1.3.3)
or
toxicokinetic
analysis.

The
second
step
in
using
MOE
analysis
to
establish
AWQC
is
the
selection
of
an
appropriate
margin
or
UF
to
apply
to
the
POD.
This
is
supported
by
analyses
in
the
MOE
discussion
in
the
risk
assessment.
The
following
issues
should
be
considered
when
establishing
the
overall
UF
for
the
derivation
of
AWQC
using
the
MOE
approach
(
others
may
be
found
appropriate
in
specific
cases):

°
The
nature
of
the
response
used
for
the
dose­
response
assessment,
for
instance,
whether
it
is
a
precursor
effect
or
a
tumor
response.
The
latter
may
support
a
greater
MOE.

°
The
slope
of
the
observed
dose­
response
relationship
at
the
POD
and
its
uncertainties
and
implications
for
risk
reduction
associated
with
exposure
reduction.
(
A
steeper
slope
implies
a
greater
reduction
in
risk
as
exposure
decreases.
This
may
support
a
smaller
MOE).

°
Human
sensitivity
compared
with
that
of
experimental
animals.
3­
13
(
Equation
3­
4)
°
Nature
and
extent
of
human
variability
and
sensitivity.

°
Human
exposure.
The
MOE
evaluation
also
takes
into
account
the
magnitude,
frequency,
and
duration
of
exposure.
If
the
population
exposed
in
a
particular
scenario
is
wholly
or
largely
composed
of
a
subpopulation
of
special
concern
(
e.
g.,
children)
for
whom
evidence
indicates
a
special
sensitivity
to
the
agent's
MOA,
an
adequate
MOE
would
be
larger
than
for
general
population
exposure.

E.
Both
Linear
and
Nonlinear
Approaches
Any
of
the
following
conclusions
leads
to
selection
of
both
a
linear
and
nonlinear
approach
to
dose­
response
assessment.
Relative
support
for
each
dose­
response
method
and
advice
on
the
use
of
that
information
needs
to
be
documented
for
the
AWQC.
In
some
cases,
evidence
for
one
MOA
is
stronger
than
for
the
other,
allowing
emphasis
to
be
placed
on
that
dose­
response
approach.
In
other
cases,
both
modes
of
action
are
equally
possible,
and
both
dose­
response
approaches
should
be
emphasized.

C
Modes
of
action
for
a
single
tumor
type
support
both
linear
and
nonlinear
dose
response
in
different
parts
of
the
dose­
response
curve
(
e.
g.,
4,4'
methylene
chloride).

C
A
tumor
mode
of
action
supports
different
approaches
at
high
and
low
doses;
e.
g.,
at
high
dose,
nonlinearity,
but,
at
low
dose,
linearity
(
e.
g.,
formaldehyde).

C
The
agent
is
not
DNA­
reactive
and
all
plausible
modes
of
action
are
consistent
with
nonlinearity,
but
not
fully
established.

C
Modes
of
action
for
different
tumor
types
support
differing
approaches,
e.
g.,
nonlinear
for
one
tumor
type
and
linear
for
another
due
to
lack
of
MOA
information
(
e.
g.,
trichloroethylene).

3.1.3.5
AWQC
Calculation
A.
Linear
Approach
The
following
equation
is
used
for
the
calculation
of
the
AWQC
for
carcinogens
where
an
RSD
is
obtained
from
the
linear
approach:
9
Although
appearing
in
this
equation
as
a
factor
to
be
multiplied,
the
RSC
can
also
be
an
amount
subtracted.

3­
14
(
Equation
3­
5)
AWQC
=
Ambient
water
quality
criterion
(
mg/
L)
RSD
=
Risk­
specific
dose
(
mg/
kg­
day)
BW
=
Human
body
weight
(
kg)
DI
=
Drinking
water
intake
(
L/
day)
FI
i
=
Fish
intake
at
trophic
level
I
(
I
=
2,
3,
and
4)
(
kg/
day)
BAF
i
=
Bioaccumulation
factor
for
trophic
level
I
(
I
=
2,
3,
and
4),
lipid
normalized
(
L/
kg)

B.
Nonlinear
Approach
In
those
cases
where
the
nonlinear,
MOE
approach
is
used,
a
similar
equation
is
used
to
calculate
the
AWQC
9
where
variables
are
defined
as
for
Equation
3­
4
and:

POD
=
Point
of
departure
(
mg/
kg­
day)
UF
=
Uncertainty
factor
(
unitless)
RSC
=
Relative
source
contribution
(
percentage
or
subtraction)

Differences
between
the
AWQC
values
obtained
using
the
linear
and
nonlinear
approaches
should
be
noted.
First,
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
obtained
using
the
nonlinear
approach
does
not
describe
a
specific
cancer
risk.
The
AWQC
calculations
shown
above
are
appropriate
for
waterbodies
that
are
used
as
sources
of
drinking
water.

The
actual
AWQC
chosen
for
the
protection
of
human
health
is
based
on
a
review
of
all
relevant
information,
including
cancer
and
noncancer
data.
The
AWQC
may,
or
may
not,
utilize
the
value
obtained
from
the
cancer
analysis
in
the
final
AWQC
value.
The
endpoint
selected
for
the
AWQC
will
be
based
on
consideration
of
the
weight
of
evidence
and
a
complete
analysis
of
all
toxicity
endpoints.

3.1.3.6
Risk
Characterization
Risk
assessment
is
an
integrative
process
that
is
documented
in
a
risk
characterization
summary.
Risk
characterization
is
the
final
step
of
the
risk
assessment
process
in
which
all
3­
15
preceding
analyses
(
i.
e.,
hazard,
dose­
response,
and
exposure
assessments)
are
tied
together
to
convey
the
overall
conclusions
about
potential
human
risk.
This
component
of
the
risk
assessment
process
characterizes
the
data
in
nontechnical
terms,
explaining
the
extent
and
weight
of
evidence,
major
points
of
interpretation
and
rationale,
and
strengths
and
weaknesses
of
the
evidence,
and
discussing
alternative
approaches,
conclusions,
uncertainties,
and
variability
that
deserve
serious
consideration.

Risk
characterization
information
accompanies
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
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.

3.1.3.7
Use
of
Toxicity
Equivalence
Factors
and
Relative
Potency
Estimates
The
1999
draft
revised
cancer
guidelines
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
1999
draft
revised
cancer
guidelines
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,
the
TEF
should
be
replaced
or
revised.
To
date,
adequate
data
to
support
use
of
TEFs
have
been
found
only
for
dibenzofurans
(
dioxins)
and
coplanar
polychlorinated
biphenyls
(
PCBs)
(
USEPA,
1989,
1999b).

The
uncertainties
associated
with
TEFs
must
be
described
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.
The
RPF
is
conceptually
similar
to
TEFs,
but
does
not
have
the
same
level
of
data
to
support
it
and
thus
has
a
less
rigorous
definition
compared
with
the
TEF.
TEFs
and
RPFs
are
used
only
when
there
is
no
better
alternative.
When
they
are
used,
assumptions
and
uncertainties
associated
with
them
are
discussed.
As
of
today,
there
are
only
3­
16
three
classes
of
compounds
for
which
relative
potency
approaches
have
been
examined
by
EPA:
dibenzofurans
(
dioxins),
polychlorinated
biphenyls
(
PCBs),
and
polycyclic
aromatic
hydrocarbons
(
PAHs).
There
are
limitations
to
the
use
of
TEF
and
RFP
approaches,
and
caution
should
be
exercised
when
using
them.
More
guidance
can
be
found
in
the
draft
document
for
conducting
health
risk
assessment
of
chemical
mixtures,
published
by
the
EPA
Risk
Assessment
Forum
(
USEPA,
1999b).

3.1.4
References
for
Cancer
Section
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.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1980.
Water
quality
criteria
documents.
Federal
Register
45:
79318­
79379.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986.
Guidelines
for
carcinogen
risk
assessment.
Federal
Register
51:
33992­
34003.

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

USEPA
(
U.
S.
Environmental
Protection
Agency).
1992a.
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
(
U.
S.
Environmental
Protection
Agency).
1993.
Revision
of
Methodology
for
Deriving
National
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Report
of
Workshop
and
EPA's
Preliminary
Recommendations
for
Revision.
Submitted
to
EPA
Science
Advisory
Board
Drinking
Water
Committee,
January
8,
1993.
Office
of
Science
and
Technology,
Office
of
Water.
Water
Docket
W­
97­
20.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996.
Proposed
Guidelines
for
Carcinogen
Risk
Assessment.
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
P­
92/
003C.
(
Federal
Register
61:
17960)

USEPA
(
U.
S.
Environmental
Protection
Agency).
1998a.
Draft
Water
Quality
Criteria
Methodology:
Human
Health.
Federal
Register
Notice.
Office
of
Water.
Washington,
DC.
EPA­
822­
Z­
98­
001.
3­
17
USEPA
(
U.
S.
Environmental
Protection
Agency).
1998b.
Ambient
Water
Quality
Criteria
Derivation
Methodology
­
Human
Health.
Technical
Support
Document.
Final
Draft.
Office
of
Water.
Washington,
DC.
EPA­
822­
B­
98­
005.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999a.
Guidelines
for
Carcinogen
Risk
Assessment.
Review
Draft.
Risk
Assessment
Forum.
Washington,
DC.
EPA/
NCEA­
F­
0644.
July.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999b.
Guidance
for
Conducting
Health
Risk
Assessment
of
Chemical
Mixtures.
External
Peer
Review
Draft.
Risk
Assessment
Forum.
Washington,
DC.
EPA/
NCEA­
C­
0148.
April.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1999c.
Revisions
to
the
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Peer
Review
Workshop
Summary
Report.
Office
of
Water.
Washington,
DC.
EPA­
822­
R­
99­
015.
September.

USEPA
(
U.
S.
Environmental
Protection
Agency).
2000.
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000).
Technical
Support
Document
Volume
1:
Risk
Assessment.
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
EPA­
822­
B­
00­
005.
August.

3.2
NONCANCER
EFFECTS
3.2.1
1980
AWQC
National
Guidelines
for
Noncancer
Effects
In
the
1980
AWQC
National
Guidelines,
the
Agency
evaluated
noncancer
human
health
effects
from
exposure
to
chemical
contaminants
using
Acceptable
Daily
Intake
(
ADI)
levels.
ADIs
were
calculated
by
dividing
NOAELs
by
safety
factors
(
SFs)
to
obtain
estimates
of
doses
of
chemicals
that
would
not
be
expected
to
cause
adverse
effects
over
a
lifetime
of
exposure.
In
accordance
with
the
National
Research
Council
report
of
1977
(
NRC,
1977),
EPA
used
SFs
of
10,
100,
or
1,000,
depending
on
the
quality
and
quantity
of
the
overall
database.
In
general,
a
factor
of
10
was
suggested
when
good­
quality
data
identifying
a
NOAEL
from
human
studies
were
available.
A
factor
of
100
was
suggested
if
no
human
data
were
available,
but
the
database
contained
valid
chronic
animal
data.
For
chemicals
with
no
human
data
and
scant
animal
data,
a
factor
of
1,000
was
recommended.
Intermediate
SFs
could
also
be
used
for
databases
that
fell
between
these
categories.

AWQC
were
calculated
using
the
ADI
levels
together
with
standard
exposure
assumptions
about
the
rates
of
human
ingestion
of
water
and
fish,
and
also
accounting
for
intake
from
other
sources
(
see
Equation
1­
1
in
the
Introduction).
Surface
water
concentrations
at
or
below
the
calculated
criteria
concentrations
would
be
expected
to
result
in
human
exposure
levels
at
or
3­
18
(
Equation
3­
6)
below
the
ADI.
Inherent
in
these
calculations
is
the
assumption
that,
generally,
adverse
effects
from
noncarcinogens
exhibit
a
threshold.

3.2.2
Noncancer
Risk
Assessment
Developments
Since
1980
Since
1980,
the
risk
assessment
of
noncarcinogenic
chemicals
has
changed.
To
remove
the
value
judgments
implied
by
the
words
"
acceptable"
and
"
safety,"
the
ADI
and
SF
terms
have
been
replaced
with
the
terms
RfD
and
UF/
modifying
factor
(
MF),
respectively.

For
the
risk
assessment
of
general
systemic
toxicity,
the
Agency
currently
uses
the
guidelines
contained
in
the
IRIS
background
document
entitled
Reference
Dose
(
RfD):
Description
and
Use
in
Health
Risk
Assessments
(
hereafter
the
"
IRIS
background
document".
That
document
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"
(
USEPA,
1993a).
The
most
common
approach
for
deriving
the
RfD
does
not
involve
dose­
response
modeling.
Instead,
an
RfD
for
a
given
chemical
is
usually
derived
by
first
identifying
the
NOAEL
for
the
most
sensitive
known
toxicity
endpoint,
that
is,
the
toxic
effect
that
occurs
at
the
lowest
dose.
This
effect
is
called
the
critical
effect.
Factors
such
as
the
study
protocol,
the
species
of
experimental
animal,
the
nature
of
the
toxicity
endpoint
assessed
and
its
relevance
to
human
effects,
the
route
of
exposure,
and
exposure
duration
are
critically
evaluated
in
order
to
select
the
most
appropriate
NOAEL
from
among
all
available
studies
in
the
chemical's
database.
If
no
appropriate
NOAEL
can
be
identified
from
any
study,
then
the
LOAEL
for
the
critical
effect
endpoint
is
used
and
an
uncertainly
factor
for
LOAEL­
to­
NOAEL
extrapolation
is
applied.
Using
this
approach,
the
RfD
is
equal
to
the
NOAEL
(
or
LOAEL)
divided
by
the
product
of
UFs
and,
occasionally,
an
MF:

The
definitions
and
guidance
for
use
of
the
UFs
and
the
MFs
are
provided
in
the
IRIS
background
document
and
are
repeated
in
Table
3­
1.

The
IRIS
background
document
on
the
RfD
(
USEPA,
1993a)
provides
guidance
for
critically
assessing
noncarcinogenic
effects
of
chemicals
and
for
deriving
the
RfD.
Another
reference
on
this
topic
is
Dourson
(
1994).
Furthermore,
the
Agency
has
also
published
separate
guidelines
for
assessing
specific
toxic
endpoints,
such
as
developmental
toxicity
(
USEPA,
1991a),
reproductive
toxicity
(
USEPA,
1996a),
and
neurotoxicity
risk
assessment
(
USEPA,
1995).
These
endpoint­
specific
guidelines
will
be
used
for
their
respective
areas
in
the
hazard
assessment
step
and
will
complement
the
overall
toxicological
assessment.
It
should
be
noted,
however,
that
an
RfD,
derived
using
the
most
sensitive
known
endpoint,
is
considered
protective
against
all
noncarcinogenic
effects.
3­
19
TABLE
3­
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
factor
of
1,
3,
or
10
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
factor
of
1,
3,
or
10
when
extrapolating
from
less­
thanchronic
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
factor
of
1,
3,
or
10
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
3­
or
10­
fold
factor
when
deriving
an
RfD
from
an
"
incomplete"
database.
This
factor
is
meant
to
account
for
the
inability
of
any
single
type
of
study
to
consider
all
toxic
endpoints.
The
intermediate
factor
of
3
(
approximately
½
log10
unit,
i.
e.,
the
square
root
of
10)
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
database
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.
The
total
product
of
the
uncertainty
factors
and
modifying
factor
should
not
exceed
3,000.
3­
20
Similar
to
the
procedure
used
in
the
1980
AWQC
National
Guidelines,
the
revised
method
of
deriving
AWQC
for
noncarcinogens
uses
the
RfD
together
with
various
assumptions
concerning
intake
of
the
contaminant
from
both
water
and
non­
water
sources
of
exposure.
The
objective
of
an
AWQC
for
noncarcinogens
is
to
ensure
that
human
exposure
to
a
substance
related
to
its
presence
in
surface
water,
combined
with
exposure
from
other
sources,
does
not
exceed
the
RfD.
The
algorithm
for
deriving
AWQC
for
noncarcinogens
using
the
RfD
is
presented
as
Equation
1­
1
in
the
Introduction.

3.2.3
Issues
and
Recommendations
Concerning
the
Derivation
of
AWQC
for
Noncarcinogens
During
a
review
of
the
1980
AWQC
National
Guidelines
(
USEPA,
1993b),
the
Agency
identified
several
issues
that
must
be
resolved
in
order
to
develop
a
final
revised
methodology
for
deriving
AWQC
based
on
noncancer
effects.
These
issues,
as
discussed
below,
mainly
concern
the
derivation
of
the
RfD
as
the
basis
for
such
an
AWQC.
Foremost
among
these
issues
is
whether
the
Agency
should
revise
the
present
method
or
adopt
entirely
new
procedures
that
use
quantitative
dose­
response
modeling
for
the
derivation
of
the
RfD.
Other
issues
include
the
following:

°
Presenting
the
RfD
as
a
single
point
value
or
as
a
range
to
reflect
the
inherent
imprecision
of
the
RfD;

°
Selecting
specific
guidance
documents
for
derivation
of
noncancer
health
effect
levels;

°
Considering
severity
of
effect
in
the
development
of
the
RfD;

°
Using
less­
than­
90­
day
studies
as
the
basis
for
RfDs;

°
Integrating
reproductive/
developmental,
immunotoxicity,
and
neurotoxicity
data
into
the
RfD
calculation;

°
Applying
toxicokinetic
data
in
risk
assessments;
and
°
Considering
the
possibility
that
some
noncarcinogenic
effects
do
not
exhibit
a
threshold.

3.2.3.1
Using
the
Current
NOAEL/
UF­
Based
RfD
Approach
or
Adopting
More
Quantitative
Approaches
for
Noncancer
Risk
Assessment
The
current
NOAEL/
UF­
based
RfD
methodology,
or
its
predecessor
ADI/
SF
methodology,
have
been
used
since
1980.
This
approach
assumes
that
there
is
a
threshold
exposure
below
which
adverse
noncancer
health
effects
are
not
expected
to
occur.
Exposures
above
this
threshold
are
believed
to
pose
some
risk
to
exposed
individuals;
however,
the
current
approach
does
not
address
the
nature
and
magnitude
of
the
risk
above
the
threshold
level
(
i.
e.,
the
shape
of
the
dose­
response
curve
above
the
threshold).
The
NOAEL/
UF­
based
RfD
approach
is
3­
21
intended
primarily
to
ensure
that
the
RfD
value
derived
from
the
available
data
falls
below
the
population
effects
threshold.
However,
the
NOAEL/
UF­
based
RfD
procedure
has
limitations.
In
particular,
this
method
requires
that
one
of
the
actual
experimental
doses
used
by
the
researchers
in
the
critical
study
be
selected
as
the
NOAEL
or
LOAEL
value.
The
determination
that
a
dose
is
a
NOAEL
or
LOAEL
will
depend
on
the
biological
endpoints
used
and
the
statistical
significance
of
the
data.
Statistical
significance
will
depend
on
the
number
and
spacing
of
dose
groups
and
the
numbers
of
animals
used
in
each
dose
group.
Studies
using
a
small
number
of
animals
can
limit
the
ability
to
distinguish
statistically
significant
differences
among
measurable
responses
seen
in
dose
groups
and
control
groups.
Furthermore,
the
determination
of
the
NOAEL
or
LOAEL
also
depends
on
the
dose
spacing
of
the
study.
Doses
are
often
widely
spaced,
typically
differing
by
factors
of
three
to
ten.
A
study
can
identify
a
NOAEL
and
a
LOAEL
from
among
the
doses
studied,
but
the
"
true"
effects
threshold
cannot
be
determined
from
those
results.
The
study
size
and
dose
spacing
limitations
also
limit
the
ability
to
characterize
the
nature
of
the
expected
response
to
exposures
between
the
observed
NOAEL
and
LOAEL
values.

The
limitations
of
the
NOAEL/
UF
approach
have
prompted
development
of
alternative
approaches
that
incorporate
more
quantitative
dose­
response
information.
The
traditional
NOAEL
approach
for
noncancer
risk
assessment
has
often
been
a
source
of
controversy
and
has
been
criticized
in
several
ways.
For
example,
experiments
involving
fewer
animals
tend
to
produce
higher
NOAELs
and,
as
a
consequence,
may
produce
higher
RfDs.
Larger
sample
sizes,
on
the
other
hand,
should
provide
greater
experimental
sensitivity
and
lower
NOAELs.
The
focus
of
the
NOAEL
approach
is
only
on
the
dose
that
is
the
NOAEL,
and
the
NOAEL
must
be
one
of
the
experimental
doses.
It
also
ignores
the
shape
of
the
dose­
response
curve.
Thus,
the
slope
of
the
dose­
response
plays
little
role
in
determining
acceptable
exposures
for
human
beings.
Therefore,
in
addition
to
the
NOAEL/
UF­
based
RfD
approach
described
above,
EPA
will
accept
other
approaches
that
incorporate
more
quantitative
dose­
response
information
in
appropriate
situations
for
the
evaluation
of
noncancer
effects
and
the
derivation
of
RfDs.
However,
the
Agency
wishes
to
emphasize
that
it
still
believes
the
NOAEL/
UF
RfD
methodology
is
valid
and
can
continue
to
be
used
to
develop
RfDs.

Two
alternative
approaches
that
may
have
relevance
in
assisting
in
the
derivation
of
the
RfD
for
a
chemical
are
the
BMD
and
the
categorical
regression
approaches.
These
alternative
approaches
may
overcome
some
of
the
inherent
limitations
in
the
NOAEL/
UF
approach.
For
example,
the
BMD
analyses
for
developmental
effects
show
that
NOAELs
from
studies
correlate
well
with
a
5
percent
response
level
(
Allen
et
al.,
1994).
The
BMD
and
the
categorical
regression
approaches
usually
have
greater
data
requirements
than
the
RfD
approach.
Thus,
it
is
unlikely
that
any
one
approach
will
apply
to
every
circumstance;
in
some
cases,
different
approaches
may
be
needed
to
accommodate
the
varying
databases
for
the
range
of
chemicals
for
which
water
quality
criteria
must
be
developed.
Acceptable
approaches
will
satisfy
the
following
criteria:
(
1)
meet
the
appropriate
risk
assessment
goal;
(
2)
adequately
describe
the
toxicity
database
and
its
quality;
(
3)
characterize
the
endpoints
properly;
(
4)
provide
a
measure
of
the
quality
of
the
"
fit"
of
the
model
when
a
model
is
used
for
dose­
response
analysis;
and
(
5)
describe
the
key
assumptions
and
uncertainties.
3­
22
A.
The
Benchmark
Dose
The
BMD
is
defined
as
the
dose
estimated
to
produce
a
predetermined
level
of
change
in
response
(
the
Benchmark
Response
level,
or
BMR)
relative
to
control.
The
BMDL
is
defined
as
the
statistical
lower
confidence
limit
on
the
BMD.
In
the
derivation
of
an
RfD,
the
BMD
is
used
as
the
dose
to
which
uncertainty
factors
are
applied
instead
of
the
NOAEL.
The
BMD
approach
first
models
a
dose­
response
curve
for
the
critical
effect(
s)
using
available
experimental
data.
Several
mathematical
algorithms
can
be
used
to
model
the
dose­
response
curve,
such
as
polynomial
or
Weibull
functions.
To
define
a
BMD
from
the
modeled
curve
for
quantal
data,
the
assessor
first
selects
the
BMR.
The
choice
of
the
BMR
is
critical.
For
quantal
endpoints,
a
particular
level
of
response
is
chosen
(
e.
g.,
1
percent,
5
percent,
or
10
percent).
For
continuous
endpoints,
the
BMR
is
the
degree
of
change
from
controls
and
is
based
on
what
is
considered
a
biologically
significant
change.
The
BMD
is
derived
from
the
BMR
dose
by
applying
the
desired
confidence
limit
calculation.
The
RfD
is
obtained
by
dividing
the
BMD
by
one
or
more
uncertainty
factors,
similar
to
the
NOAEL
approach.
Because
the
BMD
is
used
like
the
NOAEL
to
obtain
the
RfD,
the
BMR
should
be
selected
at
or
near
the
low
end
of
the
range
of
increased
risks
that
can
be
detected
in
a
study
of
typical
size.
Generally,
this
falls
in
the
range
between
the
ED
01
and
the
ED
10.

The
Agency
will
accept
use
of
a
BMD
approach
to
derive
RfDs
for
those
agents
for
which
there
is
an
adequate
database.
There
are
a
number
of
technical
decisions
associated
with
the
application
of
the
BMD
technique.
These
include
the
following:

°
The
definition
of
an
adverse
response;

°
Selection
of
response
data
to
model;

°
The
form
of
the
data
used
(
continuous
versus
quantal);

°
The
choice
of
the
measures
of
increased
risk
(
extra
risk
versus
additional
risk);

°
The
choice
of
mathematical
model
(
including
use
of
nonstandard
models
for
unusual
data
sets);

°
The
selection
of
the
BMR;

°
Methods
for
calculating
the
confidence
interval;

°
Selection
of
the
appropriate
BMD
as
the
basis
for
the
RfD
(
when
multiple
endpoints
are
modeled
from
a
single
study,
when
multiple
models
are
applied
to
a
single
response,
and
when
multiple
BMDs
are
calculated
from
different
studies);
and
°
The
use
of
uncertainty
factors
with
the
BMD
approach.
3­
23
These
topics
are
discussed
in
detail
in
Crump
et
al.
(
1995)
and
in
the
Risk
Assessment
TSD
Volume
(
USEPA,
2000).
The
use
of
the
BMD
approach
has
been
discussed
in
general
terms
by
several
authors
(
Gaylor,
1983;
Crump,
1984;
Dourson
et
al.,
1985;
Kimmel
and
Gaylor,
1988;
Brown
and
Erdreich,
1989;
Kimmel,
1990).
The
International
Life
Sciences
Institute
(
ILSI)
also
held
a
major
workshop
on
the
BMD
in
September
1993;
the
workshop
proceedings
are
summarized
in
ILSI
(
1993)
and
in
Barnes
et
al.
(
1995).
For
further
information
on
these
technical
issues,
the
reader
is
referred
to
the
publications
referenced
above.

The
BMD
approach
addresses
several
of
the
quantitative
or
statistical
criticisms
of
the
NOAEL
approach.
These
are
discussed
at
greater
length
in
Crump
et
al.
(
1995)
and
are
summarized
here.
First,
the
BMD
approach
uses
all
the
dose­
response
information
in
the
selected
study
rather
than
just
a
single
data
point,
such
as
the
NOAEL
or
LOAEL.
By
using
response
data
from
all
of
the
dose
groups
to
model
a
dose­
response
curve,
the
BMD
approach
allows
for
consideration
of
the
steepness
of
the
slope
of
the
curve
when
estimating
the
ED
10.
The
use
of
the
full
data
set
also
makes
the
BMD
approach
less
sensitive
to
small
changes
in
data
than
the
NOAEL
approach,
which
relies
on
the
statistical
comparison
of
individual
dose
groups.
The
BMD
approach
also
allows
consistency
in
the
consideration
of
the
level
of
effect
(
e.
g.,
a
10
percent
response
rate)
across
endpoints.

The
BMD
approach
accounts
more
appropriately
for
the
size
of
each
dose
group
than
the
NOAEL
approach.
Laboratory
tests
with
fewer
animals
per
dose
group
tend
to
yield
higher
NOAELs,
and
thus
higher
RfDs,
because
statistically
significant
differences
in
response
rates
are
harder
to
detect.
Therefore,
in
the
NOAEL
approach,
dose
groups
with
fewer
animals
lead
to
a
higher
(
less
conservative)
RfD.
In
contrast,
with
the
BMD
approach,
smaller
dose
groups
will
tend
to
have
the
effect
of
extending
the
confidence
interval
around
the
ED
10;
therefore,
the
lower
confidence
limit
on
the
ED
10
(
the
BMD)
will
be
lower.
With
the
BMD
approach,
greater
uncertainty
(
smaller
test
groups)
leads
to
a
lower
(
more
conservative)
RfD.

There
are
some
issues
to
be
resolved
before
the
BMD
approach
is
used
routinely.
These
were
identified
in
a
1996
Peer
Consultation
Workshop
(
USEPA,
1996b).
Methods
for
routine
use
of
the
BMD
are
currently
under
development
by
EPA.
Several
RfCs
and
RfDs
based
on
the
BMD
approach
are
included
in
EPA's
IRIS
database.
These
include
reference
values
for
methylmercury
based
on
delayed
postnatal
development
in
humans;
carbon
disulfide
based
on
neurotoxicity;
1,1,1,2­
tetrafluoroethane
based
on
testicular
effects
in
rats;
and
antimony
trioxide
based
on
chronic
pulmonary
interstitial
inflammation
in
female
rats.

Various
mathematical
approaches
have
been
proposed
for
modeling
developmental
toxicity
data
(
e.
g.,
Crump,
1984;
Kimmel
and
Gaylor,
1988;
Rai
and
Van
Ryzin,
1985;
Faustman
et
al.,
1989),
which
could
be
used
to
calculate
a
BMD.
Similar
methods
can
be
used
to
model
other
types
of
toxicity
data,
such
as
neurotoxicity
data
(
Gaylor
and
Slikker,
1990,
1992;
Glowa
and
MacPhail,
1995).
The
choice
of
the
mathematical
model
may
not
be
critical,
as
long
as
estimation
is
within
the
observed
dose
range.
Since
the
model
fits
a
mathematical
equation
to
the
observed
data,
the
assumptions
in
a
particular
model
regarding
the
existence
or
absence
of
a
threshold
for
the
effect
may
not
be
pertinent
(
USEPA,
1997).
Thus,
any
model
that
suitably
fits
3­
24
the
empirical
data
is
likely
to
provide
a
reasonable
estimate
of
a
BMD.
However,
research
has
shown
that
flexible
models
that
are
nonsymmetric
(
e.
g.,
the
Weibull)
are
superior
to
symmetric
models
(
e.
g.,
the
probit)
in
estimating
the
BMD
because
the
data
points
at
the
higher
doses
have
less
influence
on
the
shape
of
the
curve
than
at
low
doses.
In
addition,
models
should
incorporate
fundamental
biological
factors
where
such
factors
are
known
(
e.
g.,
intralitter
correlation
for
developmental
toxicity
data)
in
order
to
account
for
as
much
variability
in
the
data
as
possible.
The
Agency
is
currently
using
the
BMD
approach
in
risk
assessments
where
the
data
support
its
use.
Draft
guidelines
for
application
of
the
BMD
approach
also
are
being
developed
by
the
Agency.

Use
of
BMD
methods
involves
fitting
mathematical
models
to
dose­
response
data
obtained
primarily
from
toxicology
studies.
When
considering
available
models
to
use
for
a
BMD
analysis,
it
is
important
to
select
the
model
that
fits
the
data
the
best
and
is
the
most
biologically
appropriate.
EPA
has
developed
software
following
several
years
of
research
and
development,
expert
peer
review,
public
comment,
subsequent
revision,
and
quality
assurance
testing.
The
software
(
BMDS,
Version
1.2)
can
be
downloaded
from
http://
www.
epa.
gov/
ncea/
bmds.
htm.
BMDS
facilitates
these
operations
by
providing
simple
data­
management
tools,
a
comprehensive
help
manual,
an
online
help
system,
and
an
easy­
to­
use
interface
to
run
multiple
models
on
the
same
dose­
response
data.

As
part
of
this
software
package,
EPA
has
included
sixteen
(
16)
different
models
that
are
appropriate
for
the
analysis
of
dichotomous
(
quantal)
data
(
Gamma,
Logistic,
Log­
Logistic,
Multistage,
Probit,
Log­
Probit,
Quantal­
Linear,
Quantal­
Quadratic,
Weibull),
continuous
data
(
Linear,
Polynomial,
Power,
Hill),
and
nested
developmental
toxicology
data
(
NLogistic,
NCTR,
Rai
&
Van
Ryzin).
Results
from
all
models
include
a
reiteration
of
the
model
formula
and
model
run
options
chosen
by
the
user,
goodness­
of­
fit
information,
the
BMD,
and
the
estimate
of
the
lower­
bound
confidence
limit
on
the
benchmark
dose
(
BMDL).
Model
results
are
presented
in
textual
and
graphical
output
files
which
can
be
printed
or
saved
and
incorporated
into
other
documents.

B.
Categorical
Regression
Categorical
regression
is
an
emerging
technique
that
may
have
relevance
for
the
derivation
of
RfDs
or
for
estimating
risk
above
the
RfD
(
Dourson
et
al.,
1997;
Guth
et
al.,
1997).
The
categorical
regression
approach,
like
the
BMD
approach,
can
be
used
to
estimate
a
dose
that
corresponds
to
a
given
probability
of
adverse
effects.
This
dose
would
then
be
divided
by
UFs
to
establish
an
RfD.
However,
unlike
the
BMD
approach,
the
Categorical
regression
approach
can
incorporate
information
on
different
health
endpoints
in
a
single
dose­
response
analysis.
For
those
health
effects
for
which
studies
exist,
responses
to
the
substance
in
question
are
grouped
into
severity
categories;
for
example
(
1)
no
effect,
(
2)
no
adverse
effect,
(
3)
mild­
to­
moderate
adverse
effect,
and
(
4)
frank
effect.
These
categories
correspond
to
the
dose
categories
currently
used
in
setting
the
RfD,
namely,
the
no­
observed­
effect
level
(
NOEL),
NOAEL,
LOAEL,
and
frank­
effect
level
(
FEL),
respectively.
Logistic
transformation
or
other
applicable
mathematical
operations
are
used
to
model
the
probability
of
experiencing
effects
in
a
certain
category
as
a
3­
25
function
of
dose
(
Harrell,
1986;
Hertzberg,
1989).
The
"
acceptability"
of
the
fit
of
the
model
to
the
data
can
be
judged
using
several
statistical
measures,
including
the
P
2
statistic,
correlation
coefficients,
and
the
statistical
significance
of
its
model
parameter
estimates.

The
resulting
mathematical
equation
can
be
used
to
find
a
dose
(
or
the
lower
confidence
bound
on
the
dose)
at
which
the
probability
of
experiencing
adverse
effects
does
not
exceed
a
selected
level,
e.
g.,
10
percent.
This
dose
(
like
the
NOAEL
or
BMD)
would
then
be
divided
by
relevant
UFs
to
calculate
an
RfD.
For
more
detail
on
how
to
employ
the
categorical
regression
approach,
see
the
discussion
in
the
Risk
Assessment
TSD
(
USEPA,
2000).

As
with
the
BMD
approach,
the
categorical
regression
approach
has
the
advantage
of
using
more
of
the
available
dose­
response
data
to
account
for
response
variability
as
well
as
accounting
for
uncertainty
due
to
sample
size
through
the
use
of
confidence
intervals.
Additional
advantages
of
categorical
regression
include
the
combining
of
data
sets
prior
to
modeling,
thus
allowing
the
calculation
of
the
slope
of
a
dose­
response
curve
for
multiple
adverse
effects
rather
than
only
one
effect
at
a
time.
Another
advantage
is
the
ability
to
estimate
risks
for
different
levels
of
severity
from
exposures
above
the
RfD.

On
the
other
hand,
as
with
BMD,
opinions
differ
over
the
amount
and
adequacy
of
data
necessary
to
implement
the
method.
The
categorical
regression
approach
also
requires
judgments
regarding
combining
data
sets,
judging
goodness­
of­
fit,
and
assigning
severity
to
a
particular
effect.
Furthermore,
this
approach
is
still
in
the
developmental
stage.
It
is
not
recommended
for
routine
use,
but
may
be
used
when
data
are
available
and
justify
the
extensive
analyses
required.

C.
Summary
Whether
a
NOAEL/
UF­
based
methodology,
a
BMD,
a
categorical
regression
model,
or
other
approach
is
used
to
develop
the
RfD,
the
dose­
response­
evaluation
step
of
a
risk
assessment
process
should
include
additional
discussion
about
the
nature
of
the
toxicity
data
and
its
applicability
to
human
exposure
and
toxicity.
The
discussion
should
present
the
range
of
doses
that
are
effective
in
producing
toxicity
for
a
given
agent;
the
route,
timing,
and
duration
of
exposure;
species
specificity
of
effects;
and
any
toxicokinetic
or
other
considerations
relevant
to
extrapolation
from
the
toxicity
data
to
human­
health­
based
AWQC.
This
information
should
always
accompany
the
characterization
of
the
adequacy
of
the
data.

3.2.3.2
Presenting
the
RfD
as
a
Single
Point
or
as
a
Range
for
Deriving
AWQC
Although
the
RfD
has
traditionally
been
presented
and
used
as
a
single
point,
its
definition
contains
the
phrase
".
.
.
an
estimate
(
with
uncertainty
spanning
perhaps
an
order
of
magnitude)
.
.
."
(
USEPA,
1993a).
Underlying
this
concept
is
the
reasoning
that
the
selection
of
the
critical
effect
and
the
total
uncertainty
factor
used
in
the
derivation
of
the
RfD
is
based
on
the
"
best"
scientific
judgment,
and
that
competent
scientists
examining
the
same
database
could
derive
RfDs
which
varied
within
an
order
of
magnitude.
3­
26
In
one
instance,
IRIS
presented
the
RfD
as
a
point
value
within
an
accompanying
range.
EPA
derived
a
single
number
as
the
RfD
for
arsenic
(
0.3
:
g/
kg­
day),
but
added
that
"
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
:
g/
kg/
day"
(
USEPA,
1993c).
EPA
noted
that
regulatory
managers
should
be
aware
of
the
flexibility
afforded
them
through
this
action.

There
are
situations
in
which
the
risk
manager
can
select
an
alternative
value
to
use
in
place
of
the
RfD
in
the
AWQC
calculations.
The
domain
from
which
this
alternative
value
can
be
selected
is
restricted
to
a
defined
range
around
the
point
estimate.
As
explained
further
below,
the
Agency
is
recommending
that
sometimes
the
use
of
a
value
other
than
the
calculated
RfD
point
estimate
is
appropriate
in
characterizing
risk.
The
selection
of
an
alternative
value
within
an
appropriate
range
must
be
determined
for
each
individual
situation,
since
several
factors
affect
the
selection
of
the
alternative
value.
Observing
similar
effects
in
several
animal
species,
including
humans,
can
increase
confidence
in
the
selection
of
the
critical
effect
and
thereby
narrow
the
range
of
uncertainty.
There
are
other
factors
that
can
affect
the
precision.
These
include
the
slope
of
the
dose­
response
curve,
seriousness
of
the
observed
effect,
dose
spacing,
and
possibly
the
route
for
the
experimental
doses.
Dose
spacing
and
the
number
of
animals
in
the
study
groups
used
in
the
experiment
can
also
affect
the
confidence
in
the
RfD.

To
derive
the
AWQC,
the
calculated
point
estimate
of
the
RfD
is
the
default.
Based
on
consideration
of
the
available
data,
the
use
of
another
number
within
the
range
defined
by
the
product
of
the
UF(
s)
(
and
MF,
if
used)
could
be
justified
in
some
specific
situations.
This
means
that
there
are
risk
considerations
which
indicate
that
some
value
in
the
range
other
than
the
point
estimate
may
be
more
appropriate,
based
on
human
health
or
environmental
fate
considerations.
For
example,
the
bioavailability
of
the
contaminant
in
fish
tissues
is
one
factor
to
consider.
If
bioavailability
from
fish
tissues
is
much
lower
than
that
from
water
and
the
RfD
was
derived
from
a
study
in
which
the
contaminant
exposure
was
from
drinking
water,
the
alternative
to
the
calculated
RfD
could
be
selected
from
the
high
end
of
the
range
and
justified
using
the
quantitative
difference
in
bioavailability.

Most
inorganic
contaminants,
particularly
divalent
cations,
have
bioavailability
values
of
20
percent
or
less
from
a
food
matrix,
but
are
much
more
available
(
about
80
percent
or
higher)
from
drinking
water.
Accordingly,
the
external
dose
necessary
to
produce
a
toxic
internal
dose
would
likely
be
higher
for
a
study
where
the
exposure
occurred
through
the
diet
rather
than
the
drinking
water.
As
a
result,
the
RfD
from
a
dietary
study
would
likely
be
higher
than
that
for
the
drinking
water
study
if
equivalent
external
doses
had
been
used.
Conversely,
in
cases
where
the
NOAEL
that
was
the
basis
for
the
RfD
came
from
a
dietary
study,
the
alternative
value
could
be
slightly
lower
than
the
calculated
RfD.

Because
the
uncertainty
around
the
dose­
response
relationship
increases
as
extrapolation
below
the
observed
data
increases,
the
use
of
an
alternative
point
within
the
range
may
be
more
appropriate
in
characterizing
the
risk
than
the
use
of
the
calculated
RfD,
especially
in
situations
when
the
uncertainty
is
high.
Therefore,
as
a
matter
of
policy,
the
2000
Human
Health
Methodology
permits
the
selection
of
a
single
point
within
a
range
about
the
calculated
RfD
to
be
3­
27
used
as
the
basis
of
the
AWQC
if
an
adequate
justification
of
the
alternative
point
is
provided.
More
complete
discussion
of
this
option,
including
limitations
on
the
span
of
the
range,
is
provided
in
the
Risk
Assessment
TSD
(
USEPA,
2000).

3.2.3.3
Guidelines
to
be
Adopted
for
Derivation
of
Noncancer
Health
Effects
Values
The
Agency
currently
is
using
the
IRIS
background
document
as
the
general
basis
for
the
risk
assessment
of
noncarcinogenic
effects
of
chemicals
(
USEPA,
1993a).
EPA
recommends
continued
use
of
this
document
for
this
purpose.
However,
it
should
be
noted
that
the
process
for
evaluating
chemicals
for
inclusion
in
IRIS
is
undergoing
revision
(
USEPA,
1996c).
The
revised
assessments
for
many
chemicals
are
now
available
on
IRIS
and
can
be
consulted
as
examples
of
the
RfD
development
process
and
required
supporting
documentation.

3.2.3.4
Treatment
of
Uncertainty
Factors/
Severity
of
Effects
During
the
RfD
Derivation
and
Verification
Process
During
the
RfD
derivation
and
toxicology
review
process,
EPA
considers
the
uncertainty
in
extrapolating
between
animal
species
and
within
individuals
of
a
species,
as
well
as
specific
uncertainties
associated
with
the
completeness
of
the
database.
The
Agency's
RfD
Work
Group
has
always
considered
the
severity
of
the
observed
effects
induced
by
the
chemical
under
review
when
choosing
the
value
of
the
UF
with
a
LOAEL.
For
example,
during
the
derivation
and
verification
of
the
RfD
for
zinc
(
USEPA,
1992),
an
uncertainty
factor
less
than
the
standard
factor
of
10
(
UF
of
3)
was
assigned
to
the
relatively
mild
decrease
in
erythrocyte
superoxide
dismutase
activity
in
human
subjects.
EPA
recommends
that
the
severity
of
the
critical
effect
be
assessed
when
deriving
an
RfD
and
that
risk
managers
be
made
aware
of
the
severity
of
the
effect
and
the
weight
placed
on
this
attribute
of
the
effect
when
the
RfD
was
derived.

