United
States
EPA
822­
R­
03­
026
Environmental
Protection
November
2003
Agency
2003
UPDATE
OF
AMBIENT
WATER
QUALITY
CRITERIA
FOR
COPPER
2003
UPDATE
OF
AMBIENT
WATER
QUALITY
CRITERIA
FOR
COPPER
(
CAS
Registry
Number
7440­
50­
8)

November
2003
U.
S.
Environmental
Protection
Agency
Office
of
Water
Office
of
Science
and
Technology
Washington,
DC
ii
NOTICES
This
document
has
been
reviewed
by
the
Health
and
Ecological
Criteria
Division,
Office
of
Science
and
Technology,
U.
S.
Environmental
Protection
Agency,
and
approved
for
publication.
iii
ACKNOWLEDGMENTS
Document
Update:
2003
Cindy
Roberts
(
document
coordinator
and
contributor)
U.
S.
EPA
Health
and
Ecological
Effects
Criteria
Division
Washington,
DC
Mary
Reiley
(
contributor)
U.
S.
EPA
Health
and
Ecological
Effects
Criteria
Division
Washington,
DC
Robert
Santore
(
contributor)
HydroQual,
Inc.
Syracuse,
New
York
Paul
Paquin
(
contributor)
HydroQual,
Inc.
Syracuse,
New
York
Gary
Chapman
(
contributor)
Great
Lakes
Environmental
Center
Columbus,
Ohio
Jennifer
Mitchell
(
contributor)
U.
S.
EPA
(
formerly)
Health
and
Ecological
Effects
Criteria
Division
Washington,
DC
Charles
Delos
(
contributor)
U.
S.
EPA
Health
and
Ecological
Effects
Criteria
Division
Washington,
DC
Joseph
Meyer
(
contributor)
University
of
Wyoming
Laramie,
Wyoming
Rooni
Mathew
(
contributor)
HydroQual,
Inc.
Syracuse,
New
York
Tyler
K.
Linton
(
contributor)
Great
Lakes
Environmental
Center
Columbus,
Ohio
Statistical
Support
and
Contributor:

Russell
Erickson
Office
of
Research
and
Development
Environmental
Research
Laboratory
Duluth,
Minnesota
iv
CONTENTS
Notices
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ii
Acknowledgments
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Acronyms
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1.0
INTRODUCTION
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1
1.1
Background
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1
1.2
Copper
in
the
Environment
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1
1.3
Update
of
Copper
Criteria
with
the
Biotic
Ligand
Model
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1
1.4
Copper
Criteria
Document
Information
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2
2.0
THE
CONCEPT
OF
BIOAVAILABILITY
AND
REGULATORY
APPROACHES
FOR
COPPER
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2
2.1
Empirical
Models
Relating
Water
Chemistry
to
Toxicity
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4
2.2
Mechanistic
Models
 
Relating
Water
Chemistry
to
Toxicity
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5
3.0
INCORPORATION
OF
BLM
INTO
CRITERIA
DEVELOPMENT
PROCEDURES
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7
3.1
Implications
for
Criteria
 
Criteria
Calculations
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7
3.2
BLM
Input
Parameters
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7
3.3
Model
Prediction
Modes
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8
3.4
Data
Acceptability
and
Screening
Procedures
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8
3.5
Estimation
of
Test
Water
Chemistry
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9
3.6
Water
Chemistry
Data
Acquisition
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9
3.7
Ranking
of
Quality
of
Test
Chemistry
Characterization
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9
3.8
Criteria
Computations
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10
4.0
CONVERSION
FACTORS
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11
5.0
DATA
SUMMARY
AND
CRITERIA
CALCULATION
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11
5.1
Summary
of
Acute
Toxicity
to
Freshwater
Animals
and
Criteria
Calculation
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11
5.1.1
Comparison
with
Hardness­
Adjusted
Values
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15
5.2
Summary
of
Acute
Toxicity
to
Saltwater
Animals
and
Criteria
Calculation
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16
5.3
Formulation
of
the
CCC
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17
5.3.1
Statistical
Evaluation
of
Chronic
Toxicity
Data
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17
5.3.2
Calculation
of
Freshwater
CCC
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19
5.3.3
Evaluation
of
the
Chronic
Data
Available
for
Saltwater
Species
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21
6.0
PLANT
DATA
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21
7.0
BIOACCUMULATION
OF
COPPER
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23
8.0
OTHER
DATA
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23
9.0
NATIONAL
CRITERIA
STATEMENT
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24
10.0
IMPLEMENTATION
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24
11.0
REFERENCES
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58
v
FIGURES
Figure
1.
Conceptual
Diagram
of
Copper
Speciation
and
Copper­
Gill
Model
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5
Figure
2.
Comparison
of
Predicted
and
Measured
Acute
Copper
Toxicity
to
P.
promelas
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6
Figure
3.
Quality
Scale
for
D.
magna
BLM
Input
Data
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12
Figure
4.
Ranges
and
Distribution
of
Normalized
LC50
Values
for
Species
Listed
in
Table
1
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13
Figure
5.
Ranked
Freshwater
Genus
Mean
Acute
Values
(
GMAVs)
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14
Figure
6.
Comparison
of
Existing
Hardness
Based
WQC
and
BLM
Based
WQC
in
Synthetic
Laboratory
Water
and
EPA
Standard
Recipe
Water
for
DOC
=
2.3
mg/
L
.
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15
Figure
7.
Ranked
Saltwater
Genus
Mean
Acute
Values
(
GMAVs)
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18
Figure
8.
Relationship
Between
Freshwater
Acute
Copper
Sensitivity
(
LC50
or
EC50)
and
Acute­
Chronic
Ratios
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20
TABLES
Table
1a.
Acute
Toxicity
of
Copper
to
Freshwater
Animals
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26
Table
1b.
Acute
Toxicity
of
Copper
to
Saltwater
Animals
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.
36
Table
2a.
Chronic
Toxicity
of
Copper
to
Freshwater
Animals
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42
Table
2b.
Chronic
Toxicity
of
Copper
to
Saltwater
Animals
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44
Table
2c.
Acute­
Chronic
Ratios
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45
Table
3a.
Ranked
Freshwater
Genus
Mean
Acute
Values
with
Species
Mean
Acute­
Chronic
Ratios
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46
Table
3b.
Ranked
Saltwater
Genus
Mean
Acute
Values
with
Species
Mean
Acute­
Chronic
Ratios
.
.
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.
.
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.
.
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.
.
.
.
.
.
.
47
Table
3c.
Freshwater
and
Saltwater
Final
Acute
Value
(
FAV)
and
Criteria
Calculations
.
.
.
.
.
.
.
.
.
.
.
.
49
Table
4a.
Toxicity
of
Copper
to
Freshwater
Plants
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
50
Table
4b.
Toxicity
of
Copper
to
Saltwater
Plants
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
53
Table
5a.
Bioaccumulation
of
Copper
by
Freshwater
Organisms
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
55
Table
5b.
Bioaccumulation
of
Copper
by
Saltwater
Organisms
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
56
Table
6.
Species
Numbers
Used
in
Figure
4
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
57
APPENDICES
Appendix
A.
Ranges
in
Calibration
and
Application
Data
Sets
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
A­
1
Appendix
B.
Biotic
Ligand
Model
(
BLM)
User's
Guide
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
B­
1
Appendix
C.
Other
Data
on
Effects
of
Copper
on
Freshwater
and
Saltwater
Organisms
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
C­
1
Appendix
D.
Estimation
of
Water
Chemistry
Parameters
for
Acute
Copper
Toxicity
Tests
.
.
.
.
.
.
.
.
.
D­
1
Appendix
E.
Saltwater
Conversion
Factors
for
Dissolved
Values
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
E­
1
Appendix
F.
BLM
Input
Data
and
Notes
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
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.
.
.
.
.
.
.
F­
1
Appendix
G.
Hardness
Slopes
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
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.
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.
.
.
.
.
.
.
.
G­
1
Appendix
H.
Regression
Plots
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
.
.
.
.
H­
1
Appendix
I.
Unused
Data
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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.
.
.
I­
1
vi
ACRONYMS
ACR
Acute­
Chronic
Ratio
BL
Biotic
Ligand
BLM
Biotic
Ligand
Model
CCC
Criterion
Continuous
Concentration
CF
Conversion
Factors
CHESS
Chemical
Equilibria
in
Soils
and
Solutions
CMC
Criterion
Maximum
Concentration
CWA
Clean
Water
Act
DIC
Dissolved
Inorganic
Carbon
DOC
Dissolved
Organic
Carbon
DOM
Dissolved
Organic
Matter
ELS
Early
Life
Stage
EPA
Environmental
Protection
Agency
FACR
Final
Acute­
Chronic
Ratio
FAV
Final
Acute
Value
OR
Final
Accumulation
Value
FCV
Final
Chronic
Value
FIAM
Free
Ion
Activity
Model
GMAV
Genus
Mean
Acute
Value
GSIM
Gill
Surface
Interaction
Model
HA
Humic
Acid
LA50
Lethal
Level
of
Accumulation
at
50
Percent
Effect
Level
LOAEC
Lowest
Observed
Adverse
Effect
Concentration
Me:
BL
Metal­
Biotic
Ligand
Complex
MSE
Mean
Square
Error
NASQAN
National
Stream
Quality
Accounting
Network
NOAEC
No
Observed
Adverse
Effect
Concentration
NOM
Natural
Organic
Matter
PLC
Partial
Life­
Cycle
SMAV
Species
Mean
Acute
Values
TSS
Total
Suspended
Solids
WER
Water­
Effect
Ratio
WET
Whole
Effluent
Toxicity
WHAM
Windermere
Humic
Aqueous
Model
WQC
Water
Quality
Criteria
1
1.0
INTRODUCTION
1.1
Background
Over
the
past
20
years
the
U.
S.
Environmental
Protection
Agency
(
EPA)
has
published
a
number
of
guidance
documents
containing
aquatic
life
criteria
recommendations
for
copper
(
e.
g.,
U.
S.
EPA
1980,
1985,
1986,
1996).
The
present
document
contains
EPA's
latest
criteria
recommendations
for
protection
of
aquatic
life
in
ambient
water
from
acute
and
chronic
toxic
effects
from
copper.
These
criteria
are
based
on
the
latest
available
scientific
information
and
supersede
EPA's
previously
published
recommendations
for
copper.

This
document
provides
updated
guidance
to
States
and
authorized
Tribes
to
establish
water
quality
standards
under
the
Clean
Water
Act
(
CWA)
to
protect
aquatic
life
from
copper.
Under
the
CWA,
States
and
authorized
Tribes
are
to
establish
water
quality
criteria
to
protect
designated
uses.
Although
this
document
constitutes
EPA's
scientific
recommendations
regarding
ambient
concentrations
of
copper,
it
does
not
substitute
for
the
CWA
or
EPA's
regulations,
nor
is
it
a
regulation
itself.
Thus,
it
cannot
impose
legally
binding
requirements
on
EPA,
States,
Tribes,
or
the
regulated
community,
and
might
not
apply
to
a
particular
situation
based
on
the
circumstances.
State
and
Tribal
decisionmakers
retain
the
discretion
in
adopting
approaches,
on
a
case­
by­
case
basis,
that
differ
from
this
guidance
when
appropriate.
EPA
may
change
this
guidance
in
the
future.

1.2
Copper
in
the
Environment
Copper
is
an
abundant
trace
element
found
in
the
earth's
crust
and
is
also
a
naturally
occurring
element
that
is
generally
present
in
surface
waters
(
Nriagu
1979).
Copper
is
a
micronutrient
for
both
plants
and
animals
at
low
concentrations;
however,
it
may
become
toxic
to
some
forms
of
aquatic
life
at
elevated
concentrations.
Thus,
copper
concentrations
in
natural
environments,
and
its
biological
availability,
are
important.
Naturally
occurring
concentrations
of
copper
have
been
reported
from
0.03
to
0.23

g/
L
in
surface
seawaters
and
from
0.2
to
30

g/
L
in
freshwater
systems
(
Bowen
1985).
Copper
concentrations
in
locations
receiving
anthropogenic
inputs
such
as
mine
tailing
discharges
can
vary
anywhere
from
natural
background
to
100

g/
L
(
Hem
1989;
Lopez
and
Lee
1977)
and
have
in
some
cases
been
reported
in
the
200,000

g/
L
range
in
mining
areas
(
Davis
and
Ashenberg
1989;
Robins
et
al.
1997).
Mining,
leather
and
leather
products,
fabricated
metal
products,
and
electric
equipment
are
a
few
of
the
industries
with
copperbearing
discharges
that
contribute
to
anthropogenic
inputs
of
copper
to
surface
waters
(
Patterson
et
al.
1998).