3.2.3.5
Use
of
Less­
Than­
90­
Day
Studies
to
Derive
RfDs
Generally,
less­
than­
90­
day
experimental
studies
are
not
used
to
derive
an
RfD.
This
is
based
on
the
rationale
that
studies
lasting
for
less
than
90
days
may
be
too
short
to
detect
various
toxic
effects.
However,
EPA,
has
in
certain
circumstances,
derived
an
RfD
based
on
a
less­
than­
90­
day
study.
For
example,
the
RfD
for
nonradioactive
effects
of
uranium
is
based
on
a
30­
day
rabbit
study
(
USEPA,
1989).
The
short­
term
exposure
period
was
used,
because
it
was
adequate
for
determining
doses
that
cause
chronic
toxicity.
In
other
cases,
it
may
be
appropriate
to
use
a
less­
than­
90­
day
study
because
the
critical
effect
is
expressed
in
less
than
90
days.
For
example,
the
RfD
for
nitrate
was
derived
and
verified
using
studies
that
were
less
than
3­
months
in
duration
(
USEPA,
1991b).
For
nitrate,
the
critical
effect
of
methemoglobinemia
in
infants
occurs
in
less
than
90
days.
When
it
can
be
demonstrated
from
other
data
in
the
toxicological
database
that
the
critical
adverse
effect
is
expressed
within
the
study
period
and
that
a
longer
exposure
duration
would
not
exacerbate
the
observed
effect
or
cause
the
appearance
of
some
other
adverse
effect,
the
Agency
may
choose
to
use
less­
than­
90­
day
studies
as
the
basis
of
the
RfD.
Such
values
would
have
to
be
used
with
care
because
of
the
uncertainty
in
determining
if
other
effects
might
be
expressed
if
exposure
was
of
greater
duration
than
90
days.
3­
28
3.2.3.6
Use
of
Reproductive/
Developmental,
Immunotoxicity,
and
Neurotoxicity
Data
as
the
Basis
for
Deriving
RfDs
All
relevant
toxicity
data
have
some
bearing
on
the
RfD
derivation
and
verification
and
are
considered
by
EPA.
The
"
critical"
effect
is
the
adverse
effect
most
relevant
to
humans
or,
in
the
absence
of
an
effect
known
to
be
relevant
to
humans,
the
adverse
effect
that
occurs
at
the
lowest
dose
in
animal
studies.
If
the
critical
effect
is
neurotoxicity,
EPA
will
use
that
endpoint
as
the
basis
for
the
derivation
and
verification
of
an
RfD,
as
it
did
for
the
RfD
for
acrylamide.
Moreover,
the
Agency
is
continually
revising
its
procedures
for
noncancer
risk
assessment.
For
example,
EPA
has
released
guidelines
for
deriving
developmental
RfDs
(
RfD
DT,
USEPA,
1991a),
for
using
reproductive
toxicity
(
USEPA,
1996a),
and
neurotoxicity
(
USEPA,
1995)
data
in
risk
assessments.
The
Agency
is
currently
working
on
guidelines
for
using
immunotoxicity
data
to
derive
RfDs.
In
addition,
the
Agency
is
proceeding
with
the
process
of
generating
acceptable
emergency
health
levels
for
hazardous
substances
in
acute
exposure
situations
based
on
established
guidelines
(
NRC,
1993).

3.2.3.7
Applicability
of
Toxicokinetic
Data
in
Risk
Assessment
All
pertinent
toxicity
data
should
be
used
in
the
risk
assessment
process,
including
toxicokinetic
and
mechanistic
data.
The
Agency
has
used
toxicokinetic
data
in
deriving
the
RfD
for
cadmium
and
other
compounds
and
currently
is
using
toxicokinetic
data
to
better
characterize
human
inhalation
exposures
from
animal
inhalation
experiments
during
derivation/
verification
of
RfCs.
In
analogy
to
the
RfD,
the
RfC
is
considered
to
be
an
estimate
of
a
concentration
in
the
air
that
is
not
anticipated
to
cause
adverse
noncancer
effects
over
a
lifetime
of
inhalation
exposure
(
USEPA,
1994;
Jarabek,
1995a).
For
RfCs,
different
dosimetry
adjustments
are
made
to
account
for
the
differences
between
laboratory
animals
and
humans
in
gas
uptake
and
disposition
or
in
particle
clearance
and
retention.
This
procedure
results
in
calculation
of
a
"
human
equivalent
concentration."
Based
on
the
use
of
these
procedures,
an
interspecies
UF
of
3
(
i.
e.,
approximately
100.5),
instead
of
the
standard
factor
of
10,
is
used
in
the
RfC
derivation
(
Jarabek,
1995b).

Toxicokinetics
and
toxicodynamics
of
a
chemical
each
contribute
to
a
chemical's
observed
toxicity,
and
specifically,
to
observed
differences
among
species
in
sensitivity.
Toxicokinetics
describes
the
disposition
(
i.
e.,
deposition,
absorption,
distribution,
metabolism,
and
elimination
of
chemicals
in
the
body)
and
can
be
approximated
using
toxicokinetic
models.
Toxicodynamics
describes
the
toxic
interaction
of
the
agent
with
the
target
cell.
In
the
absence
of
specific
data
on
their
relative
contributions
to
the
toxic
effects
observed
in
species,
each
is
considered
to
account
for
approximately
one­
half
of
the
difference
in
observed
effects
for
humans
compared
with
laboratory
animals.
The
implication
of
this
assumption
is
that
an
interspecies
uncertainty
factor
of
3
rather
than
10
could
be
used
for
deriving
an
RfD
when
valid
toxicokinetic
data
and
models
can
be
applied
to
obtain
an
oral
"
human
equivalent
applied
dose"
(
Jarabek,
1995b).
If
specific
data
exist
on
the
relative
contribution
of
either
element
to
observed
effects,
that
proportion
will
be
used.
The
role
exposure
duration
may
play,
and
whether
or
not
the
chemical
or
its
damage
may
3­
29
accumulate
over
time
in
a
particular
scenario,
also
requires
careful
consideration
(
Jarabek,
1995c).

3.2.3.8
Consideration
of
Linearity
(
or
Lack
of
a
Threshold)
for
Noncarcinogenic
Chemicals
It
is
quite
possible
that
there
are
chemicals
with
noncarcinogenic
endpoints
that
have
no
threshold
for
effects.
For
example,
in
the
case
of
lead,
it
has
not
been
possible
to
identify
a
threshold
for
effects
on
neurological
development.
Other
examples
could
include
genotoxic
teratogens
and
germline
mutagens.
Genotoxic
teratogens
act
by
causing
mutational
events
during
organogenesis,
histogenesis,
or
other
stages
of
development.
Germline
mutagens
interact
with
germ
cells
to
produce
mutations
which
may
be
transmitted
to
the
zygote
and
expressed
during
one
or
more
stages
of
development.
However,
there
are
few
chemicals
which
currently
have
sufficient
mechanistic
information
about
these
possible
modes
of
action.
It
should
be
recognized
that
although
an
MOA
consistent
with
linearity
is
possible
(
especially
for
agents
known
to
be
mutagenic),
this
has
yet
to
be
reasonably
demonstrated
for
most
toxic
endpoints
other
than
cancer.

EPA
has
recognized
the
potential
for
nonthreshold
noncarcinogenic
endpoints
and
discussed
this
issue
in
the
Guidelines
for
Developmental
Toxicity
Risk
Assessment
(
USEPA,
1991a)
and
in
the
1986
Guidelines
for
Mutagenicity
Risk
Assessment
(
USEPA,
1986).
An
awareness
of
the
potential
for
such
teratogenic/
mutagenic
effects
should
be
established
in
order
to
deal
with
such
data.
However,
without
adequate
data
to
support
a
genetic
or
mutational
basis
for
developmental
or
reproductive
effects,
the
default
becomes
a
UF
or
MOA
approach,
which
are
procedures
utilized
for
noncarcinogens
assumed
to
have
a
threshold.
Therefore,
genotoxic
teratogens
and
germline
mutagens
should
be
considered
an
exception
while
the
traditional
uncertainty
factor
approach
is
the
general
rule
for
calculating
criteria
or
values
for
chemicals
demonstrating
developmental/
reproductive
effects.
For
the
exceptional
cases,
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.
Other
types
of
nonthreshold
noncarcinogens
must
also
be
handled
on
a
case­
by­
case
basis.

3.2.3.9
Minimum
Data
Guidance
For
details
on
minimum
data
guidance
for
RfD
development,
see
the
Risk
Assessment
TSD
(
USEPA,
2000).
3­
30
3.2.4
References
for
Noncancer
Effects
Allen,
B.
C.,
R.
T.
Kavlock,
C.
A.
Kimmel,
and
E.
M.
Faustman.
1994.
Dose­
response
assessment
for
developmental
toxicity.
Fund.
Appl.
Toxicol.
23:
496­
509.

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.
Reg.
Toxicol.
Pharmacol.
21:
296­
306.

Brown,
K.
G.
and
L.
S.
Erdreich.
1989.
Statistical
uncertainty
in
the
no­
observed­
adverse­
effect
level.
Fund.
Appl.
Toxicol.
13:
235­
244.

Crump,
K.
S.,
B.
Allen,
and
E.
Faustman.
1995.
The
Use
of
the
Benchmark
Dose
Approach
in
Health
Risk
Assessment.
Prepared
for
U.
S.
Environmental
Protection
Agency's
Risk
Assessment
Forum.
EPA/
630/
R­
94/
007.

Crump,
K.
S.
1984.
A
new
method
for
determining
acceptable
daily
intakes.
Fund.
Appl.
Toxicol.
4:
854­
871.

Dourson,
M.
L.
1994.
Methodology
for
establishing
oral
reference
doses
(
RfDs).
In:
Risk
Assessment
of
Essential
Elements.
W.
Mertz,
C.
O.
Abernathy,
and
S.
S.
Olin
(
eds.)
ILSI
Press.
Washington,
DC.
Pp.
51­
61.

Dourson,
M.
L.,
R.
C.
Hertzberg,
R.
Hartung
and
K.
Blackburn.
1985.
Novel
approaches
for
the
estimation
of
acceptable
daily
intake.
Toxicol.
Ind.
Health
1:
23­
41.

Dourson,
M.
L.,
L.
K.
Teuschler,
P.
R.
Durkin,
and
W.
M.
Stiteler.
1997.
Categorical
regression
of
toxicity
data,
a
case
study
using
aldicarb.
Regul.
Toxicol.
Pharmacol.
25:
121­
129.

Faustman,
E.
M.,
D.
G.
Wellington,
W.
P.
Smith
and
C.
A.
Kimmel.
1989.
Characterization
of
a
developmental
toxicity
dose­
response
model.
Environ.
Health
Perspect.
79:
229­
241.

Gaylor,
D.
W.
1983.
The
use
of
safety
factors
for
controlling
risk.
J.
Toxicol.
Environ.
Health
11:
329­
336.

Gaylor,
D.
W.
and
W.
Slikker.
1990.
Risk
assessment
for
neurotoxic
effects.
Neurotoxicology
11:
211­
218.

Gaylor,
D.
W.
and
W.
Slikker.
1992.
Risk
assessment
for
neurotoxicants.
In:
Neurotoxicology.
H.
Tilson
and
C.
Mitchel
(
eds).
Raven
Press.
New
York,
NY.
Pp.
331­
343.

Glowa,
J.
R.
and
R.
C.
MacPhail.
1995.
Quantitative
approaches
to
risk
assessment
in
neurotoxicology.
In:
Neurotoxicology:
Approaches
and
Methods.
Academic
Press.
New
York,
NY.
Pp.
777­
787.
3­
31
Guth,
D.
J.,
R.
J.
Carroll,
D.
G.
Simpson,
and
H.
Zhou.
1997.
Categorical
regression
analysis
of
acute
exposure
to
tetrachloroethylene.
Risk
Anal.
17(
3):
321­
332.

Harrell,
F.
1986.
The
logist
procedure.
SUGI
Supplemental
Library
Users
Guide,
Ver.
5th
ed.
SAS
Institute.
Cary,
NC.

Hertzberg,
R.
C.
1989.
Fitting
a
model
to
categorical
response
data
with
application
to
species
extrapolation
of
toxicity.
Health
Physics
57:
405­
409.

ILSI
(
International
Life
Sciences
Institute).
1993.
Report
of
the
Benchmark
Dose
Workshop.
ISLI
Risk
Science
Institute.
Washington,
DC.

Jarabek,
A.
M.
1995a.
The
application
of
dosimetry
models
to
identify
key
processes
and
parameters
for
default
dose­
response
assessment
approaches.
Toxicol.
Lett.
79:
171­
184.

Jarabek,
A.
M.
1995b.
Interspecies
extrapolation
based
on
mechanistic
determinants
of
chemical
disposition.
Human
Eco.
Risk
Asses.
1(
5):
41­
622.

Jarabek,
A.
M.
1995c.
Consideration
of
temporal
toxicity
challenges
current
default
assumptions.
Inhalation
Toxicol.
7:
927­
946.

Kimmel,
C.
A.
1990.
Quantitative
approaches
to
human
risk
assessment
for
noncancer
health
effects.
Neurotoxicology
11:
189­
198.

Kimmel,
C.
A.
and
D.
W.
Gaylor.
1988.
Issues
in
qualitative
and
quantitative
risk
analysis
for
developmental
toxicity.
Risk
Anal.
8:
15­
20.

NRC
(
National
Research
Council).
1977.
Decision
Making
in
the
Environmental
Protection
Agency.
Vol.
2.
National
Academy
of
Sciences.
Washington,
DC.
Pp.
32­
33
and
241­
242.

NRC
(
National
Research
Council).
1993.
Guidelines
for
Developing
Emergency
Exposure
Levels
for
Hazardous
Substances.
Subcommittee
on
Guidelines
for
Developing
Community
Emergency
Exposure
Levels
(
CEELs)
for
Hazardous
Substances.
Committee
on
Toxicology,
NRC.
National
Academy
Press.
Washington,
DC.

Rai,
K.
and
J.
Van
Ryzin.
1985.
A
dose­
response
model
for
teratological
experiments
involving
quantal
responses.
Biometrics
41:
1­
10.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1986.
Guidelines
for
mutagenicity
assessment.
Federal
Register
51:
34006­
34012.
September
24.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1989.
Reference
dose
(
RfD)
for
oral
exposure
for
uranium
(
soluble
salts).
Integrated
Risk
Information
System
(
IRIS).
Online.
3­
32
(
Verification
date
10/
1/
89).
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1991a.
Final
guidelines
for
developmental
toxicity
risk
assessment.
Federal
Register
56:
63798­
63826.
December
5.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1991b.
Reference
dose
(
RfD)
for
oral
exposure
for
nitrate.
Integrated
Risk
Information
System
(
IRIS).
Online.
(
Verification
date
10/
01/
91).
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1992.
Reference
dose
(
RfD)
for
oral
exposure
for
inorganic
zinc.
Integrated
Risk
Information
System
(
IRIS).
Online.
(
Verification
date
10/
1/
92).
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1993a.
Reference
dose
(
RfD):
Description
and
use
in
health
risk
assessments.
Integrated
Risk
Information
System
(
IRIS).
Online.
Intra­
Agency
Reference
Dose
(
RfD)
Work
Group,
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.
March
15.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1993b.
Revision
of
Methodology
for
Deriving
National
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health:
Report
of
Workshop
and
EPA's
Preliminary
Recommendations
for
Revision.
Submitted
to
the
EPA
Science
Advisory
Board
by
the
Human
Health
Risk
Assessment
Branch,
Health
and
Ecological
Criteria
Division,
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
January
8.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1993c.
Reference
dose
(
RfD)
for
oral
exposure
for
inorganic
arsenic.
Integrated
Risk
Information
System
(
IRIS).
Online.
(
Verification
date
02/
01/
93).
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1994.
Methods
for
Derivation
of
Inhalation
Reference
Concentrations
and
Application
of
Inhalation
Dosimetry.
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Research
Triangle
Park,
NC.
EPA/
600/
8­
90/
066F.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1995.
Proposed
guidelines
for
neurotoxicity
risk
assessment.
Federal
Register
60:
52032­
52056.
October
4.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996a.
Reproductive
toxicity
risk
assessment
guidelines.
Federal
Register
61:
56274­
56322.
October
31.
3­
33
USEPA
(
U.
S.
Environmental
Protection
Agency).
1996b.
Report
on
the
Benchmark
Dose
Peer
Consultation
Workshop.
Risk
Assessment
Forum.
Washington,
DC.
EPA/
630/
R­
96/
011.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1996c.
Integrated
Risk
Information
System
(
IRIS);
announcement
of
pilot
program;
request
for
information.
Federal
Register.
61:
14570.
April
2.

USEPA
(
U.
S.
Environmental
Protection
Agency).
1997.
Mercury
Study:
Report
to
Congress.
Volume
5:
Health
Effects
of
Mercury
and
Mercury
Compounds.
Office
of
Air
Quality
Planning
and
Standards,
and
Office
of
Research
and
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Research
Triangle
Park,
NC.
EPA­
452­
R­
97­
007.

USEPA
(
U.
S.
Environmental
Protection
Agency).
2000.
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000).
Technical
Support
Document
Volume
1:
Risk
Assessment.
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
EPA­
822­
B­
00­
005.
August.
4­
1
4.
EXPOSURE
The
derivation
of
AWQC
for
the
protection
of
human
health
requires
information
about
both
the
toxicological
endpoints
of
concern
for
water
pollutants
and
the
pathways
of
human
exposure
to
those
pollutants.
The
two
primary
pathways
of
human
exposure
to
pollutants
present
in
a
particular
ambient
waterbody
that
have
been
considered
in
deriving
AWQC
are
direct
ingestion
of
drinking
water
obtained
from
that
waterbody
and
the
consumption
of
fish/
shellfish
obtained
from
that
waterbody.
The
water
pathway
also
includes
other
exposures
from
household
uses
(
e.
g.,
showering).
The
derivation
of
an
AWQC
involves
the
calculation
of
the
maximum
water
concentration
for
a
pollutant
(
i.
e.,
the
water
quality
criteria
level)
that
ensures
drinking
water
and/
or
fish
ingestion
exposures
will
not
result
in
human
intake
of
that
pollutant
in
amounts
that
exceed
a
specified
level
based
upon
the
toxicological
endpoint
of
concern.

The
equation
for
noncancer
effects
is
presented
again
here,
in
simplified
form,
to
emphasize
the
exposure­
related
parameters
(
in
bold).
[
Note:
the
RSC
parameter
also
applies
to
nonlinear
low­
dose
extrapolation
for
cancer
effects
and
the
other
exposure
parameters
apply
to
all
three
of
the
equations
(
see
Section
1.6).]

(
Equation
4­
1)
(
)
(
)
[
]
AWQC
RfD
BAF
=
·
·
+
·
RSC
BW
DI
FI
where:
AWQC
=
Ambient
Water
Quality
Criterion
(
mg/
L)
RfD
=
Reference
dose
for
noncancer
effects
(
mg/
kg­
day)
RSC
=
Relative
source
contribution
factor
to
account
for
nonwater
sources
of
exposure
BW
=
Human
body
weight
(
kg)
DI
=
Drinking
water
intake
(
L/
day)
FI
=
Fish
intake
(
kg/
day)
BAF
=
Bioaccumulation
factor
(
L/
kg)

The
following
subsections
discuss
exposure
issues
relevant
to
the
2000
Human
Health
Methodology:
exposure
policy
issues;
consideration
of
non­
water
sources
of
exposure
(
the
Relative
Source
Contribution
approach);
and
the
factors
used
in
AWQC
computation.
In
relevant
sections,
science
policy
and
risk
management
decisions
made
by
EPA
are
discussed.

4.1
EXPOSURE
POLICY
ISSUES
This
section
discusses
broad
policy
issues
related
to
exposure
concerning
the
major
objectives
that
the
Agency
believes
should
be
met
in
setting
AWQC.
4­
2
An
Exposure
Assessment
TSD
provides
greater
detail
on
numerous
topics
discussed
in
this
guidance:
suggested
sources
of
contaminant
concentration
and
exposure
intake
information;
suggestions
of
survey
methods
for
obtaining
and
analyzing
exposure
data
necessary
for
deriving
AWQC;
summaries
of
studies
on
fish
consumption
among
sport
fishers
and
subsistence
fishers;
more
detailed
presentation
of
parameter
values
(
e.
g.,
fish
consumption
rates,
body
weights);
and
additional
guidance
on
the
application
of
the
RSC
approach.

4.1.1
Sources
of
Exposure
Associated
With
Ambient
Water
4.1.1.1
Appropriateness
of
Including
the
Drinking
Water
Pathway
in
AWQC
EPA
intends
to
continue
including
the
drinking
water
exposure
pathway
in
the
derivation
of
its
national
default
human
health
criteria
(
AWQC),
as
has
been
done
since
the
1980
AWQC
National
Guidelines
were
first
published.

EPA
recommends
inclusion
of
the
drinking
water
exposure
pathway
where
drinking
water
is
a
designated
use
for
the
following
reasons:
(
1)
Drinking
water
is
a
designated
use
for
surface
waters
under
the
CWA
and,
therefore,
criteria
are
needed
to
assure
that
this
designated
use
can
be
protected
and
maintained.
(
2)
Although
rare,
there
are
some
public
water
supplies
that
provide
drinking
water
from
surface
water
sources
without
treatment.
(
3)
Even
among
the
majority
of
water
supplies
that
do
treat
surface
waters,
existing
treatments
may
not
necessarily
be
effective
for
reducing
levels
of
particular
contaminants.
(
4)
In
consideration
of
the
Agency's
goals
of
pollution
prevention,
ambient
waters
should
not
be
contaminated
to
a
level
where
the
burden
of
achieving
health
objectives
is
shifted
away
from
those
responsible
for
pollutant
discharges
and
placed
on
downstream
users
to
bear
the
costs
of
upgraded
or
supplemental
water
treatment.

This
policy
decision
has
been
supported
by
the
States,
most
of
the
public
stakeholders,
and
by
external
peer
reviewers.
As
with
the
other
exposure
parameters,
States
and
authorized
Tribes
have
the
flexibility
to
use
alternative
intake
rates
if
they
believe
that
drinking
water
consumption
is
substantively
different
than
EPA's
recommended
default
assumptions
of
2
L/
day
for
adults
and
1
L/
day
for
children.
EPA
recommends
that
States
and
authorized
Tribes
use
an
intake
rate
that
would
be
protective
of
a
majority
of
consumers
and
will
consider
whether
an
alternative
assumption
is
adequately
protective
of
a
State's
or
Tribe's
population
based
on
the
information
or
rationale
provided
at
the
time
EPA
reviews
State
and
Tribal
water
quality
standards
submissions.

4.1.1.2
Setting
Separate
AWQC
for
Drinking
Water
and
Fish
Consumption
In
conjunction
with
the
issue
of
the
appropriateness
of
including
the
drinking
water
pathway
explicitly
in
the
derivation
of
AWQC
for
the
protection
of
human
health,
EPA
intends
to
continue
its
practice
of
setting
a
single
AWQC
for
both
drinking
water
and
fish/
shellfish
consumption,
and
a
separate
AWQC
based
on
ingestion
of
fish/
shellfish
alone.
This
latter
criterion
applies
in
those
cases
where
the
designated
uses
of
a
waterbody
include
supporting
4­
3
fishable
uses
under
Section
101(
a)
of
the
CWA
and,
thus,
fish
or
shellfish
for
human
consumption,
but
not
as
a
drinking
water
supply
source
(
e.
g.,
non­
potable
estuarine
waters).

EPA
does
not
believe
that
national
water
quality
criteria
for
protection
of
drinking
water
uses
only
are
particularly
useful
for
two
reasons.
First,
State
and
Tribal
standards
for
human
health
are
set
to
protect
Section
101(
a)
uses
(
e.
g.,
"
fishable,
swimmable
uses")
under
the
CWA.
Second,
most
waters
have
multiple
designated
uses.
Additionally,
the
water
quality
standards
program
protects
aquatic
life.
The
2000
Human
Health
Methodology
revisions
do
not
change
EPA's
policy
to
apply
aquatic
life
criteria
to
protect
aquatic
species
where
they
are
more
sensitive
(
i.
e.,
when
human
health
criteria
would
not
be
protective
enough)
or
where
human
health
via
fish
or
water
ingestion
is
not
an
issue.

4.1.1.3
Incidental
Ingestion
from
Ambient
Surface
Waters
The
2000
Human
Health
Methodology
does
not
routinely
include
criteria
to
address
incidental
ingestion
of
water
from
recreational
uses.
EPA
has
considered
whether
there
are
cases
where
water
quality
criteria
for
the
protection
of
human
health
based
only
on
fish
ingestion
(
or
only
criteria
for
the
protection
of
aquatic
life)
may
not
adequately
protect
recreational
users
from
health
effects
resulting
from
incidental
water
ingestion.

EPA
reviewed
information
that
provided
estimates
of
incidental
water
ingestion
rates
averaged
over
time.
EPA
generally
believes
that
the
averaged
amount
is
negligible
and
will
not
have
any
impact
on
the
chemical
criteria
values
representative
of
both
drinking
water
and
fish
ingestion.
A
lack
of
impact
on
the
criteria
values
would
likely
also
be
true
for
chemical
criteria
based
on
fish
consumption
only,
unless
the
chemical
exhibits
no
bioaccumulation
potential.
However,
EPA
also
believes
that
incidental/
accidental
water
ingestion
could
be
important
for
the
development
of
microbial
contaminant
water
quality
criteria,
and
for
either
chemical
or
microbial
criteria
for
States
where
recreational
uses
such
as
swimming
and
boating
are
substantially
higher
than
the
national
average.
EPA
also
notes
that
some
States
have
indicated
they
already
have
established
incidental
ingestion
rates
for
use
in
developing
criteria.
Therefore,
although
EPA
will
not
use
this
intake
parameter
when
deriving
its
national
304(
a)
chemical
criteria,
limited
guidance
is
provided
in
the
Exposure
Assessment
TSD
volume
in
order
to
assist
States
and
authorized
Tribes
that
face
situations
where
this
intake
parameter
could
be
of
significance.

4.2
CONSIDERATION
OF
NON­
WATER
SOURCES
OF
EXPOSURE
WHEN
SETTING
AWQC
4.2.1
Policy
Background
The
2000
Human
Health
Methodology
uses
different
approaches
for
addressing
nonwater
exposure
pathways
in
setting
AWQC
for
the
protection
of
human
health
depending
upon
the
toxicological
endpoint
of
concern.
With
those
substances
for
which
the
appropriate
toxic
endpoint
is
carcinogenicity
based
on
a
linear
low­
dose
extrapolation,
only
the
two
water
sources
(
i.
e.,
drinking
water
and
fish
ingestion)
are
considered
in
the
derivation
of
the
AWQC.
Non­
water
4­
4
sources
are
not
considered
explicitly.
In
the
case
of
carcinogens
based
on
linear
low­
dose
extrapolation,
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.
Thus,
the
AWQC
represents
the
water
concentration
that
would
be
expected
to
increase
an
individual's
lifetime
risk
of
carcinogenicity
from
exposure
to
the
particular
pollutant
by
no
more
than
one
chance
in
one
million,
regardless
of
the
additional
lifetime
cancer
risk
due
to
exposure,
if
any,
to
that
particular
substance
from
other
sources.

Furthermore,
health­
based
criteria
values
for
one
medium
based
on
linear
low­
dose
extrapolation
typically
vary
from
values
for
other
media
in
terms
of
the
concentration
value,
and
often
the
associated
risk
level.
Therefore,
the
RSC
concept
could
not
even
theoretically
apply
unless
all
risk
assessments
for
a
particular
carcinogen
based
on
linear
low­
dose
extrapolation
resulted
in
the
same
concentration
value
and
same
risk
level;
that
is,
an
apportionment
would
need
to
be
based
on
a
single
risk
value
and
level.

In
the
case
of
substances
for
which
the
AWQC
is
set
on
the
basis
of
a
carcinogen
based
on
a
nonlinear
low­
dose
extrapolation
or
for
a
noncancer
endpoint
where
a
threshold
is
assumed
to
exist,
non­
water
exposures
are
considered
when
deriving
the
AWQC
using
the
RSC
approach.
The
rationale
for
this
approach
is
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.

There
has
been
some
discussion
of
whether
it
is,
in
fact,
necessary
in
most
cases
to
explicitly
account
for
other
sources
of
exposure
when
computing
the
AWQC
for
pollutants
exhibiting
threshold
effects.
It
has
been
argued
that
because
of
the
conservative
assumptions
generally
incorporated
in
the
calculation
of
RfDs
(
or
POD/
UF
values)
used
as
the
basis
for
the
AWQC
derivation,
total
exposures
slightly
exceeding
the
RfD
are
unlikely
to
produce
adverse
effects.

EPA
emphasizes
that
the
purpose
of
the
RSC
is
to
ensure
that
the
level
of
a
chemical
allowed
by
a
criterion
or
multiple
criteria,
when
combined
with
other
identified
sources
of
exposure
common
to
the
population
of
concern,
will
not
result
in
exposures
that
exceed
the
RfD
or
the
POD/
UF.
The
policy
of
considering
multiple
sources
of
exposure
when
deriving
healthbased
criteria
has
become
common
in
EPA's
program
office
risk
characterizations
and
criteria
and
standard­
setting
actions.
Numerous
EPA
workgroups
have
evaluated
the
appropriateness
of
factoring
in
such
exposures,
and
the
Agency
concludes
that
it
is
important
for
adequately
protecting
human
health.
Consequently,
EPA
risk
management
policy
has
evolved
significantly
over
the
last
six
years.
Various
EPA
program
initiatives
and
policy
documents
regarding
aggregate
exposure
and
cumulative
risk
have
been
developed,
including
the
consideration
of
inhalation
and
dermal
exposures.
Additionally,
accounting
for
other
exposures
has
been
included
in
recent
mandates
(
e.
g.,
the
Food
Quality
Protection
Act)
and,
thus,
is
becoming
a
requirement
for
the
Agency.
The
Exposure
Decision
Tree
approach
has
been
shared
with
other
EPA
offices,
and
efforts
to
coordinate
policies
on
aggregate
exposure,
where
appropriate,
have
begun.
EPA
intends
to
continue
developing
policy
guidance
on
the
RSC
issue
and
guidance
to
address
the
concern
that
human
health
may
not
be
adequately
protected
if
criteria
allow
for
higher
levels
of
4­
5
exposure
that,
combined,
may
exceed
the
RfD
or
POD/
UF.
EPA
also
intends
to
refine
the
2000
Human
Health
Methodology
in
the
future
to
incorporate
additional
guidance
on
inhalation
and
dermal
exposures.
As
stated
previously,
EPA
is
required
to
derive
national
water
quality
criteria
under
Section
304(
a)
of
the
CWA
and
does
not
intend
to
derive
site­
specific
criteria.
However,
States
and
authorized
Tribes
have
the
flexibility
to
make
alternative
exposure
and
RSC
estimates
based
on
local
data,
and
EPA
strongly
encourages
this.

Uncertainty
factors
used
in
the
derivation
of
the
RfD
(
or
POD/
UF)
to
account
for
intraand
interspecies
variability
and
the
incompleteness
of
the
toxicity
data
set(
s)/
animal
studies
are
specifically
relevant
to
the
chemical's
internal
toxicological
action,
irrespective
of
the
sources
of
exposure
that
humans
may
be
experiencing.
The
Agency's
policy
is
to
consider
and
account
for
other
sources
of
exposure
in
order
to
set
protective
health
criteria.
EPA
believes
that
multiple
route
exposures
may
be
particularly
important
when
uncertainty
factors
associated
with
the
RfD
are
small.
Although
EPA
is
well
aware
that
RfDs
are
not
all
equivalent
in
their
derivation,
EPA
does
not
believe
that
uncertainty
in
the
toxicological
data
should
result
in
less
stringent
criteria
by
ignoring
exposure
sources.
However,
the
RSC
policy
approach
does
allow
less
stringent
assumptions
when
multiple
sources
of
exposure
are
not
anticipated.

The
AWQC
are
designed
to
be
protective
criteria,
generally
applicable
to
the
waters
of
the
United
States.
While
EPA
cannot
quantitatively
predict
the
actual
human
health
risk
associated
with
combined
exposures
above
the
RfD
or
POD/
UF,
a
combination
of
health
criteria
for
multiple
media
exceeding
the
RfD
or
POD/
UF
may
not
be
sufficiently
protective.
Therefore,
EPA's
policy
is
to
routinely
account
for
all
sources
and
routes
of
non­
occupational
exposure
when
setting
AWQC
for
noncarcinogens
and
for
carcinogens
based
on
nonlinear
low­
dose
extrapolations.
EPA
believes
that
maintaining
total
exposure
below
the
RfD
(
or
POD/
UF)
is
a
reasonable
health
goal
and
that
there
are
circumstances
where
health­
based
criteria
for
a
chemical
should
not
exceed
the
RfD
(
or
POD/
UF),
either
alone
(
if
only
one
criterion
is
relevant,
along
with
other
intake
sources
considered
as
background
exposures)
or
in
combination.
EPA
believes
its
RSC
policy
ensures
this
goal.

Also,
given
the
inability
to
reasonably
predict
future
changes
in
exposure
patterns,
the
uncertainties
in
the
exposure
estimates
due
to
typical
data
inadequacy,
possible
unknown
sources
of
exposure,
and
the
potential
for
some
populations
to
experience
greater
exposures
than
indicated
by
the
available
data,
EPA
believes
that
utilizing
the
entire
RfD
(
or
POD/
UF)
does
not
ensure
adequate
protection.

4.2.2
The
Exposure
Decision
Tree
Approach
As
indicated
in
Section
1,
EPA
has,
in
the
past,
used
a
"
subtraction"
method
to
account
for
multiple
sources
of
exposure
to
pollutants.
In
the
subtraction
method,
other
sources
of
exposure
(
i.
e.,
those
other
than
the
drinking
water
and
fish
exposures)
are
subtracted
from
the
RfD
(
or
POD/
UF).
However,
EPA
also
previously
used
a
"
percentage"
method
for
the
same
purpose.
In
this
approach,
the
percentage
of
total
exposure
typically
accounted
for
by
the
exposure
source
for
which
the
criterion
is
being
determined,
referred
to
as
the
relative
source
4­
6
contribution
(
RSC),
is
applied
to
the
RfD
to
determine
the
maximum
amount
of
the
RfD
"
apportioned"
to
that
source.
With
both
procedures,
a
"
ceiling"
level
of
80
percent
of
the
RfD
and
a
"
floor
level"
of
20
percent
of
the
RfD
are
applied.

The
subtraction
method
is
considered
acceptable
when
only
one
criterion
is
relevant
for
a
particular
chemical.
The
percentage
method
is
recommended
in
the
context
of
the
above
goals
when
multiple
media
criteria
are
at
issue.
The
percentage
method
does
not
simply
depend
on
the
amount
of
a
contaminant
in
the
prospective
criterion
source
only.
It
is
intended
to
reflect
health
considerations,
the
relative
portions
of
other
sources,
and
the
likelihood
for
ever­
changing
levels
in
each
of
those
multiple
sources
(
due
to
ever­
changing
sources
of
emissions
and
discharges).
Rather
than
simply
defaulting
in
every
instance,
the
Agency
attempts
to
compare
multiple
source
exposures
with
one
another
to
estimate
their
relative
contribution
to
the
total
 
given
that
understanding
the
degree
to
which
their
concentrations
vary,
or
making
any
distributional
analysis,
is
often
not
possible.
The
criteria
levels,
when
multiple
criteria
are
at
issue,
are
based
on
the
actual
levels,
with
an
assumption
that
there
may
be
enough
relative
variability
such
that
an
apportionment
(
relating
that
percentage
to
the
RfD)
is
a
reasonable
way
of
accounting
for
the
uncertainty
regarding
that
variability.

The
specific
RSC
approach
recommended
by
EPA,
which
we
will
use
for
the
derivation
of
AWQC
for
noncarcinogens
and
carcinogens
assessed
using
nonlinear
low­
dose
extrapolation,
is
called
the
Exposure
Decision
Tree
and
is
described
below.
To
account
for
exposures
from
other
media
when
setting
an
AWQC
(
i.
e.,
non­
drinking
water/
non­
fish
ingestion
exposures,
and
inhalation
or
dermal
exposures),
the
Exposure
Decision
Tree
for
determining
proposed
RfD
or
POD/
UF
apportionments
represents
a
method
of
comprehensively
assessing
a
chemical
for
water
quality
criteria
development.
This
method
considers
the
adequacy
of
available
exposure
data,
levels
of
exposure,
relevant
sources/
media
of
exposure,
and
regulatory
agendas
(
i.
e.,
whether
there
are
multiple
health­
based
criteria
or
regulatory
standards
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
apportionment
of
the
RfD
or
POD/
UF
desirable.
Both
subtraction
and
percentage
methods
are
potentially
utilized
under
different
circumstances
with
the
Exposure
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.
EPA
recognizes
that
there
may
be
other
valid
approaches
in
addition
to
the
Exposure
Decision
Tree.

The
Exposure
Decision
Tree
approach
allows
flexibility
in
the
RfD
(
or
POD/
UF)
apportionment
among
sources
of
exposure.
When
adequate
data
are
available,
they
are
used
to
make
protective
exposure
estimates
for
the
population(
s)
of
concern.
When
other
sources
or
routes
of
exposure
are
anticipated
but
data
are
not
adequate,
there
is
an
even
greater
need
to
make
sure
that
public
health
protection
is
achieved.
For
these
circumstances,
a
series
of
4­
7
qualitative
alternatives
is
used
(
with
the
less
adequate
data
or
default
assumptions)
that
allow
for
the
inadequacies
of
the
data
while
protecting
human
health.
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
determination
of
a
reasonable
exposure
characterization
for
the
chemical
will
result
in
a
water
quality
criterion
that
more
accurately
reflects
exposures
than
automatically
using
a
default
value.
Although
the
20
percent
default
will
still
generally
be
used
when
information
is
not
adequate,
the
need
for
using
it
should
be
reduced.
There
may
also
be
some
situations
where
EPA
would
consider
the
use
of
an
80
percent
default
(
see
Section
4.2.3).

The
Decision
Tree
also
allows
for
use
of
either
the
subtraction
or
percentage
method
to
account
for
other
exposures,
depending
on
whether
one
or
more
health­
based
criterion
is
relevant
for
the
chemical
in
question.
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
(
or
POD/
UF).

EPA
cautions
States
and
Tribes
when
using
the
subtraction
method
in
these
circumstances.
The
subtraction
method
results
in
a
criterion
allowing
the
maximum
possible
chemical
concentration
in
water
after
subtracting
other
sources.
As
such,
it
removes
any
cushion
between
pre­
criteria
levels
(
i.
e.,
actual
"
current"
levels)
and
the
RfD,
thereby
setting
criteria
at
the
highest
levels
short
of
exceeding
the
RfD.
It
is
somewhat
counter
to
the
goals
of
the
CWA
for
maintaining
and
restoring
the
nation's
waters.
It
is
also
directly
counter
to
Agency
policies,
explicitly
stated
in
numerous
programs,
regarding
pollution
prevention.
EPA
has
advocated
that
it
is
good
health
policy
to
set
criteria
such
that
exposures
are
kept
low
when
current
levels
are
already
low.
The
subtraction
method
generally
results
in
criteria
levels
of
a
contaminant
in
a
particular
medium
at
significantly
higher
levels
than
the
percentage
method
and,
in
this
respect,
is
contradictory
to
such
goals.
In
fact,
many
chemicals
have
pre­
criteria
levels
in
environmental
media
substantially
lower
(
compared
to
the
RfD)
than
the
resulting
criteria
allow.

When
more
than
one
criterion
is
relevant
to
a
particular
chemical,
apportioning
the
RfD
(
or
POD/
UF)
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
(
or
POD/
UF).
The
Exposure
Decision
Tree
(
with
numbered
boxes)
is
shown
in
Figure
4­
1.
The
explanation
in
the
text
on
the
following
pages
must
be
read
in
tandem
with
the
Decision
Tree
figure;
the
text
in
each
box
of
the
figure
only
nominally
identifies
the
process
and
conditions
for
determining
the
outcome
for
that
step
of
the
Decision
Tree.
The
underlying
objective
is
to
maintain
total
exposure
below
the
RfD
(
or
POD/
UF)
while
generally
avoiding
an
extremely
low
limit
in
a
single
medium
that
represents
just
a
nominal
fraction
of
the
total
exposure.
To
meet
this
objective,
all
proposed
numeric
limits
lie
between
80
percent
and
20
percent
of
the
RfD
(
or
POD/
UF).
Again,
EPA
will
use
the
Exposure
Decision
Tree
approach
when
deriving
its
AWQC
but
also
recognizes
that
departures
from
the
approach
may
be
appropriate
in
certain
cases.
EPA
understands
that
there
may
be
situations
where
the
Decision
Tree
procedure
is
not
practicable
or
4­
8
Are
exposures
from
multiple
sources
(
due
to
a
sum
of
sources
or
an
individual
source)
potentially
at
levels
near
(
i.
e.,
over
80%),
at
or
in
excess
of
the
RfD
(
or
POD/
UF)?
Exposure
Decision
Tree
for
Defining
Proposed
RfD
(
or
POD/
UF)
Apportionment
1.
Identify
population(
s)
of
concern.

2.
Identify
relevant
exposure
sources/
pathways.
*

3.

4.
Are
there
sufficient
data,
physical/
chemical
property
information,
fate
and
transport
information,
and/
or
generalized
information
available
to
characterize
the
likelihood
of
exposure
to
relevant
sources?

Is
there
some
information
available
on
each
source
to
make
a
characterization
of
exposure?
Apportion
the
RfD
(
or
POD/
UF)
including
80%
ceiling/
20%
floor
using
the
percentage
approach
(
with
ceiling
and
floor).
Is
there
more
than
one
regulatory
action
(
i.
e.,
criteria,
standard,
guidance)
relevant
for
the
chemical
in
question?
Describe
exposures,
uncertainties,
toxicityrelated
information,
control
issues,
and
other
information
for
management
decision.
Perform
calculations
associated
with
Boxes
12
or
13
as
applicable.
No
Yes
9.

Yes
10.

11.

Use
subtraction
of
appropriate
intake
levels
from
sources
other
than
source
of
concern,
including
80%
ceiling/
20%
floor.
12.

13.
Are
there
significant
known
or
potential
uses/
sources
other
than
the
source
of
concern?

Use
50%
of
the
RfD
(
or
POD/
UF).
7.
8A.
No
No
Yes
Yes
Yes
No
Are
adequate
data
available
to
describe
central
tendencies
and
high­
ends
for
relevant
exposure
sources/
pathways?