1.3
Update
of
Copper
Criteria
with
the
Biotic
Ligand
Model
The
freshwater
criteria
in
this
document
differ
from
EPA's
previous
metals
criteria
primarily
with
regard
to
how
metal
availability
to
organisms
is
addressed.
Previous
criteria
were
based
on
empirical
relationships
of
toxicity
to
water
hardness.
These
criteria
combine
the
effects
of
various
water
quality
variables
correlated
with
hardness.
Such
criteria
are
most
applicable
to
waters
where
these
correlations
were
similar
to
the
data
set
used
to
derive
the
relationships.
The
criteria
presented
here
instead
use
the
biotic
ligand
model
(
BLM)
(
Di
Toro
et
al.
2001).
The
BLM
is
based
on
the
premise
that
toxicity
is
related
to
metal
bound
to
a
biochemical
site
(
the
biotic
ligand)
and
that
binding
is
related
to
total
dissolved
metal
concentrations
and
complexing
ligands
in
the
water.
The
complexing
ligands
compete
with
the
biotic
ligand
for
metals
and
other
cations
in
the
water.
Unlike
the
empirical
harness
relationships,
the
BLM
explicitly
accounts
for
individual
water
quality
variables,
is
not
linked
to
a
particular
correlation
among
these
variables,
and
can
address
variables
that
were
not
a
factor
in
the
hardness
relationship.
2
1.4
Copper
Criteria
Document
Information
Although
the
new
BLM
model
has
now
been
adopted
for
use
in
place
of
the
formerly
applied
hardness­
based
approach
the
updated
freshwater
criteria
derivations
in
this
document
are
still
based
on
the
principles
set
forth
in
the
1985
Guidelines
(
or
Guidelines,
Stephan
et
al.
1985).
Therefore,
it
is
useful
to
have
some
understanding
of
how
the
Guidelines
are
ordinarily
applied:
(
1)
Acute
toxicity
test
data
must
be
available
for
species
from
a
minimum
of
eight
genera
with
a
minimum
required
taxonomic
diversity.
The
diversity
of
tested
species
is
intended
to
ensure
protection
of
various
components
of
an
aquatic
ecosystem.
(
2)
The
final
acute
value
(
FAV)
is
an
estimate
of
the
fifth
percentile
of
a
sensitivity
distribution
represented
by
the
average
LC50s
and
EC50s,
the
Genus
Mean
Acute
Values
(
GMAVs),
of
the
tested
genera.
The
criterion
maximum
concentration
(
CMC)
is
set
to
one­
half
of
the
FAV
to
correspond
to
a
lower
level
of
effect
than
the
LC50s/
EC50s
used
to
derive
the
FAV.
(
3)
Chronic
toxicity
test
data
(
longer
term
survival,
growth,
or
reproduction)
must
be
available
for
at
least
three
taxa
to
derive
a
final
chronic
value
(
FCV).
A
criterion
continuous
concentration
(
CCC)
can
be
established
from
an
FCV
calculated
similarly
to
an
FAV,
if
chronic
toxicity
data
are
available
for
eight
genera
with
a
minimum
required
taxonomic
diversity;
or
most
often
the
chronic
criterion
is
set
by
determining
an
appropriate
acute­
chronic
ratio
(
ACR)
(
the
ratio
of
acutely
toxic
concentrations
to
the
chronically
toxic
concentrations)
and
applying
that
ratio
to
the
FAV.
(
4)
When
necessary,
the
acute
and/
or
chronic
criterion
may
be
lowered
to
protect
recreationally
or
commercially
important
species.

The
body
of
this
document
contains
information
on
acute
and
chronic
toxicity
of
copper
relevant
to
the
derivation
of
the
freshwater
and
saltwater
acute
and
chronic
criteria.
It
also
includes
information
on
the
effects
of
water
quality
parameters
on
bioavailability
and
toxicity
of
copper
as
well
as
some
BLM
development
information.
Additional
information
on
the
generalized
BLM
framework,
theoretical
background,
model
calibration,
and
application
can
be
found
in
the
Technical
Support
Document
for
the
BLM
or
in
the
published
literature.
The
data
that
were
reviewed
and
not
used
to
derive
the
criteria
and
other
supporting
information
are
also
provided
in
tables
and
appendices.

2.0
THE
CONCEPT
OF
BIOAVAILABILITY
AND
REGULATORY
APPROACHES
FOR
COPPER
Copper
occurs
in
natural
waters
primarily
as
Cu
(
II)
predominately
in
complexed
form.
Free
Cu
may
be
present,
but
is
generally
a
minor
species
(
Stumm
and
Morgan
1981).
Copper
reacts
with
both
inorganic
and
organic
chemicals
in
solution
and
in
suspension,
resulting
in
a
multitude
of
chemical
forms.
Because
the
cupric
ion
is
highly
reactive,
it
forms
moderate
to
strongly
complexed
solutes
and
precipitates
with
many
inorganic
and
organic
constituents
of
natural
waters
(
e.
g.,
carbonate,
phosphate,
and
organic
materials)
and
is
readily
sorbed
onto
surfaces
of
suspended
solids.
Even
though
it
is
present
in
water
in
many
forms,
the
toxicity
of
copper
to
aquatic
life
has
been
shown
to
be
related
primarily
to
activity
of
the
cupric
ion,
and
possibly
to
some
of
the
hydroxy
complexes
(
Allen
and
Hansen
1996;
Andrew
1976;
Andrew
et
al.
1977;
Borgmann
and
Ralph
1983;
Chakoumakos
et
al.
1979;
Chapman
and
McCrady
1977;
Dodge
and
Theis
1979;
Howarth
and
Sprague
1978;
Pagenkopf
1983;
Petersen
1982;
Rueter
1983).
Many
examples
of
this
classic
response
of
organisms
to
cupric
ion
activity,
as
well
as
some
limited
exceptions,
are
reviewed
by
Campbell
(
1995).
A
formal
description
of
these
metal­
organism
interactions,
now
commonly
referred
to
as
the
Free
Ion
Activity
Model
(
FIAM),
was
first
provided
by
Morel
(
1983).
Pagenkopf
(
1983)
using
a
similar
approach
applied
the
Gill
Surface
Interaction
Model
(
GSIM)
to
predict
metal
effect
levels
over
a
range
of
water
quality
characteristics.

Based
on
the
mechanistic
principles
underlying
the
BLM,
the
following
general
trends
of
copper
toxicity
are
expected
because
individual
water
quality
parameters
and
their
combinations
are
varied
among
exposure
waters.
Any
changes
in
water
quality
that
would
be
expected
to
decrease
the
activity
of
the
free
3
copper
ion
would
be
expected
to
decrease
the
bioavailability
of
copper.
For
example,
increases
in
pH,
increases
in
alkalinity,
and
increases
in
natural
organic
matter
would
all
tend
to
decrease
copper
bioavailability
and
would
therefore
tend
to
be
associated
with
increased
copper
LC50
values.
Metal
bioavailability
may
also
be
modified
by
competitive
interactions
at
the
biotic
ligand.
Increased
concentrations
of
sodium
and
calcium,
for
example,
can
result
in
reduced
binding
of
copper
to
physiologically
active
gill
binding
sites
and
can
thereby
reduce
copper
bioavailability.
Competition
with
protons
is
included
in
the
copper
model
and
could
result
in
lower
bioavailability
at
low
pH.
But
these
effects
occur
at
relatively
lower
pH
values
than
are
typically
used
in
toxicity
tests
and,
as
a
result,
the
primary
effect
of
changing
pH
is
to
decrease
bioavailability
at
high
pH.
Cation
competition
also
has
an
effect
on
complexation
of
Cu
by
natural
organic
matter
(
NOM),
and
this
interaction
will
to
some
degree
offset
competitive
interactions
that
occur
at
the
gill
or
other
sites
of
action
of
toxicity.

Historically,
aqueous
discharges
of
metals
have
been
regulated
based
on
concentrations
of
total
metal
 
usually
measured
as
the
concentration
of
total
recoverable
metal
(
i.
e.,
the
sum
of
the
dissolved
metal
and
the
metal
that
can
be
liberated
from
solids
during
extraction
in
hot,
dilute
mineral
acid).
This
regulatory
approach
was
the
basis
for
previous
EPA
water
quality
criteria
for
copper.
In
1993,
EPA
altered
the
traditional
regulatory
approach
for
protection
of
aquatic
life
to
account
for
the
influence
of
suspended
solids
on
metal
toxicity.
EPA
authorized
States
to
regulate
discharges
based
on
dissolved
metal
concentration
instead
of
total
recoverable
metal
concentration
(
Prothro
1993).
This
change
was
an
attempt
to
incorporate
into
the
regulatory
process
the
notion
that
the
concentration
of
dissolved
metal
better
approximates
the
toxic
fraction
than
does
the
concentration
of
total
metal
(
i.
e.,
the
presence
of
suspended
solids
tends
to
decrease
metal
toxicity;
see
review
by
Meyer
et
al.
2002).
Nevertheless,
a
regulatory
approach
based
solely
on
the
concentration
of
dissolved
metal
did
not
address
concerns
that
other
water
quality
parameters
besides
total
suspended
solids
(
TSS)
concentration
alter
metal
toxicity.

EPA
has
already
incorporated
linear
regression
equations
into
criteria
calculation
procedures
to
account
for
decreases
of
acute
and
chronic
toxicity
of
copper
to
freshwater
organisms
as
water
hardness
increases.
However,
these
regression
equations
account
for
other
parameters
that
vary
in
addition
to
hardness
(
at
least
among
some
of
the
data)
but
do
not
explicitly
account
for
effects
of
these
other
water
quality
parameters
on
toxicity.

In
response
to
concerns
that
the
metal
criteria
did
not
provide
a
mechanism
to
account
for
the
modifying
effects
of
water
quality
parameters
other
than
hardness
on
metal
toxicity,
EPA
issued
guidance
in
the
early
1980s
on
the
use
of
a
water­
effect
ratio
(
WER)
method
(
Carlson
et
al.
1984;
U.
S.
EPA
1983,
1992,
1994).
The
WER
is
"
a
biological
method
to
compare
bioavailability
and
toxicity
in
receiving
waters
versus
laboratory
test
waters"
(
U.
S.
EPA
1992).
Extensive
guidance
has
been
developed
on
how
to
evaluate
a
WER
(
U.
S.
EPA
1994).
The
essence
of
the
approach
is
as
follows.
The
WER
is
calculated
by
dividing
the
acute
LC50
of
the
metal,
determined
in
water
collected
from
the
receiving
water
of
interest,
by
the
LC50
of
the
metal
determined
in
a
standard
laboratory
water,
after
adjusting
both
test
waters
to
the
same
hardness.
The
national
hardness­
based
acute
criterion
concentration
is
then
multiplied
by
this
ratio
(
i.
e.,
the
WER)
to
establish
a
site­
specific
criterion
that
reflects
the
effect
of
site
water
characteristics
on
toxicity.

However,
a
WER
accounts
only
for
interactions
of
water
quality
parameters
and
their
effects
on
metal
toxicity
to
the
species
tested,
in
the
water
sample
collected
at
a
specific
location
and
at
a
specific
time.
Although
the
WER
approach
remains
an
important
component
in
establishing
site­
specific
variations
to
ambient
water
quality
criteria
for
metals,
a
complementary
approach
is
needed
that
(
1)
explicitly
accounts
for
water
quality
parameters
that
modify
metal
toxicity
and
(
2)
can
be
applied
more
frequently
across
spatial
and
temporal
scales.
4
Because
of
the
influence
of
water
quality
parameters
such
as
pH,
alkalinity,
and
organic
matter
on
the
formation
of
compounds
that
affect
the
amount
of
cupric
ion
present,
not
all
of
the
copper
in
the
water
column
contributes
directly
to
toxicity.
In
other
words,
not
all
of
the
copper
appears
to
be
bioavailable.
Although
the
term
"
bioavailability"
eludes
a
consensus
definition
(
Dickson
et
al.
1994),
in
the
context
of
this
document
it
is
used
to
convey
the
general
concept
that
total
Cu
(
or,
more
generally,
the
total
concentration
of
any
metal
in
an
exposure
water)
is
not
a
good
predictor
of
toxicity
(
Campbell
1995;
Meyer
2002;
Morel
1983).
This
concept
has
led
to
research
and
regulatory
activity
to
develop
better
ways
to
predict
metal
toxicity
and
regulate
aqueous
discharges
(
Bergman
and
Dorward­
King
1997;
Di
Toro
et
al.
2001;
Hamelink
et
al.
1994;
Morel
1983).

2.1
Empirical
Models
Relating
Water
Chemistry
to
Toxicity
Early
copper
criteria
documents
(
U.
S.
EPA
1980,
1985,
1996)
incorporated
linear
regression
equations
into
the
criterion­
calculation
procedure
to
account
for
attenuation
of
acute
and
chronic
toxicity
of
copper
to
freshwater
biota
as
water
hardness
increases.
Previously
though,
the
only
parameter
with
enough
useful
data
to
provide
an
acceptable
predictive
capability
of
copper
toxicity
was
hardness.
Temperature
ranges
were
not
sufficiently
wide
with
most
species,
pH
values
were
often
not
reported
or
were
highly
variable,
and
alkalinity
and
dissolved
organic
carbon
(
DOC)
were
rarely
reported.
As
a
result,
criteria
for
copper,
and
those
for
several
other
metals,
were
established
as
functions
of
water
hardness.
These
equations
were
determined
from
meta­
analyses
in
which
variables
other
than
hardness
varied
among
at
least
some
of
the
data
sets
that
were
used.
Therefore,
the
regression
coefficients
for
hardness
did
not
only
reflect
how
hardness
affected
copper
toxicity;
additionally,
hardness
was
a
surrogate
for
other
co­
varying
water
quality
parameters
not
explicitly
included
in
the
regression
analyses.
Moreover,
these
criteria
did
not
include
methods
to
explicitly
account
for
modifying
effects
of
other
water
quality
parameters
when
those
parameters
varied
and
hardness
did
not.

An
alternate
approach
that
has
been
proposed
to
predict
metal
toxicity
is
to
(
1)
identify
the
bioavailable
fraction
of
the
metal;
(
2)
analyze
or
calculate
the
concentration(
s)
of
the
bioavailable
form(
s)
in
the
exposure
water;
and
(
3)
predict
the
toxicity
based
on
an
empirical
relationship
between
the
biological
response
and
the
concentration(
s)
of
the
bioavailable
form(
s).
According
to
this
approach,
only
direct
measurement
of
the
concentration
of
the
free
metal
ion
or
calculation
of
its
concentration
(
using
a
geochemical­
speciation
model)
is
needed.
Supporting
this
bioavailable­
fraction
approach,
the
concentration
of
cupric
ion
is
a
constant
predictor
of
acute
toxicity
even
in
the
presence
of
varying
levels
of
inorganic
or
organic
ligands,
which
complex
copper
and
alter
the
cupric
ion
concentration
(
i.
e.,
the
cupric
ion
LC50
remains
constant
even
though
the
concentrations
of
the
ligands
differ
considerably
in
different
exposure
waters)
(
e.
g.,
Borgmann
1983;
Santore
et
al.
2001).
However,
this
approach
is
not
correct
when
other
cations
in
the
water
can
interact
with
the
biota.
For
example,
the
LC50
of
Cu2+
increases
significantly
as
the
concentration
of
Ca2+
(
a
major
component
of
water
hardness)
is
increased
(
Meyer
et
al.
1999).
Thus,
the
concentration
of
cupric
ion
alone
is
not
always
sufficient
to
predict
toxicity.

More
generally,
there
is
no
universally
constant
bioavailable
fraction
of
a
metal
that
can
be
identified
by
chemical
analyses
(
Meyer
et
al.
2002).
The
interactions
among
the
abiotic
components
in
the
exposure
water
are
important
to
consider,
as
well
as
the
interactions
of
those
components
with
the
biota.
Hence,
although
the
simple
concept
of
predicting
metal
toxicity
based
on
the
chemical
analysis
of
a
bioavailable
fraction
is
qualitatively
appealing,
in
practice,
it
is
quantitatively
elusive
(
Meyer
2002).
Instead,
the
complex
interactions
of
Cu2+
with
dissolved
components,
suspended
particles,
and
the
biota
must
be
simultaneously
considered
in
order
to
accurately
predict
copper
toxicity
(
see
Mechanistic
Models
section).
5
Figure
1.
Conceptual
Diagram
of
Copper
Speciation
and
Copper­
Gill
Model
(
after
Pagenkopf
1983)
2.2
Mechanistic
Models
 
Relating
Water
Chemistry
to
Toxicity
Although
the
current
water
quality
criteria
for
several
metals,
including
copper,
are
hardnessdependent
it
has
long
been
recognized
that
many
other
factors
affect
copper
toxicity.
The
chemical
speciation
of
copper
in
natural
waters
and
the
explanatory
power
of
the
free
copper
ion
in
determining
copper
toxicity
were
first
recognized
more
than
30
years
ago
(
Anderson
and
Morel
1978;
Sunda
and
Gillespie
1979;
Sunda
and
Guillard
1976;
Sunda
and
Lewis
1978;
Zitko
et
al.
1973).
These
concepts
were
eventually
formalized
in
models
that
linked
metal
chemistry
and
biological
effects
including
the
gill
surface
interaction
model
(
GSIM)
(
Pagenkopf
1983)
and
the
free
ion
activity
model
(
FIAM)
(
Morel
1983).
Playle
and
others
demonstrated
that
copper
binding
to
fish
gills
can
be
modeled
using
a
chemical
speciation
approach
(
Playle
et
al.
1993a,
b).
Recently,
MacRae
and
others
demonstrated
that
copper
accumulation
at
the
gill
shows
a
dose­
response
relationship
with
mortality
(
MacRae
et
al.
1999).
A
more
comprehensive
review
of
these
historical
developments
is
presented
in
Paquin
et
al.
(
2002).

Although
early
models
showed
remarkable
utility,
several
critical
issues
remained.
A
considerable
amount
of
information
about
speciation
of
metals
in
the
environment
has
become
available
and
computing
techniques
have
been
developed
to
simulate
metal
speciation
(
Nordstrom
et
al.
1979).
Still,
the
interactions
of
metals
with
natural
organic
matter
remained
a
topic
of
intense
research
and
debate
for
the
next
few
decades.
Until
recently,
few
available
models
could
predict
metal
chemistry
in
the
presence
of
natural
organic
matter
over
a
range
of
environmental
conditions.

The
biotic
ligand
model
is
a
recent
attempt
to
develop
a
metal
bioavailability
model
based
on
the
latest
chemical
and
physiological
effects
information
of
metals
in
aquatic
environments
(
Di
Toro
et
al.
2001;
Paquin
et
al.
1999;
Santore
et
al.
2001).
The
approach
was
presented
to
EPA's
Science
Advisory
Board
during
1999
and
it
received
a
generally
favorable
response
(
U.
S.
EPA
1999,
2000).
Like
the
FIAM
and
GSIM,
the
BLM
is
based
on
a
description
of
the
chemical
speciation
of
metals
in
aqueous
systems
(
Figure
1).
Chemical
speciation
is
simulated
as
an
equilibrium
system
that
includes
complexation
of
inorganic
ions
and
NOM.
The
chemical
system
is
simulated
by
the
chemical
equilibria
in
soils
and
solutions
(
CHESS)
model
(
Santore
and
Driscoll
1995),
including
a
description
of
metal
interactions
with
NOM
based
on
the
Windermere
humic
aqueous
model
(
WHAM)
(
Tipping
1994).
A
significant
advantage
6
Figure
2.
Comparison
of
Predicted
and
Measured
Acute
Copper
Toxicity
to
P.
promelas
of
the
NOM
chemistry
developed
for
WHAM
is
that
reactions
and
parameter
values
were
developed
by
simultaneously
considering
numerous
NOM
samples
and
numerous
metals.

The
BLM
also
includes
reactions
that
describe
the
chemical
interactions
of
copper
and
other
cations
to
physiologically
active
sites
(
or
"
biotic
ligands")
that
correspond
to
the
proximate
site
of
action
of
toxicity.
The
model
parameters
define
the
degree
of
interaction
based
on
binding
affinity
characteristics
measured
in
gill­
loading
experiments
(
Playle
et
al.
1993a,
b).
That
is,
the
biotic
ligand
(
BL)
is
represented
by
a
characteristic
binding
site
density
and
conditional
stability
constant
for
each
of
the
dissolved
chemical
species
with
which
it
reacts.
Predictions
of
metal
toxicity
are
made
by
assuming
that
the
dissolved
metal
LC50,
which
varies
with
water
chemistry,
is
always
associated
with
a
fixed
critical
level
of
metal
accumulation
at
the
biotic
ligand.
This
fixed
level
of
accumulation
at
50
percent
mortality,
referred
to
as
the
LA50,
is
the
concentration
of
the
metal­
biotic
ligand
complex
(
Me:
BL)
that
is
associated
with
50
percent
mortality
for
a
fixed
exposure.
It
is
assumed
to
be
constant,
regardless
of
the
chemical
characteristics
of
the
water
(
Meyer
et
al.
1999,
2002).
This
combination
of
reactions
that
describe
aqueous
metal
speciation
and
organism
interactions
allows
the
BLM
to
predict
copper
toxicity
to
a
variety
of
organisms
over
a
variety
of
water
quality
conditions
(
Santore
et
al.
2001).
Appendix
A
describes
the
range
of
water
quality
values
and
species
to
which
the
model
has
been
applied.

A
significant
advantage
of
the
BLM
is
that
most
of
the
parameters
are
invariant
for
different
organisms,
despite
the
complexity
of
the
modeling
framework.
All
of
the
thermodynamic
constants
used
to
simulate
inorganic
and
organic
chemical
equilibrium
reactions
are
determined
by
characteristics
of
the
metal
and
the
available
ligands.
As
such,
the
constants
do
not
change
for
simulations
involving
different
organisms.
Binding
constants
for
copper
and
other
cations
to
the
biotic
ligand
were
developed
from
data
reported
by
Playle
and
others
using
fathead
minnow
(
Playle
et
al.
1993a,
b).
Similar
measurements
would
be
difficult
or
impossible
to
obtain
for
many
organisms,
especially
invertebrates,
because
of
the
difficulty
associated
with
isolating
and
excising
gill
tissue,
or
an
appropriate
analog.
Nevertheless,
the
parameter
values
developed
from
fathead
minnow
measurements
appear
to
work
adequately
for
other
organisms
(
Santore
et
al.
2001).
Figure
2
shows
the
predictive
capabilities
of
the
model
with
fathead
minnows.
7
3.0
INCORPORATION
OF
BLM
INTO
CRITERIA
DEVELOPMENT
PROCEDURES
3.1
Implications
for
Criteria
 
Criteria
Calculations
The
use
of
the
BLM
to
predict
the
bioavailability
and
toxicity
of
copper
to
aquatic
organisms
under
site­
specific
conditions
is
a
significant
change
from
the
previous
CMC
derivation
methodology.
Previous
aquatic
life
criteria
documents
for
copper
(
e.
g.,
U.
S.
EPA
1980,
1985,
1996)
expressed
the
CMC
as
a
function
of
water
hardness.
Now,
EPA
chooses
to
utilize
the
BLM
to
update
its
freshwater
acute
criterion
because
the
BLM
accounts
for
all
important
inorganic
and
organic
ligand
interactions
of
copper
while
also
considering
competitive
interactions
that
influence
binding
of
copper
at
the
site
of
toxicity,
or
the
"
biotic
ligand."
The
BLM's
ability
to
incorporate
metal
speciation
reactions
and
organism
interactions
allows
prediction
of
metal
effect
levels
to
a
variety
of
organisms
over
a
wide
range
of
water
quality
conditions.
Accordingly,
the
BLM
is
an
attractive
tool
for
deriving
water
quality
criteria.
Application
of
the
BLM
may
reduce,
if
not
eliminate,
the
need
for
site­
specific
modifications,
such
as
Water
Effect
Ratios,
to
account
for
site­
specific
chemistry
influences
on
metal
toxicity.

While
the
BLM
is
currently
considered
appropriate
for
use
to
derive
an
updated
freshwater
CMC,
further
development
is
required
before
it
will
be
suitable
for
use
to
evaluate
a
saltwater
CMC
or
a
CCC
or
chronic
value.

3.2
BLM
Input
Parameters
For
copper
simulations,
the
necessary
water
quality
input
parameters
are:
pH;
dissolved
organic
carbon
(
DOC)
(
in
mg/
L);
percent
humic
acid;
temperature;
major
cations
(
Ca+,
Mg+,
Na+,
and
K+);
major
anions
(
SO4
­,
Cl­);
dissolved
inorganic
carbon
(
DIC);
and
sulfide.

Dissolved
cations
compete
with
Cu2+
for
dissolved
organic
matter
(
DOM)
binding
sites.
For
example,
pH
is
important
in
determining
the
metal
complexation
capacity
of
dissolved
organic
matter
(
DOM).
It
also
is
important
in
determining
speciation
of
inorganic
carbon,
which
relates
to
formation
of
metal
carbonate
complexes.
DOM
can
likewise
play
a
critical
role
in
determining
metal
speciation
and
bioavailability.
Its
concentration
is
entered
into
the
BLM
in
terms
of
the
concentration
of
DOC.
Because
the
representation
of
metal­
NOM
complexes
in
the
BLM
adopted
from
WHAM,
characterizes
metal
complexation
with
both
humic
and
fulvic
organic
matter,
it
is
necessary
to
specify
the
distribution
of
these
two
humic
acid
forms
of
natural
organic
matter.
Ca
and
Na
can
directly
compete
with
copper
at
DOM
and
biotic
ligand
binding
sites,
and
these
cations
will
therefore
have
a
direct
effect
on
model
predictions.
Magnesium
may
have
a
critical
role
as
well
for
some
organisms.
In
that
SO4
may
be
the
dominant
anion
in
freshwater,
it
is
important
for
determining
the
charge
balance
and
ionic
strength
in
BLM
calculations.
Chloride
can
also
contribute
to
ionic
strength
computations
for
copper.
The
sum
of
three
inorganic
species
in
the
BLM
 
carbonate
(
CO3),
bicarbonate
(
HCO3),
and
carbonic
acid
(
H2CO3)
 
is
considered
inorganic
carbon.
Inorganic
carbon
is
a
critical
input
to
the
BLM
because
many
metals
including
copper
form
carbonate
complexes.
DIC
measurements
are
typically
not
made
in
the
environment,
so
even
though
it
is
the
preferred
measurement,
DIC
can
be
estimated
from
alkalinity
and
pH
when
a
DIC
measurement
is
not
available.
Sulfide
has
a
strong
affinity
for
many
metals,
and
although
the
sulfide
concentration
is
traditionally
assumed
to
be
negligible
in
aerated
waters;
its
concentration
may
be
impacted
by
wastewater
treatment
plant
effluents.