No
Problem
Formulation
Use
20%
of
the
RfD
(
or
POD/
UF).
8B.
No
8C.
Yes
5A.

6.
Figure
4­
1
Perform
apportionment
as
described
in
Box
12
or
13,
with
a
50%
ceiling/
20%
floor.
5B.

Gather
more
information
and
rereview
Use
20%
of
the
RfD
or
POD/
UF
OR
*
Sources
and
pathways
include
both
ingestion
and
routes
other
than
oral
for
water­
related
exposures,
and
nonwater
sources
of
exposure,
including
ingestion
exposures
(
e.
g.,
food),
inhalation,
and/
or
dermal.
4­
9
may
be
simply
irrelevant
after
considering
the
properties,
uses,
and
sources
of
the
chemical
in
question.
EPA
endorses
such
flexibility
by
States
and
authorized
Tribes
when
developing
alternative
water
quality
criteria
in
order
to
choose
other
procedures
that
are
more
appropriate
for
setting
health­
based
criteria
and,
perhaps,
apportioning
the
RfD
or
POD/
UF,
as
long
as
reasons
are
given
as
to
why
it
is
not
appropriate
to
follow
the
Exposure
Decision
Tree
approach
and
as
long
as
the
steps
taken
to
evaluate
the
potential
sources
and
levels
of
exposure
are
clearly
described.
Often,
however,
the
common
situation
of
multiple
exposure
sources
for
a
chemical
is
likely
to
merit
a
Decision
Tree
evaluation
for
the
purpose
of
developing
human
health
water
quality
criteria
for
a
given
chemical.

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
alternative
water
quality
criteria
are
of
great
importance.

Descriptions
of
the
boxes
within
the
Decision
Tree
are
separated
by
the
following
process
headings
to
facilitate
an
understanding
of
the
major
considerations
involved.
The
decision
to
perform,
or
not
to
perform,
an
apportionment
could
actually
be
made
at
several
points
during
the
Decision
Tree
process.
Working
through
the
process
is
most
helpful
for
identifying
possible
exposure
sources
and
the
potential
for
exposure,
determining
the
relevancy
of
the
Decision
Tree
to
developing
an
AWQC
for
a
particular
chemical
and,
possibly,
determining
the
appropriateness
of
using
an
alternative
approach
to
account
for
overall
exposure.
"
Relevancy"
here
means
determining
whether
more
than
one
criterion,
standard,
or
other
guidance
is
being
planned
or
is
in
existence
for
the
chemical
in
question.
Additional
guidance
for
States
and
Tribes
that
wish
to
use
the
Exposure
Decision
Tree
is
provided
in
the
Exposure
Assessment
TSD.

4.2.2.1
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
a
manner
that
allows
determination
of
the
potential
for
the
population
of
concern
to
experience
exposures
from
multiple
sources
for
the
chemical
in
question.
Also,
the
data
for
the
chemical
in
question
must
be
representative
of
each
source/
medium
of
exposure
and
be
relevant
to
the
identified
population(
s).
Evaluation
includes
determining
whether
the
levels,
multiple
criteria
or
regulatory
standards,
or
other
circumstances
make
apportionment
of
the
RfD
or
POD/
UF
reasonable.
The
initial
problem
formulation
also
determines
the
exposure
parameters
chosen,
the
intake
assumptions
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.
4­
10
4.2.2.2
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.
The
adequacy
of
data
is
a
professional
judgment
for
each
individual
chemical
of
concern,
but
EPA
recommends
that
the
minimum
acceptable
data
for
Box
3
are
exposure
distributions
that
can
be
used
to
determine,
with
an
acceptable
95
percent
confidence
interval,
the
central
tendency
and
high­
end
exposure
levels
for
each
source.
In
fact,
distributional
data
may
exist
for
some
or
most
of
the
sources
of
exposure.

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
data
set
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
value);
(
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
sufficient
summary
statistics
(
and
rarely
the
raw
data)
to
completely
characterize
data
adequacy.
Thus,
a
case­
by­
case
determination
of
data
adequacy
may
be
necessary.

That
being
stated,
there
are
some
guidelines,
as
presented
below,
that
lead
to
a
rough
rule­
of­
thumb
on
what
constitutes
an
"
adequate"
sample
size
for
exposure
assessment.
Again,
first
and
foremost,
the
representativeness
of
the
data
for
the
population
evaluated
and
the
analytical
quality
of
the
data
must
be
acceptable.
If
so,
the
primary
objective
then
becomes
estimating
an
upper
percentile
(
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
exposures
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
0.9
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:
4­
11
where:

r
=
the
rank
order
of
the
observation
n
=
the
number
of
observations
I
=
integer
from
0
to
r
­
1
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
(
see
for
example
Gibbons,
1971).

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
high­
end
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
(
i.
e.,
the
standard
deviation
divided
by
the
mean)
is
between
0.5
and
2.0,
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
nondetects
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
also
depends
on
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.
(
Equation
4­
2)
4­
12
If
the
answer
to
Box
3
is
no,
based
on
the
above
determination
of
adequacy,
then
the
decision
tree
moves
to
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
limited
data
that
are
available
(
in
addition
to
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
should
always
be
reviewed
at
the
outset,
it
is
recommended
that
this
information
also
be
used
to
estimate
the
health­
based
water
quality
criteria.
The
estimate
should
be
rather
conservative
(
as
indicated
in
the
Decision
Tree),
given
that
it
is
either
not
based
on
actual
monitoring
data
or
is
based
on
data
that
has
been
considered
to
be
inadequate
for
a
more
accurate
quantitative
estimate.
Therefore,
greater
uncertainties
exist
and
accounting
for
variability
is
not
really
possible.
Whether
the
available
data
are
adequate
and
sufficiently
representative
will
likely
vary
from
chemical
to
chemical
and
may
depend
on
the
population
of
concern.
If
there
are
some
data
and/
or
other
information
to
make
a
characterization
of
exposure,
a
determination
can
be
made
as
to
whether
there
are
significant
known
or
potential
uses
for
the
chemical/
sources
of
exposure
other
than
the
source
of
concern
(
i.
e.,
in
this
case,
the
drinking
water
and
fish
intakes
relevant
to
developing
an
AWQC)
that
would
allow
one
to
anticipate/
quantify
those
exposures
(
Box
6).
If
there
are
not,
then
it
is
recommended
that
50
percent
of
the
RfD
or
POD/
UF
can
be
safely
apportioned
to
the
source
of
concern
(
Box
7).
While
this
leaves
half
of
the
RfD
or
POD/
UF
unapportioned,
it
is
recommended
as
the
maximum
apportionment
due
to
the
lack
of
data
needed
to
more
accurately
quantify
actual
or
potential
exposures.
If
the
answer
to
the
question
in
Box
6
is
yes
(
there
is
multiple
source
information
available
for
the
exposures
of
concern),
and
some
information
is
available
on
each
source
of
exposure
(
Box
8A),
apply
the
procedure
in
either
Box
12
or
Box
13
(
depending
on
whether
one
or
more
criterion
is
relevant
to
the
chemical),
using
a
50
percent
ceiling
(
Box
8C)
 
again
due
to
the
lack
of
adequate
data.
If
the
answer
to
the
question
in
Box
8A
is
no
(
there
is
no
available
information
to
characterize
exposure),
then
the
20
percent
default
of
the
RfD
or
POD/
UF
is
used
(
Box
8B).

If
the
answer
to
the
question
in
Box
4
is
no;
that
is,
there
are
not
sufficient
data/
information
to
characterize
exposure,
EPA
intends
to
generally
use
the
"
default"
assumption
of
20
percent
of
the
RfD
or
POD/
UF
(
Box
5A)
when
deriving
or
revising
the
AWQC.
It
may
be
better
to
gather
more
data
or
information
and
re­
review
when
this
information
becomes
available
(
Box
5B).
EPA
has
done
this
on
occasion
when
resources
permit
the
acquisition
of
additional
data
to
enable
better
estimates
of
exposure
instead
of
the
default.
If
this
is
not
possible,
then
the
assumption
of
20
percent
of
the
RfD
or
POD/
UF
(
Box
5A)
should
be
used.
Box
5A
is
likely
to
be
used
infrequently
with
the
Exposure
Decision
Tree
approach,
given
that
the
information
described
in
Box
4
should
be
available
in
most
cases.
However,
EPA
intends
to
use
20
percent
of
the
RfD
(
or
POD/
UF),
which
has
also
been
used
in
past
water
program
regulations,
as
the
default
value.
4­
13
4.2.2.3
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
POD/
UF
are
compared
(
Box
9).
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
POD/
UF,
then
a
subsequent
determination
is
made
(
Box
11)
as
to
whether
there
is
more
than
one
health­
based
criterion
or
regulatory
action
relevant
for
the
given
chemical
(
i.
e.,
more
than
one
medium­
specific
criterion,
standard
or
other
guidance
being
planned,
performed
or
in
existence
for
the
chemical).
The
subtraction
method
is
considered
acceptable
when
only
one
criterion
(
standard,
etc.)
is
relevant
for
a
particular
chemical.
In
these
cases,
other
sources
of
exposure
can
be
considered
"
background"
and
can
be
subtracted
from
the
RfD
(
or
POD/
UF).
When
more
than
one
criterion
is
relevant
to
a
particular
chemical,
apportioning
the
RfD
(
or
POD/
UF)
via
the
percentage
method
is
considered
appropriate
to
ensure
that
the
combination
of
health
criteria,
and
thus
the
potential
for
resulting
exposures,
do
not
exceed
the
RfD
(
or
POD/
UF).

As
indicated
in
Section
2,
for
EPA's
national
304(
a)
criteria,
the
RSC
intake
estimates
of
non­
water
exposures
(
e.
g.,
non­
fish
dietary
exposures)
will
be
based
on
arithmetic
mean
values
when
data
are
available.
The
assumed
body
weight
used
in
calculating
the
national
criteria
will
also
be
based
on
average
values.
The
drinking
water
and
fish
intake
values
are
90th
percentile
estimates.
EPA
believes
that
these
assumptions
will
be
protective
of
a
majority
of
the
population
and
recommends
them
for
State
and
Tribal
use.
However,
States
and
authorized
Tribes
have
the
flexibility
to
choose
alternative
intake
rate
and
exposure
estimate
assumptions
to
protect
specific
population
groups
that
they
have
chosen.

4.2.2.4
Apportionment
Decisions
If
the
answer
to
the
question
in
Box
11
is
no
(
there
is
not
more
than
one
relevant
medium­
specific
criterion/
regulatory
action),
then
the
recommended
method
for
setting
a
healthbased
water
quality
criterion
is
to
utilize
a
subtraction
calculation
(
Box
12).
Specifically,
appropriate
intake
values
for
each
exposure
source
other
than
the
source
of
concern
are
subtracted
out.
EPA
will
rely
on
average
values
commonly
used
in
the
Agency
for
food
ingestion
and
inhalation
rates,
combined
with
mean
contaminant
concentration
values,
for
calculating
RSC
estimates
to
subtract.
Alternatively,
contaminant
concentrations
could
be
selected
based
on
the
variability
associated
with
those
concentrations
for
each
source.
This
implies
that
a
case­
by­
case
determination
of
the
variability
and
the
resulting
intake
chosen
would
be
made,
as
each
chemical
evaluated
can
be
expected
to
have
different
variations
in
concentration
associated
with
each
source
of
intake.
However,
EPA
anticipates
that
the
available
data
for
most
contaminants
will
not
allow
this
for
determination
(
based
on
past
experience).
Guidance
addressing
this
possibility
is
addressed
in
the
Exposure
Assessment
TSD.
EPA
does
not
recommend
that
high­
end
intakes
be
subtracted
for
every
exposure
source,
since
the
combination
may
not
be
representative
of
any
actually
exposed
population
or
individual.
The
subtraction
method
would
also
include
an
80
percent
ceiling
and
a
20
percent
floor.
4­
14
If
the
answer
to
the
question
in
Box
11
is
yes
(
there
is
more
than
one
medium­
specific
criterion/
regulation
relevant),
then
the
recommended
method
for
setting
health­
based
water
quality
criteria
is
to
apportion
the
RfD
or
POD/
UF
among
those
sources
for
which
health­
based
criteria
are
being
set
(
Box
13).
This
is
done
via
a
percentage
approach
(
with
a
ceiling
and
floor).
This
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
(
or
RSC)
would
be
50
percent.
The
health­
based
criteria
would,
in
turn,
be
set
at
50
percent
of
the
RfD
or
POD/
UF.
This
method
also
utilizes
an
appropriate
combination
of
intake
values
for
each
exposure
source
based
on
values
commonly
used
in
the
Agency
for
food
ingestion
and
inhalation
rates,
combined
with
mean
contaminant
concentration
values.

Finally,
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
POD/
UF
(
i.
e.,
the
answer
in
Box
9
is
yes),
then
the
estimates
of
exposures
and
related
uncertainties,
recommended
apportionment
(
either
box
12
or
13),
toxicity­
related
information,
control
issues,
and
other
information
are
to
be
presented
to
managers
for
a
decision
(
Box
10).
The
high
levels
referred
to
in
Box
9
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
POD/
UF.
Management
input
may
be
necessary
due
to
the
control
issues
(
i.
e.,
cost
and
feasibility
concerns),
especially
when
multiple
criteria
are
at
issue.
In
practice,
risk
managers
are
routinely
a
part
of
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
approach
or
exceed
the
RfD
or
POD/
UF
and
because
the
feasibility
of
controlling
different
sources
of
exposure
are
complicated
issues,
risk
managers
will
especially
need
to
be
directly
involved
in
final
decisions
in
these
circumstances.

It
is
emphasized
here
that
the
procedures
in
these
circumstances
are
not
different
than
the
procedures
when
exposures
are
not
at
or
above
the
RfD
(
or
POD/
UF).
Therefore,
in
these
cases,
estimates
should
be
performed
as
with
Boxes
11,
12,
and
13.
The
recommendation
should
be
made
based
on
health­
based
considerations
only,
just
as
when
the
chemical
in
question
was
not
a
Box
10
situation.
If
the
chemical
is
relevant
to
one
health
criterion
or
regulatory
action
only,
the
other
sources
of
exposure
could
be
subtracted
from
the
RfD
or
POD/
UF
to
determine
if
there
is
any
leftover
amount
for
setting
the
criterion.
If
the
chemical
is
a
multiple
media
criteria
issue,
then
an
apportionment
should
be
made,
even
though
it
is
possible
that
all
sources
would
need
to
be
reduced.
Regardless
of
the
outcome
of
Box
9,
all
apportionments
made
(
via
the
methods
of
Boxes
12
or
13)
should
include
a
presentation
of
the
uncertainty
in
the
estimate
and
in
the
RfD
or
POD/
UF
for
a
more
complete
characterization.

The
process
for
a
Box
10
situation
(
versus
a
situation
that
is
not)
differs
in
that
the
presentations
for
Boxes
12
and
13
are
based
on
apportionments
(
following
the
review
of
available
information
and
a
determination
of
appropriate
exposure
parameters)
that
must
address
additional
control
issues
and
may
result
in
more
selective
reductions.
With
Box
10,
one
or
several
criteria
possibilities
("
scenarios")
could
be
presented
for
comparison
along
with
implications
of
the
effects
4­
15
of
various
control
options.
It
is
appropriate
to
present
information
in
this
manner
to
risk
managers
given
the
complexity
of
these
additional
control
issues.

4.2.3
Additional
Points
of
Clarification
on
the
Exposure
Decision
Tree
Approach
for
Setting
AWQC
As
with
Box
9,
if
a
determination
is
made
in
Box
8A
(
i.
e.,
information
is
available
to
characterize
exposure)
that
exposures
are
near,
at,
or
above
the
RfD
(
or
POD/
UF)
based
on
the
available
information,
the
apportionments
made
need
to
be
presented
to
risk
managers
for
decision.
If
information
is
lacking
on
some
of
the
multiple
exposure
sources,
then
EPA
would
use
a
default
of
20
percent
of
the
RfD
or
POD/
UF
(
Box
8B).

Results
of
both
Boxes
12
and
13
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
that
point,
it
is
more
appropriate
to
reduce
other
sources
of
exposure,
rather
than
promulgating
standards
for
de
minimus
reductions
in
overall
exposure.

If
it
can
be
demonstrated
that
other
sources
and
routes
of
exposure
are
not
anticipated
for
the
pollutant
in
question
(
based
on
information
about
its
known/
anticipated
uses
and
chemical/
physical
properties),
then
EPA
would
use
the
80
percent
ceiling.
EPA
qualifies
this
policy
with
the
understanding
that
as
its
policy
on
cumulative
risk
assessment
continues
to
develop,
the
80
percent
RSC
may
prove
to
be
underprotective.

In
the
cases
of
pollutants
for
which
substantial
data
sets
describing
exposures
across
all
anticipated
pathways
of
exposure
exist,
and
probabilistic
analyses
have
been
conducted
based
on
those
data,
consideration
will
be
given
to
the
results
of
those
assessments
as
part
of
the
Exposure
Decision
Tree
approach
for
setting
AWQC.

For
many
chemicals,
the
rate
of
absorption
from
ingestion
can
differ
substantially
from
absorption
by
inhalation.
There
is
also
available
information
for
some
chemicals
that
demonstrates
appreciable
differences
in
gastrointestinal
absorption
depending
on
whether
the
chemical
is
ingested
from
water,
soil,
or
food.
For
some
contaminants,
the
absorption
of
the
contaminant
from
food
can
differ
appreciably
for
plant
compared
with
animal
food
products.
Regardless
of
the
apportionment
approach
used,
EPA
recommends
using
existing
data
on
differences
in
bioavailability
between
water,
air,
soils,
and
different
foods
when
estimating
total
exposure
for
use
in
apportioning
the
RfD
or
POD/
UF.
The
Agency
has
developed
such
exposure
estimates
for
cadmium
(
USEPA,
1994).
In
the
absence
of
data,
EPA
will
assume
equal
rates
of
absorption
from
different
routes
and
sources
of
exposure.
4­
16
4.2.4
Quantification
of
Exposure
When
selecting
contaminant
concentration
values
in
environmental
media
and
exposure
intake
values
for
the
RSC
analysis,
it
is
important
to
realize
that
each
value
selected
(
including
those
recommended
as
default
assumptions
in
the
AWQC
equation)
may
be
associated
with
a
distribution
of
values
for
that
parameter.
Determining
how
various
subgroups
fall
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
considered
to
generally
represent
a
particular
population
group
or
a
national
average.
Therefore,
describing
with
certainty
the
exact
percentile
of
a
particular
population
that
is
protected
with
a
resulting
criteria
is
often
not
possible.

By
and
large,
the
AWQC
are
derived
to
protect
the
majority
of
the
general
population
from
chronic
adverse
health
effects.
However,
as
stated
above
in
Section
4.1.1.1,
States
and
authorized
Tribes
are
encouraged
to
consider
protecting
population
groups
that
they
determine
are
at
greater
risk
and,
thus,
would
be
better
protected
using
alternative
exposure
assumptions.
The
ultimate
choice
of
the
contaminant
concentrations
used
in
the
RSC
estimate
and
the
exposure
intake
rates
requires
the
use
of
professional
judgment.
This
is
discussed
in
greater
detail
in
the
Exposure
Assessment
TSD.

4.2.5
Inclusion
of
Inhalation
and
Dermal
Exposures
EPA
intends
to
develop
policy
guidelines
to
apply
to
this
Methodology
for
explicitly
incorporating
inhalation
and
dermal
exposures.
When
estimating
overall
exposure
to
pollutants
for
AWQC
development,
EPA
believes
that
the
sources
of
inhalation
and
dermal
exposures
considered
should
include,
on
a
case­
by­
case
basis,
both
non­
oral
exposures
from
water
and
other
inhalation
and
dermal
sources
(
e.
g.,
ambient
or
indoor
air,
soil).
When
the
policy
guidelines
are
completed,
this
Methodology
will
be
refined
to
include
that
guidance.

A
number
of
drinking
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,
thus,
there
is
at
least
the
possibility
that
some
contaminants
in
water
may
be
dermally
absorbed.
Volatilization
may
increase
exposure
via
inhalation
and
decrease
exposure
via
ingestion
and
dermal
absorption.
The
net
effect
of
volatilization
and
dermal
absorption
upon
total
exposure
to
volatile
drinking
water
contaminants
is
unclear
in
some
cases
and
varies
from
chemical
to
chemical.
Dermal
exposures
are
also
important
to
consider
for
certain
population
groups,
such
as
children
and
other
groups
with
high
soil
contact.

With
regard
to
additional
non­
water
related
exposures,
it
is
clear
that
the
type
and
magnitude
of
toxicity
produced
via
inhalation,
ingestion,
and
dermal
contact
may
differ;
that
is,
the
route
of
exposure
can
affect
absorption
of
a
chemical
and
can
otherwise
modify
its
toxicity.
For
example,
an
inhaled
chemical
such
as
hydrogen
fluoride
may
produce
localized
effects
on
the
4­
17
lung
that
are
not
observed
(
or
only
observed
at
much
higher
doses)
when
the
chemical
is
administered
orally.
Also,
the
active
form
of
a
chemical
(
and
principal
toxicity)
can
be
the
parent
compound
and/
or
one
or
more
metabolites.
With
this
Methodology,
EPA
recommends
that
differences
in
absorption
and
toxicity
by
different
routes
of
exposure
be
determined
and
accounted
for
in
dose
estimates
and
applied
to
the
exposure
assessment.
EPA
acknowledges
that
the
issue
of
whether
the
doses
received
from
inhalation
and
ingestion
exposures
are
cumulative
(
i.
e.,
toward
the
same
threshold
of
toxicity)
is
complicated.
Such
a
determination
involves
evaluating
the
chemical's
physical
characteristics,
speciation,
and
reactivity.
A
chemical
may
also
exhibit
different
metabolism
by
inhalation
versus
oral
exposure
and
may
not
typically
be
metabolized
by
all
tissues.
In
addition,
a
metabolite
may
be
much
more
or
much
less
toxic
than
the
parent
compound.
Certainly
with
a
systemic
effect,
if
the
chemical
absorbed
via
different
routes
enters
the
bloodstream,
then
there
is
some
likelihood
that
it
will
contact
the
same
target
organ.
Attention
also
needs
to
be
given
to
the
fact
that
both
the
RfD
and
RfC
are
derived
based
on
the
administered
level.
Toxicologists
generally
believe
that
the
effective
concentration
of
the
active
form
of
a
chemical(
s)
at
the
site(
s)
of
action
determines
the
toxicity.
If
specific
differences
between
routes
of
exposure
are
not
known,
it
may
be
reasonable
to
assume
that
the
internal
concentration
at
the
site
from
any
route
contributes
as
much
to
the
same
effect
as
any
other
route.
A
default
of
assuming
equal
absorption
has
often
been
used.
However,
for
many
of
the
chemicals
that
the
Agency
has
reviewed,
there
is
a
substantial
amount
of
information
already
known
to
determine
differences
in
rates
of
absorption.
For
example,
absorption
is,
in
part,
a
function
of
blood
solubility
(
i.
e.,
Henry's
Constant)
and
better
estimations
than
the
default
can
be
made.

The
RSC
analyses
that
accompany
the
2000
Human
Health
Methodology
accommodate
inclusion
of
inhalation
exposures.
Even
if
different
target
organs
are
involved
between
different
routes
of
exposure,
a
conservative
policy
may
be
appropriate
to
keep
all
exposures
below
a
certain
level.
A
possible
alternative
is
to
set
allowable
levels
(
via
an
equation)
such
that
the
total
of
ingestion
exposures
over
the
ingestion
RfD
added
to
the
total
of
inhalation
exposures
over
the
inhalation
RfC
is
not
greater
than
1
(
Note:
the
RfD
is
typically
presented
in
mg/
kg­
day
and
the
RfC
is
in
mg/
m3).
Again,
EPA
intends
to
develop
guidance
for
this
Methodology
to
explicitly
incorporate
inhalation
and
dermal
exposures,
and
will
refine
the
Methodology
when
that
guidance
is
completed.

4.3
EXPOSURE
FACTORS
USED
IN
THE
AWQC
COMPUTATION
This
section
presents
values
for
the
specific
exposure
factors
that
EPA
will
use
in
the
derivation
of
AWQC.
These
include
human
body
weight,
drinking
water
consumption
rates,
and
fish
ingestion
rates.

When
choosing
exposure
factor
values
to
include
in
the
derivation
of
a
criterion
for
a
given
pollutant,
EPA
recommends
considering
values
that
are
relevant
to
population(
s)
that
is
(
are)
most
susceptible
to
that
pollutant.
In
addition,
highly
exposed
populations
should
be
considered
when
setting
criteria.
In
general,
exposure
factor
values
specific
to
adults
and
relevant
to
lifetime
exposures
are
the
most
appropriate
values
to
consider
when
determining
criteria
to
protect
against
effects
from
long­
term
exposure
which,
by
and
large,
the
human
health
criteria
are
4­
18
derived
to
protect.
However,
infants
and
children
may
have
higher
rates
of
water
and
food
consumption
per
unit
body
weight
compared
with
adults
and
also
may
be
more
susceptible
to
some
pollutants
than
adults
(
USEPA,
1997a).
There
may
be
instances
where
acute
or
subchronic
developmental
toxicity
makes
children
the
population
group
of
concern.
In
addition,
exposure
of
pregnant
women
to
certain
toxic
chemicals
may
cause
developmental
effects
in
the
fetus
(
USEPA,
1997b).
Exposures
resulting
in
developmental
effects
may
be
of
concern
for
some
contaminants
and
should
be
considered
along
with
information
applicable
to
long­
term
health
effects
when
setting
AWQC.
(
See
Section
3.2
for
further
discussion
of
this
issue.)
Short­
term
exposure
may
include
multiple
intermittent
or
continuous
exposures
occurring
over
a
week
or
so.
Exposure
factor
values
relevant
for
considering
chronic
toxicity,
as
well
as
exposure
factor
values
relevant
for
short­
term
exposure
developmental
concerns,
that
could
result
in
adverse
health
effects
are
discussed
in
the
sections
below.
In
appropriate
situations,
EPA
may
consider
developing
criteria
for
developmental
health
effects
based
on
exposure
factor
values
specific
to
children
or
to
women
of
childbearing
age.
EPA
encourages
States
and
Tribes
to
do
the
same
when
health
risks
are
associated
with
short­
term
exposures.

EPA
believes
that
the
recommended
exposure
factor
default
intakes
for
adults
in
chronic
exposure
situations
are
adequately
protective
of
the
population
over
a
lifetime.
In
providing
additional
exposure
intake
values
for
highly
exposed
subpopulations
(
e.
g.,
sport
anglers,
subsistence
fishers),
EPA
is
providing
flexibility
for
States
and
authorized
Tribes
to
establish
criteria
specifically
targeted
to
provide
additional
protection
using
adjusted
values
for
exposure
parameters
for
body
weight,
drinking
water
intake,
and
fish
consumption.
The
exposure
factor
values
provided
for
women
of
childbearing
age
and
children
would
only
be
used
in
the
circumstances
indicated
above.

Each
of
the
following
sections
recommends
exposure
parameter
values
for
use
in
developing
AWQC.
These
are
based
on
both
science
policy
decisions
that
consider
the
best
available
data,
as
well
as
risk
management
judgments
regarding
the
overall
protection
afforded
by
the
choice
in
the
derivation
of
AWQC.
These
will
be
used
by
EPA
to
derive
new,
or
revise
existing,
304(
a)
national
criteria.

4.3.1
Human
Body
Weight
Values
for
Dose
Calculations
The
source
of
data
for
default
human
body
weights
used
in
deriving
the
AWQC
is
the
third
National
Health
and
Nutrition
Examination
Survey
(
NHANES
III).
NHANES
III
represents
a
very
large
interview
and
examination
endeavor
of
the
National
Center
for
Health
Statistics
(
NCHS)
and
included
participation
from
the
Centers
for
Disease
Control
(
CDC).
The
NHANES
III
was
conducted
on
a
nationwide
probability
sample
of
over
30,000
persons
from
the
civilian,
non­
institutionalized
population
of
the
United
States.
The
survey
began
in
October
1988
and
was
completed
in
October
1994
(
WESTAT,
2000;
McDowell,
2000).
Body
weight
data
were
taken
from
the
NHANES
III
Examination
Data
File.
Sampling
weights
were
applied
to
all
persons
examined
in
the
Mobile
Examination
Centers
(
MECs)
or
at
home,
as
was
recommended
by
the
NHANES
data
analysts
(
WESTAT,
2000).
4­
19
The
NHANES
III
survey
has
numerous
strengths
and
very
few
weaknesses.
Its
primary
strengths
are
the
national
representativeness,
large
sample
size,
and
precise
estimates
due
to
this
large
sample
size.
Another
strength
is
its
high
response
rate;
the
examination
rate
was
73
percent
overall,
89
percent
for
children
under
1
year
old,
and
approximately
85
percent
for
children
1
to
5
years
old
(
McDowell,
2000).
Interview
response
rates
were
even
higher,
but
the
body
weight
data
come
from
the
NHANES
examinations;
that
is,
all
body
weights
were
carefully
measured
by
survey
staff,
rather
than
the
use
of
self­
reported
body
weights.
The
only
significant
potential
weakness
of
the
NHANES
data
is
the
fact
that
the
data
are
now
between
6
and
12
years
old.
Given
that
there
were
upward
trends
in
body
weight
from
NHANES
II
to
NHANES
III,
and
that
NCHS
has
indicated
the
prevalence
of
overweight
people
increased
in
all
age
groups,
the
data
could
underestimate
current
body
weights
if
that
trend
has
continued
(
WESTAT,
2000).

The
NHANES
III
collected
standard
body
measurements
of
sample
subjects,
including
height
and
weight,
that
were
made
at
various
times
of
the
day
and
in
different
seasons
of
the
year.
This
technique
was
used
because
one's
weight
may
vary
between
winter
and
summer
and
may
fluctuate
with
recency
of
food
and
water
intake
and
other
daily
activities
(
McDowell,
2000).

As
with
the
other
exposure
assumptions,
States
and
authorized
Tribes
are
encouraged
to
use
alternative
body
weight
assumptions
for
population
groups
other
than
the
general
population
and
to
use
local
or
regional
data
over
default
values
as
more
representative
of
their
target
population
group(
s).

4.3.1.1
Rate
Protective
of
Human
Health
from
Chronic
Exposure
EPA
recommends
maintaining
the
default
body
weight
of
70
kg
for
calculating
AWQC
as
a
representative
average
value
for
both
male
and
female
adults.
As
previously
indicated,
exposure
factor
values
specific
to
adults
are
recommended
to
protect
against
effects
from
longterm
exposure.
The
value
of
70
kg
is
based
on
the
following
information.
In
the
analysis
of
the
NHANES
III
database,
median
and
mean
values
for
female
adults
18­
74
years
old
are
65.8
and
69.5
kg,
respectively
(
WESTAT,
2000).
For
males
in
the
same
age
range,
the
median
and
mean
values
are
79.9
and
82.1
kg,
respectively.
The
mean
body
weight
value
for
men
and
women
ages
18
to
74
years
old
from
this
survey
is
75.6
kg
(
WESTAT,
2000).
This
mean
value
is
higher
than
the
mean
value
for
adults
ages
20­
64
years
old
of
70.5
kg
from
a
study
by
the
National
Cancer
Institute
(
NCI)
which
primarily
measured
drinking
water
intake
(
Ershow
and
Cantor,
1989).
The
NCI
study
is
described
in
the
subsection
on
Drinking
Water
Intake
Rates
that
follows
(
Section
4.3.2).
The
value
from
the
NHANES
III
database
is
also
higher
than
the
value
given
in
the
revised
EPA
Exposure
Factors
Handbook
(
USEPA,
1997b),
which
recommends
71.8
kg
for
adults,
based
on
the
older
NHANES
II
data.
The
Handbook
also
acknowledges
the
commonly
used
70
kg
value
and
encourages
risk
assessors
to
use
values
which
most
accurately
reflect
the
exposed
population.
However,
the
point
is
also
made
that
the
70
kg
value
is
used
in
the
derivation
of
cancer
slope
factors
and
unit
risks
that
appear
in
IRIS.
Consistency
is
advocated
between
the
dose­
response
relationship
and
exposure
factors
assumed.
Therefore,
if
a
value
higher
than
70
kg
is
used,
the
assessor
needs
to
adjust
the
dose­
response
relationship
as
described
in
the
Appendix
to
Chapter
1,
Volume
1
of
the
Handbook
(
USEPA,
1997b).
4­
20
4.3.1.2
Rates
Protective
of
Developmental
Human
Health
Effects
As
noted
above,
pregnant
women
may
represent
a
more
appropriate
population
for
which
to
assess
risks
from
exposure
to
chemicals
in
ambient
waters
in
some
cases,
because
of
the
potential
for
developmental
effects
in
fetuses.
In
these
cases,
body
weights
representative
of
women
of
childbearing
age
may
be
appropriate
to
adequately
protect
offspring
from
such
health
effects.
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
were
analyzed
from
the
NHANES
III
data
(
WESTAT,
2000).
The
resulting
median
and
mean
body
weight
values
are
63.2
and
67.3
kg,
respectively.
Ershow
and
Cantor
(
1989)
present
body
weight
values
specifically
for
pregnant
women
included
in
the
survey;
median
and
mean
weights
are
64.4
and
65.8
kilograms,
respectively.
Ershow
and
Cantor
(
1989),
however,
do
not
indicate
the
ages
of
these
pregnant
women.
Based
on
this
information
for
women
of
childbearing
age
and
pregnant
women,
EPA
recommends
use
of
a
body
weight
value
of
67
kg
in
cases
where
pregnant
women
are
the
specific
population
of
concern
and
the
chemical
of
concern
exhibits
reproductive
and/
or
developmental
effects
(
i.
e.,
the
critical
effect
upon
which
the
RfD
or
POD/
UF
is
based).
Using
the
67
kg
assumption
would
result
in
lower
(
more
protective)
criteria
than
criteria
based
on
70
kg.

As
discussed
earlier,
because
infants
and
children
generally
have
a
higher
rate
of
water
and
food
consumption
per
unit
body
weight
compared
with
adults,
a
higher
intake
rate
per
unit
body
weight
may
be
needed
when
comparing
estimated
exposure
doses
with
critical
doses
when
RfDs
are
based
on
health
effects
in
children.
To
calculate
intake
rates
relevant
to
such
effects,
the
body
weight
of
children
should
be
used.
As
with
the
default
body
weight
for
pregnant
women,
EPA
is
not
recommending
the
development
of
additional
AWQC
(
i.
e.,
similar
to
drinking
water
health
advisories)
that
focus
on
acute
or
short­
term
effects,
since
these
are
not
seen
routinely
as
having
a
meaningful
role
in
the
water
quality
criteria
program.
However,
there
may
be
circumstances
where
the
consideration
of
exposures
for
these
groups
is
warranted.
Although
the
AWQC
generally
are
based
on
chronic
health
effects
data,
they
are
intended
to
also
be
protective
with
respect
to
adverse
effects
that
may
reasonably
be
expected
to
occur
as
a
result
of
elevated
shorter­
term
exposures.
EPA
acknowledges
this
as
a
potential
course
of
action
and
is,
therefore,
recommending
these
default
values
which
EPA
would
consider
in
an
appropriate
circumstance
and
for
States
and
authorized
Tribes
to
utilize
in
such
situations.

EPA
is
recommending
an
assumption
of
30
kg
as
a
default
child's
body
weight
to
calculate
AWQC
to
provide
additional
protection
for
children
when
the
chemical
of
concern
indicates
health
effects
in
children
are
of
predominant
concern
(
i.
e.,
test
results
show
children
are
more
susceptible
due
to
less
developed
immune
systems,
neurological
systems,
and/
or
lower
body
weights).
The
value
is
based
on
the
mean
body
weight
value
of
29.9
kg
for
children
ages
1
to14
years
old,
which
combines
body
weight
values
for
individual
age
groups
within
this
larger
group.
The
mean
value
is
based
on
body
weight
information
from
NHANES
III
for
individual­
year
age
groups
between
one
and
14
years
old
(
WESTAT,
2000).
A
mean
body
weight
of
28
kg
is
obtained
using
body
weight
values
from
Ershow
and
Cantor
(
1989)
for
five
age
groups
within
this
range
of
0­
14
years
and
applying
a
weighting
method
for
different
ages
by
population
percentages
from
the
U.
S.
Bureau
of
the
Census.
The
30
kg
assumption
is
also
consistent
with
the
age
range
4­
21
for
children
used
with
the
estimated
fish
intake
rates.
Unfortunately,
fish
intake
rates
for
finer
age
group
divisions
are
not
possible
due
to
the
limited
sampling
base
from
the
fish
intake
survey;
there
is
limited
confidence
in
calculated
values
(
e.
g.,
the
mean)
for
such
fine
age
groups.
Given
this
limitation,
the
broad
age
category
of
body
weight
for
children
is
suitable
for
use
with
the
default
fish
intake
assumption.

Given
the
hierarchy
of
preferences
regarding
the
use
of
fish
intake
information
(
see
Section
4.3.3),
States
may
have
more
comprehensive
data
and
prefer
to
target
a
more
narrow,
younger
age
group.
If
States
choose
to
specifically
evaluate
toddlers,
EPA
recommends
using
13
kg
as
a
default
body
weight
assumption
for
children
ages
1
to
3
years
old.
The
median
and
mean
values
of
body
weight
for
children
1
to
3
years
old
are
13.2
and
13.1
kg,
respectively,
based
on
an
analysis
of
the
NHANES
III
database
(
WESTAT,
2000).
The
NHANES
III
median
and
mean
values
for
females
between
1
and
3
years
old
are
13.0
and
12.9
kg,
respectively,
and
are
13.4
and
13.4
kg
for
males,
respectively.
Median
and
mean
body
weight
values
from
the
earlier
Ershow
and
Cantor
(
1989)
study
for
children
ages
1
to
3
years
old
were
13.6
and
14.1
kg,
respectively.
Finally,
if
infants
are
specifically
evaluated,
EPA
recommends
a
default
body
weight
of
7
kg
based
on
the
NHANES
III
analysis.
Median
and
mean
body
weights
for
both
male
and
female
infants
(
combined)
2
months
old
were
6.3
and
6.3
kg,
respectively,
and
for
infants
3
months
old
were
7.0
and
6.9
kg,
respectively.
With
the
broader
age
category
of
males
and
females
2
to
6
months
old,
median
and
mean
body
weights
were
7.4
and
7.4
kg,
respectively.
The
NHANES
analysis
did
not
include
infants
under
2
months
of
age.
Although
EPA
is
not
recommending
body
weight
values
for
newborns,
the
NCHS
National
Vital
Statistics
Report
indicates
that,
for
1997,
the
median
birth
weight
ranged
from
3
to
3.5
kg,
according
to
WESTAT
(
2000).

Body
weight
values
for
individual
ages
within
the
larger
range
of
0­
14
years
are
listed
in
the
Exposure
Assessment
TSD
for
those
States
and
authorized
Tribes
who
wish
to
use
body
weight
values
for
these
individual
groups.
States
and
Tribes
may
wish
to
consider
certain
general
developmental
ages
(
e.
g.,
infants,
pre­
adolescents,
etc.),
or
certain
specific
developmental
landmarks
(
e.
g.,
neurological
development
in
the
first
four
years),
depending
on
the
chemical
of
concern.
EPA
encourages
States
and
authorized
Tribes
to
choose
a
body
weight
intake
from
the
tables
presented
in
the
TSD,
if
they
believe
a
particular
age
subgroup
is
more
appropriate.

4.3.2
Drinking
Water
Intake
Rates
The
basis
for
the
drinking
water
intake
rates
(
also
for
the
fish
intake
rates
presented
in
Section
4.3.3)
is
the
1994­
96
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII)
conducted
by
the
U.
S.
Department
of
Agriculture
(
USDA,
1998).
The
CSFII
survey
collects
dietary
intake
information
from
nationally
representative
samples
of
non­
institutionalized
persons
residing
in
United
States
households.
Households
in
these
national
surveys
are
sampled
from
the
50
states
and
the
District
of
Columbia.
Each
survey
collects
daily
consumption
records
for
approximately
10,000
food
codes
across
nine
food
groups.
These
food
groups
are
(
1)
milk
and
milk
products;
(
2)
meat,
poultry,
and
fish;
(
3)
eggs;
(
4)
dry
beans,
peas,
legumes,
nuts,
and
4­
22
seeds;
(
5)
grain
products;
(
6)
fruit;
(
7)
vegetables;
(
8)
fats,
oils,
and
salad
dressings;
and
(
9)
sweets,
sugars,
and
beverages.
The
survey
also
asks
each
respondent
how
many
fluid
ounces
of
plain
drinking
water
he
or
she
drank
during
each
of
the
survey
days.
In
addition,
the
CSFII
collects
household
information,
including
the
source
of
plain
drinking
water,
water
used
to
prepare
beverages,
and
water
used
to
prepare
foods.
Data
provide
"
up­
to­
date
information
on
food
intakes
by
Americans
for
use
in
policy
formation,
regulation,
program
planning
and
evaluation,
education,
and
research."
The
survey
is
"
the
cornerstone
of
the
National
Nutritional
Monitoring
and
Related
Research
Program,
a
set
of
related
federal
activities
intended
to
provide
regular
information
on
the
nutritional
status
of
the
United
States
population"
(
USDA,
1998).

The
1994­
96
CSFII
was
conducted
according
to
a
stratified,
multi­
area
probability
sample
organized
using
estimates
of
the
1990
United
States
population.
Stratification
accounted
for
geographic
location,
degree
of
urbanization,
and
socioeconomics.
Each
year
of
the
survey
consisted
of
one
sample
with
oversampling
for
low­
income
households.

Survey
participants
provided
two
non­
consecutive,
24­
hour
days
of
dietary
data.
Both
days'
dietary
recall
information
was
collected
by
an
in­
home
interviewer.
Interviewers
provided
participants
with
an
instructional
booklet
and
standard
measuring
cups
and
spoons
to
assist
them
in
adequately
describing
the
type
and
amount
of
food
ingested.
If
the
respondent
referred
to
a
cup
or
bowl
in
their
own
home,
a
2­
cup
measuring
cup
was
provided
to
aid
in
the
calculation
of
the
amount
consumed.
The
sample
person
could
fill
their
own
bowl
or
cup
with
water
to
represent
the
amount
eaten
or
drunk,
and
the
interviewer
could
then
measure
the
amount
consumed
by
pouring
it
into
the
2­
cup
measure.
The
Day
2
interview
occurred
three
to
10
days
after
the
Day
1
interview,
but
not
on
the
same
day
of
the
week.
The
interviews
allowed
participants
"
three
passes"
through
the
daily
intake
record
to
maximize
recall
(
USDA,
1998).
Proxy
interviews
were
conducted
for
children
aged
six
and
younger
and
sampled
individuals
unable
to
report
due
to
mental
or
physical
limitations.
The
average
questionnaire
administration
time
for
Day
1
intake
was
30
minutes,
while
Day
2
averaged
27
minutes.