A
number
of
fixed
parameters
or
constants
are
also
used
in
the
BLM
along
with
the
input
parameters
specified
above
for
speciation
or
toxicity
mode
computations.
Some
of
the
key
fixed
constants
are
the
binding
constants
for
the
interactions
between
copper
and
protons
and
the
"
biotic
ligand."
The
8
values
contained
in
the
model
were
derived
by
Playle
and
coworkers
by
conducting
gill­
loading
experiments
(
Janes
and
Playle
1995;
Playle
et
al.
1992,
1993a,
b).
Playle
et
al.
(
1993a,
b)
also
developed
the
gill
site
density
parameter
of
30
nmol/
g
wet
weight
used
in
the
model
from
measured
copper
gill
concentrations.

3.3
Model
Prediction
Modes
The
graphical
user
interface
that
has
been
developed
for
the
BLM
allows
the
user
to
run
the
model
in
either
the
"
Metal
Toxicity
Mode"
or
in
the
"
Metal
Speciation
Mode."
Run
in
the
toxicity
mode,
the
BLM
predicts
the
dissolved
concentration
of
copper
required
to
cause
acute
mortality
for
water
characteristics
specified
by
the
user.
Run
in
the
speciation
mode,
the
BLM
calculates
the
chemical
speciation
of
a
dissolved
metal,
including
complexation
with
inorganic
and
organic
ligands,
and
the
biotic
ligand.
Each
computational
mode
requires
the
user
to
specify
the
chemical
parameters
discussed
above
and
either
a
dissolved
copper
concentration
or
a
copper
accumulation
associated
with
the
biotic
ligand.

The
biotic
ligand
represents
a
discrete
receptor
or
the
site
of
action
of
toxicity
to
an
organism,
where
accumulation
of
metal
at
or
above
a
critical
threshold
concentration
leads
to
acute
toxicity.
The
lethal
accumulation
level
on
the
BL
that
results
in
an
effect
on
50
percent
of
the
individuals
is
termed
the
"
LA50"
for
that
species.
The
LA50
concentration
of
copper
on
the
BL
is
expected
to
result
in
50
percent
mortality
in
a
toxicological
exposure
for
a
fixed
exposure
duration.
The
LA50
is
expressed
in
units
of
nmol
Cu/
g
wet
weight
of
the
BL.
Since
the
BLM
includes
inorganic
and
organic
speciation
and
competitive
complexation
of
copper
with
the
BL,
the
amount
of
dissolved
copper
required
to
reach
this
threshold
will
vary,
depending
on
the
water
chemistry.
Therefore,
in
addition
to
calculating
chemical
speciation,
use
of
the
BLM
to
evaluate
the
dissolved
Cu
concentration
that
is
associated
with
the
LA50
provides
a
prediction
of
the
concentration
of
copper
that
would
result
in
acute
toxicity
(
e.
g.,
LC50)
for
a
given
set
of
water
quality
characteristics.

When
run
in
the
metal
toxicity
mode,
the
BLM
will
predict
the
LC50
of
copper
using
an
LA50
value
from
a
parameter
file
specific
to
a
particular
species
for
all
of
the
observations
with
a
complete
set
of
BLM
input
parameters.
However,
the
BLM
can
also
be
run
with
"
User
Defined"
LA50s.
That
is,
the
BLM
will
predict
LC50s
based
on
the
LA50
values
specified
by
the
user
rather
than
the
default
LA50
value
specified
in
the
parameter
files
for
particular
organisms.
Instructions
for
constructing
BLM
input
files
and
running
the
model
can
be
found
in
the
Biotic
Ligand
Model
User's
Guide
(
Appendix
B).

3.4
Data
Acceptability
and
Screening
Procedures
Data
screening
procedures
for
this
effort
differed
from
data
screening
procedures
for
previous
copper
criteria
documents,
in
that
studies
previously
considered
unacceptable
for
deriving
criteria
are
acceptable
when
utilizing
the
BLM.
For
example,
studies
with
DOC
content
exceeding
5
mg/
L
or
studies
that
were
fed
were
not
always
acceptable
in
the
past,
but
are
now
acceptable
for
use
with
the
BLM,
because
the
BLM
is
designed
to
account
for
these
differences.
Conversely,
some
previously
acceptable
freshwater
acute
toxicity
tests
were
relegated
to
Appendix
C
(
other
data)
because
of
poor
chemical
characterization,
together
with
several
other
freshwater
tests
in
which
copper
concentrations
in
the
test
chambers
were
not
measured.
Detailed
chemical
analyses
of
the
dilution
water,
test
water,
and
measured
copper
concentrations
are
critical
parameters
for
the
BLM
(
see
Mechanistic
Models
section).
The
lack
of
any
or
all
of
these
major
ion
concentrations,
including
measurements
of
total
or
dissolved
copper,
without
reliable
estimates
of
surrogate
values,
precludes
the
use
of
a
particular
study's
results
(
see
next
section,
Estimation
of
Test
Water
Chemistry).

3.5
Estimation
of
Test
Water
Chemistry
9
To
incorporate
the
BLM
into
the
copper
aquatic
life
criteria
document,
a
data
table
was
generated
summarizing
the
acute
toxicity
of
copper
to
freshwater
organisms
that
included
the
necessary
BLM
water
chemistry
parameters.
Studies
lacking
measured
copper
concentrations
were
not
considered
for
further
evaluation.
A
literature
review
was
conducted,
searching
AQUIRE,
BIOSYS,
and
CAS.
The
literature
was
reviewed,
and
the
appropriate
measurements
were
tabulated.

As
the
understanding
by
the
scientific
community
of
the
important
influence
of
water
chemistry
on
metals
toxicity
has
increased,
measurements
(
and
reporting)
of
relevant
water
quality
parameters
has
also
increased.
Still,
much
of
the
currently
available
aquatic
toxicity
literature
for
metals
does
not
include
measurements
for
all
of
the
key
BLM
inputs.
Many
of
these
key
BLM
inputs
were
not
measured
or
reported
in
the
published
material
reviewed
for
this
update
of
the
WQC.
Consequently,
additional
data
were
obtained
from
the
authors;
additional
measurements
were
made
in
relevant
water
sources;
or,
finally,
input
parameters
were
estimated.
A
detailed
description
of
the
methods
used
to
obtain
or
estimate
these
input
parameters
is
included
in
Estimation
of
Water
Chemistry
Parameters
for
Acute
Copper
Toxicity
Tests
(
Appendix
D).
Below
is
a
summary
of
the
effort
undertaken
to
estimate
the
various
test
water
chemistry
conditions.

3.6
Water
Chemistry
Data
Acquisition
Studies
included
in
Table
1a
of
the
ambient
water
quality
criteria
document
for
copper
were
reviewed
to
record
all
reported
information
on
dilution
and
test
water
chemistry.
Any
additional
references
to
which
the
authors
referred
while
describing
their
test
waters
were
retrieved.
When
critical
water
chemistry
parameters
were
not
available,
authors
were
asked
to
measure
missing
water
chemistry
parameters
in
the
toxicity
test
source
waters.
If
primary
or
corresponding
authors
could
not
be
contacted,
an
attempt
was
made
to
contact
secondary
authors
or
personnel
from
the
laboratories
where
the
studies
had
been
conducted.
Failing
this,
the
U.
S.
Geological
Survey
National
Stream
Quality
Accounting
Network
(
NASQAN)
and
the
EPA
STOrage
and
RETrieval
(
STORET)
data
were
used
to
obtain
data
for
tests
conducted
in
ambient
surface
water.
Where
actual
water
chemistry
data
were
unavailable,
data
from
other
studies
with
the
same
water
were
used
as
surrogate
values
if
appropriate.
In
some
instances,
other
available
sources
were
contacted
to
obtain
water
chemistry
data
(
e.
g.,
city
drinking
water
treatment
officials).
The
acquired
data
were
scrutinized
for
representativeness
and
usefulness
for
estimating
surrogate
values
to
complete
the
water
quality
information
for
the
dilution
and/
or
test
water
that
was
used
in
the
original
studies.
When
the
above
sources
could
not
be
used
geochemical
ion
input
parameters
were
based
on
the
reported
hardness
measurement
and
regression
relationships
constructed
for
various
input
parameters
from
NASQAN
data.

As
with
any
modeling
effort,
the
reliability
of
model
output
depends
on
the
reliability
of
model
input.
Although
the
input
data
have
been
carefully
scrutinized
and
filtered,
the
reliability
of
the
BLMderived
accumulation
and
toxicity
values
for
this
project
are
subject
to
the
limitations
of
the
input
measurements
and
estimation
procedures
described
above.

3.7
Ranking
of
Quality
of
Test
Chemistry
Characterization
A
ranking
system
was
devised
to
evaluate
only
the
quality
of
the
chemical
characterization
of
the
test
water,
not
the
overall
quality
of
the
study
itself.
Studies
with
a
rank
of
1
contain
all
of
the
necessary
parameters
for
BLM
input
based
on
measurements
from
either
the
test
chambers
or
the
water
source.
In
general,
studies
in
which
the
BLM
input
parameters
were
reported
for
test
chamber
samples
take
precedence
over
studies
in
which
the
parameters
were
reported
only
for
the
source
water.
A
characterization
ranking
of
2
denotes
those
studies
where
not
all
parameters
were
measured,
but
reliable
estimates
of
the
requisite
concentrations
could
be
made.
Similarly,
a
rank
of
3
denotes
studies
in
which
all
10
parameters
except
DOC
were
measured,
but
reliable
estimates
of
DOC
could
be
made.
For
the
majority
of
the
tests,
a
chemical
characterization
of
4+
was
assigned
because
hardness,
alkalinity,
and
pH
were
measured,
and
the
ionic
composition
could
be
reliably
estimated
or
calculated.
A
4­
was
assigned
to
those
studies
conducted
using
standard
reconstituted
water
in
which
hardness,
alkalinity,
or
pH
was
either
measured
or
referenced,
and
the
recipe
for
the
water
is
known
(
ASTM
2000;
U.
S.
EPA
1993).
The
chemical
characterization
rank
of
5
was
ascribed
to
studies
in
which
one
of
the
key
parameters
(
DOC,
Ca,
pH,
alkalinity)
was
not
measured,
and
when
it
could
not
be
reliably
estimated.
If
two
or
more
key
parameters
(
DOC,
Ca,
pH,
alkalinity)
were
not
measured
and
could
not
be
reliably
estimated,
a
study
was
given
a
chemical
characterization
rank
of
6.
Studies
receiving
a
quality
rating
of
greater
than
4
were
not
used
in
the
criteria
development
procedures
because
the
estimates
for
some
of
the
key
input
parameters
were
not
thought
to
be
reliable.

3.8
Criteria
Computations
To
calculate
the
acute
criterion
or
CMC,
reported
acute
toxicity
values
(
e.
g.,
LC50s)
(
Table
1a)
and
individual
test
water
chemistry
parameters
were
used
to
calculate
LA50
values
by
running
the
model
in
the
speciation
mode.
These
LA50
values
were
then
normalized
to
a
standard
water
condition
(
Table
1a,
footnote
d)
by
running
the
model
in
the
toxicity
mode
and
specifying
user­
defined
LA50s.
As
used
here,
"
normalization"
refers
to
the
procedure
whereby
all
of
the
measured
effect
levels
were
adjusted,
via
use
of
the
BLM,
to
the
predicted
LC50
that
would
have
been
expected
in
a
standard
test
water.
These
normalized
LC50s
were
used
to
calculate
Species
Mean
Acute
Values
(
SMAVs),
Genus
Mean
Acute
Values
(
GMAVs),
and
a
Final
Acute
Value
(
FAV)
pursuant
to
the
1985
Guidelines
procedure.
The
FAV
represents
a
hypothetical
genus
more
sensitive
than
95
percent
of
the
tested
genera.
The
FAV
was
derived
from
the
four
GMAVs
that
have
cumulative
probabilities
closest
to
the
5th
percentile
toxicity
value
for
all
the
tested
genera
(
Table
3a).
Inputting
this
FAV
as
an
LC50
concentration
and
running
the
model
in
speciation
mode
determines
the
lethal
accumulation
associated
with
the
FAV
in
the
standard
test
water.
Since
it
is
assumed
that
the
LA50
does
not
vary
with
changes
in
water
chemistry,
this
LA50
is
programmed
into
the
model
as
a
constant.
To
derive
a
criterion
for
a
specific
site,
the
site
water
chemistry
data
are
input
to
the
model.
The
model
then
uses
an
iterative
approach
to
determine
the
dissolved
copper
concentration
needed
to
achieve
a
Cu­
biotic
ligand
concentration
equal
to
the
criterion
LA50.
This
dissolved
Cu
concentration
is
in
effect
the
FAV
based
on
site
water
chemistry.
The
site­
specific
CMC
is
this
predicted
dissolved
metal
concentration
divided
by
two.
The
site­
specific
CCC
is
the
CMC
divided
by
the
final
acutechronic
ratio
(
FACR).