Two
days
of
dietary
recall
data
were
provided
by
15,303
individuals
across
the
three
survey
years.
This
constitutes
an
overall
two­
day
response
rate
of
75.9
percent.
Survey
weights
were
corrected
by
the
USDA
for
nonresponse.

All
three
1994­
96
CSFII
surveys
are
multistage,
stratified­
cluster
samples.
Sample
weights,
which
project
the
data
from
a
sampled
individual
to
the
population,
are
based
on
the
probability
of
an
individual
being
sampled
at
each
stage
of
the
sampling
design.
The
sample
weights
associated
with
each
individual
reporting
two
days
of
consumption
data
were
adjusted
to
correct
for
nonresponse
bias.

The
1994­
96
CSFII
surveys
have
advantages
and
limitations
for
estimating
per
capita
water
(
or
fish)
consumption.
The
primary
advantage
of
the
CSFII
surveys
is
that
they
were
designed
and
conducted
by
the
USDA
to
support
unbiased
estimation
of
food
consumption
across
the
population
in
the
United
States
and
the
District
of
Columbia.
Second,
the
survey
is
designed
to
record
daily
intakes
of
foods
and
nutrients
and
support
estimation
of
food
consumption.
4­
23
One
limitation
of
the
1994­
96
CSFII
surveys
is
that
individual
food
consumption
data
were
collected
for
only
two
days
 
a
brief
period
which
does
not
necessarily
depict
"
usual
intake."
Usual
dietary
intake
is
defined
as
"
the
long­
run
average
of
daily
intakes
by
an
individual."
Upper
percentile
estimates
may
differ
for
short­
term
and
longer­
term
data
because
short­
term
food
consumption
data
tend
to
be
inherently
more
variable.
It
is
important
to
note,
however,
that
variability
due
to
duration
of
the
survey
does
not
result
in
bias
of
estimates
of
overall
mean
consumption
levels.
Also,
the
multistage
survey
design
does
not
support
interval
estimates
for
many
of
the
subpopulations
of
interest
because
of
sparse
representation
in
the
sample.
Subpopulations
with
sparse
representation
include
Native
Americans
on
reservations
and
certain
ethnic
groups.
While
these
individuals
are
participants
in
the
survey,
they
are
not
present
in
sufficient
numbers
to
support
consumption
estimates.

Despite
these
limitations,
the
CSFII
is
considered
one
of
the
best
sources
of
current
information
on
consumption
of
water
and
fish­
containing
foods.
The
objective
of
estimating
per
capita
water
and
fish
consumption
by
the
United
States
population
is
compatible
with
the
statistical
design
and
scope
of
the
CSFII
survey.

4.3.2.1
Rate
Protective
of
Human
Health
from
Chronic
Exposure
EPA
recommends
maintaining
the
default
drinking
water
intake
rate
of
2
L/
day
to
protect
most
consumers
from
contaminants
in
drinking
water.
EPA
believes
that
the
2
L/
day
assumption
is
representative
of
a
majority
of
the
population
over
the
course
of
a
lifetime.
EPA
also
notes
that
there
is
comparatively
little
variability
in
water
intake
within
the
population
compared
with
fish
intake
(
i.
e.,
drinking
water
intake
varies,
by
and
large,
by
about
a
three­
fold
range,
whereas
fish
intake
can
vary
by
100­
fold).
EPA
believes
that
the
2
L/
day
assumption
continues
to
represent
an
appropriate
risk
management
decision.
The
results
of
the
1994­
96
CSFII
analysis
indicate
that
the
arithmetic
mean,
75th,
and
90th
percentile
values
for
adults
20
years
and
older
are
1.1,
1.5,
and
2.2
L/
day,
respectively
(
USEPA,
2000a).
The
2
L/
day
value
represents
the
86th
percentile
for
adults.
These
values
can
also
be
compared
to
data
from
an
older
National
Cancer
Institute
(
NCI)
study,
which
estimated
intakes
of
tapwater
in
the
United
States
based
on
the
USDA's
1977­
78
Nationwide
Food
Consumption
Survey
(
NFCS).
The
arithmetic
mean,
75th,
and
90th
percentile
values
for
adults
20
­
64
years
old
were
1.4,
1.7,
and
2.3
L/
day,
respectively
(
Ershow
and
Cantor,
1989).
The
2
L/
day
value
represents
the
88th
percentile
for
adults
from
the
NCI
study.

The
2
L/
day
assumption
was
used
with
the
original
1980
AWQC
National
Guidelines
and
has
also
been
used
in
EPA's
drinking
water
program.
EPA
believes
that
the
newer
studies
continue
to
support
the
use
of
2
L/
day
as
a
reasonable
and
protective
consumption
rate
that
represents
the
intake
of
most
water
consumers
in
the
general
population.
However,
individuals
who
work
or
exercise
in
hot
climates
could
have
water
consumption
rates
significantly
above
2
L/
day,
and
EPA
believes
that
States
and
Tribes
should
consider
regional
or
occupational
variations
in
water
consumption.
4­
24
4.3.2.2
Rates
Protective
of
Developmental
Human
Health
Effects
Based
on
the
1994­
96
CSFII
study
data,
EPA
also
recommends
2
L/
day
for
women
of
childbearing
age.
The
analysis
for
women
of
childbearing
age
(
ages
15­
44)
indicate
mean,
75th,
and
90th
percentile
values
of
0.9,
1.3,
and
2.0
L/
day,
respectively.
These
rates
compare
well
with
those
based
on
an
analysis
of
tapwater
intake
by
pregnant
and
lactating
women
by
Ershow
et
al.
(
1991),
based
on
the
older
USDA
data,
for
women
ages
15­
49.
Arithmetic
mean,
75th
and
90th
percentile
values
were
1.2,
1.5,
and
2.2
L/
day,
respectively,
for
pregnant
women.
For
lactating
women,
the
arithmetic
mean,
75th
and
90th
percentile
values
were
1.3,
1.7,
and
1.9
L/
day,
respectively.

As
noted
above,
because
infants
and
children
have
a
higher
daily
water
intake
per
unit
body
weight
compared
with
adults,
a
water
consumption
rate
measured
for
children
is
recommended
for
use
when
RfDs
are
based
on
health
effects
in
children.
Use
of
this
water
consumption
rate
should
result
in
adequate
protection
for
infants
and
children
when
setting
criteria
based
on
health
effects
for
this
target
population.
EPA
recommends
a
drinking
water
intake
of
1
L/
day
to,
again,
represent
a
majority
of
the
population
of
children
that
consume
drinking
water.
The
results
of
the
1994­
96
CSFII
analysis
indicate
that
for
children
from
1
to
10
years
of
age,
the
arithmetic
mean,
75th,
and
90th
percentile
values
are
0.4,
0.6,
and
0.9
L/
day,
respectively
(
USEPA,
2000a).
The
1
L/
day
value
represents
the
93rd
percentile
for
this
group.
The
arithmetic
mean,
75th,
and
90th
percentile
values
for
smaller
children,
ages
1
to
3
years,
are
0.3,
0.5,
and
0.7
L/
day,
respectively.
The
1
L/
day
value
represents
the
97th
percentile
of
the
group
ages
1
to
3
years
old.
For
the
category
of
infants
under
1
year
of
age,
the
arithmetic
mean,
75th,
and
90th
percentile
values
are
0.3,
0.7,
and
0.9
L/
day,
respectively.
These
data
can
similarly
be
compared
to
those
of
the
older
National
Cancer
Institute
(
NCI)
study.
The
arithmetic
mean,
75th,
and
90th
percentile
values
for
children
1
to
10
years
old
were
0.74,
0.96,
and
1.3
L/
day,
respectively.
The
mean,
75th,
and
90th
percentile
values
for
children
1
to
3
years
old
in
the
NCI
study
were
0.6,
0.8,
and
1.2
L/
day,
respectively.
Finally,
the
mean,
75th,
and
90th
percentile
values
for
infants
less
than
6
months
old
were
0.3,
0.3,
and
0.6
L/
day,
respectively
(
Ershow
and
Cantor,
1989).

4.3.2.3
Rates
Based
on
Combining
Drinking
Water
Intake
and
Body
Weight
As
an
alternative
to
considering
body
weight
and
drinking
water
intake
rates
separately,
EPA
is
providing
rates
based
on
intake
per
unit
body
weight
data
(
in
units
of
ml/
kg)
in
the
Exposure
Assessment
TSD,
with
additional
discussion
on
their
use.
These
rates
are
based
on
selfreported
body
weights
from
the
CSFII
survey
respondents
for
the
1994­
96
data.
While
EPA
intends
to
derive
or
revise
national
default
criteria
on
the
separate
intake
values
and
body
weights,
in
part
due
to
the
strong
input
received
from
its
State
stakeholders,
the
ml/
kg­
BW/
day
values
are
provided
in
the
TSD
for
States
or
authorized
Tribes
that
prefer
their
use.
It
should
be
noted
that
in
their
1993
review,
EPA's
Science
Advisory
Board
(
SAB)
felt
that
using
drinking
water
intake
rate
assumptions
on
a
per
unit
body
weight
basis
would
be
more
accurate,
but
did
not
believe
this
change
would
appreciably
affect
the
criteria
values
(
USEPA,
1993).
4­
25
4.3.3
Fish
Intake
Rates
The
basis
for
the
fish
intake
rates
is
the
1994­
96
CSFII
conducted
by
the
USDA,
and
described
above
in
Section
4.3.2.

4.3.3.1
Rates
Protective
of
Human
Health
from
Chronic
Exposure
EPA
recommends
a
default
fish
intake
rate
of
17.5
grams/
day
to
adequately
protect
the
general
population
of
fish
consumers,
based
on
the
1994
to
1996
data
from
the
USDA's
CSFII
Survey.
EPA
will
use
this
value
when
deriving
or
revising
its
national
304(
a)
criteria.
This
value
represents
the
90th
percentile
of
the
1994­
96
CSFII
data.
This
value
also
represents
the
uncooked
weight
estimated
from
the
CSFII
data,
and
represents
intake
of
freshwater
and
estuarine
finfish
and
shellfish
only.
For
deriving
AWQC,
EPA
has
also
considered
the
States'
and
Tribes'
needs
to
provide
adequate
protection
from
adverse
health
effects
to
highly
exposed
populations
such
as
recreational
and
subsistence
fishers,
in
addition
to
the
general
population.
Based
on
available
studies
that
characterize
consumers
of
fish,
recreational
fishers
and
subsistence
fishers
are
two
distinct
groups
whose
intake
rates
may
be
greater
than
the
general
population.
It
is,
therefore,
EPA's
decision
to
discuss
intakes
for
these
two
groups,
in
addition
to
the
general
population.

EPA
recommends
default
fish
intake
rates
for
recreational
and
subsistence
fishers
of
17.5
grams/
day
and
142.4
grams/
day,
respectively.
These
rates
are
also
based
on
uncooked
weights
for
fresh/
estuarine
finfish
and
shellfish
only.
However,
because
the
level
of
fish
intake
in
highly
exposed
populations
varies
by
geographical
location,
EPA
suggests
a
four
preference
hierarchy
for
States
and
authorized
Tribes
to
follow
when
deriving
consumption
rates
that
encourages
use
of
the
best
local,
State,
or
regional
data
available.
A
thorough
discussion
of
the
development
of
this
policy
method
and
relevant
data
sources
is
contained
in
the
Exposure
Assessment
TSD.
The
hierarchy
is
also
presented
here
because
EPA
strongly
emphasizes
that
States
and
authorized
Tribes
should
consider
developing
criteria
to
protect
highly
exposed
population
groups
and
use
local
or
regional
data
over
the
default
values
as
more
representative
of
their
target
population
group(
s).
The
four
preference
hierarchy
is:
(
1)
use
of
local
data;
(
2)
use
of
data
reflecting
similar
geography/
population
groups;
(
3)
use
of
data
from
national
surveys;
and
(
4)
use
of
EPA's
default
intake
rates.

The
recommended
four
preference
hierarchy
is
intended
for
use
in
evaluating
fish
intake
from
fresh
and
estuarine
species
only.
Therefore,
to
protect
humans
who
additionally
consume
marine
species
of
fish,
the
marine
portion
should
be
considered
an
other
source
of
exposure
when
calculating
an
RSC
for
dietary
intake.
Refer
to
the
Exposure
Assessment
TSD
for
further
discussion.
States
and
Tribes
need
to
ensure
that
when
evaluating
overall
exposure
to
a
contaminant,
marine
fish
intake
is
not
double­
counted
with
the
other
dietary
intake
estimate
used.
Coastal
States
and
authorized
Tribes
that
believe
accounting
for
total
fish
consumption
(
i.
e.,
fresh/
estuarine
and
marine
species)
is
more
appropriate
for
protecting
the
population
of
concern
may
do
so,
provided
that
the
marine
intake
component
is
not
double­
counted
with
the
RSC
estimate.
Tables
of
fish
consumption
intakes
based
on
the
CSFII
in
the
TSD
provide
rates
for
fresh/
estuarine
species,
marine
species,
and
total
(
combined)
values
to
facilitate
this
option
for
4­
26
States
and
Tribes.
Throughout
this
section,
the
terms
"
fish
intake"
or
"
fish
consumption"
are
used.
These
terms
refer
to
the
consumption
of
finfish
and
shellfish,
and
the
CSFII
survey
includes
both.
States
and
Tribes
should
ensure
that
when
selecting
local
or
regionally­
specific
studies,
both
finfish
and
shellfish
are
included
when
the
population
exposed
are
consumers
of
both
types.

EPA's
first
preference
is
that
States
and
authorized
Tribes
use
the
results
from
fish
intake
surveys
of
local
watersheds
within
the
State
or
Tribal
jurisdiction
to
establish
fish
intake
rates
that
are
representative
of
the
defined
populations
being
addressed
for
the
particular
waterbody.
Again,
EPA
recommends
that
data
indicative
of
fresh/
estuarine
species
only
be
used
which
is,
by
and
large,
most
appropriate
for
developing
AWQC.
EPA
also
recommends
the
use
of
uncooked
weight
intake
values,
which
is
discussed
in
greater
detail
with
the
fourth
preference.
States
and
authorized
Tribes
may
use
either
high­
end
values
(
such
as
the
90th
or
95th
percentile
values)
or
average
values
for
an
identified
population
that
they
plan
to
protect
(
e.
g.,
subsistence
fishers,
sport
fishers,
or
the
general
population).
EPA
generally
recommends
that
arithmetic
mean
values
should
be
the
lowest
value
considered
by
States
or
Tribes
when
choosing
intake
rates
for
use
in
criteria
derivation.
When
considering
geometric
mean
(
median)
values
from
fish
consumption
studies,
States
and
authorized
Tribes
need
to
ensure
that
the
distribution
is
based
on
survey
respondents
who
reported
consuming
fish
because
surveys
based
on
both
consumers
and
nonconsumers
can
often
result
in
median
values
of
zero.
If
a
State
or
Tribe
chooses
values
(
whether
the
central
tendency
or
high­
end
values)
from
studies
that
particularly
target
high­
end
consumers,
these
values
should
be
compared
to
high­
end
fish
intake
rates
for
the
general
population
to
make
sure
that
the
high­
end
consumers
within
the
general
population
would
be
protected
by
the
chosen
intake
rates.
EPA
believes
this
is
a
reasonable
procedure
and
is
also
consistent
with
the
recent
Great
Lakes
Water
Quality
Initiative
(
known
as
the
"
GLI")
(
USEPA,
1995).
States
and
authorized
Tribes
may
wish
to
conduct
their
own
surveys
of
fish
intake,
and
EPA
guidance
is
available
on
methods
to
conduct
such
studies
in
Guidance
for
Conducting
Fish
and
Wildlife
Consumption
Surveys
(
USEPA,
1998).
Results
from
broader
geographic
regions
in
which
the
State
or
Tribe
is
located
can
also
be
used,
but
may
not
be
as
applicable
as
results
from
local
watersheds.
Since
such
studies
would
ultimately
form
the
basis
of
a
State
or
Tribe's
AWQC,
EPA
would
review
any
surveys
of
fish
intake
for
consistency
with
the
principles
of
EPA's
guidance
as
part
of
the
Agency's
review
of
water
quality
standards
under
Section
303(
c).

If
surveys
conducted
in
the
geographic
area
of
the
State
or
Tribe
are
not
available,
EPA's
second
preference
is
that
States
and
authorized
Tribes
consider
results
from
existing
fish
intake
surveys
that
reflect
similar
geography
and
population
groups
(
e.
g.,
from
a
neighboring
State
or
Tribe
or
a
similar
watershed
type),
and
follow
the
method
described
above
regarding
target
values
to
derive
a
fish
intake
rate.
Again,
EPA
recommends
the
use
of
uncooked
weight
intake
values
and
the
use
of
fresh/
estuarine
species
data
only.
Results
of
existing
local
and
regional
surveys
are
discussed
in
greater
detail
in
the
TSD.

If
applicable
consumption
rates
are
not
available
from
local,
State,
or
regional
surveys,
EPA's
third
preference
is
that
States
and
authorized
Tribes
select
intake
rate
assumptions
for
different
population
groups
from
national
food
consumption
surveys.
EPA
has
analyzed
one
such
4­
27
national
survey,
the
1994­
96
CSFII.
As
described
in
Section
4.3.2,
this
survey,
conducted
annually
by
the
USDA,
collects
food
consumption
information
from
a
probability
sample
of
the
population
of
all
50
states.
Respondents
to
the
survey
provide
two
days
of
dietary
recall
data.
A
detailed
description
of
the
combined
1994­
96
CSFII
survey,
the
statistical
methodology,
and
the
results
and
uncertainties
of
the
EPA
analyses
are
provided
in
a
separate
EPA
report
(
USEPA,
2000b).
The
Exposure
Assessment
TSD
for
this
Methodology
presents
selected
results
from
this
report
including
point
and
interval
estimates
of
combined
finfish
and
shellfish
consumption
for
the
mean,
50th
(
median),
90th,
95th,
and
99th
percentiles.
The
estimated
fish
consumption
rates
are
by
fish
habitat
(
i.
e.,
freshwater/
estuarine,
marine
and
all
habitats)
for
the
following
population
groups:
(
1)
all
individuals;
(
2)
individuals
age
18
and
over;
(
3)
women
ages
15­
44;
and
(
4)
children
age
14
and
under.
Three
kinds
of
estimated
fish
consumption
rates
are
provided:
(
1)
per
capita
rates
(
i.
e.,
rates
based
on
consumers
and
nonconsumers
of
fish
from
the
survey
period
 
refer
to
the
TSD
for
further
discussion);
(
2)
consumers­
only
rates
(
i.
e.,
rates
based
on
respondents
who
reported
consuming
finfish
or
shellfish
during
the
two­
day
reporting
period);
and
(
3)
per
capita
consumption
by
body
weight
(
i.
e.,
per
capita
rates
reported
as
milligrams
of
fish
per
kilogram
of
body
weight
per
day).

EPA's
fourth
preference
is
that
States
and
authorized
Tribes
use
as
fish
intake
assumptions
the
following
default
rates,
based
on
the
1994­
96
CSFII
data,
that
EPA
believes
are
representative
of
fish
intake
for
different
population
groups:
17.5
grams/
day
for
the
general
adult
population
and
sport
fishers,
and
142.4
grams/
day
for
subsistence
fishers.
These
are
risk
management
decisions
that
EPA
has
made
after
evaluating
numerous
fish
intake
surveys.
These
values
represent
the
uncooked
weight
intake
of
freshwater/
estuarine
finfish
and
shellfish.
As
with
the
other
preferences,
EPA
requests
that
States
and
authorized
Tribes
routinely
consider
whether
there
is
a
substantial
population
of
sport
fishers
or
subsistence
fishers
when
developing
sitespecific
estimates,
rather
than
automatically
basing
them
on
the
typical
individual.
Because
the
combined
1994­
96
CSFII
survey
is
national
in
scope,
EPA
will
use
the
results
from
this
survey
to
estimate
fish
intake
for
deriving
national
criteria.
EPA
has
recognized
the
data
gaps
and
uncertainties
associated
with
the
analysis
of
the
1994­
96
CSFII
survey
in
the
process
of
making
its
default
recommendations.
The
estimated
mean
of
freshwater
and
estuarine
fish
ingestion
for
adults
is
7.50
grams/
day,
and
the
median
is
0
grams/
day.
The
estimated
90th
percentile
is
17.53
grams/
day;
the
estimated
95th
percentile
is
49.59
grams/
day;
and
the
estimated
99th
percentile
is
142.41
grams/
day.
The
median
value
of
0
grams/
day
may
reflect
the
portion
of
individuals
in
the
population
who
never
eat
fish
as
well
as
the
limited
reporting
period
(
2
days)
over
which
intake
was
measured.
By
applying
as
a
default
17.5
grams/
day
for
the
general
adult
population,
EPA
intends
to
select
an
intake
rate
that
is
protective
of
a
majority
of
the
population
(
again,
the
90th
percentile
of
consumers
and
nonconsumers
according
to
the
1994­
96
CSFII
survey
data).
Trophic
level
breakouts
are:
TL2
=
3.8
grams/
day;
TL3
=
8.0
grams/
day;
and
TL4
=
5.7
grams/
day.
EPA
further
considers
17.5
grams/
day
to
be
indicative
of
the
average
consumption
among
sport
fishers
based
on
averages
in
the
studies
reviewed,
which
are
presented
in
the
Exposure
Assessment
TSD.
Similarly,
EPA
believes
that
the
assumption
of
142.4
grams/
day
is
within
the
range
of
average
consumption
estimates
for
subsistence
fishers
based
on
the
studies
reviewed.
Experts
at
the
1992
National
Workshop
that
initiated
the
effort
to
revise
this
Methodology
acknowledged
that
the
national
survey
high­
end
values
are
representative
of
4­
28
average
rates
for
highly
exposed
groups
such
as
subsistence
fishermen,
specific
ethnic
groups,
or
other
highly
exposed
people.
EPA
is
aware
that
some
local
and
regional
studies
indicate
greater
consumption
among
Native
American,
Pacific
Asian
American,
and
other
subsistence
consumers,
and
recommends
the
use
of
those
studies
in
appropriate
cases,
as
indicated
by
the
first
and
second
preferences.
Again,
States
and
authorized
Tribes
have
the
flexibility
to
choose
intake
rates
higher
than
an
average
value
for
these
population
groups.
If
a
State
or
authorized
Tribe
has
not
identified
a
separate
well­
defined
population
of
high­
end
consumers
and
believes
that
the
national
data
from
the
1994­
96
CSFII
are
representative,
they
may
choose
these
recommended
rates.

As
indicated
above,
the
default
intake
values
are
based
on
the
uncooked
weights
of
the
fish
analyzed.
There
has
been
some
question
regarding
whether
to
use
cooked
or
uncooked
weights
of
fish
intake
for
deriving
the
AWQC.
Studies
show
that,
typically,
with
a
filet
or
steak
of
fish,
the
weight
loss
in
cooking
is
about
20
percent;
that
is,
the
uncooked
weight
is
approximately
20
percent
higher
(
Jacobs
et
al.,
1998).
This
obviously
means
that
using
uncooked
weights
results
in
a
slightly
higher
intake
rate
and
slightly
more
stringent
AWQC.
In
researching
consumption
surveys
for
this
proposal,
EPA
has
found
that
some
surveys
have
reported
rates
for
cooked
fish,
others
have
reported
uncooked
rates,
and
many
more
are
unclear
as
to
whether
cooked
or
uncooked
rates
are
used.
The
basis
of
the
CSFII
survey
was
prepared
or
as
consumed
intakes;
that
is,
the
survey
respondents
estimated
the
weight
of
fish
that
they
consumed.
This
was
also
true
with
the
GLI
(
which
was
specifically
based
on
studies
describing
consumption
rates
of
cooked
fish)
and,
by
and
large,
cooked
fish
is
what
people
consume.
However,
EPA's
Guidance
For
Assessing
Chemical
Contaminant
Data
For
Use
In
Fish
Advisories
recommends
analysis
and
advisories
based
on
uncooked
fish
(
USEPA,
1997a).
EPA
considered
the
potential
confusion
over
the
fact
that
the
uncooked
weights
are
used
in
the
fish
advisory
program.
Further,
the
measures
of
a
contaminant
in
fish
tissue
samples
that
are
applicable
to
compliance
monitoring
and
the
permitting
program
are
related
to
the
uncooked
weights.
The
choice
of
intakes
is
also
complicated
by
factors
such
as
the
effect
of
the
cooking
process,
the
different
parts
of
a
fish
where
a
chemical
may
accumulate,
and
the
method
of
preparation.

After
considering
all
of
the
above
(
in
addition
to
public
input
received),
EPA
will
derive
its
national
default
criteria
based
on
the
uncooked
weight
fish
intakes.
The
Exposure
Assessment
TSD
provides
additional
guidance
on
site­
specific
modifications.
Specifically,
an
alternate
approach
is
described
for
calculating
AWQC
with
the
as
consumed
weight
 
which
is
more
directly
associated
with
human
exposure
and
risk
 
and
then
adjusting
the
value
by
the
approximate
20
percent
loss
to
an
uncooked
equivalent
(
thereby
representing
the
same
relative
risk
as
the
as
consumed
value).
This
approach
results
in
a
different
AWQC
value
(
than
using
the
uncooked
weights)
and
represents
a
more
direct
translation
of
the
as
consumed
risk
to
the
uncooked
equivalent.
However,
EPA
understands
that
it
is
more
scientifically
rigorous
and
may
be
too
intensive
of
a
process
for
States
and
Tribes
to
rely
on.
The
option
is
presented
in
the
TSD
to
offer
States
and
authorized
Tribes
greater
flexibility
with
their
water
quality
standards
program.

The
default
fish
intake
values
also
reflect
specific
designations
of
species
classified
in
accordance
with
information
regarding
the
life
history
of
the
species
or
based
on
landings
information
form
the
National
Marine
Fisheries
Service.
Most
significantly,
salmon
has
been
4­
29
reclassified
from
a
freshwater/
estuarine
species
to
a
marine
species.
As
marine
harvested
salmon
represents
approximately
99
percent
of
salmon
consumption
in
the
1994­
96
CSFII
Survey,
removal
reduces
the
overall
fresh/
estuarine
fish
consumption
rate
by
13
percent.
Although
they
represent
a
very
small
percentage
of
freshwater/
estuarine
intake,
land­
locked
and
farm­
raised
salmon
consumed
by
1994­
96
CSFII
respondents
are
still
included.
The
rationale
for
the
default
intake
species
designations
is
explained
in
the
Exposure
Assessment
TSD.
Once
again,
EPA
emphasizes
the
flexibility
for
States
and
authorized
Tribes
to
use
alternative
assumptions
based
on
local
or
regional
data
to
better
represent
their
population
groups
of
concern.

4.3.3.2
Rates
Protective
of
Developmental
Human
Health
Effects
Exposures
resulting
in
health
effects
in
children
or
developmental
effects
in
fetuses
may
be
of
primary
concern.
As
discussed
at
the
beginning
of
this
section
on
exposure
factors
used,
in
a
situation
where
acute
or
sub­
chronic
toxicity
and
exposure
are
the
basis
of
an
RfD
(
or
POD/
UF),
EPA
will
consider
basing
its
national
default
criteria
on
children
or
women
of
childbearing
age,
depending
on
the
target
population
at
greatest
risk.
EPA
recommends
that
States
and
authorized
Tribes
use
exposure
factors
for
children
or
women
of
childbearing
age
in
these
situations.
As
stated
previously,
EPA
is
not
recommending
the
development
of
additional
AWQC
but
is
acknowledging
that
basing
a
criterion
on
these
population
groups
is
a
potential
course
of
action
and
is,
therefore,
recommending
the
following
default
intake
rates
for
such
situations.

EPA's
preferences
for
States
and
authorized
Tribes
in
selecting
values
for
intake
rates
relevant
for
children
is
the
same
as
that
discussed
above
for
establishing
values
for
average
daily
consumption
rates
for
chronic
effects;
i.
e.,
in
decreasing
order
of
preference,
results
from
fish
intake
surveys
of
local
watersheds,
results
from
existing
fish
intake
surveys
that
reflect
similar
geography
and
population
groups,
the
distribution
of
intake
rates
from
nationally
based
surveys
(
e.
g.,
the
CSFII),
or
lastly,
the
EPA
default
rates.
When
an
RfD
is
based
on
health
effects
in
children,
EPA
recommends
a
default
intake
rate
of
156.3
grams/
day
for
assessing
those
contaminants
that
exhibit
adverse
effects.
This
represents
the
90th
percentile
consumption
rate
for
actual
consumers
of
freshwater/
estuarine
finfish
and
shellfish
for
children
ages
14
and
under
using
the
combined
1994
to
1996
results
from
the
CSFII
survey.
The
value
was
calculated
based
on
data
for
only
those
children
who
ate
fish
during
the
2­
day
survey
period,
and
the
intake
was
averaged
over
the
number
of
days
during
which
fish
was
actually
consumed.
EPA
believes
that
by
selecting
the
data
for
consumers
only,
the
90th
percentile
is
a
reasonable
intake
rate
to
approximate
consumption
of
fresh/
estuarine
finfish
and
shellfish
within
a
short
period
of
time
for
use
in
assessments
where
adverse
effects
in
children
are
of
primary
concern.
As
discussed
previously,
EPA
will
use
a
default
body
weight
of
30
kg
to
address
potential
acute
or
subchronic
effects
from
fish
consumption
by
children.
EPA
is
also
providing
these
default
intake
values
for
States
and
authorized
Tribes
that
choose
to
provide
additional
protection
when
developing
criteria
that
they
believe
should
be
based
on
health
effects
in
children.
This
is
consistent
with
the
rationale
in
the
recent
GLI
(
USEPA,
1995)
and
is
an
approach
that
EPA
believes
is
reasonable.
Distributional
information
on
intake
values
relevant
for
assessing
exposure
when
health
effects
to
children
are
of
concern
is
presented
in
the
Exposure
Assessment
TSD.
4­
30
There
are
also
cases
in
which
pregnant
women
may
be
the
population
of
most
concern,
due
to
the
possibility
of
developmental
effects
that
may
result
from
exposures
of
the
mother
to
toxicants.
In
these
cases,
fish
intake
rates
specific
to
females
of
childbearing
age
are
most
appropriate
when
assessing
exposures
to
developmental
toxicants.
When
an
RfD
is
based
on
developmental
toxicity,
EPA
proposes
a
default
intake
rate
of
165.5
grams/
day
for
assessing
exposures
for
women
of
childbearing
age
from
contaminants
that
cause
developmental
effects.
This
is
equivalent
to
the
90th
percentile
consumption
rate
for
actual
consumers
of
freshwater/
estuarine
finfish
and
shellfish
for
women
ages
15
to
44
using
the
combined
1994
to1996
results
from
the
CSFII
survey.
As
with
the
rate
for
children,
this
value
represents
only
those
women
who
ate
fish
during
the
2­
day
survey
period.
As
discussed
previously,
EPA
will
use
a
default
body
weight
of
67
kg
for
women
of
childbearing
age.

4.3.3.3
Rates
Based
on
Combining
Fish
Intake
and
Body
Weight
As
with
the
drinking
water
intake
values,
EPA
is
providing
values
for
fish
intake
based
on
a
per
unit
body
weight
basis
(
in
units
of
mg/
kg)
in
the
Exposure
Assessment
TSD.
These
rates
use
the
self­
reported
body
weights
of
the
1994­
96
CSFII
survey.
Again,
while
EPA
intends
to
derive
or
revise
national
default
criteria
on
the
separate
intake
values
and
body
weights,
the
mg/
kg­
BW/
day
values
are
provided
in
the
TSD
for
States
or
authorized
Tribes
that
prefer
their
use.

4.4
REFERENCES
FOR
EXPOSURE
Ershow
A.
G.,
Brown
L.
M.
and
Cantor
K.
P.
1991.
Intake
of
tapwater
and
total
water
by
pregnant
and
lactating
women.
Am.
J.
Public
Health.
81:
328­
334.

Ershow
A.
G.
and
K.
P.
Cantor.
1989.
Total
Water
and
Tap
Water
Intake
in
the
United
States:
Population­
based
Estimates
of
Quantities
and
Sources.
National
Cancer
Institute.
Bethesda,
MD.
Order
#
263­
MD­
810264.

Gibbons,
J.
D.
1971.
Nonparametric
Statistical
Inference.
Chapter
2:
Order
Statistics.
McGraw­
Hill,
Inc.
New
York,
NY.

Jacobs,
H.
L.,
H.
D.
Kahn,
K.
A.
Stralka,
and
D.
B.
Phan.
1998.
Estimates
of
per
capita
fish
consumption
in
the
U.
S.
based
on
the
continuing
survey
of
food
intake
by
individuals
(
CSFII).
Risk
Analysis:
An
International
Journal
18(
3).

McDowell,
M.
2000.
Personal
communication
between
Denis
R.
Borum,
U.
S.
Environmental
Protection
Agency,
and
Margaret
McDowell,
Health
Statistician,
National
Health
and
Nutrition
Examination
Survey,
National
Center
for
Health
Statistics.
March
24,
2000.

USDA.
1998.
U.
S.
Department
of
Agriculture.
1994
 
1996
Continuing
Survey
of
Food
Intakes
by
Individuals
and
1994
 
1996
Diet
and
Health
Knowledge
Survey.
Agricultural
Research
Service,
USDA.
NTIS
CD
 
ROM,
accession
number
PB98
 
500457.
4­
31
[
Available
from
the
National
Technical
Information
Service,
5285
Port
Royal
Road,
Springfield,
VA
22161.
Phone:
(
703)
487
 
4650.]

USEPA.
1993.
Review
of
the
Methodology
for
Developing
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health.
Prepared
by
the
Drinking
Water
Committee
of
the
Science
Advisory
Board.
EPA­
SAB­
DWC
USEPA.
1994.
Reference
dose
(
RfD)
for
oral
exposure
for
cadmium.
Integrated
Risk
Information
System
(
IRIS).
Online.
(
Verification
date
02/
01/
94.)
Office
of
Health
and
Environmental
Assessment,
Environmental
Criteria
and
Assessment
Office.
Cincinnati,
OH.

USEPA.
1995.
Great
Lakes
Water
Quality
Initiative
Technical
Support
Document
for
the
Procedure
to
Determine
Bioaccumulation
Factors.
Office
of
Water.
Washington,
DC.
EPA/
820/
B­
95/
005.

USEPA.
1997a.
Guidance
for
Assessing
Chemical
Contaminant
Data
for
Use
in
Fish
Advisories.
Volume
II:
Risk
Assessment
and
Fish
Consumption
Limits.
Second
Edition.
Office
of
Water.
Washington
DC.
EPA/
823/
B­
97/
009.

USEPA.
1997b.
Exposure
Factors
Handbook.
National
Center
for
Environmental
Assessment,
Office
of
Research
and
Development.
Washington,
DC.
EPA/
600/
P­
95/
002Fa.
August.

USEPA.
1998.
Guidance
for
Conducting
Fish
and
Wildlife
Consumption
Surveys.
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
EPA­
823­
B­
98­
007.
November.

USEPA.
2000a.
Estimated
Per
Capita
Water
Ingestion
in
the
United
States:
Based
on
Data
Collected
by
the
United
States
Department
of
Agriculture's
1994­
96
Continuing
Survey
of
Food
Intakes
by
Individuals.
Office
of
Science
and
Technology,
Office
of
Water.
Washington,
DC.
EPA­
822­
00­
008.
April.

USEPA.
2000b.
Estimated
Per
Capita
Fish
Consumption
in
the
United
States:
Based
on
Data
Collected
by
the
United
States
Department
of
Agriculture's
1994­
1996
Continuing
Survey
of
Food
Intake
by
Individuals.
Office
of
Science
and
Technology,
Office
of
Water,
Washington,
DC.
March.

WESTAT.
2000.
Memorandum
on
Body
Weight
Estimates
Based
on
NHANES
III
data,
Including
Data
Tables
and
Graphs.
Analysis
conducted
and
prepared
by
WESTAT,
under
EPA
Contract
No.
68­
C­
99­
242.
March
3,
2000.
Methodology
for
Deriving
Ambient
Water
Quality
Criteria
for
the
Protection
of
Human
Health
(
2000)

Chapter
5
5.
BIOACCUMULATION
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
1
5.1
Introduction
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
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.
.
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.
.
.
5­
1
5.2
Definitions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
5­
6
5.3
Framework
for
Determining
National
Bioaccumulation
Factors
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
10
5.4
National
Bioaccumulation
Factors
for
Nonionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
16
5.5
National
Bioaccumulation
Factors
for
Ionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
55
5.6
National
Bioaccumulation
Factors
for
Inorganic
and
Organometallic
Chemicals
.
.
.
.
5­
57
5.7
References
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
.
5­
63
TABLES
AND
FIGURES
Figure
5­
1
Framework
for
Deriving
a
National
BAF
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
13
Figure
5­
2
BAF
Derivation
for
Nonionic
Organic
Chemicals
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
17
Table
5­
1
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3
and
4
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
5­
36
5­
1
(
Equation
5­
1)
5.
BIOACCUMULATION
5.1
INTRODUCTION
Aquatic
organisms
can
accumulate
certain
chemicals
in
their
bodies
when
exposed
to
these
chemicals
through
water,
their
diet,
and
other
sources.
This
process
is
called
bioaccumulation.
The
magnitude
of
bioaccumulation
by
aquatic
organisms
varies
widely
depending
on
the
chemical
but
can
be
extremely
high
for
some
highly
persistent
and
hydrophobic
chemicals.
For
such
highly
bioaccumulative
chemicals,
concentrations
in
aquatic
organisms
may
pose
unacceptable
human
health
risks
from
fish
and
shellfish
consumption
even
when
concentrations
in
water
are
too
low
to
cause
unacceptable
health
risks
from
drinking
water
consumption
alone.
These
chemicals
may
also
biomagnify
in
aquatic
food
webs,
a
process
whereby
chemical
concentrations
increase
in
aquatic
organisms
of
each
successive
trophic
level
due
to
increasing
dietary
exposures
(
e.
g.,
increasing
concentrations
from
algae,
to
zooplankton,
to
forage
fish,
to
predatory
fish).

In
order
to
prevent
harmful
exposures
to
waterborne
chemicals
through
the
consumption
of
contaminated
fish
and
shellfish,
national
304(
a)
water
quality
criteria
for
the
protection
of
human
health
must
address
the
process
of
chemical
bioaccumulation
in
aquatic
organisms.
For
deriving
national
304(
a)
criteria
to
protect
human
health,
EPA
accounts
for
potential
bioaccumulation
of
chemicals
in
fish
and
shellfish
through
the
use
of
national
bioaccumulation
factors
(
BAFs).
A
national
BAF
is
a
ratio
(
in
L/
kg)
that
relates
the
concentration
of
a
chemical
in
water
to
its
expected
concentration
in
commonly
consumed
aquatic
organisms
in
a
specified
trophic
level.
An
illustration
of
how
national
BAFs
are
used
in
the
derivation
of
304(
a)
criteria
for
carcinogens
using
linear
low­
dose
extrapolation
is
shown
in
the
following
equation:

where:

RSD
=
Risk
specific
dose
(
mg/
kg­
day)
BW
=
Human
body
weight
(
kg)
DI
=
Drinking
water
intake
(
L/
day)
FI
i
=
Fish
intake
at
trophic
level
I,
where
I=
2,
3,
and
4;
BAF
i
=
National
bioaccumulation
factor
at
trophic
level
I,
where
I=
2,
3,
and
4
The
purpose
of
this
chapter
is
to
present
EPA's
recommended
methodology
for
deriving
national
bioaccumulation
factors
for
setting
national
304(
a)
water
quality
criteria
to
protect
human
health.
A
detailed
scientific
basis
of
the
recommended
national
BAF
methodology
is
provided
in
the
Bioaccumulation
TSD.
While
the
methodology
detailed
in
this
chapter
is
5­
2
intended
to
be
used
by
EPA
for
deriving
national
BAFs,
EPA
encourages
States
and
authorized
Tribes
to
derive
BAFs
that
are
specific
to
certain
regions
or
waterbodies,
where
appropriate.
Guidance
to
States
and
authorized
Tribes
for
deriving
site­
specific
BAFs
is
provided
in
the
Biaccumulation
TSD.

5.1.1
Important
Bioaccumulation
and
Bioconcentration
Concepts
Several
attributes
of
the
bioaccumulation
process
are
important
to
understand
when
deriving
national
BAFs
for
use
in
setting
national
304(
a)
criteria.
First,
the
term
"
bioaccumulation"
refers
to
the
uptake
and
retention
of
a
chemical
by
an
aquatic
organism
from
all
surrounding
media
(
e.
g.,
water,
food,
sediment).
The
term
"
bioconcentration"
refers
to
the
uptake
and
retention
of
a
chemical
by
an
aquatic
organism
from
water
only.
For
some
chemicals
(
particularly
those
that
are
highly
persistent
and
hydrophobic),
the
magnitude
of
bioaccumulation
by
aquatic
organisms
can
be
substantially
greater
than
the
magnitude
of
bioconcentration.
Thus,
an
assessment
of
bioconcentration
alone
would
underestimate
the
extent
of
accumulation
in
aquatic
biota
for
these
chemicals.
Accordingly,
EPA's
guidelines
presented
in
this
chapter
emphasize
the
measurement
of
chemical
bioaccumulation
by
aquatic
organisms,
whereas
EPA's
1980
Methodology
emphasized
the
measurement
of
bioconcentration.

Another
noteworthy
aspect
of
bioaccumulation
process
is
the
issue
of
steady­
state
conditions.
Specifically,
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.
The
rates
of
chemical
uptake
and
depuration
can
be
affected
by
various
factors
including
the
properties
of
the
chemical,
the
physiology
of
the
organism
in
question,
water
quality
and
other
environmental
conditions,
ecological
characteristics
of
the
waterbody
(
e.
g.,
food
web
structure),
and
the
concentration
and
loadings
history
of
the
chemical.
When
the
rates
of
chemical
uptake
and
depuration
are
equal,
tissue
concentrations
remain
constant
over
time
and
the
distribution
of
the
chemical
between
the
organism
and
its
source(
s)
is
said
to
be
at
steady­
state.
For
constant
chemical
exposures
and
other
conditions,
the
steady­
state
concentration
in
the
organism
represents
the
highest
accumulation
potential
of
the
chemical
in
that
organism
under
those
conditions.
The
time
required
for
a
chemical
to
achieve
steady
state
has
been
shown
to
vary
according
to
the
properties
of
the
chemical
and
other
factors.
For
example,
some
highly
hydrophobic
chemicals
can
require
long
periods
of
time
to
reach
steady
state
between
environmental
compartments
(
e.
g.,
many
months),
while
highly
hydrophilic
chemicals
usually
reach
steady­
state
relatively
quickly
(
e.
g.,
hours
to
days).

Since
national
304(
a)
criteria
for
the
protection
of
human
health
are
typically
designed
to
protect
humans
from
harmful
lifetime
or
long­
term
exposures
to
waterborne
contaminants,
the
assessment
of
bioaccumulation
that
equals
or
approximates
steady­
state
accumulation
is
one
of
the
principles
underlying
the
derivation
of
national
BAFs.
For
some
chemicals
that
require
relatively
long
periods
of
time
to
reach
steady­
state
in
tissues
of
aquatic
organisms,
changes
in
water
column
concentrations
may
occur
on
a
much
more
rapid
time
scale
compared
to
the
corresponding
changes
in
tissue
concentrations.
Thus,
if
the
system
departs
substantially
from
steady­
state
conditions
and
water
concentrations
are
not
averaged
over
a
sufficient
time
period,
5­
3
the
ratio
of
the
tissue
concentration
to
a
water
concentration
may
have
little
resemblance
to
the
steady­
state
ratio
and
have
little
predictive
value
of
long­
term
bioaccumulation
potential.
Therefore,
BAF
measurements
should
be
based
on
water
column
concentrations
which
are
averaged
over
a
sufficient
period
of
time
(
e.
g.,
a
duration
comparable
to
the
time
required
for
the
chemical
to
reach
steady­
state).
In
addition,
BAF
measurements
should
be
based
on
adequate
spatial
averaging
of
both
tissue
and
water
column
concentrations
for
use
in
deriving
304(
a)
criteria
for
the
protection
of
human
health.

For
this
reason,
a
BAF
is
defined
in
this
Methodology
as
representing
the
ratio
(
in
L/
kgtissue
of
a
concentration
of
a
chemical
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
(
i.
e.,
the
ratio
which
reflects
bioaccumulation
at
or
near
steady­
state).
A
bioconcentration
factor
(
BCF)
is
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.

5.1.2
Goal
of
the
National
BAF
The
goal
of
EPA's
national
BAF
is
to
represent
the
long­
term,
average
bioaccumulation
potential
of
a
chemical
in
edible
tissues
of
aquatic
organisms
that
are
commonly
consumed
by
humans
throughout
the
United
States.
National
BAFs
are
not
intended
to
reflect
fluctuations
in
bioaccumulation
over
short
time
periods
(
e.
g.,
a
few
days)
because
304(
a)
human
health
criteria
are
generally
designed
to
protect
humans
from
long­
term
exposures
to
waterborne
chemicals.
National
BAFs
are
also
intended
to
account
for
some
major
chemical,
biological,
and
ecological
attributes
that
can
affect
bioaccumulation
in
bodies
of
water
across
the
United
States.
For
example,
separate
procedures
are
provided
for
deriving
national
BAFs
depending
on
the
type
of
chemical
(
i.
e.,
nonionic
organic,
ionic
organic,
inorganic
and
organometallic).
In
addition,
EPA's
national
BAFs
are
derived
separately
for
each
trophic
level
to
account
for
potential
biomagnification
of
some
chemicals
in
aquatic
food
webs
and
broad
physiological
differences
between
trophic
levels
that
may
influence
bioaccumulation.
Because
lipid
content
of
aquatic
organisms
and
the
amount
of
organic
carbon
in
the
water
column
have
been
shown
to
affect
bioaccumulation
of
nonionic
organic
chemicals,
EPA's
national
BAFs
are
adjusted
to
reflect
the
lipid
content
of
commonly
consumed
fish
and
shellfish
and
the
freely
dissolved
fraction
of
the
chemical
in
ambient
water
for
these
chemicals.

5.1.3
Changes
to
the
1980
Methodology
Numerous
scientific
advances
have
occurred
in
the
area
of
bioaccumulation
since
the
publication
of
the
1980
Methodology
for
deriving
AWQC
for
the
protection
of
human
health
(
USEPA,
1980).
These
advances
have
significantly
increased
our
ability
to
assess
and
predict
the
bioaccumulation
of
chemicals
in
aquatic
biota.
As
a
result,
EPA
has
revised
the
bioaccumulation
portion
of
the
1980
Methodology
to
reflect
the
current
state
of
the
science
and
to
improve
accuracy
in
assessing
bioaccumulation
for
setting
304(
a)
criteria
for
the
protection
of
5­
4
human
health.
The
changes
contained
in
the
bioaccumulation
portion
of
the
2000
Human
Health
Methodology
are
mostly
designed
to:

C
Improve
the
ability
to
incorporate
chemical
exposure
from
sediments
and
aquatic
food
webs
in
assessing
bioaccumulation
potential,

C
Expand
the
ability
to
account
for
site­
specific
factors
which
affect
bioaccumulation,
and
C
Incorporate
new
data
and
assessment
tools
into
the
bioaccumulation
assessment
process.

A
summary
of
the
key
changes
that
have
been
incorporated
into
the
bioaccumulation
portion
of
the
2000
Human
Health
Methodology
and
appropriate
comparisons
to
the1980
Methodology
are
provided
below.

5.1.3.1
Overall
Approach
The
1980
Methodology
for
deriving
304(
a)
criteria
for
the
protection
of
human
health
emphasized
the
assessment
of
bioconcentration
(
uptake
from
water
only)
through
the
use
of
the
BCF.
Based
on
the
1980
Methodology,
measured
BCFs
were
usually
determined
from
laboratory
data
unless
field
data
demonstrated
consistently
higher
or
lower
accumulation
compared
with
laboratory
data.
In
these
cases,
"
field
BCFs"
(
currently
termed
field­
measured
BAFs)
were
recommended
for
use.
For
lipophilic
chemicals
where
lab
or
field­
measured
data
were
unavailable,
EPA
recommended
predicting
BCFs
from
the
octanol­
water
partition
coefficient
and
the
following
equation
from
Veith
et
al.
(
1979):
"
log
BCF
=
(
0.85
log
K
ow)
­
0.70".

The
2000
Human
Health
Methodology
revisions
contained
in
this
chapter
emphasize
the
measurement
of
bioaccumulation
(
uptake
from
water,
sediment,
and
diet)
through
the
use
of
the
BAF.
Consistent
with
the
1980
Methodology,
measured
data
are
preferred
over
predictive
approaches
for
determining
the
BAF
(
i.
e.,
field­
measured
BAFs
are
generally
preferred
over
predicted
BAFs).
However,
the
2000
Human
Health
Methodology
contains
additional
methods
for
deriving
a
national
BAF
that
were
not
available
in
1980.
The
preference
for
using
the
BAF
methods
also
differs
depending
on
the
type
and
properties
of
the
chemical.
For
example,
the
BAF
derivation
procedure
differs
for
each
of
three
broadly
defined
chemical
categories:
(
1)
nonionic
organic,
(
2)
ionic
organic,
and
(
3)
inorganic
and
organometallic
chemicals.
Furthermore,
within
the
category
of
nonionic
organic
chemicals,
different
procedures
are
used
to
derive
the
BAF
depending
on
a
chemicals'
hydrophobicity
and
extent
of
chemical
metabolism
that
would
be
expected
to
occur
in
aquatic
biota.

5.1.3.2
Lipid
Normalization
In
the
1980
Methodology,
BCFs
for
lipophilic
chemicals
were
normalized
by
the
lipid
fraction
in
the
tissue
of
fish
and
shellfish
used
to
determine
the
BCF.
Lipid
normalization
enabled
BCFs
to
be
averaged
across
tissues
and
organisms.
Once
the
average
lipid­
normalized
5­
5
BCF
was
determined,
it
was
adjusted
by
the
consumption­
weighted
lipid
content
of
commonly
consumed
aquatic
organisms
in
the
United
States
to
obtain
an
overall
consumption­
weighted
BCF.
A
similar
procedure
has
been
retained
in
the
2000
Human
Health
Methodology,
whereby
BAFs
for
nonionic
organic
chemicals
are
lipid
normalized
and
adjusted
by
the
consumptionweighted
lipid
content
of
commonly
consumed
organisms
to
obtain
a
BAF
for
criteria
calculations.
However,
the
2000
Human
Health
Methodology
uses
more
up­
to­
date
lipid
data
and
consumption
data
for
deriving
the
consumption­
weighted
BAFs.

5.1.3.3
Bioavailability
Bioconcentration
factors
derived
according
to
the
1980
Methodology
were
based
on
the
total
concentration
of
the
chemical
in
water,
for
both
lipophilic
and
nonlipophilic
chemicals.
In
the
2000
Human
Health
Methodology,
BAFs
for
nonionic
organic
chemicals
are
derived
using
the
most
bioavailable
fraction
(
i.
e.,
the
freely
dissolved
fraction)
to
account
for
the
influence
of
particulate
and
dissolved
organic
carbon
on
a
chemical's
bioavailability.
Such
BAFs
are
then
adjusted
to
reflect
the
expected
bioavailability
at
the
sites
of
interest
(
i.
e.,
by
adjusting
for
organic
carbon
concentrations
at
the
sites
of
interest).
Procedures
for
accounting
for
the
effect
of
organic
carbon
on
bioaccumulation
were
published
previously
by
EPA
under
the
Great
Lakes
Water
Quality
Initiative
(
GLWQI
or
GLI)
rulemaking
(
USEPA,
1995a,
b).
Bioavailability
is
also
considered
in
developing
BAFs
for
the
other
chemical
classes
defined
in
the
2000
Human
Health
Methodology
(
e.
g.,
ionic
organics,
inorganics/
organometallics)
but
is
done
so
on
a
chemical­
bychemical
basis.

5.1.3.4
Trophic
Level
Considerations
In
the
1980
Methodology,
BCFs
were
determined
and
used
for
criteria
derivation
without
explicit
regard
to
the
trophic
level
of
the
aquatic
organism
(
e.
g.,
benthic
filter
feeder,
forage
fish,
predatory
fish).
Over
the
past
two
decades,
much
information
has
been
assembled
which
demonstrates
that
an
organism's
trophic
position
in
the
aquatic
food
web
can
have
an
important
effect
on
the
magnitude
of
bioaccumulation
of
certain
chemicals.
In
order
to
account
for
the
variation
in
bioaccumulation
that
is
due
to
trophic
position
of
the
organism,
the
2000
Human
Health
Methodology
recommends
that
BAFs
be
determined
and
applied
on
a
trophic
level­
specific
basis.

5.1.3.5
Site­
Specific
Adjustments
The
1980
Methodology
contained
little
guidance
for
making
adjustments
to
the
national
BCFs
to
reflect
site­
or
region­
specific
conditions.
The
2000
Human
Health
Methodology
has
greatly
expanded
the
guidance
to
States
and
authorized
Tribes
for
making
adjustments
to
national
BAFs
to
reflect
local
conditions.
This
guidance
is
contained
in
the
Bioaccumulation
TSD.
In
the
Bioaccumulation
TSD,
guidance
and
data
are
provided
for
adjusting
national
BAFs
to
reflect
the
lipid
content
in
locally
consumed
aquatic
biota
and
the
organic
carbon
content
in
the
waterbodies
of
concern.
This
guidance
also
allows
the
use
of
appropriate
bioaccumulation
models
for
deriving
site­
specific
BAFs.
EPA
also
plans
to
publish
detailed
guidance
on
designing
and
conducting
field
5­
6
(
Equation
5­
2)
bioaccumulation
studies
for
measuring
BAFs
and
biota­
sediment
accumulation
factors
(
BSAFs).
In
general,
EPA
encourages
States
and
authorized
Tribes
to
make
site­
specific
modifications
to
EPA's
national
BAFs
provided
such
adjustments
are
scientifically
defensible
and
adequately
protect
the
designated
use
of
the
waterbody.

While
the
aforementioned
revisions
are
new
to
EPA's
Methodology
for
deriving
national
304(
a)
criteria
for
the
protection
of
human
health,
many
of
these
refinements
have
been
incorporated
in
prior
Agency
guidance
and
regulations.
For
example,
the
use
of
food
chain
multipliers
to
account
for
the
biomagnification
of
nonionic
organic
chemicals
in
aquatic
food
webs
when
measured
data
are
unavailable
was
introduced
by
EPA
in
three
documents:
Technical
Support
Document
for
Water
Quality­
Based
Toxics
Control
(
USEPA,
1991),
a
draft
document
entitled
Assessment
and
Control
of
Bioconcentratable
Contaminants
in
Surface
Waters
(
USEPA,
1993),
and
in
the
Great
Lakes
Water
Quality
Initiative
(
GLI)
(
USEPA,
1995b).
Similarly,
procedures
for
predicting
BAFs
using
BSAFsand
incorporating
the
effect
of
organic
carbon
on
bioavailability
were
used
to
derive
water
quality
criteria
under
the
GLI.

5.1.4
Organization
of
This
Section
The
methodology
for
deriving
national
BAFs
for
use
in
deriving
National
304(
a)
Human
Health
AWQC
is
provided
in
the
following
sections.
Important
terms
used
throughout
this
chapter
are
defined
in
Section
5.2.
Section
5.3
provides
an
overview
of
the
BAF
derivation
guidelines.
Detailed
procedures
for
deriving
national
BAFs
are
provided
in
Section
5.4
for
nonionic
organic
chemicals,
in
Section
5.5
for
ionic
organic
chemicals,
and
in
Section
5.6
for
inorganics
and
organometallic
chemicals.
Literature
cited
is
provided
in
Section
5.7.

5.2
DEFINITIONS
The
following
terms
and
definitions
are
used
throughout
this
chapter.

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

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.

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:
5­
7
(
Equation
5­
3)

(
Equation
5­
4)
where:

C
t
=
Concentration
of
the
chemical
in
the
specified
wet
tissue
C
w
=
Concentration
of
chemical
in
water
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:

where:

C
t
=
Concentration
of
the
chemical
in
the
specified
wet
tissue
C
w
=
Concentration
of
chemical
in
water
Baseline
BAF
(
BAF
R
f
d).
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
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.

Baseline
BCF
(
BCF
R
f
d).
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
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.

Biomagnification.
The
increase
in
tissue
concentration
of
a
chemical
in
organisms
at
successive
trophic
levels
through
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
chemical
in
a
predator
at
a
particular
trophic
level
to
the
tissue
concentration
in
its
prey
at
the
next
lower
trophic
level
for
a
given
waterbody
and
chemical
exposure.
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
a
BMF
can
be
calculated
using
lipid­
normalized
concentrations
in
the
tissue
of
organisms
at
two
successive
trophic
levels
as:
5­
8
(
Equation
5­
5)

(
Equation
5­
6)
where:

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

For
inorganic,
organometallic,
and
certain
ionic
organic
chemicals
where
lipid
and
organic
carbon
partitioning
does
not
apply,
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
lower
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)

Biota­
Sediment
Accumulation
Factor
(
BSAF).
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
the
ratio
of
the
lipid­
normalized
concentration
of
a
substance
in
tissue
of
an
aquatic
organism
to
its
organic
carbon­
normalized
concentration
in
surface
sediment
(
expressed
as
kg
of
sediment
organic
carbon
per
kg
of
lipid),
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
(
µ
g/
g
lipid)
5­
9
(
Equation
5­
7)

(
Equation
5­
8)
C
soc
=
The
organic
carbon­
normalized
concentration
of
the
chemical
in
the
surface
sediment
(
µ
g/
g
sediment
organic
carbon)

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

Food
Chain
Multiplier
(
FCM).
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
the
ratio
of
a
baseline
BAF
R
f
d
for
an
organism
of
a
particular
trophic
level
to
the
baseline
BCF
R
f
d
(
usually
determined
for
organisms
in
trophic
level
one).
For
inorganic,
organometallic,
and
certain
ionic
organic
chemicals
where
lipid
and
organic
carbon
partitioning
does
not
apply,
a
FCM
is
based
on
total
(
wet
or
dry
weight)
concentrations
of
the
chemical
in
tissue.

Freely
Dissolved
Concentration.
For
nonionic
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:

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
Hydrophilic.
A
term
that
refers
to
the
extent
to
which
a
chemical
is
attracted
to
partitioning
into
the
water
phase.
Hydrophilic
organic
chemicals
have
a
greater
tendency
to
partition
into
polar
phases
(
e.
g.,
water)
compared
to
chemicals
of
hydrophobic
chemicals.

Hydrophobic.
A
term
that
refers
to
the
extent
to
which
a
chemical
avoids
partitioning
into
the
water
phase.
Highly
hydrophobic
organic
chemicals
have
a
greater
tendency
to
partition
into
nonpolar
phases
(
e.
g.,
lipid,
organic
carbon)
compared
with
chemicals
of
lower
hydrophobicity.

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
lipidnormalized
concentration
can
be
calculated
as:
5­
10
(
Equation
5­
9)
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.

5.3
FRAMEWORK
FOR
DETERMINING
NATIONAL
BIOACCUMULATION
FACTORS
5.3.1
Four
Different
Methods
Bioaccumulation
factors
used
to
derive
national
BAFs
can
be
measured
or
predicted
using
some
or
all
of
the
following
four
methods,
depending
on
the
type
of
chemical
and
its
properties.
These
methods
are:

(
1)
a
measured
BAF
obtained
from
a
field
study
(
i.
e.,
a
field­
measured
BAF);

(
2)
a
BAF
predicted
from
a
field­
measured
BSAF;

(
3)
a
BAF
predicted
from
a
laboratory­
measured
BCF
(
with
or
without
adjustment
by
an
FCM);
and
5­
11
(
4)
a
BAF
predicted
from
a
chemical's
octanol­
water
partition
coefficient
(
K
ow
),
with
or
without
adjustment
using
an
FCM.

A
brief
summary
of
each
of
the
four
methods
is
provided
below.
Additional
details
on
the
use
of
these
four
methods
is
provided
in
Section
5.4
(
for
nonionic
organics),
Section
5.5
(
for
ionic
organics)
and
Section
5.6
(
for
inorganics
and
organometallics).

1.
Field­
Measured
BAF.
Use
of
a
field­
measured
BAF,
which
is
the
most
direct
measure
of
bioaccumulation,
is
the
only
method
that
can
be
used
to
derive
a
national
BAF
for
all
types
of
chemicals
(
i.
e.,
nonionic
organic,
ionic
organic,
and
inorganic
and
organometallic
chemicals).
A
field­
measured
BAF
is
determined
from
a
field
study
using
measured
chemical
concentrations
in
the
aquatic
organism
and
its
surrounding
water.
Because
field
studies
are
conducted
in
natural
aquatic
ecosystems,
a
field­
measured
BAF
reflects
an
organism's
exposure
to
a
chemical
through
all
relevant
exposure
pathways
(
i.
e.,
water,
sediment,
and
diet).
A
field­
measured
BAF
also
reflects
any
metabolism
of
a
chemical
that
might
occur
in
the
aquatic
organism
or
its
food
web.
Therefore,
field­
measured
BAFs
are
appropriate
for
all
chemicals,
regardless
of
the
extent
of
chemical
metabolism
in
biota.

2.
Field­
measured
BSAF.
For
nonionic
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies),
a
BAF
can
also
be
predicted
from
BSAFs.
A
BSAF
is
similar
to
a
field­
measured
BAF
in
that
the
concentration
of
a
chemical
in
biota
is
measured
in
the
field
and
reflects
an
organism's
exposure
to
all
relevant
exposure
routes.
A
BSAF
also
reflects
any
chemical
metabolism
that
might
occur
in
the
aquatic
organism
or
its
food
web.
However,
unlike
a
fieldmeasured
BAF
which
references
the
biota
concentration
to
the
water
concentration,
a
BSAF
references
the
biota
concentration
to
the
sediment
concentration.
Use
of
the
BSAF
procedure
is
restricted
to
organic
chemicals
which
are
classified
as
being
moderately
to
highly
hydrophobic.

3.
Lab­
measured
BCF.
A
laboratory­
measured
BCF
can
also
be
used
to
estimate
a
BAF
for
organic
and
inorganic
chemicals.
However,
unlike
a
field­
measured
BAF
or
a
BAF
predicted
from
a
field­
measured
BSAF,
a
laboratory­
measured
BCF
only
reflects
the
accumulation
of
chemical
through
the
water
exposure
route.
Laboratory­
measured
BCFs
may
therefore
under
estimate
BAFs
for
chemicals
where
accumulation
from
sediment
or
dietary
sources
is
important.
In
these
cases,
laboratory­
measured
BCFs
can
be
multiplied
by
a
FCM
to
reflect
accumulation
from
non­
aqueous
(
i.
e.,
food
chain)
pathways
of
exposure.
Since
a
laboratory­
measured
BCF
is
determined
using
the
measured
concentration
of
a
chemical
in
an
aquatic
organism
and
its
surrounding
water,
a
laboratory­
measured
BCF
reflects
any
metabolism
of
the
chemical
that
occurs
in
the
organism,
but
not
in
the
food
web.

4.
Kow.
A
chemical's
octanol­
water
partition
coefficient,
or
K
ow,
can
also
be
used
to
predict
a
BAF
for
nonionic
organic
chemicals.
This
procedure
is
appropriate
only
for
nonionic
5­
12
organic
chemicals
(
and
certain
ionic
organic
chemicals
where
similar
lipid
and
organic
carbon
partitioning
behavior
applies).
The
K
ow
has
been
extensively
correlated
with
the
BCF
for
nonionic
organic
chemicals
that
are
poorly
metabolized
by
aquatic
organisms.
Therefore,
where
substantial
metabolism
is
known
to
occur
in
biota,
the
K
ow
is
not
used
to
predict
the
BAF.
For
nonionic
organic
chemicals
where
chemical
exposure
through
the
food
web
is
important,
use
of
the
K
ow
alone
will
under
predict
the
BAF.
In
such
cases,
the
K
ow
is
adjusted
with
a
FCM
similar
to
the
BCF
procedure
above.

5.3.2
Overview
of
BAF
Derivation
Framework
Although
up
to
four
methods
can
be
used
to
derive
a
BAF
as
described
in
the
previous
section,
it
is
evident
that
these
methods
do
not
apply
equally
to
all
types
of
chemicals.
In
addition,
experience
demonstrates
that
the
required
data
will
usually
not
be
available
to
derive
a
BAF
value
using
all
of
the
applicable
methods.
As
a
result,
EPA
has
developed
the
following
guidelines
to
direct
users
in
selecting
the
most
appropriate
method(
s)
for
deriving
a
national
BAF.

Figure
5­
1
shows
the
overall
framework
of
EPA's
national
BAF
methodology.
This
framework
illustrates
the
major
steps
and
decisions
that
will
ultimately
lead
to
calculating
a
national
BAF
using
one
of
six
hierarchical
procedures
shown
at
the
bottom
of
Figure
5­
1.
Each
procedure
contains
a
hierarchy
of
the
BAF
derivation
methods
discussed
above,
the
composition
of
which
depends
on
the
chemical
type
and
certain
chemical
properties
(
e.
g.,
its
degree
of
hydrophobicity
and
expected
degree
of
metabolism
and
biomagnification).
The
number
assigned
to
each
BAF
method
within
a
procedure
indicates
its
general
order
of
preference
for
deriving
a
national
BAF
value.
The
goal
of
the
framework
and
accompanying
guidelines
is
to
enable
full
use
of
available
data
and
methods
for
deriving
a
national
BAF
value
while
appropriately
restricting
the
use
of
certain
methods
to
reflect
their
inherent
limitations.

The
first
step
in
the
framework
is
to
define
the
chemical
of
concern.
As
described
in
Section
5.3.3,
the
chemical
used
to
derive
the
national
BAF
should
be
consistent
with
the
chemical
used
to
derive
the
critical
health
assessment
value.
The
second
step
is
to
collect
and
review
all
relevant
data
on
bioconcentration
and
bioaccumulation
of
the
chemical
of
concern
(
see
Section
5.3.4).
Once
pertinent
data
are
reviewed,
the
third
step
is
to
classify
the
chemical
of
concern
into
one
of
three
broadly
defined
chemical
categories:
(
1)
nonionic
organic
chemicals,
(
2)
ionic
organic
chemicals,
and
(
3)
and
inorganic
and
organometallic
chemicals.
Guidance
for
classifying
chemicals
into
these
three
categories
is
provided
in
Section
5.3.5.

After
a
chemical
has
been
classified
into
one
of
the
three
categories,
other
information
is
used
to
select
one
of
six
hierarchical
procedures
to
derive
the
national
BAF.
The
specific
procedures
for
deriving
a
BAF
for
each
chemical
group
are
discussed
in
Section
5.4
for
nonionic
organics,
Section
5.5
for
ionic
organics,
and
Section
5.6
for
inorganics
and
organometallics.
5­
13
CLASSIFY
CHEMICAL
OF
CONCERN
Nonionic
Organic
Figure
5­
1.
Framework
for
Deriving
a
National
BAF
Ionic
Organic
Inorganic
&
Organometallic
Yes
(
Log
KOW
>
4)
(
Log
KOW
<
4)

PROCEDURE
#
4
HYDROPHOBICITY?

METABOLISM?

Low
High
Low
High
METABOLISM?

1.
Field
BAF
or
PROCEDURE
#
3
Lab
BCF
2.
KOW
1.
Field
BAF
PROCEDURE
#
2
3.
Lab
BCF
1.
Field
BAF
PROCEDURE
#
1
2.
BSAF
4.
KOW*
FCM
BIOMAGNIFICATION?

PROCEDURE
#
6
3.
Lab
BCF*
FCM
Low
COLLECT
&
REVIEW
DATA
DEFINE
CHEMICAL
OF
CONCERN
Moderate­
High
PROCEDURE
#
5
No
IONIZATION
NEGLIGIBLE?

No
Yes
2.
BSAF
1.
Field
BAF
or
Lab
BCF
1.
Field
BAF
or
Lab
BCF
1.
Field
BAF
2.
Lab
BCF*
FCM
5­
14
Detailed
guidance
concerning
the
first
three
steps
of
the
derivation
process
(
i.
e,
defining
the
chemical
of
concern,
collecting
and
reviewing
data,
and
classifying
the
chemical
of
concern)
is
provided
in
the
following
three
sections.

5.3.3
Defining
the
Chemical
of
Concern
Defining
the
chemical
of
concern
is
the
first
step
in
deriving
a
national
BAF.
This
step
involves
precisely
defining
the
form(
s)
of
the
chemical
upon
which
the
national
BAF
value
will
be
derived.
Although
this
step
is
usually
straightforward
for
single
chemicals,
complications
can
arise
when
the
chemical
of
concern
occurs
as
a
mixture.
The
following
guidelines
should
be
followed
for
defining
the
chemical
of
concern.

1.
Information
for
defining
the
chemical
of
concern
should
be
obtained
from
the
health
and
exposure
assessment
portions
of
the
criteria
derivation
effort.
The
chemical(
s)
used
to
derive
the
national
BAF
should
be
consistent
with
the
chemical(
s)
used
to
derive
the
reference
dose
(
RfD),
point
of
departure/
uncertainty
factor
(
POD/
UF),
or
cancer
potency
factor.

2.
In
most
cases,
the
RfD,
POD/
UF,
or
cancer
potency
factor
will
be
based
on
a
single
chemical.
In
some
cases,
the
RfD,
POD/
UF,
or
cancer
potency
factor
will
be
based
on
a
mixture
of
compounds,
typically
within
the
same
chemical
class
(
e.
g.,
toxaphene,
chlordane).
In
these
situations,
the
national
BAF
should
be
derived
in
a
manner
that
is
consistent
with
the
mixture
used
to
express
the
health
assessment.

a.
If
sufficient
data
are
available
to
reliably
assess
the
bioaccumulation
of
each
relevant
compound
contained
in
the
mixture,
then
the
national
BAF(
s)
should
be
derived
using
the
BAFs
for
the
individual
compounds
of
the
mixture
and
appropriately
weighted
to
reflect
the
mixture
composition
used
to
establish
the
RfD,
POD/
UF,
or
cancer
potency
factor.
An
example
of
this
approach
is
shown
in
the
derivation
of
BAFs
for
PCBs
in
the
GLI
Rulemaking
(
USEPA,
1997).

b.
If
sufficient
data
are
not
available
to
reliably
assess
the
bioaccumulation
of
individual
compounds
of
the
mixture,
then
the
national
BAF(
s)
should
be
derived
using
BAFs
for
the
same
or
appropriately
similar
chemical
mixture
as
that
used
to
establish
the
RfD,
POD/
UF,
or
cancer
potency
value.

5.3.4
Collecting
and
Reviewing
Data
The
second
step
in
deriving
a
national
BAF
is
to
collect
and
review
all
relevant
bioaccumulation
data
for
the
chemical
of
concern.
The
following
guidance
should
be
followed
for
collecting
and
reviewing
bioaccumulation
data
for
deriving
national
BAFs.

1.
All
data
on
the
occurrence
and
accumulation
of
the
chemical
of
concern
in
aquatic
animals
and
plants
should
be
collected
and
reviewed
for
adequacy.
5­
15
2.
A
comprehensive
literature
search
strategy
should
be
used
for
gathering
bioaccumulationrelated
data.
An
example
of
a
comprehensive
literature
search
strategy
is
provided
in
the
Bioaccumulation
TSD.

3.
All
data
that
are
used
should
contain
sufficient
supporting
information
to
indicate
that
acceptable
measurement
procedures
were
used
and
that
the
results
are
probably
reliable.
In
some
cases
it
may
be
appropriate
to
obtain
additional
written
information
from
the
investigator.

4.
Questionable
data,
whether
published
or
unpublished,
should
not
be
used.
Guidance
for
assessing
the
acceptability
of
bioaccumulation
and
bioconcentration
studies
is
found
in
Sections
5.4,
5.5,
and
5.6.

5.3.5
Classifying
the
Chemical
of
Concern
The
next
step
in
deriving
a
national
BAF
consists
of
classifying
the
chemical
of
concern
into
one
of
three
categories:
nonionic
organic,
ionic
organic,
and
inorganic
and
organometallic
(
Figure
5­
1).
This
step
helps
to
determine
which
of
the
four
methods
described
in
Section
5.3.1
are
appropriate
for
deriving
BAFs.
The
following
guidance
applies
for
classifying
the
chemical
of
concern.

1.
Nonionic
Organic
Chemicals.
For
the
purposes
of
the
2000
Human
Health
Methodology,
nonionic
organic
chemicals
are
those
organic
compounds
that
do
not
ionize
substantially
in
natural
bodies
of
water.
These
chemicals
are
also
referred
to
as
neutral
or
nonpolar
organics
in
the
scientific
literature.
Due
to
their
neutrality,
nonionic
organic
chemicals
tend
to
associate
with
other
neutral
(
or
near
neutral)
compartments
in
aquatic
ecosystems
(
e.
g.,
lipid,
organic
carbon).
Examples
of
nonionic
organic
chemicals
which
have
been
widely
studied
in
terms
of
their
bioaccumulation
include
polychlorinated
biphenyls
(
PCBs),
polychlorinated
dibenzo­
p­
dioxins
and
furans,
many
chlorinated
pesticides,
and
polynuclear
aromatic
hydrocarbons
(
PAHs).
Procedures
for
deriving
a
national
BAF
for
nonionic
organic
chemicals
are
provided
in
Section
5.4.

2.
Ionic
Organic
Chemicals.
For
the
purposes
of
the
2000
Human
Health
Methodology,
ionic
organic
chemicals
are
considered
to
include
those
chemicals
that
contain
functional
groups
with
exchangeable
protons
such
as
hydroxyl,
carboxylic,
and
sulfonic
groups
and
functional
groups
that
readily
accept
protons
such
as
amino
and
aromatic
heterocyclic
nitrogen
(
pyridine)
groups.
Ionic
organic
chemicals
undergo
ionization
in
water,
the
extent
of
which
depends
on
pH
and
the
pKa
of
the
chemical.
Because
the
ionized
species
of
these
chemicals
behave
differently
from
the
neutral
species,
separate
guidance
is
provided
for
deriving
BAFs
for
ionic
organic
chemicals.
Procedures
for
deriving
national
BAFs
for
ionic
organic
chemicals
are
provided
in
Section
5.5.

3.
Inorganic
and
Organometallic
Chemicals.
The
inorganic
and
organometallic
category
is
considered
to
include
inorganic
minerals,
other
inorganic
compounds
and
elements,
5­
16
metals
(
e.
g.,
copper,
cadmium,
chromium,
zinc),
metalloids
(
selenium,
arsenic)
and
organometallic
compounds
(
e.
g.,
methylmercury,
tributyltin,
tetraalkyllead).
Procedures
for
deriving
BAFs
for
inorganic
and
organometallic
chemicals
are
provided
in
Section
5.6.

5.4
NATIONAL
BIOACCUMULATION
FACTORS
FOR
NONIONIC
ORGANIC
CHEMICALS
5.4.1
Overview
This
section
contains
the
methodology
for
deriving
national
BAFs
for
nonionic
organic
chemicals
as
defined
in
Section
5.3.5.
The
four
general
steps
of
this
methodology
are:

1.
Selecting
the
BAF
derivation
procedure,
2.
Calculating
individual
baseline
BAF
R
f
ds,
3.
Selecting
the
final
baseline
BAF
R
f
ds,
and
4.
Calculating
the
national
BAFs
from
the
final
baseline
BAF
R
f
ds.

A
schematic
of
this
four­
step
process
is
shown
in
Figure
5­
2.

Step
1
of
the
methodology
(
selecting
the
BAF
derivation
procedure)
determines
which
of
the
four
BAF
procedures
summarized
in
Figure
5­
1
will
be
appropriate
for
deriving
the
national
BAF.
Step
2
involves
calculating
individual,
species­
specific
BAF
R
f
ds
using
all
of
the
methods
available
within
the
selected
BAF
derivation
procedure.
Calculating
the
individual
baseline
BAF
R
f
ds
involves
using
data
from
the
field
site
or
laboratory
where
the
original
data
were
collected
to
account
for
site­
specific
factors
which
affect
the
bioavailability
of
the
chemical
to
aquatic
organisms
(
e.
g.,
lipid
content
of
study
organisms
and
freely
dissolved
concentration
in
study
water).
Step
3
of
the
methodology
consists
of
selecting
the
final
baseline
BAF
R
f
ds
from
the
individual
baseline
BAF
R
f
ds
by
taking
into
account
the
uncertainty
in
the
individual
BAFs
and
the
data
preference
hierarchy
selected
in
Step
1.
The
final
step
is
to
calculate
a
BAF
(
or
BAFs)
that
will
be
used
in
the
derivation
of
304(
a)
criteria
(
i.
e.,
referred
to
as
the
national
BAF).
This
step
involves
adjusting
the
final
baseline
BAF
R
f
d(
s)
to
reflect
certain
factors
that
affect
bioavailablity
of
the
chemical
to
aquatic
organisms
in
waters
to
which
the
national
304(
a)
criteria
will
apply
(
e.
g.,
the
freely
dissolved
fraction
expected
in
U.
S.
waters
and
the
lipid
content
of
consumed
aquatic
organisms).
Baseline
BAF
R
f
ds
are
not
used
directly
in
the
derivation
of
the
304(
a)
criteria
because
they
do
not
reflect
the
conditions
that
affect
bioavailability
in
U.
S.
waters.

Section
5.4.2
below
provides
detailed
guidance
for
selecting
the
appropriate
BAF
derivation
procedure
(
Step
1
of
the
process).
Guidance
on
calculating
individual
baseline
BAF
R
f
ds,
selecting
the
final
baseline
BAF,
and
calculating
the
national
BAF
(
Steps
2
through
4
of
the
process)
is
provided
in
separate
sections
under
each
of
the
four
BAF
derivation
procedures.
5­
17
5­
18
5.4.2
Selecting
the
BAF
Derivation
Procedure
This
section
describes
the
decisions
that
should
be
made
to
select
one
of
the
four
available
hierarchical
procedures
for
deriving
a
national
BAF
for
nonionic
organic
chemicals
(
Procedures
#
1
through
#
4
of
Figure
5­
1).
As
shown
in
Figure
5­
1,
two
decision
points
exist
in
selecting
the
BAF
derivation
procedure.
The
first
decision
point
requires
knowledge
of
the
chemical's
hydrophobicity
(
i.
e.,
the
K
ow
of
the
chemical).
Guidance
for
selecting
the
K
ow
for
a
chemical
is
provided
in
the
Bioaccumulation
TSD.
The
K
ow
provides
an
initial
basis
for
assessing
whether
biomagnification
may
be
a
concern
for
nonionic
organic
chemicals.
The
second
decision
point
is
based
on
the
rate
of
metabolism
for
the
chemical
in
the
target
organism.
Guidance
for
assessing
whether
a
high
or
low
rate
of
metabolism
is
likely
for
a
chemical
of
concern
is
provided
below
in
Section
5.4.2.3.
With
the
appropriate
information
for
these
two
decision
points,
the
BAF
derivation
procedure
should
be
selected
using
the
following
guidelines.

5.4.2.1
Chemicals
with
Moderate
to
High
Hydrophobicity
1.
For
the
purposes
of
the
2000
Human
Health
Methodology,
nonionic
organic
chemicals
with
log
K
ow
values
equal
to
or
greater
than
4.0
should
be
classified
as
moderately
to
highly
hydrophobic.
For
moderately
to
highly
hydrophobic
nonionic
organic
chemicals,
available
data
indicate
that
exposure
through
the
diet
and
other
non­
aqueous
routes
can
become
important
in
determining
chemical
residues
in
aquatic
organisms
(
e.
g.,
Russell
et
al.,
1999;
Fisk
et
al.,
1998;
Oliver
and
Niimi,
1983;
Oliver
and
Niimi,
1988;
Niimi,
1985;
Swackhammer
and
Hites,
1988).
Dietary
and
other
non­
aqueous
exposure
can
become
extremely
important
for
those
nonionic
organic
chemicals
that
are
poorly
metabolized
by
aquatic
biota
(
e.
g.,
certain
PCB
congeners,
chlorinated
pesticides,
and
polychlorinated
dibenzo­
p­
dioxins
and
furans).

2.
Procedure
#
1
should
be
used
to
derive
national
BAFs
for
moderately
to
highly
hydrophobic
nonionic
organic
chemicals
in
cases
where:

(
a)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
expected
to
be
sufficiently
low
such
that
biomagnification
is
of
concern,
or
(
b)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
not
sufficiently
known.

Procedure
#
1
accounts
for
non­
aqueous
exposure
and
the
potential
for
biomagnification
in
aquatic
food
webs
through
the
use
of
field­
measured
values
for
bioaccumulation
(
i.
e.,
field
measured
BAF
or
BSAF)
and
FCMs
when
appropriate
field
data
are
unavailable.
Guidance
on
deriving
national
BAFs
using
Procedure
#
1
is
found
below
in
Section
5.4.3.

3.
Procedure
#
2
should
be
used
to
derive
the
national
BAFs
for
moderately
to
highly
hydrophobic
nonionic
organic
chemicals
in
cases
where:
5­
19
(
a)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
expected
to
be
sufficiently
high
such
that
biomagnification
is
not
of
concern.

Procedure
#
2
relaxes
the
requirement
of
using
FCMs
and
eliminates
the
use
of
K
ow­
based
estimates
of
the
BAF,
two
procedures
that
are
most
appropriate
for
poorly
metabolized
nonionic
organic
chemicals.
Guidance
on
deriving
national
BAFs
using
Procedure
#
2
is
found
below
in
Section
5.4.4.

5.4.2.2
Chemicals
with
Low
Hydrophobicity
1.
For
the
purposes
of
these
guidelines,
nonionic
organic
chemicals
with
log
K
ow
values
less
than
4.0
should
be
classified
as
exhibiting
low
hydrophobicity.
For
nonionic
organic
chemicals
that
exhibit
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
available
information
indicates
that
non­
aqueous
exposure
to
these
chemicals
is
not
likely
to
be
important
in
determining
chemical
residues
in
aquatic
organisms
(
e.
g.,
Fisk
et
al.,
1998;
Gobas
et
al.,
1993;
Connolly
and
Pedersen,
1988;
Thomann,
1989).
For
this
group
of
chemicals,
laboratory­
measured
BCFs
and
K
ow­
predicted
BCFs
do
not
require
adjustment
with
FCMs
for
determining
the
national
BAF
(
Procedures
#
3
and
#
4),
unless
other
appropriate
data
indicate
differently.

Other
appropriate
data
include
studies
clearly
indicating
that
non­
aqueous
exposure
is
important
such
that
use
of
a
BCF
would
substantially
underestimate
residues
in
aquatic
organisms.
In
these
cases,
Procedure
#
1
should
be
used
to
derive
the
BAF
for
nonionic
organic
chemicals
with
log
K
ow
<
4.0.
Furthermore,
the
data
supporting
the
K
ow
determination
should
be
carefully
reviewed
for
accuracy
and
appropriate
interpretation,
since
the
apparent
discrepancy
may
be
due
to
errors
in
determining
K
ow.

2.
Procedure
#
3
should
be
used
to
derive
national
BAFs
for
nonionic
organic
chemicals
of
low
hydrophobicity
in
cases
where:

(
a)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
expected
to
be
negligible,
such
that
tissue
residues
of
the
chemical
of
concern
are
not
substantially
reduced
compared
to
an
assumption
of
no
metabolism,
or
(
b)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
not
sufficiently
known.

Procedure
#
3
includes
the
use
of
K
ow­
based
estimates
of
the
BCF
to
be
used
when
lab
or
field
data
are
absent.
Guidance
on
deriving
national
BAFs
using
Procedure
#
3
is
found
below
in
Section
5.4.5.

3.
Procedure
#
4
should
be
used
to
derive
national
BAFs
for
nonionic
organic
chemicals
of
low
hydrophobicity
in
cases
where:
5­
20
(
a)
the
rate
of
chemical
metabolism
by
target
aquatic
organisms
is
expected
to
be
sufficiently
high,
such
that
tissue
residues
of
the
chemical
of
concern
are
substantially
reduced
compared
with
an
assumption
of
no
metabolism.

Procedure
#
4
eliminates
the
option
of
using
K
ow­
based
estimates
of
the
BAF
because
the
K
ow
may
over­
predict
accumulation
when
a
chemical
is
metabolized
substantially
by
an
aquatic
organism.
Guidance
on
deriving
national
BAFs
using
Procedure
#
4
is
found
below
in
Section
5.4.6.