The
LA50s
used
in
criteria
computations
were
calculated
for
each
test
in
which
water
quality
characteristics
could
be
reasonably
well
characterized.
Because
an
underlying
premise
of
the
BLM
is
that
the
LA50
is
invariant
for
a
given
organism,
for
any
test
condition,
the
fact
that
some
residual
variability
in
LA50s
exists
may
reflect
model
uncertainty,
including:
(
1)
among­
strain
variability;
(
2)
among­
life­
stage
variability;
and
(
3)
potential
physiological
effects
of
the
site
water
on
the
test
organism
that
alter
organism
sensitivity
rather
than
metal
bioavailability.

Ultimately,
the
final
freshwater
criteria
depend
on
a
number
of
varying
water
quality
parameters
(
e.
g.,
Ca+,
Mg+,
and
DOC),
and
any
number
of
test
water
chemistries
could
be
used
to
normalize
the
Table
1a
data.
Table
1a
data
(
LC50s
and
EC50s)
are
standardized
to
the
water
chemistry
condition
specified
in
footnote
f,
for
illustrative
purposes
only
as
is
typical
in
hardness­
dependent
metals
criteria
documents.
Be
that
as
it
may,
the
normalization
chemistry
selected
may
influence
the
species
sensitivity
distribution,
particularly
when
two
or
more
species
have
similar
sensitivities
to
copper
toxicity.
Example
criteria
for
several
water
chemistry
conditions
are
provided
in
Figure
6.
11
4.0
CONVERSION
FACTORS
Although
past
water
quality
criteria
for
copper
(
and
other
metals)
had
been
established
upon
total
metals'
concentrations,
EPA
made
the
decision
to
allow
the
expression
of
metals
criteria
on
the
basis
of
dissolved
metal
(
operationally
defined
as
metal
that
passes
through
a
0.45­
micron
filter,
[
U.
S.
EPA
1993])
because
it
was
thought
to
better
represent
the
bioavailable
fraction
of
the
metal.
At
that
time,
most
data
in
existing
databases
were
from
tests
that
were
either
conducted
using
nominal
concentrations,
or
provided
only
total
copper
measurements,
such
that
some
procedure
was
required
to
estimate
their
dissolved
equivalents.
Now,
dissolved
metals
toxicity
values
are
required
as
BLM
input
in
order
to
obtain
lethal
accumulation
values.
EPA
used
conversion
factors
(
CF)
that
when
multiplied
by
the
total
metal
concentrations
result
in
a
dissolved
metal
concentration.
CF
corresponds
to
the
percentage
of
the
total
recoverable
metal
that
is
dissolved.

CFs
for
the
conversion
of
total
copper
concentrations
in
water
from
freshwater
toxicity
tests
to
dissolved
copper
concentrations
were
developed
by
conducting
a
number
of
laboratory
toxicity
tests
(
Stephan
1995;
University
of
Wisconsin­
Superior
1995).
Simulation
tests
were
conducted
to
determine
the
influence
of
copper
concentrations,
presence
or
absence
of
food,
duration
of
the
test,
hardness,
and
species
of
test
organism
on
the
concentration
of
dissolved
copper
in
the
test
water.
The
simulation
tests
were
designed
to
mimic
conditions
that
existed
during
the
toxicity
tests
used
to
derive
the
earlier
metals
criteria,
such
as
sorption
of
metal
onto
test
chambers,
uptake
of
metal
by
test
organisms,
and
precipitation.
The
recommended
conversion
factors
from
the
Stephan
(
1995)
report
(
0.96
for
both
the
CMC
and
CCC)
were
utilized
to
convert
total
recoverable
measurements
to
dissolved
values,
when
necessary.

In
the
case
of
saltwater,
several
studies
are
available
that
report
nominal,
total,
and
dissolved
concentrations
of
copper
in
laboratory
water
(
Table
1b)
from
site­
specific
WER
studies
(
refer
to
Appendix
E
for
further
details).
These
studies
show
relatively
consistent
ratios
for
the
nominal­
to­
dissolved
concentrations
and
for
total­
to­
dissolved
concentrations.
The
dissolved­
to­
nominal
conversion
requires
a
larger
correction
factor
than
does
the
dissolved­
to­
total
correction.
The
data
provided
in
Appendix
E
bear
this
out
in
all
but
one
case
(
SAIC
1993
data
for
the
blue
mussel).
Nominal
copper
concentrations
for
this
series
of
tests
may
have
been
overstated
or
the
measured
total
copper
concentrations
may
have
been
proportionally
lower
than
for
the
other
studies.
The
overall
ratio
for
correcting
saltwater
total
copper
concentrations
to
dissolved
copper
concentrations
is
0.909,
based
on
the
results
of
six
studies
(
Appendix
E).
This
is
comparable
to
its
equivalent
conversion
factor
in
freshwater,
which
is
0.960
(
Stephan
1995).
When
it
is
necessary
to
convert
nominal
saltwater
copper
concentrations
to
dissolved
copper
concentrations,
the
conversion
factor
is
0.838
based
on
the
same
six
studies.

5.0
DATA
SUMMARY
AND
CRITERIA
CALCULATION
5.1
Summary
of
Acute
Toxicity
to
Freshwater
Animals
and
Criteria
Calculation
This
effort
identified
approximately
600
acute
freshwater
toxicity
tests
with
aquatic
organisms
and
copper
considered
acceptable
for
deriving
criteria.
Of
these
acceptable
studies,
approximately
100
were
eliminated
from
the
criteria
derivation
process
because
they
did
not
report
measured
copper
concentrations.
Nearly
150
additional
studies
were
eliminated
from
the
calculation
of
the
FAV
because
they
received
a
quality
rating
of
greater
than
4
in
the
quality
rating
scheme
described
above.

The
BLM
version
AP08­
Build
2002­
05­
07
was
used
to
calculate
lethal
accumulation
values
for
each
individual
test
result
included
in
Table
1a
by
running
the
model
in
the
metal
speciation
mode
(
see
Appendix
B,
BLM
User's
Guide).
Reported
effect
levels
(
i.
e.,
LC50s
or
EC50s)
and
the
chemistry
characterization
for
each
test
were
input
parameters
for
the
model
(
Appendix
F).
LC50s
or
EC50s
reported
12
Figure
3.
Quality
Scale
for
D.
magna
BLM
Input
Data
in
terms
of
total
recoverable
metal
were
converted
to
dissolved
concentrations
as
discussed
above
in
the
Conversion
Factors
section.
Lethal
accumulation
values
were
then
converted
to
toxicity
values
(
e.
g.,
LC50s)
at
standard
water
condition
by
running
the
model
in
the
metal
toxicity
mode.

Data
from
approximately
350
test
were
used
to
derive
normalized
LC50
values,
including
15
species
of
invertebrates,
22
species
of
fish,
and
1
amphibian
species
(
Table
1a).
Large
variations
in
toxicity
values
were
observed
for
some
species.
Examination
of
the
nature
of
these
individual
values
showed
that
a
majority
of
them
corresponded
to
observations
where
key
BLM
parameters
were
missing
and
thus
estimated
(
i.
e.,
a
quality
ranking
of
3
or
4
range
is
typical
for
these
values),
and
for
many
species
the
variation
in
LC50
was
seen
to
increase
in
observations
with
more
missing
BLM
parameters
(
e.
g.,
D.
magna,
Figure
3).
The
large
variability
in
LC50
for
some
species,
therefore,
seems
to
be
related
to
the
use
of
estimated
BLM
parameters
for
some
of
the
data.
For
other
organisms
(
such
as
rainbow
trout),
significant
variations
in
LC50s
were
likely
due
to
the
mixture
of
life­
stages
represented
in
the
acute
toxicity
datasets.
In
general,
an
objective
approach
that
could
be
used
to
automatically
screen
anomalous
LC50
values
was
needed.
For
a
given
species
with
more
than
five
test
results,
relatively
extreme
values
were
13
Figure
4.
Ranges
and
Distribution
of
Normalized
LC50
Values
for
Species
Listed
in
Table
1
Species
are
identified
by
unique
species
number
listed
in
Table
6.
For
each
species
the
range
between
the
1st
and
3rd
quartile
of
all
available
normalized
LC50
values
is
represented
by
the
box.
Extreme
values
are
plotted
as
individual
symbols,
with
the
number
of
vertices
indicating
the
quality
scale
(
extreme
values
and
quality
scales
are
discussed
in
Section
5.1).
Statistics
shown
for
normalized
LC50
values
after
excluding
extreme
values
include
the
geometric
mean
shown
as
a
circle,
and
minimum
and
maximum
values
shown
as
whisker
bars
around
the
mean.
defined
within
the
distribution
of
LC50
values
using
a
simple
statistical
method
that
identifies
those
individual
values
that
are
far
from
most
of
the
rest
of
the
population
of
values
(
Chambers
et
al.
1983).
To
characterize
these
extreme
values,
a
range
was
established
by
first
calculating
the
difference
between
the
1st
and
3rd
quartiles
for
the
entire
dataset.
This
difference
was
then
multiplied
by
1.5
and
either
added
to
the
3rd
quartile,
or
subtracted
from
the
1st
quartile
to
establish
the
"
inside
range."
Any
points
falling
outside
this
range
were
identified
as
extreme
values.
While
data
limitations
preclude
the
application
of
a
more
formal
evaluation
of
"
statistical
outliers,"
this
simplified
procedure
was
considered
to
be
a
reasonable
way
to
account
for
what
appeared
to
be
anomalous
results.

As
an
example
of
this
method
applied
to
the
LC50
data,
box
plots
are
shown
of
the
range
of
LC50
values
for
each
of
the
species
in
Table
1a.
Species
are
identified
with
numbers,
as
shown
in
Table
6.
For
each
species,
the
geometric
mean
is
shown
as
the
center
symbol,
the
first
set
of
ranges
represent
the
1st
and
3rd
quartile.
The
second
set
of
ranges
represent
the
minimum
and
maximum
values
excluding
extreme
values.
Data
corresponding
to
extreme
values
are
individually
plotted
as
separate
plotting
symbols
(
Figure
4).
For
the
extreme
values,
the
number
of
vertices
in
the
plotting
symbol
represents
the
14
quality
ranking
(
e.
g.,
a
triangle
represents
an
observation
with
a
quality
ranking
of
three,
a
diamond
represents
an
observation
with
a
quality
ranking
of
4+,
a
star
represents
a
quality
ranking
of
4
or
4­).
The
LC50
values
that
corresponded
to
"
extreme
values"
were
therefore
not
considered
in
subsequent
calculation
of
the
5th­
percentile
LC50
value.

SMAVs
ranged
from
2.54

g/
L
for
the
most
sensitive
species,
Daphnia
pulicaria,
to
101,999

g/
L
for
the
least
sensitive
species,
Notemigonus
crysoleucas.
Cladocerans
were
among
the
most
sensitive
species,
with
D.
pulicaria,
D.
magna,
Ceriodaphnia
dubia,
and
Scapholeberis
sp.
being
four
out
of
the
six
most
sensitive
species.
Invertebrates
in
general
were
more
sensitive
than
fish,
representing
the
10
lowest
SMAVs.

The
27
GMAVs
calculated
from
the
above­
mentioned
SMAVs
ranged
from
3.56

g/
L
for
Daphnids
to
101,999

g/
L
for
the
Notemigonus
genus.
Nine
of
the
10
most
sensitive
genera
were
invertebrates.
The
salmonid
genus
Oncorhynchus
was
the
most
sensitive
fish
genus,
with
a
GMAV
of
29.11

g/
L
and
an
overall
GMAV
ranking
of
10.

Toxicity
values
are
available
for
more
than
one
species
in
eight
different
taxonomic
families.
The
ranked
GMAVs
are
presented
in
Figure
5.
Pursuant
to
procedures
used
to
calculate
a
FAV,
a
FAV
of
4.2
µ
g/
L
was
derived
from
the
four
GMAVs
with
cumulative
probabilities
closest
to
the
5th
percentile
toxicity
value
for
all
the
tested
genera
(
Table
3c).
The
presumption
is
that
this
acute
toxicity
value
represents
the
LC50
for
an
organism
that
is
sensitive
at
the
5
percentile
level
of
the
GMAV
distribution.
The
four
lowest
GMAVs
vary
by
less
than
a
factor
of
three
from
the
highest
to
the
lowest
value.
The
CMC
is
the
FAV
divided
by
two,
and
rounded
to
two
significant
figures.
Therefore,
the
freshwater
dissolved
copper
CMC
for
the
normalization
chemistry
presented
is
2.1

g/
L.