5.4.2.3
Assessing
Metabolism
Currently,
assessing
the
degree
to
which
a
chemical
is
metabolized
by
aquatic
organisms
is
confounded
by
a
variety
of
factors.
First,
conclusive
data
on
chemical
metabolism
in
aquatic
biota
are
largely
lacking.
Such
data
include
whole
organism
studies
where
the
metabolic
rates
and
breakdown
products
are
quantified
in
fish
and
other
aquatic
organisms
relevant
to
human
consumption.
However,
the
majority
of
information
on
metabolism
is
derived
from
in
vitro
liver
microsomal
preparations
in
which
primary
and
secondary
metabolites
may
be
identified
and
their
rates
of
formation
may
or
may
not
be
quantified.
Extrapolating
results
from
in
vitro
studies
to
the
whole
organism
involves
considerable
uncertainty.
Second,
there
are
no
generally
accepted
procedures
for
reliably
predicting
chemical
metabolism
by
aquatic
organisms
in
the
absence
of
measured
data.
Third,
the
rate
at
which
a
chemical
is
metabolized
by
aquatic
organisms
can
be
species
and
temperature
dependent.
For
example,
PAHs
are
known
to
be
metabolized
readily
by
vertebrate
aquatic
species
(
primarily
fish),
although
at
rates
much
less
than
those
observed
for
mammals.
However,
the
degree
of
metabolism
in
invertebrate
species
is
generally
much
less
than
the
degree
in
vertebrate
species
(
James,
1989).
One
hypothesis
for
this
difference
is
that
the
invertebrate
species
lack
the
detoxifying
enzymes
and
pathways
that
are
present
in
many
vertebrate
species.

Given
the
current
limitations
on
assessing
the
degree
of
chemical
metabolism
by
aquatic
organisms,
the
assessment
of
metabolism
should
be
made
on
a
case­
by­
case
basis
using
a
weightof
evidence
approach.
When
assessing
a
chemical's
likelihood
to
undergo
substantial
metabolism
in
a
target
aquatic
organism,
the
following
data
should
be
carefully
evaluated:

(
1)
in
vivo
chemical
metabolism
data,
(
2)
bioconcentration
and
bioaccumulation
data,
(
3)
data
on
chemical
occurrence
in
target
aquatic
biota,
and
(
4)
in
vitro
chemical
metabolism
data.

1.
In
vivo
Data.
In
vivo
data
on
metabolism
in
aquatic
organisms
are
from
studies
of
chemical
metabolism
using
whole
organisms.
These
studies
are
usually
conducted
using
large
fish
from
which
blood,
bile,
urine,
and
individual
tissues
can
be
collected
for
the
identification
and
quantification
of
metabolites
formed
over
time.
In
vivo
studies
are
considered
the
most
useful
for
evaluating
a
chemical's
degree
of
metabolism
in
an
organism
because
both
oxidative
(
Phase
I)
and
conjugative
(
Phase
II)
metabolism
can
be
5­
21
assessed
in
these
studies.
Mass­
balance
studies,
in
which
parent
compound
elimination
is
quantified
separately
from
biotransformation
and
elimination
of
metabolites,
allow
calculation
of
conversion
rate
of
parent
to
metabolite
as
well
as
metabolite
elimination.
This
information
might
be
used
to
estimate
loss
due
to
metabolism
separately
from
that
due
to
elimination
of
the
parent
compound
for
adjustment
of
K
ow­
predicted
BAFs.
However,
due
to
the
analytical
and
experimental
challenges
these
studies
pose,
data
of
this
type
are
limited.
Less
rigorous
in
vivo
metabolism
studies
might
include
the
use
of
metabolic
blockers
to
demonstrate
the
influence
of
metabolism
on
parent
compound
kinetics.
However,
caution
should
be
used
in
interpretation
of
absolute
rates
from
these
data
due
to
the
lack
of
specificity
of
mammalian
derived
blockers
in
aquatic
species
(
Miranda
et
al.,
1998).

2.
Bioconcentration
or
Bioaccumulation
Data.
Data
on
chemical
bioconcentration
or
bioaccumulation
in
aquatic
organisms
can
be
used
indirectly
for
assessing
metabolism.
This
assessment
involves
comparing
acceptable
lab­
measured
BCFs
or
field­
measured
BAFs
(
after
converting
to
baseline
values
using
procedures
below)
with
the
chemical's
predicted
value
based
on
K
ow.
The
theoretical
basis
of
bioconcentration
and
bioaccumulation
for
nonionic
organic
chemicals
indicates
that
a
chemical's
baseline
BCF
should
be
similar
to
its
K
ow­
predicted
value
if
metabolism
is
not
occurring
or
is
minimal
(
see
the
Bioaccumulation
TSD).
This
theory
also
indicates
that
baseline
BAFs
should
be
similar
to
or
higher
than
the
K
ow
for
poorly
metabolized
organic
chemicals,
with
highly
hydrophobic
chemicals
often
exhibiting
higher
baseline
BAFs
than
K
ow
values.
Thus,
if
a
chemical's
baseline
BCF
or
BAF
is
substantially
lower
than
its
K
ow,
this
may
be
an
indication
that
the
chemical
is
being
metabolized
by
the
aquatic
organism
of
concern.
Note,
however,
that
this
difference
may
also
indicate
problems
in
the
experimental
design
or
analytical
chemistry,
and
that
it
may
be
difficult
to
discern
the
difference.

3.
Chemical
Occurrence
Data.
Although
by
no
means
definitive,
data
on
the
occurrence
of
chemicals
in
aquatic
biota
(
i.
e.,
residue
studies)
may
offer
another
useful
line
of
evidence
for
evaluating
a
chemical's
likelihood
to
undergo
substantial
metabolism.
Such
studies
are
most
useful
if
they
have
been
conducted
repeatedly
over
time
and
over
wide
geographical
areas.
Such
studies
might
indicate
a
chemical
is
poorly
metabolized
if
data
show
that
the
chemical
is
being
biomagnified
in
the
aquatic
food
web
(
i.
e.,
higher
lipidnormalized
residues
in
successive
trophic
levels).
Conversely,
such
studies
might
indicate
a
chemical
is
being
metabolized
substantially
if
residue
data
show
a
decline
in
residues
with
increasing
trophic
level.
Again,
other
reasons
for
increases
or
decreases
in
concentrations
with
increasing
trophic
level
might
exist
and
should
be
carefully
evaluated
(
e.
g.,
incorrect
food
web
assumptions,
differences
in
exposure
concentrations).

4.
In
vitro
Data.
In
vitro
metabolism
data
include
data
from
studies
where
specific
subcellular
fractions
(
e.
g.,
microsomal,
cytosolic),
cells,
or
tissues
from
an
organism
are
tested
outside
the
body
(
i.
e.,
in
test­
tubes,
cell­
or
tissue­
culture).
Compared
with
in
vivo
studies
of
chemical
metabolism
in
aquatic
organisms,
in
vitro
studies
are
much
more
plentiful
in
the
literature,
with
the
majority
of
studies
characterizing
oxidative
(
Phase
I)
5­
22
reactions
de­
coupled
from
conjugative
(
Phase
II)
metabolism.
Cell,
tissue,
or
organ
level
in
vitro
studies
are
less
common
but
provide
a
more
complete
assessment
of
metabolism.
While
such
studies
are
particularly
useful
for
identifying
the
pathways,
rates
of
formation,
and
metabolites
formed,
as
well
as
the
enzymes
involved
and
differences
in
the
temperature
dependence
of
metabolism
across
aquatic
species,
they
suffer
from
uncertainty
when
results
are
extrapolated
to
the
whole
organism.
This
uncertainty
results
from
the
fact
that
dosimetry
(
i.
e.,
delivery
of
the
toxicant
to,
and
removal
of
metabolite
from,
the
target
tissue)
cannot
currently
be
adequately
reproduced
in
the
laboratory
or
easily
modeled.

When
assessing
chemical
metabolism
using
the
above
information,
the
following
guidelines
apply.

a.
A
finding
of
substantial
metabolism
should
be
supported
by
two
or
more
lines
of
evidence
identified
using
the
data
described
above.

b.
At
least
one
of
the
lines
of
evidence
should
be
supported
by
either
in
vivo
metabolism
data
or
acceptable
bioconcentration
or
bioaccumulation
data.

c.
A
finding
of
substantial
metabolism
in
one
organism
should
not
be
extrapolated
to
another
organism
or
another
group
of
organisms
unless
data
indicate
similar
metabolic
pathways
exist
(
or
are
very
likely
to
exist)
in
both
organisms.
In
vitro
data
may
be
particularly
useful
in
cross­
species
extrapolations.

d.
Finally,
in
situations
where
sufficient
data
are
not
available
to
properly
assess
the
likelihood
of
significant
metabolism
in
aquatic
biota
of
concern,
the
chemical
should
be
assumed
to
undergo
little
or
no
metabolism.
This
assumptions
reflects
a
policy
decision
by
EPA
to
err
on
the
side
of
public
health
protection
when
sufficient
information
on
metabolism
is
lacking.

5.4.3
Deriving
National
BAFs
Using
Procedure
#
1
This
section
contains
guidance
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
1
shown
in
Figure
5­
1.
The
types
of
nonionic
organic
chemicals
for
which
Procedure
#
1
is
most
appropriate
are
those
that
are
classified
as
moderately
to
highly
hydrophobic
and
subject
to
low
(
or
unknown)
rates
of
metabolism
by
aquatic
biota
(
see
Section
5.4.2
above).
Non­
aqueous
contaminant
exposure
and
subsequent
biomagnification
in
aquatic
food
webs
are
of
concern
for
chemicals
that
are
classified
in
this
category.
Some
examples
of
nonionic
organic
chemicals
for
which
Procedure
#
1
is
considered
appropriate
include:

C
tetra­,
penta­
&
hexachlorobenzenes;
C
PCBs;
C
octachlorostyrene;
C
hexachlorobutadiene;
5­
23
C
endrin,
dieldrin,
aldrin;
C
mirex,
photomirex;
C
DDT,
DDE,
DDD;
and
C
heptachlor,
chlordane,
nonachlor.

Under
Procedure
#
1,
the
following
four
methods
may
be
used
in
deriving
a
national
BAF:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
a
field­
measured
BAF);
C
predicting
a
BAF
from
an
acceptable
field­
measured
BSAF;
C
predicting
a
BAF
from
an
acceptable
laboratory­
measured
BCF
and
FCM;
and
C
predicting
a
BAF
from
an
acceptable
K
ow
and
FCM.

As
shown
in
Figure
5­
2,
once
the
derivation
procedure
has
been
selected,
the
next
steps
in
deriving
a
national
BAF
for
a
given
trophic
level
include:
calculating
individual
baseline
BAF
R
f
ds
(
step
2),
selecting
the
final
baseline
BAF
R
f
d
(
step
3),
and
calculating
the
national
BAF
from
the
final
baseline
BAF
R
f
d
(
step
4).
Each
of
these
three
steps
is
discussed
separately
below.

5.4.3.1
Calculating
Individual
Baseline
BAF
R
f
ds
Calculating
an
individual
baseline
BAF
R
f
d
involves
normalizing
the
field­
measured
BAF
tT
(
or
laboratory­
measured
BCF
tT
)
which
are
based
on
total
concentrations
in
tissue
and
water
by
the
lipid
content
of
the
study
organisms
and
the
freely
dissolved
concentration
in
the
study
water.
Both
the
lipid
content
in
the
organism
and
the
freely
dissolved
concentration
(
as
influenced
by
organic
carbon
in
water)
have
been
shown
to
be
important
factors
that
influence
the
bioaccumulation
of
nonionic
organic
chemicals
(
e.
g.,
Mackay,
1982;
Connolly
and
Pederson,
1988;
Thomann,
1989,
Suffet
et
al.,
1994).
Therefore,
baseline
BAF
R
f
ds
(
which
are
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis)
are
considered
more
amenable
to
extrapolating
between
different
species
and
bodies
of
water
compared
to
BAFs
expressed
using
the
total
concentration
in
the
tissue
and
water.
Because
bioaccumulation
can
be
strongly
influenced
by
the
trophic
position
of
aquatic
organisms
(
either
due
to
biomagnification
or
physiological
differences),
extrapolation
of
baseline
BAF
R
f
ds
should
not
be
performed
between
species
of
different
trophic
levels.

1.
For
each
species
for
which
acceptable
data
are
available,
calculate
all
possible
baseline
BAF
R
f
ds
using
each
of
the
four
methods
shown
above
for
Procedure
#
1.

2.
Individual
baseline
BAF
R
f
ds
should
be
calculated
from
field­
measured
BAF
tT
s,
fieldmeasured
BSAFs,
laboratory
BCF
tT
s,
and
the
K
ow
according
to
the
following
procedures.

A.
Baseline
BAF
R
f
ds
from
Field­
Measured
BAFs
A
baseline
BAF
R
f
d
should
be
calculated
from
each
field­
measured
BAF
tT
using
information
on
the
lipid
fraction
in
the
tissue
of
concern
for
the
study
organism
and
the
fraction
of
the
total
chemical
that
is
freely
dissolved
in
the
study
water.
5­
24
(
Equation
5­
10)

(
Equation
5­
11)
1.
Baseline
BAF
R
f
d
Equation.
For
each
acceptable
field­
measured
BAF
tT
,
calculate
a
baseline
BAF
R
f
d
using
the
following
equation:

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
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
The
technical
basis
of
Equation
5­
10
is
provided
in
the
Bioaccumulation
TSD.
Guidance
for
determining
each
component
of
Equation
5­
10
is
provided
below.

2.
Determining
the
Measured
BAFt
T.
The
field­
measured
BAF
tT
shown
in
Equation
5­
10
should
be
calculated
based
on
the
total
concentration
of
the
chemical
in
the
appropriate
tissue
of
the
aquatic
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:

where:

C
t
=
Total
concentration
of
the
chemical
in
the
specified
wet
tissue
C
w
=
Total
concentration
of
chemical
in
water
The
data
used
to
calculate
a
field­
measured
BAF
tT
should
be
reviewed
thoroughly
to
assess
the
quality
of
the
data
and
the
overall
uncertainty
in
the
BAF
value.
The
following
general
criteria
apply
in
determining
the
acceptability
of
field­
measured
BAFs
that
are
being
considered
for
deriving
national
BAFs
using
Procedure
#
1.

a.
Aquatic
organisms
used
to
calculate
a
field­
measured
BAF
tT
should
be
representative
of
aquatic
organisms
that
are
commonly
consumed
in
the
United
States.
An
aquatic
organism
that
is
not
commonly
consumed
in
the
United
States
can
be
used
to
calculate
an
acceptable
field­
measured
BAF
tT
provided
that
the
5­
25
organism
is
considered
to
be
a
reasonable
surrogate
for
a
commonly
consumed
organism.
Information
on
the
ecology,
physiology,
and
biology
of
the
organism
should
be
reviewed
when
assessing
whether
an
organism
is
a
reasonable
surrogate
of
a
commonly
consumed
organism.

b.
The
trophic
level
of
the
study
organism
should
be
determined
by
taking
into
account
its
life
stage,
diet,
size,
and
the
food
web
structure
at
the
study
location.
Information
from
the
study
site
(
or
similar
sites)
is
preferred
when
evaluating
trophic
status.
If
such
information
is
lacking,
general
information
for
assessing
trophic
status
of
aquatic
organisms
can
be
found
in
USEPA
(
2000a,
b,
c).

c.
The
percent
lipid
of
the
tissue
used
to
determine
the
field­
measured
BAF
tT
should
be
either
measured
or
reliably
estimated
to
permit
lipid­
normalization
of
the
chemical's
tissue
concentration.

d.
The
study
from
which
the
field­
measured
BAF
tT
is
derived
should
contain
sufficient
supporting
information
from
which
to
determine
that
tissue
and
water
samples
were
collected
and
analyzed
using
appropriate,
sensitive,
accurate,
and
precise
analytical
methods.

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

f.
The
water
concentration(
s)
used
to
derive
the
BAF
should
reflect
the
average
exposure
of
the
aquatic
organism
that
corresponds
to
the
concentration
measured
in
its
tissue
of
concern.
For
nonionic
organic
chemicals,
greater
temporal
and
spatial
averaging
of
chemical
concentrations
is
required
as
the
K
ow
increases.
In
addition,
as
variability
in
water
concentrations
increase,
greater
temporal
and
spatial
averaging
is
also
generally
required.
Greater
spatial
averaging
is
also
generally
required
for
more
mobile
organisms.

g.
The
concentrations
of
particulate
organic
carbon
and
dissolved
organic
carbon
in
the
study
water
should
be
measured
or
reliably
estimated.

EPA
is
currently
developing
guidance
for
designing
and
conducting
field
studies
for
determining
field­
measured
BAFtT
s,
including
recommendations
for
minimum
data
requirements.
A
more
detailed
discussion
of
factors
that
should
be
considered
when
determining
field­
measured
BAFtT
s
is
provided
in
the
Bioaccumulation
TSD.

3.
Determining
the
Fraction
Freely
Dissolved
(
ffd).
As
illustrated
by
Equation
5­
10,
the
fraction
of
the
nonionic
organic
chemical
that
is
freely
dissolved
in
the
study
water
is
required
for
calculating
a
baseline
BAF
R
f
d
from
a
field­
measured
BAF
tT
.
The
freely
dissolved
fraction
is
the
portion
of
the
nonionic
organic
chemical
that
is
not
bound
to
5­
26
(
Equation
5­
12)
particulate
organic
carbon
or
dissolved
organic
carbon.
Together,
the
concentration
of
a
nonionic
organic
chemical
that
is
freely
dissolved,
bound
to
dissolved
organic
carbon,
and
bound
to
particulate
organic
carbon
constitute
its
total
concentration
in
water.
As
discussed
further
in
the
Bioaccumulation
TSD,
the
freely
dissolved
fraction
of
a
chemical
is
considered
to
be
the
best
expression
of
the
bioavailable
form
of
nonionic
organic
chemicals
to
aquatic
organisms
(
e.
g.,
Suffet
et
al.,
1994;
USEPA,
1995b).
Because
the
fraction
of
a
nonionic
organic
chemical
that
is
freely
dissolved
may
vary
among
different
bodies
of
water
as
a
result
of
differences
in
dissolved
and
particulate
organic
carbon
in
the
water,
the
bioavailability
of
the
total
chemical
concentration
in
water
is
expected
to
vary
from
one
body
of
water
to
another.
Therefore,
BAFs
which
are
based
on
the
freely
dissolved
concentration
in
water
(
rather
than
the
total
concentration
in
water)
are
considered
to
be
more
reliable
for
extrapolating
and
aggregating
BAFs
among
different
bodies
of
water.
Currently,
availability
of
BAFs
based
on
measured
freely
dissolved
concentrations
is
very
limited,
partly
because
of
difficulties
in
analytically
measuring
the
freely
dissolved
concentration.
Thus,
if
a
BAF
based
on
the
total
water
concentration
is
reported
in
a
given
study,
the
fraction
of
the
chemical
that
is
freely
dissolved
should
be
predicted
using
information
on
the
organic
carbon
content
in
the
study
water.

a.
Equation
for
Determining
the
Freely
Dissolved
Fraction.
If
reliable
measured
data
are
unavailable
to
directly
determine
the
freely
dissolved
fraction
of
the
chemical
in
water,
the
freely
dissolved
fraction
should
be
estimated
using
the
following
equation.

where:

POC
=
concentration
of
particulate
organic
carbon
(
kg/
L)
DOC
=
concentration
of
dissolved
organic
carbon
(
kg/
L)
K
ow
=
n­
octanol
water
partition
coefficient
for
the
chemical
In
Equation
5­
12,
K
ow
is
being
used
to
estimate
the
partition
coefficient
to
POC
(
i.
e.,
K
POC
in
L/
kg)
and
0.08
@
K
ow
is
being
used
to
estimate
the
partition
coefficient
to
DOC
(
i.
e.,
the
K
DOC
in
L/
kg).
A
discussion
of
the
technical
basis,
assumptions,
and
uncertainty
associated
with
the
derivation
and
application
of
Equation
5­
12
is
provided
in
the
Bioaccumulation
TSD.

b.
POC
and
DOC
Values.
When
converting
from
the
total
concentration
of
a
chemical
to
a
freely
dissolved
concentration
using
Equation
5­
12
above,
the
POC
and
DOC
concentrations
should
be
obtained
from
the
original
study
from
which
the
field­
measured
BAF
is
determined.
If
POC
and
DOC
concentrations
are
not
reported
in
the
BAF
study,
reliable
estimates
of
POC
and
DOC
might
be
obtained
5­
27
from
other
studies
of
the
same
site
used
in
the
BAF
study
or
closely
related
site(
s)
within
the
same
water
body.
When
using
POC/
DOC
data
from
other
studies
of
the
same
water
body,
care
should
be
taken
to
ensure
that
environmental
and
hydrological
conditions
that
might
affect
POC
or
DOC
concentrations
(
i.
e.,
runoff
events,
proximity
to
ground
water
or
surface
water
inputs,
sampling
season)
are
reasonably
similar
to
those
in
the
BAF
study.
Additional
information
related
to
selecting
POC
and
DOC
values
is
provided
in
the
Bioaccumulation
TSD.

In
some
cases,
BAFs
are
reported
using
the
concentration
of
the
chemical
in
filtered
or
centrifuged
water.
When
converting
these
BAFs
to
a
freely
dissolved
basis,
the
concentration
of
POC
should
be
set
equal
to
zero
when
using
Equation
5­
12.
Particulates
are
removed
from
water
samples
by
filtering
or
centrifuging
the
sample.

c.
Selecting
Kow
Values.
A
variety
of
techniques
are
available
to
measure
or
predict
K
ow
values.
The
reliability
of
these
techniques
depends
to
a
large
extent
on
the
K
ow
of
the
chemical.
Because
K
ow
is
an
important
input
parameter
for
calculating
the
freely
dissolved
concentration
of
nonionic
organic
chemicals
and
for
deriving
BAFs
using
the
other
three
methods
of
Procedure
#
1,
care
should
be
taken
in
selecting
the
most
reliable
K
ow
value.
The
value
of
K
ow
for
use
in
estimating
the
freely
dissolved
fraction
and
other
procedures
used
to
derive
national
BAFs
should
be
selected
based
on
the
guidance
presented
in
the
Bioaccumulation
TSD.

4.
Determining
the
Fraction
Lipid
(
f
R
)
.
Calculating
a
baseline
BAF
R
f
d
for
a
nonionic
organic
chemical
using
Equation
5­
10
also
requires
that
the
total
chemical
concentration
measured
in
the
tissue
used
to
determine
the
field­
measured
BAF
tT
be
normalized
by
the
lipid
fraction
(
f
R
)
in
that
same
tissue.
Lipid
normalization
of
tissue
concentrations
reflects
the
assumption
that
BAFs
(
and
BCFs)
for
nonionic
organic
chemicals
are
directly
proportional
to
the
percent
lipid
in
the
tissue
upon
which
they
are
based.
This
assumption
means
that
an
organism
with
a
two
percent
lipid
content
would
be
expected
to
accumulate
twice
the
amount
of
a
chemical
at
steady
state
compared
with
an
organism
with
one
percent
lipid
content,
all
else
being
equal.
The
assumption
that
aquatic
organisms
accumulate
nonionic
organic
chemicals
in
proportion
to
their
lipid
content
has
been
extensively
evaluated
in
the
literature
(
Mackay,
1982;
Connell,
1988;
Barron,
1990)
and
is
generally
accepted.
Because
the
lipid
content
in
aquatic
organisms
can
vary
both
within
and
across
species,
BAFs
that
are
expressed
using
the
lipid­
normalized
concentration
(
rather
than
the
total
concentration
in
tissue)
are
considered
to
be
the
most
reliable
for
aggregating
multiple
BAF
values
for
a
given
species.
Additional
discussion
of
technical
basis,
assumptions,
and
uncertainties
involved
in
lipid
normalization
is
provided
in
the
Bioaccumulation
TSD.

a.
The
lipid
fraction
f
R
,
is
routinely
reported
in
bioaccumulation
studies
involving
nonionic
organic
chemicals.
If
the
lipid
fraction
is
not
reported
in
the
BAF
study,
5­
28
(
Equation
5­
13)
it
can
be
calculated
using
the
following
equation
if
the
appropriate
data
are
reported:

where:

M
R
=
Mass
of
lipid
in
specified
tissue
M
t
=
Mass
of
specified
tissue
(
wet
weight)

b.
Because
lipid
content
can
vary
within
an
aquatic
organism
(
and
among
tissues
within
that
organism)
due
to
several
factors
including
the
age
and
sex
of
the
organism,
changes
in
dietary
composition,
season
of
sampling
and
reproductive
status,
the
lipid
fraction
used
to
calculate
a
baseline
BAF
R
f
d
should
be
measured
in
the
same
tissue
and
organisms
used
to
determine
the
field­
measured
BAF
tT
,
unless
comparability
is
demonstrated
across
organisms.

c.
Experience
has
shown
that
different
solvent
systems
used
to
extract
lipids
for
analytical
measurement
can
result
in
different
quantities
of
lipids
being
extracted
and
measured
in
aquatic
organisms
(
e.
g.,
Randall
et
al.,
1991,
1998).
As
a
result,
lipid
measurements
determined
using
different
solvent
systems
might
lead
to
apparent
differences
in
lipid­
normalized
concentrations
and
lipid­
normalized
BAFs.
The
extent
to
which
different
solvent
systems
might
affect
lipid
extractions
(
and
lipid­
normalized
concentrations)
is
thought
to
vary
depending
on
the
solvent,
chemical
of
concern,
and
lipid
composition
of
the
tissue
being
extracted.
Guidance
on
measurement
of
lipid
content,
including
the
choice
of
solvent
system
and
how
different
solvent
systems
may
affect
lipid
content,
is
provided
in
the
Bioaccumulation
TSD.

B.
Baseline
BAF
R
f
d
Derived
from
BSAFs
The
second
method
of
determining
a
baseline
BAF
R
f
d
for
the
chemical
of
concern
in
Procedure
#
1
involves
the
use
of
BSAFs.
Although
BSAFs
may
be
used
for
measuring
and
predicting
bioaccumulation
directly
from
concentrations
of
chemicals
in
surface
sediment,
they
may
also
be
used
to
estimate
BAFs
(
USEPA,
1995b;
Cook
and
Burkhard,
1998).
Since
BSAFs
are
based
on
field
data
and
incorporate
effects
of
chemical
bioavailability,
food
web
structure,
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
which
are
detectable
in
fish
tissues
and
sediments,
but
are
difficult
to
detect
or
measure
precisely
in
the
water
column.
5­
29
As
shown
by
Equation
5­
14
below,
predicting
baseline
BAF
R
f
ds
using
BSAFs
requires
that
certain
types
of
data
be
used
for
the
chemicals
of
interest
(
for
which
BAFs
are
to
be
determined)
and
reference
chemicals
(
for
which
BAFs
are
measured)
from
a
common
sediment­
waterorganism
data
set.
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
reflect
the
net
effect
of
biomagnification,
metabolism,
food
chain,
bioenergetics,
and
bioavailability
factors
on
the
degree
of
each
chemical's
equilibrium/
disequilibrium
between
sediment
and
biota.
At
equilibrium,
BSAFs
are
expected
to
be
approximately
1.0.
However,
deviations
from
1.0
(
reflecting
disequilibrium)
are
common
due
to:
conditions
where
water
is
not
at
equilibrium
with
surface
sediment;
differences
in
organic
carbon
content
of
water
and
sediment;
kinetic
limitations
for
chemical
transfer
between
sediments
and
water
associated
with
specific
biota;
biomagnification;
or
biological
processes
such
as
growth
or
biotransformation.
BSAFs
are
most
useful
(
i.
e.,
most
predictable
from
one
site
to
another)
when
measured
under
steady­
state
(
or
near
steady­
state)
conditions.
The
use
of
non­
steady­
state
BSAFs,
such
as
found
with
new
chemical
loadings
or
rapid
increases
in
loadings,
increases
uncertainty
in
this
method
for
the
relative
degree
of
disequilibrium
between
the
reference
chemicals
and
the
chemicals
of
interest.
In
general,
the
fact
that
concentrations
of
hydrophobic
chemicals
in
sediment
are
less
sensitive
than
concentrations
in
water
to
fluctuations
in
chemical
loading
and
distribution
makes
the
BSAF
method
robust
for
estimating
BAFs.
Results
from
validation
of
the
BAF
procedure
in
Lake
Ontario,
the
Fox
River
and
Green
Bay,
Wisconsin,
and
the
Hudson
River,
New
York,
demonstrate
good
agreement
between
observed
and
BSAF­
predicted
BAFs
in
the
vast
majority
of
comparisons
made.
Detailed
results
of
the
validation
studies
for
the
BSAF
procedure
are
provided
in
the
Bioaccumulation
TSD.

Baseline
BAF
R
f
ds
should
be
calculated
using
acceptable
BSAFs
for
chemicals
of
interest
and
appropriate
sediment­
to­
water
fugacity
(
disequilibrium)
ratios
(
J
socw)
r
/(
K
ow)
r
for
reference
chemicals
under
the
following
guidelines.

1.
Baseline
BAF
R
f
d
Equation.
For
each
species
with
an
acceptable
field
measured
(
BSAF)
I,
a
baseline
BAF
R
f
d
for
the
chemical
of
interest
may
be
calculated
using
the
following
equation
with
an
appropriate
value
of
(
J
socw)
r
/(
K
ow)
r:

(
Equation
5­
14)

where:

(
Baseline
BAF
R
f
d)
I
=
BAF
expressed
on
a
freely
dissolved
and
lipidnormalized
basis
for
chemical
of
interest
"
I"
(
BSAF)
I
=
Biota­
sediment
accumulation
factor
for
chemical
of
interest
"
I"
5­
30
(
Equation
5­
15)

(
Equation
5­
16)

(
Equation
5­
17)
(
J
socw)
r
=
sediment
organic
carbon
to
water
freely
dissolved
concentration
ratio
of
reference
chemical
"
r"
(
K
ow)
I
=
octanol­
water
partition
coefficient
for
chemical
of
interest
"
I"
(
K
ow)
r
=
octanol­
water
partition
coefficient
for
the
reference
chemical
"
r"
D
i/
r
=
ratio
between
J
socw
/
K
ow
for
chemicals
"
I"
and
"
r"
(
normally
chosen
so
that
D
i/
r
=
1)

The
technical
basis,
assumptions,
and
uncertainties
associated
with
Equation
5­
14
are
provided
in
the
Bioaccumulation
TSD.
Guidance
for
determining
each
component
of
Equation
5­
14
is
provided
below.

2.
Determining
Field­
Measured
BSAFs.
BSAFs
should
be
determined
by
relating
lipidnormalized
concentrations
of
chemicals
in
an
organism
(
C
R
)
to
organic
carbon­
normalized
concentrations
of
the
chemicals
in
surface
sediment
samples
(
C
soc)
using
the
following
equation:

a.
Lipid­
Normalized
Concentration.
The
lipid­
normalized
concentration
of
a
chemical
in
an
organism
should
be
determined
by:

where:

C
t
=
Concentration
of
the
chemical
in
the
wet
tissue
(
either
whole
organism
or
specified
tissue)
(
µ
g/
g)
f
R
=
Fraction
lipid
content
in
the
tissue
b.
Organic
Carbon­
Normalized
Concentration.
The
organic
carbon­
normalized
concentration
of
a
chemical
in
sediment
should
be
determined
by:
5­
31
where:

C
s
=
Concentration
of
chemical
in
sediment
(
µ
g/
g
sediment)
f
oc
=
Fraction
organic
carbon
in
sediment
The
organic
carbon­
normalized
concentrations
of
the
chemicals
in
surface
sediment
samples
should
be
associated
with
the
average
exposure
environment
of
the
organism.

3.
Sediment­
to­
Water
Partition
Coefficient
(
J
socw)
r.
Sediment­
to­
water
partition
coefficients
for
reference
chemicals
should
be
determined
by:

(
Equation
5­
18)

where:

(
C
soc)
r
=
Concentration
of
a
reference
chemical
in
sediment
normalized
to
sediment
organic
carbon
(
C
w
f
d)
r
=
Concentration
of
the
reference
chemical
freely
dissolved
in
water
4.
Selecting
Reference
Chemicals.
Reference
chemicals
with
(
J
socw)
/
(
K
ow)
similar
to
that
of
the
chemical
of
interest
are
preferred
for
this
method.
Theoretically,
knowledge
of
the
difference
between
sediment­
to­
water
fugacity
ratios
for
two
chemicals,
"
I"
and
"
r"
(
D
i/
r),
could
be
used
when
reliable
reference
chemicals
that
meet
the
fugacity
equivalence
condition
are
not
available.
Similarity
of
(
J
socw)
/
(
K
ow)
for
two
chemicals
can
be
indicated
on
the
basis
of
similar
physical­
chemical
behavior
in
water
(
persistence,
volatilization),
similar
mass
loading
histories,
and
similar
concentration
profiles
in
sediment
cores.

Validation
studies
have
demonstrated
that
choosing
reference
chemicals
with
well
quantified
concentrations
in
water
is
important
because
the
uncertainty
associated
with
measurement
of
barely
detected
chemicals
is
large
(
see
the
Bioaccumulation
TSD).
Similarity
between
K
ow
values
of
the
reference
and
target
chemicals
is
generally
desirable,
although
recent
validation
studies
indicate
that
the
accuracy
of
the
method
is
not
substantially
decreased
through
use
of
reference
chemicals
with
large
differences
in
K
ow
,
as
long
as
the
chemicals
are
structurally
similar
and
have
similar
persistence
behavior
in
water
and
sediments.

5.
The
following
data,
procedural,
and
quality
assurance
requirements
should
be
met
for
predicting
baseline
BAF
R
f
ds
using
field­
measured
BSAFs:
5­
32
a.
Data
on
the
reference
chemicals
and
chemicals
of
interest
should
come
from
a
common
organism­
water­
sediment
data
set
at
a
particular
site.

b.
The
chemicals
of
interest
and
reference
chemicals
should
have
similar
physicochemical
properties
and
persistence
in
water
and
sediment.

c.
The
loadings
history
of
the
reference
chemicals
and
chemicals
of
interest
should
be
similar
such
that
their
expected
sediment­
water
disequilibrium
ratios
(
J
socw/
K
ow)
would
not
be
expected
to
be
substantially
different
(
i.
e.,
D
i/
r
~
1).

d.
The
use
of
multiple
reference
chemicals
is
generally
preferred
for
determining
the
value
of
(
J
socw)
r
so
long
as
the
concentrations
are
well
quantified
and
the
aforementioned
conditions
for
selecting
reference
chemicals
are
met.
In
some
cases,
use
of
a
single
reference
chemical
may
be
necessary
because
of
limited
data.

e.
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.

f.
The
K
ow
value
for
the
target
and
reference
chemicals
should
be
selected
as
described
in
the
Bioaccumulation
TSD.

g.
All
other
data
quality
and
procedural
guidelines
described
earlier
for
determining
field­
measured
BAFs
in
Section
5.4.3.1(
A)
should
be
met.

Further
details
on
the
requirements
for
predicting
BAFs
from
BSAF
measurements,
including
the
data,
assumptions,
and
limitations
of
this
approach
are
provided
in
the
Bioaccumulation
TSD.

C.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
tT
and
FCM
The
third
method
in
Procedure
#
1
consists
of
using
a
laboratory­
measured
BCF
tT
(
i.
e.,
a
BCF
based
on
total
concentrations
in
tissue
and
water)
and
FCMs
to
predict
a
baseline
BAF
R
f
d
for
the
chemical
of
concern.
The
BCF
tT
is
used
in
conjunction
with
an
FCM
because
non­
aqueous
routes
of
exposure
and
subsequent
biomagnification
is
of
concern
for
the
types
of
chemicals
applicable
to
Procedure
#
1.
A
laboratory­
measured
BCF
inherently
accounts
for
the
effects
of
chemical
metabolism
that
occurs
in
the
organism
used
to
calculate
the
BCF,
but
does
not
account
for
metabolism
which
may
occur
in
other
organisms
of
the
aquatic
food
web.

1.
Baseline
BAF
R
f
d
Equation.
For
each
acceptable
laboratory­
measured
BCF
tT
,
calculate
a
baseline
BAF
R
f
d
using
the
following
equation:
5­
33
(
Equation
5­
20)
(
Equation
5­
19)

where:

Baseline
BAF
R
f
d
=
BAF
expressed
on
a
freely
dissolved
and
lipidnormalized
basis
Measured
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
=
The
food
chain
multiplier
either
obtained
from
Table
5­
1
by
linear
interpolation
for
the
appropriate
trophic
level,
or
from
appropriate
field
data
The
technical
basis
for
Equation
5­
19
is
provided
in
the
Bioaccumulation
TSD.
Guidance
for
determining
each
component
of
Equation
5­
19
is
provided
below.

2.
Determining
the
Measured
BCFt
T.
The
laboratory­
measured
BCF
tT
shown
in
Equation
5­
19
should
be
calculated
using
information
on
the
total
concentration
of
the
chemical
in
the
tissue
of
the
organism
and
the
total
concentration
of
the
chemical
in
the
laboratory
test
water.
The
equation
to
derive
a
measured
BCF
tT
is:

where:

C
t
=
Total
concentration
of
the
chemical
in
the
specified
wet
tissue
C
w
=
Total
concentration
of
chemical
in
the
laboratory
test
water
The
data
used
to
calculate
a
laboratory­
measured
BCF
tT
should
be
reviewed
thoroughly
to
assess
the
quality
of
the
data
and
the
overall
uncertainty
in
the
BCF
value.
The
following
general
criteria
apply
in
determining
the
acceptability
of
laboratory­
measured
BCF
tT
.

a.
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
compared
with
healthy
organisms.

b.
The
total
concentration
of
the
chemical
in
the
water
should
be
measured
and
should
be
relatively
constant
during
the
exposure
period.
5­
34
c.
The
organisms
should
be
exposed
to
the
chemical
using
a
flow­
through
or
renewal
procedure.

d.
The
percent
lipid
of
the
tissue
used
to
normalize
the
BCF
tT
should
be
either
measured
or
reliably
estimated
to
permit
lipid
normalization
of
chemical
concentrations.

e.
The
concentrations
of
particulate
organic
carbon
and
dissolved
organic
carbon
in
the
study
water
should
be
measured
or
reliably
estimated.

f.
Aquatic
organisms
used
to
calculate
a
laboratory­
measured
BCF
tT
should
be
representative
of
those
aquatic
organisms
that
are
commonly
consumed
in
the
United
States.
An
aquatic
organism
which
is
not
commonly
consumed
in
the
United
States
can
be
used
to
calculate
an
acceptable
laboratory­
measured
BCF
tT
provided
that
the
organism
is
considered
to
be
a
reasonable
surrogate
for
a
commonly
consumed
organism.
Information
on
the
ecology,
physiology,
and
biology
of
the
organism
should
be
reviewed
when
assessing
whether
an
organism
is
a
reasonable
surrogate
of
a
commonly
consumed
organism.

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

h.
The
calculation
of
the
BCF
tT
should
appropriately
address
growth
dilution,
which
can
be
particularly
important
in
affecting
BCF
tT
determinations
for
poorly
depurated
chemicals.

I.
Other
aspects
of
the
methodology
used
should
be
similar
to
those
described
by
the
American
Society
of
Testing
and
Materials
(
ASTM,
1999)
and
USEPA
Ecological
Effects
Test
Guidelines
(
USEPA,
1996).

j.
In
addition,
the
magnitude
of
the
K
ow
and
the
availability
of
corroborating
BCF
data
should
be
considered.
For
example,
if
the
steady­
state
method
is
used
for
the
BCF
tT
determination,
exposure
periods
longer
than
28
days
will
generally
be
required
for
highly
hydrophobic
chemicals
to
reach
steady
state
between
the
water
and
the
organism.

k.
If
a
baseline
BCF
R
f
d
derived
from
a
laboratory­
measured
BCFtT
consistently
increases
or
decreases
as
the
chemical
concentration
increases
in
the
test
solutions
for
the
test
organisms,
the
BCF
tT
should
be
selected
from
the
test
concentration(
s)
that
would
most
closely
correspond
to
the
304(
a)
criterion.
Note:
a
BCF
tT
should
not
be
calculated
from
a
control
treatment.
5­
35
3.
Selecting
Food
Chain
Multipliers.
An
FCM
reflects
a
chemical's
tendency
to
biomagnify
in
the
aquatic
food
web.
Values
of
FCMs
greater
than
1.0
are
indicative
of
biomagnification
and
typically
apply
to
organic
chemicals
with
log
K
ow
values
between
4.0
and
9.0.
For
a
given
chemical,
FCMs
tend
to
be
greater
at
higher
trophic
levels,
although
FCMs
for
trophic
level
three
can
be
higher
than
those
for
trophic
level
four.

Food
chain
multipliers
used
to
derive
baseline
BAF
R
f
ds
using
Procedure
#
1
can
be
selected
from
model­
derived
or
field­
derived
estimates.

a.
Model­
Derived
FCMs.
For
nonionic
organic
chemicals
appropriate
for
Procedure
#
1,
EPA
has
calculated
FCMs
for
various
K
ow
values
and
trophic
levels
using
the
bioaccumulation
model
of
Gobas
(
1993).
The
FCMs
shown
in
Table
5­
1
were
calculated
using
the
Gobas
model
as
the
ratio
of
the
baseline
BAF
R
f
ds
for
trophic
levels
2,
3,
and
4
to
the
baseline
BCF
R
f
d.

EPA
recommends
using
the
biomagnification
model
by
Gobas
(
1993)
to
derive
FCMs
for
nonionic
organic
chemicals
for
several
reasons.
First,
the
Gobas
model
includes
both
benthic
and
pelagic
food
chains,
thereby
incorporating
exposure
of
organisms
to
chemicals
from
both
the
sediment
and
the
water
column.
Second,
the
input
data
needed
to
run
the
model
can
be
readily
defined.
Third,
the
predicted
BAFs
using
the
model
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
equilibrium
partitioning
sediment
guidelines
(
USEPA,
2000d).

The
Gobas
model
requires
input
of
specific
data
on
the
structure
of
the
food
chain
and
the
water
quality
characteristics
of
the
water
body
of
interest.
For
calculating
national
BAFs,
a
mixed
pelagic/
benthic
food
web
structure
consisting
of
four
trophic
levels
is
assumed.
Trophic
level
1
is
phytoplankton,
trophic
level
2
is
zooplankton,
trophic
level
3
is
forage
fish
(
e.
g.,
sculpin
and
smelt),
and
trophic
level
4
are
predatory
fish
(
e.
g.,
salmonids).
Additional
assumptions
are
made
regarding
the
composition
of
the
aquatic
species'
diets
(
e.
g.,
salmonids
consume
10
percent
sculpin,
50
percent
alewives,
and
40
percent
smelt),
the
physical
parameters
of
the
aquatic
species
(
e.
g.,
lipid
values),
and
the
water
quality
characteristics
(
e.
g.,
water
temperature,
sediment
organic
carbon).

A
mixed
pelagic/
benthic
food
web
structure
has
been
assumed
for
the
purpose
of
calculating
FCMs
because
it
is
considered
to
be
most
representative
of
the
types
of
food
webs
that
occur
in
aquatic
ecosystems.
FCMs
derived
using
the
mixed
pelagic/
benthic
structure
are
also
about
mid­
range
in
magnitude
between
a
100%
pelagic
and
100%
benthic
driven
food
web
(
see
the
Bioaccumulation
TSD).
The
validity
of
FCMs
derived
using
the
mixed
pelagic/
benthic
food
web
structure
has
5­
36
Table
5­
1
Food­
Chain
Multipliers
for
Trophic
Levels
2,
3
and
4
(
Mixed
Pelagic
and
Benthic
Food
Web
Structure
and
J
socw
/
KOW
=
23)

Log
KOW
Trophic
Level
2
Trophic
Level
3
Trophic
Level
4
Log
KOW
Trophic
Level
2
Trophic
Level
3
Trophic
Level
4
4.0
1.00
1.23
1.07
6.6
1.00
12.9
23.8
4.1
1.00
1.29
1.09
6.7
1.00
13.2
24.4
4.2
1.00
1.36
1.13
6.8
1.00
13.3
24.7
4.3
1.00
1.45
1.17
6.9
1.00
13.3
24.7
4.4
1.00
1.56
1.23
7.0
1.00
13.2
24.3
4.5
1.00
1.70
1.32
7.1
1.00
13.1
23.6
4.6
1.00
1.87
1.44
7.2
1.00
12.8
22.5
4.7
1.00
2.08
1.60
7.3
1.00
12.5
21.2
4.8
1.00
2.33
1.82
7.4
1.00
12.0
19.5
4.9
1.00
2.64
2.12
7.5
1.00
11.5
17.6
5.0
1.00
3.00
2.51
7.6
1.00
10.8
15.5
5.1
1.00
3.43
3.02
7.7
1.00
10.1
13.3
5.2
1.00
3.93
3.68
7.8
1.00
9.31
11.2
5.3
1.00
4.50
4.49
7.9
1.00
8.46
9.11
5.4
1.00
5.14
5.48
8.0
1.00
7.60
7.23
5.5
1.00
5.85
6.65
8.1
1.00
6.73
5.58
5.6
1.00
6.60
8.01
8.2
1.00
5.88
4.19
5.7
1.00
7.40
9.54
8.3
1.00
5.07
3.07
5.8
1.00
8.21
11.2
8.4
1.00
4.33
2.20
5.9
1.00
9.01
13.0
8.5
1.00
3.65
1.54
6.0
1.00
9.79
14.9
8.6
1.00
3.05
1.06
6.1
1.00
10.5
16.7
8.7
1.00
2.52
0.721
6.2
1.00
11.2
18.5
8.8
1.00
2.08
0.483
6.3
1.00
11.7
20.1
8.9
1.00
1.70
0.320
6.4
1.00
12.2
21.6
9.0
1.00
1.38
0.210
6.5
1.00
12.6
22.8
been
evaluated
in
several
different
ecosystems
including
Lake
Ontario,
the
tidally
influenced
Bayou
D'Inde
in
Louisiana,
the
Fox
River
and
Green
Bay,
Wisconsin,
and
the
Hudson
River
in
New
York.
Additional
details
of
the
validation
of
EPA's
national
default
FCMs
and
the
assumptions,
uncertainties,
and
input
parameters
for
the
model
are
provided
in
the
Bioaccumulation
TSD.
5­
37
Although
EPA
uses
the
FCMs
in
Table
5­
1
to
derive
its
national
304(
a)
criteria,
EPA
recognizes
that
food
webs
of
other
waterbodies
might
differ
from
the
assumptions
used
to
calculate
national
BAFs.
In
these
situations,
States
and
authorized
Tribes
may
wish
to
use
alternate
food
web
structures
for
calculating
FCMs
for
use
in
setting
State
or
Tribal
water
quality
criteria.
Additional
guidance
on
the
use
of
alternate
food
web
structures
for
calculating
State,
Tribal,
or
sitespecific
criteria
is
provided
in
the
Bioaccumulation
TSD.

b.
Field­
Derived
FCMs.
In
addition
to
model­
derived
estimates
of
FCMs,
field
data
may
also
be
used
to
derive
FCMs.
Currently,
the
use
of
field­
derived
FCMs
is
the
only
method
recommended
for
estimating
FCMs
for
inorganic
and
organometalic
chemicals
because
appropriate
model­
derived
estimates
are
not
yet
available
(
see
Section
5.6).
In
contrast
to
the
model­
based
FCMs
described
previously,
fieldderived
FCMs
account
for
any
metabolism
of
the
chemical
of
concern
by
the
aquatic
organisms
used
to
calculate
the
FCM.

Field­
derived
FCMs
should
be
calculated
using
lipid­
normalized
concentrations
of
the
nonionic
organic
chemical
in
appropriate
predator
and
prey
species
using
the
following
equations.

FCM
TL2
=
BMF
TL2
(
Equation
5­
21)

FCM
TL3
=
(
BMF
TL3)
(
BMF
TL2)
(
Equation
5­
22)

FCM
TL4
=
(
BMF
TL4)
(
BMF
TL3)
(
BMF
TL2)
(
Equation
5­
23)

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
nonionic
organic
chemicals,
BMFs
can
be
calculated
from
tissue
residue
concentrations
determined
in
biota
at
a
site
according
to
the
following
equations.

BMF
TL2
=
(
C
R
,
TL2)
/
(
C
R
,
TL1)
(
Equation
5­
24)

BMF
TL3
=
(
C
R
,
TL3)
/
(
C
R
,
TL2)
(
Equation
5­
25)

BMF
TL4
=
(
C
R
,
TL4)
/
(
C
R
,
TL3)
(
Equation
5­
26)
5­
38
where:
C
R
=
Lipid­
normalized
concentration
of
chemical
in
tissue
of
appropriate
biota
that
occupy
the
specified
trophic
level
(
TL2,
TL3,
or
TL4)

In
addition
to
the
acceptability
guidelines
pertaining
to
field­
measured
BAFs,
the
following
procedural
and
quality
assurance
requirements
apply
to
field­
measured
FCMs.

(
1)
Information
should
be
available
to
identify
the
appropriate
trophic
levels
for
the
aquatic
organisms
and
appropriate
predator­
prey
relationships
for
the
site
from
which
FCMs
are
being
determined.
General
information
on
determining
trophic
levels
of
aquatic
organisms
can
be
found
in
USEPA
2000a,
b,
c.

(
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)
The
studies
from
which
the
FCMs
are
derived
should
contain
sufficient
supporting
information
from
which
to
determine
that
tissue
samples
were
collected
and
analyzed
using
appropriate,
sensitive,
accurate,
and
precise
methods.

(
4)
The
percent
lipid
should
be
either
measured
or
reliably
estimated
for
the
tissue
used
to
determine
the
FCM.

(
5)
The
tissue
concentrations
should
reflect
average
exposure
over
the
approximate
time
required
to
achieve
steady­
state
in
the
target
species.

D.
Baseline
BAF
R
f
d
from
a
Kow
and
FCM
The
fourth
method
in
Procedure
#
1
consists
of
using
a
K
ow
and
an
appropriate
FCM
for
estimating
the
baseline
BAF
R
f
d.
In
this
method,
the
K
ow
is
assumed
to
be
equal
to
the
baseline
BCF
R
f
d.
Numerous
investigations
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.
Isnard
and
Lambert
(
1988)
list
various
regression
equations
that
illustrate
this
linear
relationship.
When
the
regression
equations
are
constructed
using
lipidnormalized
BCFs,
the
slopes
and
intercepts
are
not
significantly
different
from
one
and
zero,
respectively
(
e.
g.,
de
Wolf,
et
al.,
1992).
The
underlying
assumption
for
the
linear
relationship
between
the
BCF
and
K
ow
is
that
the
bioconcentration
process
can
be
viewed
as
the
partitioning
of
a
chemical
between
the
lipid
of
the
aquatic
organisms
and
water
and
that
the
K
ow
is
a
useful
5­
39
(
Equation
5­
27)
surrogate
for
this
partitioning
process
(
Mackay,
1982).
To
account
for
biomagnification,
Procedure
#
1
requires
the
K
ow
value
be
used
in
conjunction
with
an
appropriate
FCM.

1.
Baseline
BAF
R
f
d
Equation.
For
each
acceptable
K
ow
value
and
FCM
for
the
chemical
of
concern,
calculate
a
baseline
BAF
R
f
d
using
the
following
equation.

where:

Baseline
BAF
R
f
d
=
BAF
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis
for
a
given
trophic
level
FCM
=
The
food
chain
multiplier
for
the
appropriate
trophic
level
obtained
from
Table
5­
1
by
linear
interpolation
or
from
appropriate
field
data
(
used
with
Procedure
#
1
only)
K
ow
=
Octanol­
water
partition
coefficient
The
BCF­
K
ow
relationship
has
been
developed
primarily
for
nonionic
organic
chemicals
that
are
not
readily
metabolized
by
aquatic
organisms
and
thus
is
most
appropriate
for
poorly­
metabolized
nonionic
organic
chemicals
(
i.
e.,
Procedures
#
1
and
#
3
as
depicted
in
Figure
5­
1).
For
poorly­
metabolized
nonionic
organic
chemicals
with
large
log
K
ow
s
(
i.
e.,
>
6),
reported
log
BCFs
are
often
not
equal
to
log
K
ow.
EPA
believes
that
this
nonlinearity
is
primarily
due
to
not
accounting
for
several
factors
which
affect
the
BCF
determination.
These
factors
include
not
basing
BCFs
on
the
freely
dissolved
concentration
in
water,
not
accounting
for
growth
dilution,
not
assessing
BCFs
at
steady­
state,
inaccuracies
in
measurements
of
uptake
and
elimination
rate
constants,
and
complications
from
the
use
of
solvent
carriers
in
the
exposure.
Application
of
Equation
5­
27
for
predicting
BAFs
has
been
conducted
in
several
different
ecosystems
including
Lake
Ontario,
the
tidally
influenced
Bayou
D'Inde
in
Louisiana,
the
Fox
River
and
Green
Bay,
Wisconsin,
and
the
Hudson
River
in
New
York.
Additional
detail
on
the
validation,
technical
basis,
assumptions,
and
uncertainty
associated
with
Equation
5­
27
and
is
provided
in
the
Bioaccumulation
TSD.

2.
FCMs
and
Kows.
Food
chain
multipliers
and
K
ow
values
should
be
selected
as
described
previously
in
Procedure
#
1.

5.4.3.2
Selecting
Final
Baseline
BAF
R
f
ds
After
calculating
individual
baseline
BAF
R
f
ds
using
as
many
of
the
methods
in
Procedure
#
1
as
possible,
the
next
step
is
to
determine
a
final
baseline
BAF
R
f
d
for
each
trophic
level
from
the
individual
baseline
BAF
R
f
ds
(
see
Figures
5­
1
and
5­
2).
The
final
baseline
BAF
R
f
d
will
be
used
in
the
5­
40
last
step
to
determine
the
national
BAF
for
each
trophic
level.
The
final
baseline
BAF
R
f
d
for
each
trophic
level
should
be
determined
from
the
individual
baseline
BAF
R
f
ds
by
considering
the
data
preference
hierarchy
defined
by
Procedure
#
1
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
1
is
(
in
order
of
preference):

1.
a
baseline
BAF
R
f
d
from
an
acceptable
field­
measured
BAF
(
method
1)
2.
a
baseline
BAF
R
f
d
predicted
from
an
acceptable
field­
measured
BSAF
(
method
2),
3.
a
baseline
BAF
R
f
d
predicted
from
an
acceptable
BCF
and
FCM
(
method
3),
or
4.
a
baseline
BAF
R
f
d
predicted
from
an
acceptable
K
ow
and
FCM
(
method
4).

This
data
preference
hierarchy
reflects
EPA's
preference
for
BAFs
based
on
field­
measurements
of
bioaccumulation
(
methods
1
and
2)
over
those
based
on
laboratory­
measurements
and/
or
predictions
of
bioaccumulation
(
methods
3
and
4).
However,
this
data
preference
hierarchy
should
not
be
considered
inflexible.
Rather,
it
should
be
used
as
a
guide
for
selecting
the
final
baseline
BAF
R
f
ds
when
the
uncertainty
is
similar
among
two
or
more
baseline
BAF
R
f
ds
derived
using
different
methods.
The
following
steps
and
guidelines
should
be
followed
for
selecting
the
final
baseline
BAF
R
f
ds
using
Procedure
#
1.

1.
Calculate
Species­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
where
more
than
one
acceptable
baseline
BAF
R
f
d
is
available
for
a
given
species,
calculate
a
species­
mean
baseline
BAF
R
f
d
as
the
geometric
mean
of
all
available
individual
baseline
BAF
R
f
ds.
When
calculating
a
species­
mean
baseline
BAF
R
f
d,
individual
baseline
BAF
R
f
ds
should
be
reviewed
carefully
to
assess
the
uncertainty
in
the
BAF
values.
For
highly
hydrophobic
chemicals
applicable
to
Procedure
#
1,
particular
attention
should
be
paid
to
whether
sufficient
spatial
and
temporal
averaging
of
water
and
tissue
concentrations
was
likely
achieved
in
the
BAF,
BSAF,
or
BCF
study.
Highly
uncertain
baseline
BAF
R
f
ds
should
not
be
used.
Large
differences
in
individual
baseline
BAF
R
f
ds
for
a
given
species
(
e.
g.,
greater
than
a
factor
of
10)
should
be
investigated
further.
In
such
cases,
some
or
all
of
the
baseline
BAF
R
f
ds
for
a
given
species
might
not
be
used.
Additional
discussion
on
evaluating
acceptability
of
BAF
values
is
provided
in
the
Bioaccumulation
TSD.

2.
Calculate
Trophic­
Level­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
where
more
than
one
acceptable
species­
mean
baseline
BAF
R
f
d
is
available
within
a
given
trophic
level,
calculate
a
trophic­
level­
mean
baseline
BAF
R
f
d
as
the
geometric
mean
of
acceptable
species­
mean
baseline
BAF
R
f
ds
in
that
trophic
level.
Trophic­
level­
mean
baseline
BAF
R
f
ds
should
be
calculated
for
trophic
levels
two,
three,
and
four
because
available
data
on
U.
S.
consumers
of
fish
and
shellfish
indicate
significant
consumption
of
organisms
in
these
trophic
levels.

3.
Select
a
Final
Baseline
BAF
R
f
d
for
Each
Trophic
Level.
For
each
trophic
level,
select
the
final
baseline
BAF
R
f
d
using
best
professional
judgment
by
considering:
(
1)
the
data
preference
hierarchy
shown
previously,
(
2)
the
relative
uncertainty
in
the
trophic­
levelmean
baseline
BAF
R
f
ds
derived
using
different
methods,
and
(
3)
the
weight
of
evidence
among
the
four
methods.
5­
41
(
Equation
5­
28)
a.
In
general,
when
more
than
one
trophic­
level­
mean
baseline
BAF
R
f
d
is
available
for
a
given
trophic
level,
the
final
trophic­
level­
mean
baseline
BAF
R
f
d
should
be
selected
from
the
most
preferred
BAF
method
defined
by
the
data
preference
hierarchy
for
Procedure
#
1.

b.
If
uncertainty
in
a
trophic­
level­
mean
baseline
BAF
based
on
a
higher
tier
(
more
preferred)
method
is
judged
to
be
substantially
greater
than
a
trophic­
level­
mean
baseline
BAF
from
a
lower
tier
method,
and
the
weight
of
evidence
among
the
various
methods
suggests
that
a
BAF
value
from
lower
tier
method
is
likely
to
be
more
accurate,
then
the
final
baseline
BAF
R
f
d
should
be
selected
using
a
trophic
level­
mean
baseline
BAF
R
f
d
from
a
lower
tier
method.

c.
When
considering
the
weight
of
evidence
among
the
various
BAF
methods,
greater
confidence
in
the
final
baseline
BAF
R
f
d
is
generally
assigned
when
BAFs
from
a
greater
number
of
methods
are
in
agreement
for
a
given
trophic
level.
However,
lack
of
agreement
among
methods
does
not
necessarily
indicate
less
confidence
if
such
disagreements
can
be
adequately
explained.
For
example,
if
the
chemical
of
concern
is
metabolized
by
aquatic
organisms
represented
by
a
BAF
value,
one
would
expect
disagreement
between
a
field­
measured
BAF
(
the
highest
priority
data)
and
a
predicted
BAF
using
a
K
ow
and
model­
derived
FCM.
Thus,
field­
measured
BAFs
should
generally
be
given
the
greatest
weight
among
methods
because
they
reflect
direct
measures
of
bioaccumulation
and
incorporate
any
metabolism
which
might
occur
in
the
organism
and
its
food
web.

d.
The
above
steps
should
be
performed
for
each
trophic
level
until
a
final
baseline
BAF
R
f
d
is
selected
for
trophic
levels
two,
three,
and
four.

5.4.3.3
Calculating
National
BAFs
The
last
step
in
deriving
a
national
BAF
for
each
trophic
level
is
to
convert
the
final
baseline
BAF
R
f
d
determined
in
the
previous
step
to
a
BAF
that
reflects
conditions
to
which
the
national
304(
a)
criteria
will
apply
(
Figure
5­
2).
Since
a
baseline
BAF
R
f
d
is
by
definition
normalized
by
lipid
content
and
expressed
on
a
freely
dissolved
basis,
it
needs
to
be
adjusted
to
reflect
the
lipid
fraction
of
aquatic
organisms
commonly
consumed
in
the
U.
S.
and
the
freely
dissolved
fraction
expected
in
U.
S.
bodies
of
water.
Converting
a
final
baseline
BAF
R
f
d
to
a
national
BAF
requires
information
on:
(
1)
the
percent
lipid
of
the
aquatic
organisms
commonly
consumed
by
humans,
and
(
2)
the
freely
dissolved
fraction
of
the
chemical
of
concern
that
would
be
expected
in
the
ambient
waters
of
interest.
For
each
trophic
level,
a
national
BAF
should
be
determined
from
a
final
baseline
BAF
R
f
d
according
to
the
following
guidelines.

1.
National
BAF
Equation.
For
each
trophic
level,
calculate
a
national
BAF
using
the
following
equation.
5­
42
where:

Final
Baseline
BAF
R
f
d
=
Final
trophic­
level­
mean
baseline
BAF
expressed
on
a
freely
dissolved
and
lipid­
normalized
basis
for
trophic
level
"
n"
f
R
(
TL
n)
=
Lipid
fraction
of
aquatic
species
consumed
at
trophic
level
"
n"
f
fd
=
Fraction
of
the
total
chemical
in
water
that
is
freely
dissolved
The
technical
basis
of
Equation
5­
28
is
provided
in
the
Bioaccumulation
TSD.
Guidance
for
determining
each
component
of
Equation
5­
28
is
provided
below.

2.
Determining
the
Final
Baseline
BAF
R
f
d.
The
final
trophic­
level­
mean
baseline
BAF
R
f
ds
used
in
this
equation
are
those
which
have
been
determined
using
the
guidance
presented
in
Section
5.4.3.2
for
selecting
the
final
baseline
BAF
R
f
ds.

3.
Lipid
Content
of
Commonly
Consumed
Aquatic
Species.
As
illustrated
by
Equation
5­
28,
the
percent
lipid
of
the
aquatic
species
consumed
by
humans
is
needed
to
accurately
characterize
the
potential
exposure
to
a
chemical
from
ingestion
of
aquatic
organisms.

a.
National
Default
Lipid
Values.
For
the
purposes
of
calculating
a
national
304(
a)
criterion,
the
following
national
default
values
for
lipid
fraction
should
be
used:
1.9%
(
for
trophic
level
two
organisms),
2.6%
(
for
trophic
level
three
organisms),
and
3.0%
(
for
trophic
level
four
organisms).

These
national
default
values
for
lipid
content
reflect
national
per
capita
average
patterns
of
fish
consumption
in
the
United
States.
Specifically,
they
were
calculated
using
the
consumption­
weighted
mean
lipid
content
of
commonly
consumed
fish
and
shellfish
as
identified
by
the
USDA
Continuing
Survey
of
Food
Intake
by
Individuals
(
CSFII)
for
1994
through
1996.
This
same
national
survey
data
was
used
to
derive
national
default
values
of
fish
consumption.
To
maintain
consistency
with
the
fish
consumption
assumptions,
only
freshwater
and
estuarine
organisms
were
included
in
the
derivation
of
the
national
default
lipid
values.
Additional
details
on
the
technical
basis,
assumptions,
and
uncertainty
in
the
national
default
values
of
lipid
fraction
are
provided
in
the
Bioaccumulation
TSD.

Although
national
default
lipid
values
are
used
by
EPA
to
set
national
304(
a)
criteria,
EPA
encourages
States
and
authorized
Tribes
to
use
local
or
regional
data
on
lipid
content
of
consumed
aquatic
species
when
adopting
criteria
into
their
water
quality
standards
because
local
or
regional
consumption
patterns
(
and
lipid
content)
can
differ
from
national
consumption
patterns.
Additional
guidance
on
5­
43
(
Equation
5­
29)
developing
site­
specific
values
of
lipid
content,
including
a
database
of
lipid
content
for
many
commonly
consumed
aquatic
organisms,
is
found
in
the
Bioaccumulation
TSD.

4.
Freely
Dissolved
Fraction.
The
third
piece
of
information
required
for
deriving
a
national
BAF
is
the
freely
dissolved
fraction
of
the
chemical
of
concern
that
is
expected
in
waters
of
the
United
States.
As
noted
previously,
expressing
BAFs
on
the
freely
dissolved
concentration
in
water
allows
a
common
basis
for
averaging
BAFs
from
several
studies.
However,
for
use
in
criteria
development,
these
BAFs
should
be
converted
back
to
values
based
on
the
total
concentration
in
the
water
to
be
consistent
with
monitored
water
column
and
effluent
concentrations,
which
are
typically
based
on
total
concentrations
of
chemicals
in
the
water.
This
should
be
done
by
multiplying
the
freely
dissolved
baseline
BAF
R
f
d
by
the
fraction
of
the
freely
dissolved
chemical
expected
in
water
bodies
of
the
United
States
where
criteria
are
to
be
applied,
as
shown
in
Equation
5­
29.

where:

POC
=
national
default
value
for
the
particulate
organic
carbon
concentration
(
kg/
L)
DOC
=
national
default
value
for
the
dissolved
organic
carbon
concentration
(
kg/
L)
K
ow
=
n­
octanol
water
partition
coefficient
for
the
chemical
Equation
5­
29
is
identical
to
Equation
5­
12,
which
was
used
to
determine
the
freely
dissolved
fraction
for
deriving
baseline
BAF
R
f
ds
from
field­
measured
BAFs.
However,
the
POC
and
DOC
concentrations
used
in
Equation
5­
29
reflect
those
values
that
are
expected
in
U.
S.
bodies
of
water,
not
the
POC
and
DOC
values
in
the
study
water
used
to
derive
the
BAF.
Guidance
for
determining
each
component
of
Equation
5­
29
follows.

a.
National
Default
Values
of
POC
and
DOC.
For
estimating
the
freely
dissolved
fraction
of
the
chemical
of
concern
that
is
expected
in
U.
S.
water
bodies,
national
default
values
of
0.5
mg/
L
(
5
×
10­
7
kg/
L)
for
POC
and
2.9
mg/
L
(
2.9
×
10­
6
kg/
L)
for
DOC
should
be
used.
These
values
are
50th
percentile
values
(
medians)
based
on
an
analysis
of
over
110,000
DOC
values
and
85,000
POC
values
contained
in
EPA's
STORET
database
from
1980
through
1999.
These
default
values
reflect
a
combination
of
values
for
streams,
lakes
and
estuaries
across
the
United
States.
Additional
details
on
the
technical
basis,
assumptions,
and
uncertainty
in
the
5­
44
derivation
and
application
of
the
national
default
values
of
POC
and
DOC
are
provided
in
the
Bioaccumulation
TSD.

Although
national
default
values
of
POC
and
DOC
concentrations
are
used
by
EPA
to
set
national
304(
a)
criteria
as
described
by
this
document,
EPA
encourages
States
and
authorized
Tribes
to
use
local
or
regional
data
on
POC
and
DOC
when
adopting
criteria
into
their
water
quality
standards.
EPA
encourages
States
and
Tribes
to
consider
local
or
regional
data
on
POC
and
DOC
because
local
or
regional
conditions
may
result
in
differences
in
POC
or
DOC
concentrations
compared
with
the
values
used
as
national
defaults.
Additional
guidance
on
developing
local
or
regional
values
of
POC
and
DOC,
including
a
database
of
POC
and
DOC
values
segregated
by
waterbody
type,
is
found
in
the
Bioaccumulation
TSD.

b.
KowValue.
The
value
selected
for
the
K
ow
of
the
chemical
of
concern
should
be
the
same
value
used
in
earlier
calculations
(
e.
g.,
for
calculating
baseline
BAF
R
f
ds
and
FCMs).
Guidance
for
selecting
the
K
ow
value
is
found
in
the
Bioaccumulation
TSD.

5.4.4
Deriving
National
BAFs
Using
Procedure
#
2
This
section
provides
guidance
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
2
shown
in
Figure
5­
1.
The
types
of
nonionic
organic
chemicals
for
which
Procedure
#
2
is
most
appropriate
are
those
that
are
classified
as
moderately
to
highly
hydrophobic
and
subject
to
high
rates
of
metabolism
by
aquatic
biota
(
see
Section
5.4.2
above).
Non­
aqueous
contaminant
exposure
and
subsequent
biomagnification
in
aquatic
food
webs
are
not
generally
of
concern
for
chemicals
that
are
classified
in
this
category.
As
a
result,
FCMs
are
not
used
in
this
procedure.
In
addition,
K
ow
­
based
predictions
of
bioconcentration
are
not
used
in
this
procedure
since
the
K
ow
/
BCF
relationship
is
primarily
based
on
poorly
metabolized
chemicals.
Some
nonionic
organic
chemicals
for
which
Procedure
#
2
is
probably
appropriate
include
certain
PAHs
which
are
believed
to
be
metabolized
substantially
by
fish
(
e.
g.,
benzo[
a]
pyrene,
phenanthrene,
fluoranthene,
pyrene,
benzo[
a]
anthracene
and
chrysene/
triphenylene;
USEPA,
1980;
Burkhard
and
Lukasewycz,
2000).

According
to
Procedure
#
2,
the
following
three
methods
can
be
used
in
deriving
a
national
BAF:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
a
field­
measured
BAF)
(
method
1),
C
predicting
a
BAF
from
an
acceptable
BSAF
(
method
2),
and
C
predicting
a
BAF
from
an
acceptable
BCF
(
method
3).

Each
of
these
three
methods
relies
on
measured
data
for
assessing
bioaccumulation
and
therefore,
includes
the
effects
of
chemical
metabolism
by
the
study
organism
in
the
BAF
estimate.
5­
45
The
field­
measured
BAF
and
BSAF
methods
also
incorporate
any
metabolism
which
occurs
in
the
aquatic
food
web.

As
shown
in
Figure
5­
2,
the
next
steps
in
deriving
a
national
BAF
after
selecting
the
derivation
procedure
are:
(
1)
calculating
individual
baseline
BAF
R
f
ds,
(
2)
selecting
the
final
baseline
BAF
R
f
ds,
and
(
3)
calculating
the
national
BAFs.
Each
of
these
three
steps
is
discussed
separately
below.

5.4.4.1
Calculating
Individual
Baseline
BAF
R
f
ds
As
described
previously
in
Procedure
#
1,
calculating
individual
baseline
BAF
R
f
ds
involves
normalizing
the
measured
BAFtT
or
BCF
tT
(
which
are
based
on
the
total
chemical
in
water
and
tissue)
by
the
lipid
content
of
the
study
organisms
and
the
freely
dissolved
fraction
of
the
chemical
in
the
study
water.
Converting
measured
BAF
tT
(
or
BCF
tT
)
values
to
baseline
BAF
R
f
d
(
or
BCF
R
f
d)
values
is
designed
to
account
for
variation
in
measured
BAF
tT
s
that
is
caused
by
differences
in
lipid
content
of
study
organisms
and
differences
in
the
freely
dissolved
fraction
of
chemical
in
study
waters.
Therefore,
baseline
BAF
R
f
ds
are
considered
more
amenable
for
extrapolating
and
averaging
BAFs
across
different
species
and
different
study
waters
compared
with
total
BAF
tT
s.

1.
For
each
species
where
acceptable
data
are
available,
calculate
all
possible
baseline
BAF
R
f
ds
using
each
of
the
three
methods
shown
above
for
Procedure
#
2.

2.
Individual
baseline
BAF
R
f
ds
should
be
calculated
from
field­
measured
BAF
tT
s,
fieldmeasured
BSAFs,
and
laboratory
BCF
tT
s
according
to
the
following
procedures.

A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
a
field­
measured
BAFtT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
A)
for
determining
baseline
BAF
R
f
ds
from
field­
measured
BAFs
in
Procedure
#
1.

2.
Because
nonionic
organic
chemicals
applicable
to
Procedure
#
2
have
relatively
high
rates
of
metabolism
in
aquatic
organisms,
they
will
tend
to
reach
steady
state
more
quickly
than
nonionic
organic
chemicals
with
similar
K
ow
values
but
which
undergo
little
or
no
metabolism.
Therefore,
less
temporal
averaging
of
chemical
concentrations
would
generally
be
required
for
determining
field­
measured
BAF
tT
s
with
highly
metabolizable
chemicals
compared
with
chemicals
that
are
poorly
metabolized
by
aquatic
biota.
5­
46
B.
Baseline
BAF
R
f
d
Derived
from
Field­
measured
BSAFs
1.
A
baseline
BAF
R
f
d
should
be
calculated
from
a
field­
measured
BSAF
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
B)
for
determining
baseline
BAF
R
f
ds
from
fieldmeasured
BSAFs
in
Procedure
#
1.

C.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
a
laboratorymeasured
BCFtT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
c)
for
determining
baseline
BAF
R
f
ds
from
a
laboratory­
measured
BCF
and
FCM
in
Procedure
#
1.

2.
Because
biomagnification
is
not
an
overriding
concern
for
nonionic
organic
chemicals
applicable
to
Procedure
#
2,
food
chain
multipliers
are
not
used
in
the
derivation
of
a
baseline
BAF
R
f
d
from
a
laboratory­
measured
BCF
tT
.

5.4.4.2
Selecting
Final
Baseline
BAF
R
f
ds
After
calculating
individual,
baseline
BAF
R
f
ds
using
as
many
of
the
methods
in
Procedure
#
2
as
possible,
the
next
step
is
to
determine
a
final
baseline
BAF
R
f
d
for
each
trophic
level
from
the
individual
baseline
BAF
R
f
ds.
The
final
baseline
BAF
R
f
d
will
be
used
in
the
last
step
to
determine
the
national
BAF
for
each
trophic
level.
A
final
baseline
BAF
R
f
d
for
each
trophic
level
should
be
determined
from
the
individual
baseline
BAF
R
f
ds
by
considering
the
data
preference
hierarchy
defined
by
Procedure
#
2
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
2
is
(
in
order
of
preference):

1.
a
baseline
BAF
R
f
d
from
an
acceptable
field­
measured
BAF
(
method
1),
2.
a
baseline
BAF
R
f
d
from
an
acceptable
field­
measured
BSAF
(
method
2),
or
3.
a
baseline
BAF
R
f
d
from
an
acceptable
laboratory­
measured
BCF
(
method
3).

This
data
preference
hierarchy
reflects
EPA's
preference
for
BAFs
based
on
fieldmeasurements
of
bioaccumulation
(
methods
1
and
2)
over
those
based
on
laboratorymeasurements
(
method
3).
However,
as
explained
in
Procedure
#
1,
this
data
preference
hierarchy
should
not
be
considered
inflexible.
Rather,
it
should
be
used
as
a
guide
for
selecting
the
final
baseline
BAF
R
f
ds
when
the
underlying
uncertainty
is
similar
among
two
or
more
baseline
BAF
R
f
ds
derived
using
different
methods.
Although
biomagnification
is
not
generally
a
concern
for
chemicals
subject
to
Procedure
#
2,
trophic
level
differences
in
bioaccumulation
might
be
substantial
to
the
extent
that
the
rate
of
chemical
metabolism
by
organisms
in
different
trophic
levels
differs.
For
example,
certain
PAHs
have
been
shown
to
be
metabolized
to
a
much
greater
extent
by
some
fish
compared
with
some
invertebrate
species
(
James,
1989).
Therefore,
final
baseline
BAF
R
f
ds
for
chemicals
applicable
to
Procedure
#
2
should
be
determined
on
a
trophic­
levelspecific
basis
according
to
the
following
guidelines.
5­
47
1.
The
final
baseline
BAF
R
f
ds
in
Procedure
#
2
should
be
selected
according
to
the
same
steps
described
in
Procedure
#
1
but
with
the
substitution
of
the
data
preference
hierarchy
described
above
for
Procedure
#
2.
Specifically,
the
species­
mean
baseline
BAF
R
f
ds,
trophic­
level­
mean
baseline
BAF
R
f
ds,
and
the
final
baseline
BAF
R
f
ds
should
be
determined
according
to
the
guidelines
presented
in
Procedure
#
1
(
Section
5.4.3.2,
Steps
1,
2,
and
3).

5.4.4.3
Calculating
the
National
BAFs
As
described
in
Procedure
#
1,
the
last
step
in
deriving
national
BAFs
for
nonionic
organic
chemicals
is
to
convert
the
final
baseline
BAF
R
f
ds
determined
in
the
previous
step
to
BAFs
which
reflect
conditions
to
which
the
national
304(
a)
criteria
will
apply
(
Figure
5­
2).

1.
For
trophic
levels
two,
three,
and
four,
national
BAFs
should
be
calculated
from
the
final
baseline
BAF
R
f
ds
using
the
same
equation
and
procedures
described
previously
in
Procedure
#
1
(
see
Section
5.4.3.3
entitled
"
Calculating
the
National
BAFs").

5.4.5
Deriving
National
BAFs
Using
Procedure
#
3
This
section
provides
guidance
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
3
shown
in
Figure
5­
1.
The
types
of
nonionic
organic
chemicals
for
which
Procedure
#
3
is
most
appropriate
are
those
that
are
classified
as
low
in
hydrophobicity
(
i.
e.,
log
K
ow
values
less
than
4.0)
and
subject
to
low
(
or
unknown)
rates
of
metabolism
by
aquatic
biota
(
see
Section
5.4.2
above).
Non­
aqueous
contaminant
exposure
and
subsequent
biomagnification
in
aquatic
food
webs
are
not
generally
of
concern
for
chemicals
that
are
classified
in
this
category
(
Fisk
et
al.,
1998;
Gobas
et
al.,
1993;
Connolly
and
Pedersen,
1988;
Thomann,
1989).
As
a
result,
FCMs
are
not
used
in
this
procedure.

According
to
Procedure
#
3,
the
following
three
methods
can
be
used
in
deriving
a
national
BAF:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
a
field­
measured
BAF),
C
predicting
a
BAF
from
an
acceptable
laboratory­
measured
BCF,
and
C
predicting
a
BAF
from
an
acceptable
K
ow.

After
selecting
the
derivation
procedure,
the
next
steps
in
deriving
a
national
BAF
at
a
given
trophic
level
for
nonionic
organic
chemicals
are:
(
1)
calculating
individual
baseline
BAF
R
f
ds,
(
2)
selecting
the
final
baseline
BAF
R
f
d,
and
(
3)
calculating
the
national
BAF
(
Figure
5­
2).
Each
of
these
three
steps
is
discussed
separately
below.

5.4.5.1
Calculating
Individual
Baseline
BAF
R
f
ds
Calculating
individual
baseline
BAF
R
f
ds
involves
normalizing
each
measured
BAF
tT
or
BCF
tT
(
which
are
based
on
the
total
chemical
in
water
and
tissue)
by
the
lipid
content
of
the
study
organism
and
the
freely
dissolved
fraction
of
the
chemical
in
the
study
water.
For
additional
5­
48
discussion
of
the
technical
basis
for
calculating
baseline
BAF
R
f
ds,
see
Section
5.4.3.1
in
Procedure
#
1.

1.
For
each
species
where
acceptable
data
are
available,
calculate
all
possible
baseline
BAF
R
f
ds
using
each
of
the
three
methods
shown
above
for
Procedure
#
3.

2.
An
individual
baseline
BAF
R
f
d
should
be
calculated
from
field­
measured
BAF
tT
s,
laboratorymeasured
BCFtT
s,
and
K
ow
values
according
to
the
following
procedures.

A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
a
field­
measured
BAFtT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
A)
in
Procedure
#
1.

2.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
nonionic
organic
chemicals
applicable
to
Procedure
#
3
are
expected
to
remain
almost
entirely
in
the
freely
dissolved
form
in
natural
waters
with
dissolved
and
particulate
organic
carbon
concentrations
typical
of
most
field
BAF
studies.
Therefore,
the
freely
dissolved
fraction
should
be
assumed
to
be
equal
to
1.0,
unless
the
concentrations
of
DOC
and
POC
are
very
high
in
the
field
BAF
study.
For
studies
with
very
high
DOC
or
POC
concentrations,
(
e.
g.,
about
100
mg/
L
or
higher
for
DOC
or
10
mg/
L
or
higher
for
POC),
the
freely
dissolved
fraction
may
be
substantially
lower
than
1.0
and
therefore
should
be
calculated
using
Equation
5­
12.

3.
Temporal
Averaging
of
Concentrations.
Also
due
to
their
low
hydrophobicity,
nonionic
organic
chemicals
appropriate
to
Procedure
#
3
will
also
tend
to
reach
steady
state
quickly
compared
with
those
chemicals
to
which
Procedure
#
1
applies.
Therefore,
the
extent
of
temporal
averaging
of
tissue
and
water
concentrations
is
typically
much
less
than
that
required
for
highly
hydrophobic
chemicals
to
which
Procedure
#
1
is
applied.
In
addition,
field
studies
used
to
calculate
BAFs
for
these
chemicals
should
have
sampled
water
and
tissue
at
similar
points
in
time
because
tissue
concentrations
respond
more
rapidly
to
changes
in
water
concentrations.
EPA
will
be
providing
additional
guidance
on
appropriate
BAF
study
designs
for
nonionic
organic
chemicals
(
including
those
appropriate
to
Procedure
#
3)
in
its
forthcoming
guidance
document
on
conducting
field
BAF
and
BSAF
studies.

B.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
a
laboratorymeasured
BCFtT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
c)
of
Procedure
#
1.
5­
49
2.
Food
Chain
Multipliers.
Because
biomagnification
is
not
an
overriding
concern
for
the
minimally
hydrophobic
chemicals
applicable
to
Procedure
#
3,
FCMs
are
not
used
in
the
derivation
of
a
baseline
BAF
R
f
d
from
a
laboratory­
measured
BCF
tT
.

3.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
nonionic
organic
chemicals
to
which
Procedure
#
3
is
applied
are
expected
to
remain
almost
entirely
in
the
freely
dissolved
form
in
waters
containing
dissolved
and
particulate
organic
carbon
concentrations
typical
of
laboratory
BCF
studies.
Therefore,
the
freely
dissolved
fraction
should
usually
be
assumed
equal
to
1.0.
The
freely
dissolved
fraction
will
be
substantially
less
than
1.0
only
in
situations
where
unusually
high
concentrations
of
DOC
and
POC
are
present
in
the
laboratory
BCF
study
(
e.
g.,
above
about
100
mg/
L
for
DOC
or
about
10
mg/
L
for
POC).
In
this
situation,
the
freely
dissolved
fraction
should
be
calculated
according
to
Equation
5­
12.

C.
Baseline
BAF
R
f
d
from
a
Kow
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
an
acceptable
K
ow
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
D)
in
Procedure
#
1.

2.
Because
biomagnification
is
not
an
overriding
concern
for
nonionic
organic
chemicals
with
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
food
chain
multipliers
are
not
used
in
Procedure
#
3
for
deriving
the
baseline
BAF
R
f
d
from
a
K
ow.

5.4.5.2
Selecting
Final
Baseline
BAF
R
f
ds
After
calculating
individual
baseline
BAF
R
f
ds
using
as
many
of
the
methods
in
Procedure
#
3
as
possible,
the
next
step
is
to
determine
a
final
baseline
BAF
R
f
d
for
each
trophic
level
from
the
individual
baseline
BAF
R
f
ds
(
Figure
5­
2).
The
final
baseline
BAF
R
f
d
will
be
used
in
the
last
step
to
determine
the
national
BAF
for
each
trophic
level.
The
final
baseline
BAF
R
f
d
for
each
trophic
level
should
be
determined
from
the
individual
baseline
BAF
R
f
ds
by
considering
the
data
preference
hierarchy
defined
by
Procedure
#
3
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
3
is
(
in
order
of
preference):

1.
a
baseline
BAF
R
f
d
from
an
acceptable
field­
measured
BAF
or
laboratorymeasured
BCF,
or
2.
a
baseline
BAF
R
f
d
predicted
from
an
acceptable
K
ow
value.

This
data
preference
hierarchy
reflects
EPA's
preference
for
BAFs
that
are
based
on
measured
data
(
field­
measured
BAFs
and
laboratory­
measured
BCFs)
over
BAFs
based
on
predictive
methods
(
K
ow).
This
data
preference
hierarchy
should
be
used
as
a
guide
for
selecting
the
final
baseline
BAF
R
f
ds
when
the
uncertainty
is
similar
among
two
or
more
baseline
BAF
R
f
ds
derived
using
different
methods.
Since
bioaccumulation
via
dietary
uptake
and
subsequent
biomagnification
generally
are
not
of
concern
for
chemicals
subject
to
Procedure
#
3,
field­
5­
50
measured
BAFs
and
laboratory­
measured
BCFs
are
considered
equally
in
determining
the
national
BAF.

Final
baseline
BAF
R
f
ds
should
be
selected
for
each
trophic
level
using
the
following
steps
and
guidelines.

1.
Calculate
Species­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
(
i.
e.,
field­
measured
BAF,
BAF
from
a
lab­
measured
BCF,
or
BAF
from
a
K
ow)
where
more
than
one
acceptable
baseline
BAF
R
f
d
is
available
for
a
given
species,
calculate
a
species­
mean
baseline
BAF
R
f
d
according
to
the
guidance
described
previously
in
Procedure
#
1.

2.
Calculate
Trophic­
Level­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
where
more
than
one
acceptable
species­
mean
baseline
BAF
R
f
d
is
available
within
a
given
trophic
level,
calculate
the
trophic­
level­
mean
baseline
BAF
R
f
d
as
the
geometric
mean
of
acceptable
species­
mean
baseline
BAF
R
f
ds
in
that
trophic
level.