Site­
water
chemistry
parameters
are
needed
to
evaluate
a
criterion.
This
is
analogous
to
the
situation
that
previously
existed
for
the
hardness­
based
WQC,
where
a
hardness
concentration
was
necessary
in
order
to
derive
a
criterion.
Examples
of
CMC
calculations
at
various
water
chemistry
conditions
are
presented
in
Figure
6.

Figure
5.
Ranked
Freshwater
Genus
Mean
Acute
Values
(
GMAVs)
15
Figure
6.
Comparison
of
Existing
Hardness
Based
WQC
and
BLM
Based
WQC
in
Synthetic
Laboratory
Water
and
EPA
Standard
Recipe
Water
for
DOC
=
2.3
mg/
L
5.1.1
Comparison
With
Hardness­
Adjusted
Values
As
discussed
previously,
EPA's
earlier
freshwater
copper
criteria
recommendations
were
hardnessdependent
values.
One
would
expect
a
BLM­
based
criterion
calculation
procedure
to
yield
the
more
appropriate
criterion
 
appropriate
in
the
sense
that
it
accounts
for
the
important
water
chemistry
factors
that
affect
toxicity,
including
DOC
complexation,
where
the
hardness
correction
does
not.
While
in
principle
the
BLM
is
expected
to
improve
the
criteria
calculation
method,
the
BLM's
ability
to
accurately
predict
LC50s
or
metal
speciation
is
limited
by
the
quality
of
the
input
data.
For
this
effort,
many
input
parameters
were
estimated.
To
ascertain
if
the
BLM­
based
criterion
is
an
improvement
over
a
hardness­
dependent
criterion
in
light
of
the
necessity
of
estimating
several
of
the
required
input
parameters,
the
variations
between
measured
versus
predicted
values
for
each
of
these
approaches
were
compared.

For
the
first
comparison,
lethal
accumulation
values
were
calculated
for
each
study
result
(
uncensored
data)
utilizing
the
measured
or
estimated
chemistry
input
parameters.
Average
accumulation
values
for
each
species
were
calculated
and
used
to
run
the
BLM
with
"
User
Defined"
LA50s,
specifying
the
species
average
accumulation
value
for
all
study
results
for
that
species
and
the
original
input
chemistry
parameters.
The
predicted
LC50
values
at
each
chemistry
condition
were
compared
with
the
originally
measured
values
by
regressing
the
natural
logarithm
of
the
predicted
toxicity
value
versus
the
natural
logarithm
of
the
measured
toxicity
value.

A
similar
procedure
was
performed
for
the
hardness
adjustment.
A
pooled
hardness
slope
was
calculated
using
all
appropriate
Table
1a
data
(
considering
all
quality
ratings)
based
on
the
1985
Guideline
procedure
(
Appendix
G).
This
pooled
slope
was
used
to
normalize
all
Table
1a
data
used
for
the
BLM
analysis
to
a
standard
hardness
of
50
mg/
L
(
measured
as
CaCO3).
Species
mean
acute
values
were
calculated
and
used
to
predict
LC50s
for
each
test
result,
for
that
same
species,
at
the
test
hardness.
Again,
the
natural
logarithms
of
the
measured
versus
hardness
predicted
values
were
regressed.
16
The
mean
square
of
error
(
MSE)
from
these
two
least
squares
regression
procedures
were
compared.
The
MSE
from
the
BLM
measured
versus
predicted
analysis
(
0.403)
was
only
slightly
lower
than
the
MSE
from
the
comparable
hardness
analysis
(
0.420).
The
small
reduction
in
the
MSE
for
the
BLM
analysis
is
interpreted
to
mean
that
the
BLM,
in
this
case,
was
a
slightly
better
predictor
of
LC50
values
and
somewhat
better
at
reducing
variability
among
species
mean
values
compared
with
the
hardness
adjustment
for
these
laboratory
water
studies.
Application
of
the
BLM
in
field
situations
where
DOC
is
expected
to
be
present
at
higher
concentrations
than
those
observed
in
laboratory
studies
would
likely
improve
the
performance
of
the
BLM
compared
with
the
hardness
adjustment.
The
reason
is
that
the
BLM
would
reasonably
account
for
the
typically
observed
increase
in
effect
levels
under
such
conditions,
while
the
hardness­
based
approach
would
not.

As
a
comparison
between
the
hardness
typical
of
the
previous
copper
criterion
and
this
revised
criterion
using
the
BLM,
both
procedures
were
used
to
calculate
criterion
values
for
waters
with
a
range
in
hardness
as
specified
by
the
standard
EPA
recipes
(
U.
S.
EPA
1993).
The
EPA
recipes
specify
the
concentration
of
various
salts
and
reagents
to
be
used
in
the
synthesis
of
laboratory
test
waters
with
specific
hardness
values
(
e.
g.,
very
soft,
soft,
moderately
hard,
hard,
or
very
hard).
As
the
water
hardness
increases
in
these
recipes,
pH
and
alkalinity
also
increase.
This
has
implications
for
the
BLM
because
the
bioavailability
of
copper
would
be
expected
to
decrease
with
increasing
pH
and
alkalinity
due
to
the
increasing
degree
of
complexation
of
copper
with
hydroxides
and
carbonates
and
decreasing
proton
competition
with
the
metal
at
both
DOM
and
biotic
ligand
binding
sites.
The
BLM
was
used
to
predict
the
WQC
with
a
DOC
concentration
of
2.3
mg/
L
(
the
average
value
in
the
data
used
in
Table
1)
for
the
five
standard
hardness
waters.
The
BLM
criterion
for
these
waters
agrees
very
well
with
that
calculated
by
the
hardness
equation
used
in
previous
copper
criterion
documents
(
Figure
6).
However,
alkalinity
and
pH
change
as
hardness
changes
in
the
EPA
recipes.
The
BLM
prediction
is
taking
all
of
these
changes
in
water
quality
into
account.
It
is
possible
to
use
the
BLM
to
look
only
at
the
change
in
predicted
WQC
with
changes
in
hardness
(
e.
g.,
alkalinity
and
pH
remaining
constant).
Also
shown
in
Figure
6
are
BLM
predictions
with
only
hardness
varying.
As
can
be
seen,
these
predictions
show
a
much
flatter
response
with
increasing
hardness,
and
do
not
match
the
response
seen
in
the
hardness
equation
at
all.
The
hardness
equation,
therefore,
is
based
on
waters
where
changes
in
hardness
are
accompanied
by
changes
in
pH
and
alkalinity.
However,
there
are
many
possible
natural
waters
where
changes
in
hardness
are
not
accompanied
by
changes
in
pH
and
alkalinity
(
such
as
water
draining
a
region
rich
in
gypsum).
In
these
cases,
the
hardness
equation
based
criterion
will
still
assume
a
response
that
is
characteristic
of
waters
where
hardness,
alkalinity,
and
pH
co­
vary,
and
will
likely
be
underprotective
relative
to
the
level
of
protection
intended
by
the
Guidelines,
in
high
hardness
waters.
Conversely,
in
waters
where
the
covariation
between
hardness,
pH,
and
alkalinity
is
greater
than
is
typical
for
data
in
Table
1,
the
hardness
equation
based
criteria
may
be
overprotective.

5.2
Summary
of
Acute
Toxicity
to
Saltwater
Animals
and
Criteria
Calculation
Tests
of
the
acute
toxicity
of
copper
to
saltwater
organisms
(
acceptable
for
deriving
criteria)
have
been
conducted
with
34
species
of
invertebrates
and
18
species
of
fish
(
Table
1b).
In
general,
where
relationships
were
apparent
between
life
stage
and
sensitivity,
values
only
for
the
most
sensitive
life
stage
were
considered
in
deriving
SMAVs.
The
censoring
procedure
used
for
the
freshwater
toxicity
values
was
also
considered
for
use
in
censoring
saltwater
acute
toxicity
values.
However,
it
was
not
applied.
The
freshwater
censoring
procedure
was
not
used
because,
in
one
case,
it
resulted
in
eliminating
only
data
for
the
most
sensitive
life­
stage,
rather
than
the
insensitive
life­
stage.
In
situations
where
data
indicate
that
a
particular
life­
stage
for
the
species
is
at
least
a
factor
or
two
more
resistant
than
another,
the
Guidelines
recommend
that
the
data
for
the
more
resistant
life­
stage
not
be
used
in
the
calculation
of
the
SMAV.

Embryo­
larval
life­
stages
of
bivalve
mollusc
genera
represent
the
first
two
of
the
four
most
sensitive
genera,
including,
by
sensitivity
rank,
the
genera
Mytilus­
11.5

g/
L
and
Crassostrea­
12.6

g/
L.
Toxicity
data
for
Mytilus
edulis
were
distinguished
from
data
for
Mytilus
spp.
based
on
the
molecular
genetics
work
17
presented
by
Gaffney
(
1997)
and
information
about
the
collection
locations
of
the
test
organisms
for
the
Mytilus
studies.
The
fourth
most
sensitive
genera
(
the
sea
urchin
genus
Strongylocentrotus)
is
also
represented
by
the
embryo­
larval
life­
stage
(
Table
1b).
Comparing
the
data
for
older
mussels
(
Nelson
et
al.
1988)
and
oysters
(
Okazaki
1976)
with
data
for
embryo­
larval
forms
indicates
that
these
early
life
stages
(
ELSs)
are
appreciably
more
sensitive
than
the
older
forms.
This
is
probably
true
for
marine
invertebrates
in
general,
although
data
for
the
red
abalone
(
Martin
et
al.
1977)
indicate
that
48­
hour
larvae
are
perhaps
slightly
more
resistant
than
larger
forms.
The
mysid,
Holmesimysis
costata,
and
the
copepods,
Eurytemora
affinis
and
Acartia
tonsa,
are
among
the
most
sensitive
crustacean
species
tested.

Except
for
the
summer
flounder
and
the
cabezon,
with
GMAVs
of
12.7
and
86.4

g/
L,
respectively,
no
other
saltwater
fish
had
a
GMAV
below
100

g/
L.
Fourteen
other
genera
of
marine
fish
had
GMAVs
from
117
to
4,743

g/
L
dissolved
copper.
Two
of
the
lowest
fish
GMAVs
were
based
on
tests
with
early
life
stages,
and
the
higher
fish
GMAVs
did
not
include
tests
with
early
life
stages.
These
results
suggest
that
acute
tests
with
early
(
post­
hatch)
life
stages
can
generally
be
protective
of
acute
toxicity
to
older
life
stages,
but
not
necessarily
the
reverse.

In
sum,
several
studies
indicate
that
salinity
affects
copper
toxicity
and
those
effects
are
speciesdependent
The
brackish
water
clam,
Rangia
cuneata,
was
very
sensitive
to
copper
in
freshwater
(
LC50
210

g/
L
at
<
1
g/
kg
salinity),
but
35
to
38
times
more
resistant
at
salinities
of
5.5
and
22
g/
kg
(
Olson
and
Harrel
1973).
Similarly,
young
striped
bass
were
about
three
times
more
sensitive
to
copper
at
a
salinity
of
5
g/
kg
than
at
10
or
15
g/
kg
(
Reardon
and
Harrel
1990).
An
influence
of
salinity
was
observed
by
Ozoh
(
1992a)
in
the
previously
cited
study
of
the
influence
of
temperature
and
salinity
on
copper
toxicity
to
the
polychaete
worm,
Hediste
diversicolor.
Effects
of
salinity
were
more
consistent
than
those
for
temperature.
A
regression
of
log
LC50
versus
log
salinity
indicated
a
slope
of
0.245
for
young
worms,
and
a
slope
of
0.596
for
mature
worms.
Increasing
salinity
over
the
range
tested
(
7
 
30
g/
kg)
increased
LC50s
by
factors
of
approximately
1.4
and
2.4
for
young
worms
and
mature
worms,
respectively.
Establishing
salinitydependent
criteria
on
the
basis
of
these
limited
data
is
not
possible.
Furthermore,
salinity­
based
criteria
should
be
based
only
on
tests
with
organisms
and
life
stages
that
would
be
present
at
lower
salinities.

Acute
values
are
available
for
more
than
one
species
in
the
eight
different
taxonomic
families
recommended
in
the
Guidelines.
The
44
available
saltwater
GMAVs
ranged
from
11.5

g/
L
dissolved
copper
for
Mytilus
to
6,448

g/
L
for
Rangia,
a
factor
of
over
500
difference
(
Table
3b,
Figure
7).
In
each
of
six
genera
with
a
range
of
SMAVs,
all
SMAVs
within
the
genus
are
within
a
factor
of
3.5.
A
saltwater
FAV
of
12.3

g/
L
dissolved
copper
was
obtained
using
the
four
lowest
GMAVs
in
Table
3b
and
the
calculation
procedure
described
in
the
Guidelines.
This
FAV
was
lowered
to
6.19

g/
L
to
protect
commercially
and
recreationally
important
mussel
species.
The
CMC
is
the
FAV
divided
by
two,
and
rounded
to
two
significant
figures.
Therefore,
the
new
saltwater
dissolved
copper
CMC
is
3.1

g/
L.