3.
Select
a
Final
Baseline
BAF
R
f
d
for
Each
Trophic
Level.
For
each
trophic
level,
select
the
final
baseline
BAF
R
f
d
using
best
professional
judgment
by
considering:
(
1)
the
data
preference
hierarchy,
(
2)
the
relative
uncertainties
among
trophic­
level­
mean
baseline
BAF
R
f
ds
derived
using
different
methods,
and
(
3)
the
weight
of
evidence
among
the
three
methods.

a.
In
general,
when
more
than
one
trophic­
level­
mean
baseline
BAF
R
f
d
is
available
within
a
given
trophic
level,
the
final
baseline
BAF
R
f
d
should
be
selected
from
the
most
preferred
BAF
method
defined
by
the
data
preference
hierarchy
for
Procedure
#
3.
Within
the
first
data
preference
tier,
field­
measured
BAFs
and
laboratory­
measured
BCFs
are
considered
equally
desirable
for
deriving
a
final
trophic­
level­
mean
baseline
BAF
R
f
d
using
Procedure
#
3.
If
a
trophic­
level­
mean
baseline
BAF
R
f
d
is
available
from
both
a
field­
measured
BAF
and
a
laboratorymeasured
BCF,
the
final
baseline
BAF
R
f
d
should
be
selected
using
the
trophic­
levelmean
baseline
BAF
R
f
d
or
BCF
R
f
d
with
the
least
overall
uncertainty.

b.
If
uncertainty
in
a
trophic­
level­
mean
baseline
BAF
R
f
d
based
on
a
higher
tier
(
more
preferred)
method
is
judged
to
be
substantially
greater
than
a
trophic­
level­
mean
baseline
BAF
R
f
d
from
a
lower
tier
method,
then
the
final
baseline
BAF
R
f
d
should
be
selected
using
a
trophic­
level­
mean
baseline
BAF
R
f
d
from
a
lower
tier
method.

c.
The
above
steps
should
be
performed
for
each
trophic
level
until
a
final
baseline
BAF
R
f
d
is
selected
for
trophic
level
two,
three,
and
four.

5.4.5.3
Calculating
the
National
BAFs
As
described
in
Procedure
#
1,
the
last
step
in
deriving
a
national
BAF
for
a
given
trophic
level
for
nonionic
organic
chemicals
is
to
convert
the
final
baseline
BAF
R
f
d
determined
in
the
5­
51
previous
step
to
a
BAF
that
reflect
conditions
to
which
the
national
304(
a)
criterion
will
apply
(
Figure
5­
2).
Each
national
BAF
should
be
determined
from
a
final
baseline
BAF
R
f
d
according
to
the
following
guidelines.

1.
National
BAF
Equation.
Except
where
noted
below,
national
BAFs
for
trophic
levels
two,
three,
and
four
should
be
calculated
from
the
final,
trophic­
level­
mean
baseline
BAF
R
f
ds
using
Equation
5­
28
and
associated
guidance
described
in
Procedure
#
1
(
see
Section
5.4.3.3).

2.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
a
freely
dissolved
fraction
of
1.0
should
be
assumed
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
3.
A
freely
dissolved
fraction
of
1.0
should
be
assumed
because
at
a
log
K
ow
of
less
than
4.0,
nonionic
organic
chemicals
are
expected
to
remain
over
99
percent
in
the
freely
dissolved
form
at
POC
and
DOC
concentrations
corresponding
to
national
default
values
for
U.
S.
bodies
of
water
(
i.
e.,
0.5
mg/
L
and
2.9
mg/
L,
respectively).

5.4.6
Deriving
National
BAFs
Using
Procedure
#
4
This
section
provides
guidance
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
4
shown
in
Figure
5­
1.
The
types
of
nonionic
organic
chemicals
for
which
Procedure
#
4
is
most
appropriate
are
those
that
are
classified
as
having
low
hydrophobicity
and
subject
to
high
rates
of
metabolism
by
aquatic
biota
(
see
Section
5.4.2
above).
Non­
aqueous
contaminant
exposure
and
subsequent
biomagnification
in
aquatic
food
webs
are
not
generally
of
concern
for
chemicals
that
are
classified
in
this
category.
As
a
result,
FCMs
are
not
used
in
this
procedure.
In
addition,
K
ow
­
based
predictions
of
bioconcentration
are
not
used
in
this
procedure
since
the
K
ow
/
BCF
relationship
is
primarily
based
on
poorly
metabolized
chemicals.
One
example
of
a
nonionic
organic
chemical
for
which
Procedure
#
4
appears
appropriate
is
butyl
benzyl
phthalate
in
fish.
Using
radiolabeling
techniques
with
confirmation
by
chromatographic
analysis,
Carr
et
al.
(
1997)
present
evidence
that
indicates
butyl
benzyl
phthalate
is
extensively
metabolized
in
sunfish.
Carr
et
al.
(
1997)
also
report
measured
BCFs
(
and
subsequently
lipid­
normalized
BCFs)
which
are
substantially
below
predicted
BCFs
based
on
log
K
ow.
In
a
study
of
chlorinated
anilines
(
which
would
be
essentially
un­
ionized
at
ambient
pH),
de
Wolf
et
al.
(
1992)
reported
measured
BCFs
substantially
lower
than
those
predicted
based
on
K
ow.
The
authors
suggested
that
biotransformation
(
metabolism)
involving
the
amine
(
NH
2)
was
responsible
for
the
lower
measured
BCFs.

According
to
Procedure
#
4,
the
following
two
methods
can
be
used
in
deriving
a
national
BAF:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
a
field­
measured
BAF),
and
C
predicting
a
BAF
from
an
acceptable
BCF.
5­
52
After
selecting
the
derivation
procedure,
the
next
steps
in
deriving
a
national
BAF
for
a
given
trophic
level
for
nonionic
organic
chemicals
are:
(
1)
calculating
individual
baseline
BAF
R
f
ds,
(
2)
selecting
the
final
baseline
BAF
R
f
d,
and
(
3)
calculating
the
national
BAF
(
Figure
5­
2).
Each
of
these
three
steps
is
discussed
separately
below.

5.4.6.1
Calculating
Individual
Baseline
BAF
R
f
ds
Calculating
individual
baseline
BAF
R
f
ds
involves
normalizing
the
measured
BAF
tT
or
BCF
tT
(
which
are
based
on
the
total
chemical
in
water
and
tissue)
by
the
lipid
content
of
the
study
organism
and
the
freely
dissolved
fraction
of
the
chemical
in
the
study
water.
For
additional
discussion
of
the
technical
basis
for
calculating
baseline
BAF
R
f
ds,
see
Section
5.4.3.1
in
Procedure
#
1.

1.
For
each
species
where
acceptable
data
are
available,
calculate
all
possible
baseline
BAF
R
f
ds
using
each
of
the
two
methods
shown
above
for
Procedure
#
4.

2.
Individual
baseline
BAF
R
f
ds
should
be
calculated
from
field­
measured
BAF
tT
s
and
laboratory­
measured
BCF
tT
s
according
to
the
following
procedures.

A.
Baseline
BAF
R
f
d
from
Field­
Measured
BAFs
1.
A
baseline
BAF
R
f
d
should
be
calculated
from
a
field­
measured
BAF
tT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
A)
in
Procedure
#
1.

2.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
nonionic
organic
chemicals
applicable
to
Procedure
#
4
are
expected
to
remain
almost
entirely
in
the
freely
dissolved
form
in
natural
waters
with
dissolved
and
particulate
organic
carbon
concentrations
typical
of
most
field
BAF
studies.
Therefore,
the
freely
dissolved
fraction
should
be
assumed
equal
to
1.0
unless
the
concentrations
of
DOC
and
POC
are
very
high
in
the
field
BAF
study.
For
studies
with
very
high
DOC
or
POC
concentrations,
(
e.
g.,
about
100
mg/
L
or
higher
for
DOC
or
10
mg/
L
or
higher
for
POC),
the
freely
dissolved
fraction
may
be
substantially
lower
than
1.0
and
therefore
should
be
calculated
using
Equation
5­
12.

3.
Temporal
Averaging
of
Concentrations.
Also
due
to
their
low
hydrophobicity,
nonionic
organic
chemicals
appropriate
to
Procedure
#
4
will
also
tend
to
reach
steadystate
quickly
compared
with
those
chemicals
to
which
Procedure
#
1
applies.
Therefore,
the
extent
of
temporal
averaging
of
tissue
and
water
concentrations
is
typically
much
less
than
that
required
for
highly
hydrophobic
chemicals
to
which
Procedure
#
1
is
applied.
In
addition,
field
studies
used
to
calculate
BAFs
for
these
chemicals
should
have
sampled
water
and
tissue
at
similar
points
in
time
because
tissue
concentrations
should
respond
rapidly
to
changes
in
water
concentrations.
EPA
will
be
providing
additional
guidance
on
appropriate
BAF
study
designs
for
nonionic
organic
chemicals
(
including
those
5­
53
appropriate
to
Procedure
#
4)
in
its
forthcoming
guidance
document
on
conducting
field
BAF
and
BSAF
studies.

B.
Baseline
BAF
R
f
d
from
a
Laboratory­
Measured
BCF
1.
Except
where
noted
below,
a
baseline
BAF
R
f
d
should
be
calculated
from
a
laboratorymeasured
BCFtT
using
the
guidance
and
equations
outlined
in
Section
5.4.3.1(
c)
of
Procedure
#
1.

2.
Food
Chain
Multipliers.
Because
biomagnification
is
not
an
important
concern
for
the
minimally
hydrophobic
chemicals
applicable
to
Procedure
#
4,
FCMs
are
not
used
in
the
derivation
of
a
baseline
BAF
R
f
d
from
a
laboratory­
measured
BCF
tT
.

3.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
nonionic
organic
chemicals
to
which
Procedure
#
4
is
applied
are
expected
to
remain
almost
entirely
in
the
freely
dissolved
form
in
waters
containing
dissolved
and
particulate
organic
carbon
concentrations
typical
of
laboratory
BCF
studies.
Therefore,
the
freely
dissolved
fraction
should
usually
be
assumed
to
be
equal
to
1.0.
The
freely
dissolved
fraction
will
be
substantially
less
than
1.0
only
in
situations
where
unusually
high
concentrations
of
DOC
and
POC
are
present
in
the
lab
BCF
study
(
e.
g.,
above
about
100
mg/
L
for
DOC
or
about
10
mg/
L
for
POC).
In
this
situation,
the
freely
dissolved
fraction
should
be
calculated
according
to
Equation
5­
12.

5.4.6.2
Selecting
the
Final
Baseline
BAF
R
f
ds
After
calculating
individual
baseline
BAF
R
f
ds
using
as
many
of
the
methods
in
Procedure
#
4
as
possible,
the
next
step
is
to
determine
a
final
baseline
BAF
R
f
d
for
a
given
trophic
level
from
the
individual
baseline
BAF
R
f
ds
(
Figure
5­
2).
The
final
baseline
BAF
R
f
d
will
be
used
in
the
last
step
to
determine
the
national
BAF
for
each
trophic
level.
A
final
baseline
BAF
R
f
d
should
be
determined
for
each
trophic
level
from
the
individual
baseline
BAF
R
f
ds
by
considering
the
data
preference
hierarchy
defined
by
Procedure
#
4
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
4
is:

1.
a
baseline
BAF
R
f
d
from
an
acceptable
field­
measured
BAF
or
predicted
from
an
acceptable
laboratory­
measured
BCF.

Since
bioaccumulation
via
dietary
uptake
and
subsequent
biomagnification
generally
are
not
of
concern
for
chemicals
subject
to
Procedure
#
4,
field­
measured
BAFs
and
laboratorymeasured
BCFs
are
considered
equally
in
determining
the
national
BAF.

Final
baseline
BAF
R
f
ds
should
be
selected
for
each
trophic
level
using
the
following
steps
and
guidelines.
5­
54
1.
Calculate
Species­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
(
i.
e.,
field­
measured
BAF
or
a
BAF
from
a
lab­
measured
BCF)
where
more
than
one
acceptable
baseline
BAF
R
f
d
is
available
for
a
given
species,
calculate
a
species­
mean
baseline
BAF
R
f
d
according
to
the
guidance
described
previously
in
Procedure
#
1.

2.
Calculate
Trophic­
Level­
Mean
Baseline
BAF
R
f
ds.
For
each
BAF
method
where
more
than
one
acceptable
species­
mean
baseline
BAF
R
f
d
is
available
within
a
given
trophic
level,
calculate
the
trophic­
level­
mean
baseline
BAF
R
f
d
as
the
geometric
mean
of
acceptable
species­
mean
baseline
BAF
R
f
ds
for
that
trophic
level.

3.
Select
a
Final
Baseline
BAF
R
f
d
for
Each
Trophic
Level.
For
each
trophic
level,
select
the
final
baseline
BAF
R
f
d
using
best
professional
judgment
by
considering:
(
1)
the
data
preference
hierarchy,
and
(
2)
the
relative
uncertainties
among
trophic­
level­
mean
BAFs
derived
using
different
methods.

a.
As
discussed
above,
field­
measured
BAFs
and
laboratory­
measured
BCFs
are
considered
equally
desirable
for
deriving
a
final
trophic­
level­
mean
baseline
BAF
R
f
d
using
Procedure
#
4.
If
a
trophic­
level­
mean
baseline
BAF
R
f
d
is
available
from
both
a
field­
measured
BAF
and
a
laboratory­
measured
BCF,
the
final
baseline
BAF
R
f
d
should
be
selected
using
the
trophic­
level­
mean
baseline
BAF
R
f
d
or
BCF
R
f
d
with
the
least
overall
uncertainty.

b.
The
above
steps
should
be
performed
for
each
trophic
level
until
a
final
baseline
BAF
R
f
d
is
selected
for
trophic
levels
two,
three,
and
four.

5.4.6.3
Calculating
National
BAFs
As
described
in
Procedure
#
1,
the
last
step
in
deriving
a
national
BAF
for
a
given
trophic
level
for
nonionic
organic
chemicals
is
to
convert
the
final
baseline
BAF
R
f
d
determined
in
the
previous
step
to
a
BAF
that
reflects
conditions
to
which
the
national
304(
a)
criterion
will
apply
(
Figure
5­
2).
Each
national
BAF
should
be
determined
from
a
final
baseline
BAF
R
f
d
according
to
the
following
guidelines.

1.
National
BAF
Equation.
Except
where
noted
below,
national
BAFs
for
trophic­
levels
two,
three,
and
four
should
be
calculated
from
the
final,
trophic­
level­
mean
baseline
BAF
R
f
ds
using
the
same
equation
and
procedures
described
previously
in
Procedure
#
1
(
see
Section
5.4.3.3
in
Procedure
#
1).

2.
Freely
Dissolved
Fraction.
Due
to
their
low
hydrophobicity
(
i.
e.,
log
K
ow
<
4.0),
a
freely
dissolved
fraction
of
1.0
should
be
assumed
for
calculating
national
BAFs
for
nonionic
organic
chemicals
using
Procedure
#
4.
A
freely
dissolved
fraction
of
1.0
should
be
assumed
because
at
a
log
K
ow
value
of
less
than
4.0,
nonionic
organic
chemicals
are
expected
to
remain
over
99
percent
in
the
freely
dissolved
form
at
POC
and
DOC
5­
55
concentrations
corresponding
to
national
default
values
for
U.
S.
bodies
of
water
(
i.
e.,
0.5
mg/
L
and
2.9
mg/
L,
respectively).

5.5
NATIONAL
BIOACCUMULATION
FACTORS
FOR
IONIC
ORGANIC
CHEMICALS
This
section
contains
guidelines
for
deriving
national
BAFs
for
ionic
organic
chemicals
(
i.
e.,
organic
chemicals
which
undergo
significant
ionization
in
water).
As
defined
in
Section
5.3.5,
ionic
organic
chemicals
contain
functional
groups
which
can
either
readily
donate
protons
(
e.
g.,
organic
acids
with
hydroxyl,
carboxylic,
and
sulfonic
groups)
or
readily
accept
protons
(
e.
g.,
organic
bases
with
amino
and
aromatic
heterocyclic
nitrogen
groups).
Some
examples
of
ionic
organic
compounds
include:

C
chlorinated
phenols
(
e.
g.,
2,4,6­
trichlorophenol,
pentachlorophenol),
C
chlorinated
phenoxyalkanoic
acids
(
e.
g.,
2,4­
dichlorophenoxyacetic
acid
[
2,4­
D]),
C
nitrophenols
(
e.
g.,
2­
nitrophenol,
2,4,6­
trinitrophenol),
C
cresols
(
e.
g.,
2,4­
dinitro­
o­
cresol
[
DNOC]),
C
pyridines
(
e.
g.,
2,4­
dimethypyidine),
C
aliphatic
and
aromatic
amines
(
e.
g.,
trimethylamine,
aniline),
and
C
linear
alkylbenzenesulfonate
(
LAS)
surfactants.

Ionic
organic
chemicals
are
considered
separately
for
deriving
national
BAFs
because
the
anionic
or
cationic
species
of
these
chemicals
behave
much
differently
in
the
aquatic
environment
compared
with
their
neutral
(
un­
ionized)
counterparts.
The
neutral
species
of
ionic
organic
chemicals
are
thought
to
behave
in
a
similar
manner
as
nonionic
organic
compounds
(
e.
g.,
partitioning
to
lipids
and
organic
carbon
as
a
function
of
hydrophobicity).
However,
the
ionized
(
cationic,
anionic)
species
exhibit
a
considerably
more
complex
behavior
involving
multiple
environmental
partitioning
mechanisms
(
e.
g.,
ion
exchange,
electrostatic,
and
hydrophobic
interactions)
and
a
dependency
on
pH
and
other
factors
including
ionic
strength
and
ionic
composition
(
Jafvert
et
al.,
1990;
Jafvert
1990;
Schwarzenbach,
et
al.,
1993).
As
a
consequence,
methods
to
predict
the
environmental
partitioning
of
organic
cations
and
anions
are
less
developed
and
validated
compared
with
methods
for
nonionic
organic
chemicals
(
Spacie,
1994;
Suffet
et
al.,
1994).

Given
the
current
limitations
in
the
state
of
the
science
for
predicting
the
partitioning
and
bioaccumulation
of
the
ionized
species
of
ionic
organic
chemicals,
procedures
for
deriving
national
BAFs
for
these
chemicals
differ
depending
on
the
extent
to
which
the
fraction
of
the
total
chemical
is
likely
to
be
represented
by
the
ionized
(
cationic,
anionic)
species
in
U.
S.
surface
waters.
When
a
significant
fraction
of
the
total
chemical
concentration
is
expected
to
be
present
as
the
ionized
species
in
water,
procedures
for
deriving
the
national
BAF
rely
on
empirical
(
measured)
methods
(
i.
e.,
Procedures
#
5
and
6
in
Section
5.6).
When
an
insignificant
fraction
of
the
total
chemical
is
expected
to
be
present
as
the
ionized
species
(
i.
e.,
the
chemical
exists
essentially
in
the
neutral
form),
procedures
for
deriving
the
national
BAF
will
follow
those
5­
56
established
for
nonionic
organic
chemicals
(
e.
g.,
Procedures
#
1
through
#
4
in
Section
5.4).
The
following
guidelines
apply
for
assessing
the
occurrence
of
cationic
and
anionic
forms
at
typical
environmental
pH
ranges.

1.
For
the
ionic
organic
chemical
of
concern,
the
dissociation
constant,
pK
a,
should
be
compared
to
the
range
of
pH
values
expected
in
fresh
and
estuarine
waters
of
the
U.
S.
At
pH
equal
to
the
pK
a,
50%
of
the
organic
acid
or
base
is
expected
to
be
present
in
the
ionized
species.
The
pH
values
for
U.
S.
fresh
and
estuarine
waters
typically
range
between
6
and
9,
although
somewhat
higher
and
lower
values
can
occur
in
some
bodies
of
water
(
e.
g.,
acidic
bogs
and
lakes,
highly
alkaline
and
eutrophic
systems,
etc.).

2.
For
organic
acids,
the
chemical
will
exist
almost
entirely
in
its
un­
ionized
form
when
pH
is
about
2
or
more
units
below
the
pK
a.
For
organic
bases,
the
chemical
will
exist
almost
entirely
in
its
un­
ionized
form
when
pH
is
about
2
or
more
units
above
the
pK
a.
In
these
cases,
the
aqueous
behavior
of
the
chemical
would
be
expected
to
be
similar
to
nonionic
organic
chemicals.
Therefore,
national
BAF
should
usually
be
derived
using
Procedures
#
1
through
#
4
in
Section
5.4.

3.
When
pH
is
greater
than
the
pK
a
minus
2
for
organic
acids
(
or
less
than
the
pKa
plus
2
for
organic
bases),
the
fraction
of
the
total
chemical
that
is
expected
to
exist
in
its
ionized
form
can
become
significant
(
i.
e.,
$
1%
in
the
ionized).
In
these
cases,
the
national
BAF
should
usually
be
derived
using
Procedures
#
5
and
#
6
in
Section
5.6.

4.
In
general,
most
organic
acids
(
e.
g.,
pentachlorophenol
and
silvex),
exist
primarily
in
the
ionized
form
in
ambient
waters
because
their
pK
a's
(
4.75
and
3.07,
respectively)
are
much
smaller
than
the
pH
of
the
ambient
waters.
Conversely,
most
organic
bases,
(
e.
g.,
aniline)
exist
mostly
in
the
un­
ionized
form
in
ambient
waters
because
their
pK
a's
(
4.63
for
aniline)
are
much
smaller
than
the
pH
of
the
ambient
waters.

5.
The
above
guidelines
are
intended
to
be
a
general
guide
for
deriving
national
BAFs
for
ionic
organic
chemicals,
not
an
inflexible
rule.
Modifications
to
these
guidelines
should
be
considered
on
a
case­
by­
case
basis,
particularly
when
such
modifications
are
strongly
supported
by
measured
bioaccumulation
or
bioconcentration
data.
For
example,
initial
models
have
been
developed
for
predicting
the
solid
and
organic­
phase
partitioning
of
certain
organic
acids
(
e.
g.,
Jafvert
1990,
Jafvert
et
al.,
1990).
As
these
or
other
models
become
more
fully
developed
and
appropriately
validated
in
the
future,
they
should
be
considered
in
the
development
of
national
BAFs.
In
addition,
since
pH
is
a
controlling
factor
for
dissociation
and
subsequent
partitioning
of
ionic
organic
chemicals,
consideration
should
be
given
to
expressing
BAFs
or
BCFs
as
a
function
of
pH
(
or
other
factors)
where
sufficient
data
exist
to
reliably
establish
such
relationships.
5­
57
5.6
NATIONAL
BIOACCUMULATION
FACTORS
FOR
INORGANIC
AND
ORGANOMETALLIC
CHEMICALS
This
section
contains
guidelines
for
deriving
national
BAFs
for
inorganic
and
organometallic
chemicals
as
defined
in
Section
5.3.5.
The
derivation
of
BAFs
for
inorganic
and
organometallic
chemicals
differs
in
several
ways
from
procedures
for
nonionic
organic
chemicals.
First,
lipid
normalization
of
chemical
concentrations
in
tissues
does
not
generally
apply
for
inorganic
and
organometallic
chemicals.
Thus,
BAFs
and
BCFs
cannot
be
extrapolated
from
one
tissue
to
another
based
on
lipid­
normalized
concentrations
as
is
done
for
nonionic
organic
chemicals.
Second,
the
bioavailability
of
inorganics
and
organometallics
in
water
tends
to
be
chemical­
specific
and
thus,
the
techniques
for
expressing
concentrations
of
nonionic
organic
chemicals
based
on
the
freely
dissolved
form
do
not
generally
apply.
Third,
at
the
present
time
there
are
no
generic
bioaccumulation
models
that
can
be
used
to
predict
BAFs
for
inorganic
and
organometallic
chemicals
as
a
whole,
unlike
the
existence
of
K
ow­
based
models
for
nonionic
organic
chemicals.
While
some
chemical­
specific
bioaccumulation
models
have
been
developed
for
inorganic
and
organometallic
chemicals
(
e.
g.,
Mercury
Cycling
Model
by
Hudson
et.
al,
1994),
those
models
currently
tend
to
require
site­
specific
data
for
input
to
the
model
and
are
restricted
to
site­
specific
applications.
As
the
models
become
more
fully
developed
and
validated
in
the
future,
they
should
be
considered
on
a
case­
by­
case
basis
in
conjunction
with
the
following
procedures
for
deriving
national
BAFs.

5.6.1
Selecting
the
BAF
Derivation
Procedure
As
shown
in
Figure
5­
1,
national
BAFs
can
be
derived
using
two
procedures
for
inorganic
and
organometallic
chemicals
(
Procedures
#
5
and
#
6).
The
choice
of
the
BAF
derivation
procedure
depends
on
whether
or
not
the
chemical
undergoes
biomagnification
in
aquatic
food
webs.

1.
For
many
inorganic
and
organometallic
chemicals,
biomagnification
does
not
occur
and
the
BCF
will
be
equal
to
the
BAF.
For
these
types
of
chemicals,
Procedure
#
5
should
be
used
to
derive
the
national
BAF.
Procedure
#
5
considers
BAFs
and
BCFs
to
be
of
equal
value
in
determining
the
national
BAF
and
does
not
require
the
use
of
FCMs
with
BCF
measurements.
Guidance
for
deriving
BAFs
using
Procedure
#
5
is
provided
in
Section
5.6.3.

2.
For
some
inorganic
and
organometallic
chemicals
(
e.
g.,
methylmercury),
biomagnification
does
occur
and
Procedure
#
6
should
be
used
to
determine
the
national
BAF.
Procedure
#
6
gives
general
preference
to
the
use
of
field­
measured
BAFs
over
laboratory­
measured
BCFs
and
requires
FCMs
to
be
used
with
BCF
measurements
for
predicting
BAFs.
Guidance
for
deriving
BAFs
using
Procedure
#
6
is
provided
in
Section
5.6.4.

3.
Determining
whether
or
not
biomagnification
occurs
for
inorganic
and
organometallic
chemicals
requires
chemical­
specific
data
on
measured
concentrations
of
the
chemical
in
aquatic
organisms
and
their
prey.
Concentrations
in
aquatic
organisms
that
increase
5­
58
substantially
at
successive
trophic
levels
of
a
food
web
suggest
that
biomagnification
is
occurring.
Concentrations
in
aquatic
organisms
that
remain
about
the
same
or
decrease
at
successive
trophic
levels
of
a
food
web
suggest
that
biomagnification
is
not
occurring.
When
comparing
tissue
concentrations
for
assessing
biomagnification,
care
should
be
taken
to
ensure
that
the
aquatic
organisms
chosen
actually
represent
functional
predatorprey
relationships
and
that
all
major
prey
species
are
considered
in
the
comparisons.

5.6.2
Bioavailability
The
chemical­
specific
nature
of
inorganic
and
organometallic
bioavailability
is
likely
due
in
part
to
chemical­
specific
differences
in
several
factors
which
affect
bioavailability
and
bioaccumulation.
These
factors
include
differences
in
the
mechanisms
for
chemical
uptake
by
aquatic
organisms
(
e.
g.,
passive
diffusion,
facilitated
transport,
active
transport),
differences
in
sorption
affinities
to
biotic
and
abiotic
ligands,
and
differences
in
chemical
speciation
in
water.
Some
inorganic
and
organometallic
chemicals
exist
in
multiple
forms
and
valence
states
in
aquatic
ecosystems
that
can
differ
in
their
bioavailability
to
aquatic
organisms
and
undergo
conversions
between
forms.
For
example,
selenium
can
exist
in
various
forms
in
aquatic
ecosystems,
including
inorganic
selenite(+
4)
and
selenate(+
6)
oxyanions,
elemental
selenium
(
0)
under
reducing
conditions
(
primarily
in
sediments),
and
organoselenium
compounds
of
selenide
(­
2).
Dominant
forms
of
mercury
in
natural,
oxic
waters
include
inorganic
(+
2)
mercury
compounds
and
methylmercury;
the
latter
is
generally
considered
to
be
substantially
more
bioavailable
than
inorganic
mercury
compounds
to
higher
trophic
level
organisms.
Although
a
generic
analogue
to
the
"
freely
dissolved"
conversion
for
nonionic
organic
chemicals
does
not
presently
exist
for
inorganic
and
organometallic
chemicals
as
a
whole,
the
occurrence
and
bioavailability
of
different
forms
of
these
chemicals
should
be
carefully
considered
when
deriving
national
BAFs.

1.
If
data
indicate
that:
(
1)
a
particular
form
(
or
multiple
forms)
of
the
chemical
of
concern
largely
governs
its
bioavailability
to
target
aquatic
organisms,
and
(
2)
BAFs
are
more
reliable
when
derived
using
the
bioavailable
form(
s)
compared
with
using
other
form(
s)
of
the
chemical
of
concern,
then
BAFs
and
BCFs
should
be
based
on
the
appropriate
bioavailable
form(
s).

2.
Because
different
forms
of
many
inorganic
and
organometallic
chemicals
may
interconvert
once
released
to
the
aquatic
environment,
regulatory
and
mass
balance
considerations
typically
require
an
accounting
of
the
total
concentration
in
water.
In
these
cases,
sufficient
data
should
be
available
to
enable
conversion
between
total
concentrations
and
the
other
(
presumably
more
bioavailable)
forms
in
water.

5.6.3
Deriving
BAFs
Using
Procedure
#
5
This
section
contains
guidance
for
calculating
national
BAFs
for
inorganic
and
organometallic
chemicals
using
Procedure
#
5
as
shown
in
Figure
5­
1.
The
types
of
inorganic
and
5­
59
organometallic
chemicals
for
which
Procedure
#
5
is
appropriate
are
those
that
are
not
likely
to
biomagnify
in
aquatic
food
webs
(
see
Section
5.1
above).
In
Procedure
#
5,
two
methods
are
available
to
derive
the
national
BAF
for
a
given
trophic
level:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
field­
measured
BAF),
or
C
predicting
a
BAF
from
an
acceptable
laboratory­
measured
BCF.

Individual
BAFs
should
be
determined
from
field­
measured
BAFs
or
laboratory­
measured
BCFs
according
to
the
following
guidelines.

5.6.3.1
Determining
Field­
Measured
BAFs
1.
Except
where
noted
below,
field­
measured
BAFs
should
be
determined
using
the
guidance
provided
in
Section
5.4.3.1(
A)
of
Procedure
#
1.

2.
As
described
previously,
conversion
of
field­
measured
BAFs
to
baseline
BAF
R
f
ds
based
on
lipid­
normalized
and
freely­
dissolved
concentrations
does
not
apply
for
inorganic
and
organometallic
chemicals.
Therefore,
the
guidance
and
equations
provided
in
Procedure
#
1
which
pertain
to
converting
field­
measured
BAFs
to
baseline
BAF
R
f
ds
and
subsequently
to
national
BAFs
do
not
generally
apply
to
inorganic
chemicals.
As
discussed
in
Section
5.6.2
above,
an
analogous
procedure
in
concept
might
be
required
for
converting
total
BAFs
to
BAFs
based
on
the
most
bioavailable
form(
s)
for
some
inorganic
and
organometallic
chemicals
of
concern.
Such
procedures
should
be
applied
on
a
chemicalspecific
basis.

3.
BAFs
should
be
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.

4.
BAFs
should
be
based
on
concentrations
in
the
edible
tissue(
s)
of
the
biota
unless
it
is
demonstrated
that
whole­
body
BAFs
are
similar
to
edible
tissue
BAFs.
For
some
finfish
and
shellfish
species,
whole
body
is
considered
to
be
the
edible
tissue.

5.
The
concentrations
of
an
inorganic
or
organometallic
chemical
in
a
bioaccumulation
study
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
or
organometallic
chemical
that
is
essential
to
the
nutrition
of
aquatic
organisms
might
be
overestimated
if
concentrations
are
at
or
below
normal
background
levels
due
to
selective
accumulation
by
the
organisms
to
meet
their
nutritional
requirements.
5­
60
5.6.3.2
Determining
Laboratory­
Measured
BCFs
1.
Except
where
noted
below,
BAFs
should
be
predicted
from
laboratory­
measured
BCFs
using
the
guidance
provided
in
Section
5.4.3.1(
c)
of
Procedure
#
1.

2.
As
described
previously,
conversion
of
laboratory­
measured
BCFs
to
baseline
BCF
R
f
ds
based
on
lipid­
normalized
and
freely
dissolved
concentrations
does
not
apply
for
inorganic
and
organometallic
chemicals.
Therefore,
the
guidance
and
equations
provided
in
Procedure
#
1
which
pertain
to
converting
laboratory­
measured
BCFs
to
baseline
BCF
R
f
ds
and
subsequently
to
national
BCFs
do
not
generally
apply
to
inorganic
and
organometallic
chemicals.
As
discussed
in
Section
5.6.2
above,
an
analogous
procedure
in
concept
might
be
required
for
converting
total
BCFs
to
BCFs
based
on
the
most
bioavailable
form(
s)
of
some
inorganic
and
organometallic
chemicals
of
concern.
Such
procedures
should
be
applied
on
a
chemical­
specific
basis.
In
addition,
the
use
of
FCMs
with
BCFs
does
not
apply
to
chemicals
applicable
to
Procedure
#
5.

3.
BCFs
should
be
expressed
on
a
wet­
weight
basis;
BCFs
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
BCF.

4.
BCFs
should
be
based
on
concentrations
in
the
edible
tissue(
s)
of
the
biota
unless
it
is
demonstrated
that
whole­
body
BCFs
are
similar
to
edible
tissue
BCFs.
For
some
finfish
and
shellfish
species,
whole
body
is
considered
to
be
the
edible
tissue.

5.
The
concentrations
of
an
inorganic
or
organometallic
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
or
organometallic
chemical
that
is
essential
to
the
nutrition
of
aquatic
organisms
might
be
overestimated
if
concentrations
are
at
or
below
normal
background
levels
due
to
selective
accumulation
by
the
organisms
to
meet
their
nutritional
requirements.

5.6.3.3
Determining
the
National
BAFs
After
calculating
individual
BAFs
using
as
many
of
the
methods
in
Procedure
#
5
as
possible,
the
next
step
is
to
determine
national
BAFs
for
each
trophic
level
from
the
individual
BAFs.
The
national
BAFs
will
be
used
to
determine
the
national
304(
a)
criteria.
The
national
BAFs
should
be
determined
from
the
individual
BAFs
by
considering
the
data
preference
hierarchy
defined
for
Procedure
#
5
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
5
is:

1.
a
BAF
from
an
acceptable
field­
measured
BAF
or
predicted
from
an
acceptable
laboratory­
measured
BCF.
5­
61
Since
bioaccumulation
via
dietary
uptake
and
subsequent
biomagnification
are
not
of
concern
for
chemicals
subject
to
Procedure
#
5,
field­
measured
BAFs
and
laboratory­
measured
BCFs
are
considered
equally
in
determining
the
national
BAFs.
The
national
BAFs
should
be
selected
for
each
trophic
level
using
the
following
steps
and
guidelines.

1.
Calculate
Species­
Mean
BAFs.
For
each
BAF
method
where
more
than
one
acceptable
field­
measured
BAF
(
or
a
BAF
predicted
from
a
BCF)
is
available
for
a
given
species,
calculate
the
species­
mean
BAF
as
the
geometric
mean
of
all
acceptable
individual
measured
or
BCF­
predicted
BAFs.
When
calculating
species­
mean
BAFs,
individual
measured
or
BCF­
predicted
BAFs
should
be
reviewed
carefully
to
assess
uncertainties
in
the
BAF
values.
Highly
uncertain
BAFs
should
not
be
used.
Large
differences
in
individual
BAFs
for
a
given
species
(
e.
g.,
greater
than
a
factor
of
10)
should
be
investigated
further
and
in
such
cases,
some
or
all
of
the
BAFs
for
a
given
species
might
not
be
used.
Additional
discussion
on
evaluating
the
acceptability
of
BAF
and
BCF
values
is
provided
in
the
Bioaccumulation
TSD.

2.
Calculate
Trophic­
Level­
Mean
BAFs.
For
each
BAF
method
where
more
than
one
acceptable
species­
mean
BAF
is
available
within
a
given
trophic
level,
calculate
the
trophic­
level­
mean
BAF
as
the
geometric
mean
of
acceptable
species­
mean
BAFs
in
that
trophic
level.
Trophic­
level­
mean
BAFs
should
be
calculated
for
trophic
levels
two,
three
and
four
because
available
data
on
U.
S.
consumers
of
fish
and
shellfish
indicate
significant
consumption
of
organisms
in
these
trophic
levels.

3.
Select
a
Final
National
BAF
for
Each
Trophic
Level.
For
each
trophic
level,
select
the
final
national
BAF
using
best
professional
judgment
by
considering:
(
1)
the
data
preference
hierarchy
in
Procedure
#
5,
and
(
2)
the
relative
uncertainties
among
trophic
level­
mean
BAFs
derived
using
different
methods.

a.
As
discussed
above,
field­
measured
BAFs
and
laboratory­
measured
BCFs
are
considered
equally
desirable
for
deriving
a
final
national
BAF
using
Procedure
#
5.
If
a
trophic­
level­
mean
BAF
is
available
from
both
a
field­
measured
BAF
and
a
laboratory­
measured
BCF,
the
final
national
BAF
should
be
selected
using
the
trophic­
level­
mean
BAF
with
the
least
overall
uncertainty.

b.
The
above
steps
should
be
performed
for
each
trophic
level
until
a
national
BAF
is
selected
for
trophic
levels
two,
three,
and
four.

5.6.4
Deriving
BAFs
Using
Procedure
#
6
This
section
contains
guidance
for
calculating
national
BAFs
for
inorganic
and
organometallic
chemicals
using
Procedure
#
6
as
shown
in
Figure
5­
1.
The
types
of
inorganic
and
organometallic
chemicals
for
which
Procedure
#
6
is
appropriate
are
those
that
are
considered
likely
to
biomagnify
in
aquatic
food
webs
(
see
Section
5.6.1
above).
Methylmercury
is
an
5­
62
example
of
an
organometallic
chemical
to
which
Procedure
#
6
applies.
In
Procedure
#
6,
two
methods
are
available
to
derive
the
national
BAF:

C
using
a
BAF
from
an
acceptable
field
study
(
i.
e.,
field­
measured
BAF),
or
C
predicting
a
BAF
from
an
acceptable
laboratory­
measured
BCF
and
a
FCM.

Individual
BAFs
should
be
determined
from
field­
measured
BAFs
or
laboratory­
measured
BCFs
and
FCMs
according
to
the
following
guidelines.

5.6.4.1
Determining
Field­
Measured
BAFs
1.
Field­
measured
BAFs
should
be
determined
using
the
guidance
provided
in
Section
5.6.3.1
of
Procedure
#
5.

5.6.4.2
Determining
Laboratory­
Measured
BCFs
1.
Except
where
noted
below,
BAFs
should
be
predicted
from
laboratory­
measured
BCFs
using
the
guidance
provided
in
Section
5.6.3.2
of
Procedure
#
5.

2.
Because
biomagnification
is
of
concern
for
chemicals
applicable
to
Procedure
#
6,
BAFs
should
be
predicted
from
laboratory­
measured
BCF
using
FCMs.
Currently,
there
are
no
generic
models
from
which
to
predict
FCMs
for
inorganic
or
organometallic
chemicals.
Therefore,
FCMs
should
be
determined
using
field
data
as
described
in
the
section
entitled:
"
Field­
Derived
FCMs"
in
Section
5.4.3.1(
c)
of
Procedure
#
1.
Unlike
nonionic
organic
chemicals,
field­
derived
FCMs
for
inorganic
and
organometallic
chemicals
are
not
based
on
lipid­
normalized
concentrations
in
tissues.
For
calculating
FCMs
for
inorganic
and
organometallic
chemicals,
concentrations
in
tissues
should
be
based
on
the
consistent
use
of
either
wet­
weight
or
dry­
weight
concentrations
in
edible
tissues.
FCMs
should
be
derived
for
trophic
levels
two,
three,
and
four.

5.6.4.3
Determining
the
National
BAF
After
calculating
individual
BAFs
using
as
many
of
the
methods
in
Procedure
#
6
as
possible,
the
next
step
is
to
determine
national
BAFs
for
each
trophic
level
from
the
individual
BAFs.
The
national
BAFs
will
be
used
to
determine
the
national
304(
a)
criteria.
The
national
BAFs
should
be
determined
from
the
individual
BAFs
by
considering
the
data
preference
hierarchy
defined
for
Procedure
#
6
and
uncertainty
in
the
data.
The
data
preference
hierarchy
for
Procedure
#
6
is
(
in
order
of
preference):

1.
a
BAF
from
an
acceptable
field­
measured
BAF,
or
2.
a
predicted
BAF
from
an
acceptable
laboratory­
measured
BCF
and
FCM.

This
data
preference
hierarchy
reflects
EPA's
preference
for
field­
measured
BAFs
over
BAFs
predicted
from
a
laboratory­
measured
BCF
and
FCM,
because
field­
measured
BAFs
are
5­
63
direct
measures
of
bioaccumulation
and
biomagnification
in
aquatic
food
webs.
BAFs
predicted
from
laboratory­
measured
BCFs
and
FCMs
indirectly
account
for
biomagnification
through
the
use
of
the
FCM.
For
each
trophic
level,
the
national
BAFs
should
be
determined
using
the
following
steps
and
guidelines.

1.
Calculate
Species­
Mean
BAFs.
For
each
BAF
method
where
more
than
one
acceptable
field­
measured
BAF
or
BAF
predicted
using
a
BCF
and
FCM
is
available,
calculate
a
species­
mean
BAF
according
to
the
guidance
described
previously
in
Procedure
#
5.

2.
Calculate
Trophic
Level­
Mean
BAFs.
For
each
BAF
method
where
more
than
one
acceptable
species­
mean
BAF
is
available
within
a
given
trophic
level,
calculate
the
trophic
level­
mean
BAF
according
to
guidance
described
previously
in
Procedure
#
5.

3.
Select
a
Final
National
BAF
for
Each
Trophic
Level.
For
each
trophic
level,
select
the
final
national
BAF
using
best
professional
judgment
by
considering:
(
1)
the
data
preference
hierarchy
in
Procedure
#
6,
and
(
2)
the
relative
uncertainties
among
trophic
level­
mean
BAFs
derived
using
different
methods.

a.
When
a
trophic­
level
mean
BAF
is
available
using
both
methods
for
a
given
trophic
level
(
i.
e.,
a
field­
measured
BAF
and
a
BAF
predicted
from
a
BCF
and
FCM),
the
national
BAF
should
usually
be
selected
using
the
field­
measured
BAF
which
is
the
preferred
BAF
method
in
the
data
preference
hierarchy
in
Procedure
#
6.

b.
If
uncertainty
in
the
trophic­
level
mean
BAF
derived
using
field­
measured
BAFs
is
considered
to
be
substantially
greater
than
a
trophic­
level
mean
BAF
derived
using
a
BCF
and
FCM,
the
national
BAF
for
that
trophic
level
should
be
selected
from
the
second
tier
(
BCF
@
FCM)
method.

c.
The
above
steps
should
be
performed
for
each
trophic
level
until
a
national
BAF
is
selected
for
trophic
levels
two,
three,
and
four.

5.7
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