5.3
Formulation
of
the
CCC
5.3.1
Statistical
Evaluation
of
Chronic
Toxicity
Data
In
aquatic
toxicity
tests,
chronic
values
are
usually
defined
as
the
geometric
mean
of
the
highest
concentration
of
a
toxic
substance
at
which
no
adverse
effect
is
observed
(
highest
no
observed
adverse
effect
concentration,
or
NOAEC)
and
the
lowest
concentration
of
the
toxic
substance
that
causes
an
adverse
effect
(
lowest
observed
adverse
effect
concentration,
or
LOAEC).
The
significance
of
the
observed
effects
is
determined
by
statistical
tests
comparing
responses
of
organisms
exposed
to
low­
level
(
control)
concentrations
of
the
toxic
substance
against
responses
of
organisms
exposed
to
elevated
concentrations.
Analysis
of
variance
is
the
most
common
test
employed
for
such
comparisons.
This
18
approach,
however,
has
limitations;
it
has
the
disadvantage
of
resulting
in
marked
differences
between
the
magnitudes
of
the
effects
corresponding
to
the
individual
chronic
values,
because
of
variation
in
the
power
of
the
statistical
tests
used,
the
concentrations
tested,
and
the
size
and
variability
of
the
samples
used
(
Stephan
and
Rogers
1985).

An
alternative
approach
to
calculate
chronic
values
focuses
on
the
use
of
point
estimates
such
as
regression
analysis
to
define
the
dose­
response
relationship.
With
a
regression
equation
or
probit
analysis,
which
defines
the
level
of
adverse
effects
as
a
function
of
increasing
concentrations
of
the
toxic
substance,
it
is
possible
to
determine
the
concentration
that
causes
a
relatively
small
effect,
for
example
a
5
to
30
percent
reduction
in
response.
To
make
chronic
values
reflect
a
uniform
level
of
effect,
regression
and
probit
analyses
were
used,
where
possible,
both
to
demonstrate
that
a
significant
concentration­
effect
relationship
was
present
and
to
estimate
chronic
values
with
a
consistent
level
of
effect.
The
most
precise
estimates
of
effect
concentrations
can
generally
be
made
for
50
percent
reduction
(
EC50);
however,
such
a
major
reduction
is
not
necessarily
consistent
with
criteria
providing
adequate
protection.
In
contrast,
a
concentration
that
causes
a
low
level
of
reduction,
such
as
an
EC5
or
EC10,
is
rarely
statistically
significantly
different
from
the
control
treatment.
As
a
compromise,
the
EC20
is
used
here
to
represent
a
low
level
of
effect
that
is
generally
significantly
different
from
the
control
treatment
across
the
useful
chronic
datasets
that
are
available
for
copper.

Regression
or
probit
analysis
was
utilized
to
evaluate
a
chronic
dataset
only
in
cases
where
the
necessary
data
were
available
and
the
dataset
met
the
following
conditions:
(
1)
it
contained
a
control
treatment
(
or
low
exposure
data
point)
to
anchor
the
curve
at
the
low
end,
(
2)
it
contained
at
least
three
concentrations,
and
(
3)
two
of
the
data
points
had
effect
variable
values
below
the
control
and
above
zero
(
i.
e.,
"
partial
effects").
Control
concentrations
of
copper
were
estimated
in
cases
where
no
measurements
were
reported.
These
analyses
were
performed
using
the
Toxicity
Relationship
Analysis
Program
software
Figure
7.
Ranked
Saltwater
Genus
Mean
Acute
Values
(
GMAVs)
19
(
version
1.0;
U.
S.
EPA).
Additional
detail
regarding
the
aforementioned
statistical
procedures
is
available
in
the
cited
program.

When
the
data
from
an
acceptable
chronic
test
met
the
conditions
for
the
logistic
regression
or
probit
analysis,
the
EC20
was
the
preferred
chronic
value.
When
data
did
not
meet
the
conditions,
was
not
available,
or
did
not
lend
itself
to
regression
analysis,
best
scientific
judgment
was
used
to
determine
the
chronic
value.
In
this
case,
the
chronic
value
is
usually
the
geometric
mean
of
the
NOAEC
and
the
LOAEC.
But
when
no
treatment
concentration
was
an
NOAEC,
the
chronic
value
was
less
than
the
lowest
tested
concentration.

For
life­
cycle,
partial
life­
cycle,
and
early
life
stage
tests,
the
toxicological
variable
used
in
chronic
value
analyses
was
survival,
reproduction,
growth,
emergence,
or
intrinsic
growth
rate.
If
copper
apparently
reduced
both
survival
and
growth
(
weight
or
length),
the
product
of
variables
(
biomass)
was
analyzed,
rather
than
analyzing
the
variables
separately.
The
most
sensitive
of
the
toxicological
variables
was
selected,
for
the
most
part,
as
the
chronic
value
for
the
particular
study.

A
species­
by­
species
discussion
of
each
acceptable
chronic
test
on
copper
evaluated
for
this
document
is
presented
in
Appendix
H.
Figures
that
presents
the
data
and
regression/
probability
distribution
line
for
each
of
the
acceptable
chronic
test
which
contained
sufficient
acceptable
data
are
also
provided
in
Appendix
H.

5.3.2
Calculation
of
Freshwater
CCC
Acceptable
freshwater
chronic
toxicity
data
from
early
life
stage
tests,
partial
life­
cycle
tests,
and
full
life­
cycle
tests
are
currently
available
for
29
tests
including
data
for
6
invertebrate
species
and
10
fish
species
(
Table
2a).
The
17
chronic
values
for
invertebrate
species
range
from
2.83
(
D.
pulex)
to
34.6

g/
L
(
C.
dubia);
and
the
12
chronic
values
for
the
fish
species
range
from
<
5
(
brook
trout)
to
60.4

g/
L
(
northern
pike).
Of
the
29
chronic
tests,
comparable
acute
values
are
available
for
17
of
the
tests
(
Table
2c).
The
relationship
between
acute
toxicity
values
and
ACRs
is
presented
in
Figure
8.
The
supporting
acute
and
chronic
test
values
for
the
ACRs
and
the
species
mean
ACRs
are
presented
in
Table
3c.

The
general
effect
of
hardness
on
chronic
toxicity
is
not
evident
upon
inspection
of
the
limited
hardness­
chronic
toxicity
data
for
the
species
for
which
such
evaluations
are
marginally
possible.
Five
tests
over
a
range
of
hardness
values
were
conducted
with
D.
magna
(
Blaylock
et
al.
1985;
Chapman
et
al.
unpublished
manuscript;
van
Leeuwen
et
al.
1988).
Five
tests
over
a
range
of
hardness
values
were
also
conducted
with
C.
dubia
(
Belanger
et
al.
1989;
Carlson
et
al.
1986;
Oris
et
al.
1991).
Winner
(
1985)
conducted
eight
tests
with
D.
pulex
over
a
range
of
hardness
values,
but
humic
acid
was
also
varied
in
these
tests.
In
the
D.
magna
tests,
chronic
values
increased
when
hardness
increased
from
about
50
to
about
100
mg/
L;
however,
in
one
of
the
tests,
the
chronic
value
decreased
when
hardness
was
further
raised
to
about
200
mg/
L.
In
a
second
test
conducted
at
a
hardness
of
225
mg/
L,
the
chronic
value
was
not
much
higher
than
those
in
the
100
mg/
L
hardness
tests.
The
resulting
overall
slope
for
D.
magna
based
on
these
data
is
negative.
The
C.
dubia
test
exhibited
no
discernible
trends
between
hardness
and
toxicity.
One
possibility
is
that
daphnids
may
be
ingesting
precipitated
copper
that
might
form
at
high
hardness
and
high
pH.
Alternatively,
Winner
et
al.
(
1985)
suggest
that
Ca2+
and
Mg2+
ions
in
hard
water
may
be
displacing
Cu2+
from
binding
sites
on
humic
acid,
making
more
copper
bioavailable.
Because
the
hardness
relationship
with
chronic
toxicity
is
equivocal,
no
overall
chronic
slope
was
derived.
20
Figure
8.
Relationship
Between
Freshwater
Acute
Copper
Sensitivity
(
LC50
or
EC50)
and
Acute­
Chronic
Ratios
Because
the
minimum
eight
family
data
requirements
for
chronic
toxicity
data
were
not
met
in
order
to
use
the
FAV
approach
and
because
the
relationship
between
hardness
and
chronic
toxicity
is
equivocal,
EPA
elected
to
derive
the
CCC
utilizing
the
ACR
approach
from
the
Guidelines.
Moreover,
this
was
a
means
of
incorporating
the
improvements
of
the
acute
BLM
calculations
into
the
chronic
criterion
derivation
procedures
even
though,
as
previously
mentioned,
additional
development
is
required
before
the
BLM
will
be
suitable
for
use
in
evaluating
chronic
toxicity
data
directly.
To
calculate
the
FCV,
the
FAV
is
divided
by
the
FACR;
thus,
no
chronic
hardness
slope
is
necessary
to
derive
a
CCC.

The
freshwater
FCV
is
derived
using
acute
chronic
ratios
in
conjunction
with
the
FAV.
However,
the
FAV
is
site­
water
specific.
To
derive
a
FCV,
the
BLM
is
run
in
the
toxicity
mode,
which
utilizes
the
accumulation
value
constant
incorporated
in
the
model
to
calculate
an
LC50
based
on
the
site
water
chemistry
composition.
This
LC50
is
then
divided
by
the
freshwater
FACR
to
generate
an
FCV,
which
is
the
basis
for
the
CCC.

Overall,
individual
ACRs
varied
from
<
1
(
0.55)
for
C.
dubia
(
Oris
et
al.
1991)
to
191.6
for
the
snail,
Campeloma
decisum
(
Arthur
and
Leonard
1970).
Species
mean
acute­
chronic
ratios
ranged
from
1.48
in
saltwater
for
the
sheepshead
minnow
(
Hughes
et
al.
1989)
to
171.2
in
freshwater
for
the
snail,
C.
decisum.
The
FACR
of
3.23
was
calculated
as
the
geometric
mean
of
the
ACRs
for
sensitive
freshwater
species,
C.
dubia,
D.
magna,
D.
pulex,
O.
tshawytscha,
and
O.
mykiss
along
with
the
one
saltwater
ACR
for
C.
variegatus.
Pursuant
to
the
Guidelines,
consideration
was
given
to
calculating
the
FACR
based
on
all
ACRs
within
a
factor
of
10,
but
because
there
appeared
to
be
a
relationship
between
acutely
sensitive
species
and
increases
in
ACRs
as
sensitivity
decreased,
the
FACR
was
derived
from
data
for
species
whose
SMAVs
were
close
to
the
FAV.
Based
on
the
normalization
water
chemistry
conditions
used
for
illustrative
21
purposes
in
the
document,
the
freshwater
CMC
value
is
4.2,
which
divided
by
the
FACR
of
3.23
results
in
a
freshwater
CCC
of
1.3

g/
L
dissolved
Cu.

5.3.3
Evaluation
of
the
Chronic
Data
Available
for
Saltwater
Species
Only
one
acceptable
saltwater
chronic
copper
value
is
available
for
the
sheepshead
minnow
(
Table
2b).
This
chronic
toxicity
value
was
obtained
from
a
flow­
through
early
life
stage
test
in
which
the
concentrations
of
copper
in
the
test
chamber
were
measured.

The
ELS
test
with
sheepshead
minnow
was
one
of
the
tests
for
which
the
chronic
value
and
most
sensitive
effect
are
reported
without
providing
concentration­
response
data.
Thus,
regression
analysis
was
not
an
option
for
statistical
evaluation
of
the
data
in
this
case.
In
the
28­
day
ELS
test,
growth
was
reported
to
be
a
more
sensitive
endpoint
than
mortality,
and
the
chronic
value
for
growth
was
249

g/
L.
The
96­
hour
LC50
reported
for
copper
in
this
study
was
368

g/
L,
and
the
two
values
provide
an
acute­
chronic
ratio
of
1.48.

A
life­
cycle
test
was
conducted
with
the
mysid,
Americamysis
bahia
(
formerly
Mysidopsis
bahia).
Survival
of
mysids
was
reduced
at
140

g/
L,
and
production
of
young
virtually
ceased
at
77

g/
L
(
significant
at
P<
0.05),
but
reproduction
at
24
and
38

g/
L
was
not
different
from
that
of
controls.
Based
on
reproductive
data,
unacceptable
effects
were
observed
at
77

g/
L,
but
not
at
38

g/
L,
resulting
in
a
chronic
value
of
54.09

g/
L.
Using
the
acute
value
of
181

g/
L,
an
ACR
for
this
mysid
would
be
3.346.
Control
survival
in
this
test
however,
was
considered
inadequate;
thus,
the
chronic
value
was
not
used
to
derive
the
final
chronic
criterion.

The
ACR
value
for
saltwater
is
for
a
relatively
acutely
insensitive
saltwater
species,
with
a
GMAV
falling
in
the
upper
half
of
all
tested
saltwater
genera.
The
lowest
saltwater
acute
values
are
from
tests
with
embryos
and
larvae
of
molluscs
and
embryos
of
summer
flounder,
which
are
possibly
the
most
sensitive
life
stages
of
these
species.
Although
saltwater
ACRs
for
acutely
sensitive
saltwater
species
are
not
available,
ACRs
for
acutely
sensitive
freshwater
species
are
available.
Some
of
the
most
acutely
sensitive
freshwater
species
for
which
ACRs
are
available
are
cladocerans
C.
dubia,
D.
magna,
and
D.
pulex).
(
Data
for
D.
pulex
are
not
listed
in
Table
1a
because
of
the
ranking
based
on
the
chemical
characterization
of
the
test
water
for
the
BLM.
D.
pulex
would
be
among
the
most
acutely
sensitive
species
if
a
hardness
adjustment
were
utilized
instead
of
the
BLM.)
On
the
basis
of
data
for
the
five
sensitive
freshwater
species
along
with
the
one
available
saltwater
ACR
for
the
sheepshead
minnow,
the
saltwater
FACR
is
the
same
as
the
freshwater
ACR
of
3.23.
Thus,
for
saltwater,
the
final
chronic
value
for
copper
is
equal
to
the
FAV
of
6.188

g/
L
divided
by
the
ACR
of
3.23,
or
1.9

g/
L
(
Table
3c).

6.0
PLANT
DATA
Copper
has
been
widely
used
as
an
algicide
and
herbicide
for
nuisance
aquatic
plants
(
McKnight
et
al.
1983).
Although
copper
is
known
as
an
inhibitor
of
photosynthesis
and
plant
growth,
toxicity
data
on
individual
species
suitable
for
deriving
aquatic
life
criteria
(
Table
4a,
b)
are
not
numerous.

The
relationship
of
copper
toxicity
to
the
complexing
capacity
of
the
water
or
the
culture
medium
is
now
widely
recognized
(
Gächter
et
al.
1973;
Petersen
1982),
and
several
studies
have
used
algae
to
"
assay"
the
copper
complexing
capacity
of
both
fresh
and
salt
waters
(
Allen
et
al.
1983;
Lumsden
and
Florence
1983;
Rueter
1983).
It
has
also
been
shown
that
algae
are
capable
of
excreting
complexing
substances
in
response
to
copper
stress
(
McKnight
and
Morel
1979;
Swallow
et
al.
1978;
van
den
Berg
et
al.
1979).
Foster
(
1982)
and
Stokes
and
Hutchinson
(
1976)
have
identified
resistant
strains
and/
or
species
of
algae
from
copper
(
or
other
metal)
impacted
environments.
A
portion
of
this
resistance
probably
results
from
induction
of
the
chelate­
excretion
mechanism.
Chelate
excretion
by
algae
may
also
serve
as
a
22
protective
mechanism
for
other
aquatic
organisms
in
eutrophic
waters;
that
is,
where
algae
are
capable
of
maintaining
free
copper
activities
below
harmful
concentrations.

Copper
concentrations
from
1
to
8,000

g/
L
have
been
shown
to
inhibit
growth
of
various
freshwater
plant
species.
Very
few
of
these
tests,
though,
were
accompanied
by
analysis
of
actual
copper
exposure
concentrations.
Notable
exceptions
are
freshwater
tests
with
green
alga,
including
Chlamydomonas
reinhardtii
(
Schafer
et
al.
1993;
Winner
and
Owen
1991b),
which
is
the
only
flowthrough
measured
test
with
an
aquatic
plant,
Chlorella
vulgaris
and
Selenastrum
capricornutum
(
Blaylock
et
al.
1985).
There
is
also
a
measured
test
with
duckweed
(
Taraldsen
and
Norberg­
King
1990).

A
direct
comparison
between
the
freshwater
plant
data
and
the
BLM
derived
criteria
is
difficult
to
make
without
a
better
understanding
of
the
composition
of
the
algal
media
used
for
different
studies
(
e.
g.,
DOC,
hardness,
and
pH)
because
these
factors
influence
the
applicable
criteria
comparison.
BLM
derived
criteria
for
certain
water
conditions,
such
as
low
to
mid­
range
pH,
hardness
up
to
100
mg/
L
as
CaCO3,
and
low
DOC
are
in
the
range
of,
if
not
lower
than,
the
lowest
reported
toxic
endpoints
for
freshwater
algal
species
and
would
therefore
appear
protective
of
plant
species.
In
other
water
quality
conditions
BLMderived
criteria
may
be
significantly
higher
(
see
Figure
6).

Data
are
available
on
the
toxicity
of
copper
in
saltwater
to
several
species
of
macroalgae
and
microalgae
(
Table
4b).
A
comparison
of
effect
levels
seen
in
tests
with
saltwater
plants
and
the
CMC
and
CCC
established
to
protect
saltwater
animals
indicates
that
only
one
test
result
falls
slightly
below
the
CCC.
One
static
unmeasured
test,
with
the
microalgae
Scrippsiella
faeroense,
provides
an
8­
day
growth
EC50
of
<
1

g/
L
(
Saifullah
1978).
However,
this
result
failed
to
include
a
reported
background
copper
concentration
of
1.86­
4.18

g/
L,
placing
this
response
in
the
range
of
<
2.86
 
<
5.18.
In
addition,
the
study
included
a
second
experiment
with
the
same
species
and
an
8­
day
growth
EC50
of
5

g/
L;
adding
in
the
reported
background
range
brings
this
EC50
to
6.86
 
9.18

g/
L.
Thus,
the
animal
CCC
appears
adequate
for
protecting
against
chronic
seawater
plant
effects
observed
in
tests
included
in
Table
4b.

Two
publications
provide
data
for
the
red
algae
Champia
parvula
that
indicate
that
reproduction
of
this
species
is
especially
sensitive
to
copper.
The
methods
manual
(
U.
S.
EPA
1988)
for
whole
effluent
toxicity
(
WET)
testing
contains
the
results
of
six
experiments
showing
nominal
reproduction
LOECs
from
48­
hr
exposures
to
1.0
to
2.5

g/
L
copper
(
mean
2.0

g/
L);
these
tests
used
a
mixture
of
50
percent
sterile
seawater
and
50
percent
GP2
medium
copper.
The
second
study
by
Morrison
et
al.
(
1989)
evaluated
interlaboratory
variation
of
the
48­
hr
WET
test
procedure;
this
six­
test
study
gave
growth
EC50
values
from
0.8
to
1.9

g/
L
(
mean
1.0

g/
L).
Thus,
there
are
actually
12
tests
that
provide
evidence
of
significant
reproductive
impairment
in
C.
parvula
at
nominal
copper
concentrations
between
0.8
and
2.5

g/
L,
which
is
in
the
range
of
the
saltwater
CCC.
For
these
studies
though,
the
dilution
water
source
was
not
identified.

One
difficulty
in
assessing
these
data
is
the
uncertainty
of
the
copper
concentration
in
the
test
solutions,
primarily
with
respect
to
any
background
copper
that
might
be
found
in
the
dilution
water,
especially
with
solutions
compounded
from
sea
salts
or
reagents.
Thus,
with
a
CCC
of
1.9

g/
L
dissolved
copper,
the
significance
of
a
1
or
2

g/
L
background
copper
level
to
a
1
to
3

g/
L
nominal
effect
level
can
be
considerable.

The
reproduction
of
other
macroalgae
appears
to
be
generally
sensitive
to
copper,
but
not
to
the
extent
of
Champia.
Many
of
these
other
macroalgae
appear
to
have
greater
ecological
significance
than
Champia,
several
forming
significant
intertidal
and
subtidal
habitats
for
other
saltwater
organisms,
as
well
as
being
a
major
food
source
for
grazers.
Reproductive
and
growth
effects
on
the
other
species
of
macroalgae
sometimes
appear
to
occur
at
copper
concentrations
between
5
and
10

g/
L
(
Appendix
C,
Other
Data).
Thus,
most
major
macrophyte
groups
seem
to
be
adequately
protected
by
the
CMC
and
CCC,
but
appear
similar
in
sensitivity
to
some
of
the
more
sensitive
groups
of
saltwater
animals.
23
7.0
BIOACCUMULATION
OF
COPPER
Because
no
regulatory
action
levels
for
copper
and
human
health
are
applicable
to
aquatic
organisms,
and
no
consumption
limits
are
established
for
wildlife,
there
is
no
basis
for
developing
a
residue­
based
criterion
(
or
final
residue
value)
for
copper
based
on
EPA's
current
Guidelines.

As
more
information
is
acquired
about
food
consumption
as
a
route
of
copper
exposure
to
fish
and
macroinvertebrates,
bioaccumulation
potential
 
and
the
link
to
environmental
source
concentrations
 
may
become
a
considerably
more
important
factor
in
establishing
criteria.
Currently,
the
database
available
for
calculating
potential
bioconcentration
(
from
the
water)
or
bioaccumulation
(
from
all
sources)
is
limited.
This
is
especially
true
given
the
current
Guidelines
requirement
for
deriving
BCFs
that
all
water
concentrations
be
adequately
quantitated,
and
that
tissue
levels
be
approaching
steady
state
or
else
that
tests
be
at
least
28
days
in
duration.
Additionally,
bioconcentration
factors
for
copper
usually
are
not
constant;
instead,
they
generally
decrease
as
aqueous
copper
concentrations
increase
(
McGeer
et
al.
2003).

After
culling
the
data
according
to
the
Guidelines,
the
only
acceptable
bioaccumulation
factors
for
copper
(
Table
5a,
b)
were
juvenile
fathead
minnows
(
464),
Asiatic
clams
(
45,300),
polychaete
worms
(
1,006
 
2,950),
mussels
(
2,491
 
7,730),
and
Pacific
oysters
(
33,400
 
57,000).

8.0
OTHER
DATA
Many
of
the
data
identified
for
this
effort
are
listed
in
Appendix
C,
Other
Data,
for
various
reasons,
including
exposure
durations
other
than
96
hours
with
the
same
species
reported
in
Tables
1a
and
1b,
with
some
exposures
lasting
up
to
30
days.
Acute
values
for
test
durations
less
than
96
hours
are
available
for
several
species
not
shown
in
Tables
1a
and
1b.
Still,
these
species
have
approximately
the
same
sensitivities
to
copper
as
species
in
the
same
families
listed
in
Tables
1a
and
1b.
Reported
LC50s
at
200
hours
for
chinook
salmon
and
rainbow
trout
(
Chapman
1978)
differ
only
slightly
from
96­
hour
LC50s
reported
for
these
same
species
in
the
same
water.

A
number
of
other
acute
tests
in
Appendix
C
were
conducted
in
dilution
waters
that
were
not
considered
appropriate
for
criteria
development.
Brungs
et
al.
(
1976)
and
Geckler
et
al.
(
1976)
conducted
tests
with
many
species
in
stream
water
that
contained
a
large
amount
of
effluent
from
a
sewage
treatment
plant.
Wallen
et
al.
(
1957)
tested
mosquitofish
in
a
turbid
pond
water.
Until
chemical
measurements
that
correlate
well
with
the
toxicity
of
copper
in
a
wide
variety
of
waters
are
identified
and
widely
used,
results
of
tests
in
unusual
dilution
waters,
such
as
those
in
Appendix
C,
will
not
be
very
useful
for
deriving
water
quality
criteria.

Appendix
C
also
includes
tests
based
on
physiological
effects,
such
as
changes
in
growth,
appetite,
blood
parameters,
stamina,
etc.
These
were
included
in
Appendix
C
because
they
could
not
be
directly
interpreted
for
derivation
of
criteria.

A
direct
comparison
of
a
particular
test
result
to
a
BLM­
derived
criterion
is
not
always
straightforward,
particularly
if
complete
chemical
characterization
of
the
test
water
is
not
available.
Such
is
the
case
for
a
number
of
studies
included
in
Appendix
C.
While
there
are
some
test
results
with
effect
concentrations
below
the
example
criteria
concentrations
presented
in
this
document,
these
same
effect
concentrations
could
be
above
criteria
derived
for
other
normalization
chemistries,
raising
the
question
as
to
what
is
the
appropriate
comparison
to
make.
For
example,
Appendix
C
includes
an
EC50
for
D.
Pulex
of
3.6

g/
L
(
Koivisto
et
al.
1992)
at
an
approximate
hardness
of
25
mg/
L
(
33
mg/
L
as
CaCO3).
Yet,
example
criteria
at
a
hardness
of
25
mg/
L
(
as
CaCO3)
(
including
those
in
Figure
6)
range
from
0.23

g/
L
(
DOC
=
0.1
mg/
L)
to
4.09

g/
L
(
DOC
=
2.3
mg/
L)
based
on
the
DOC
concentration
selected
for
the
synthetic
water
recipe.
The
chemical
composition
for
the
Koivisto
et
al.
(
1992)
study
would
dictate
what
the
appropriate
BLM
criteria
comparison
should
be.
24
Based
on
the
expectation
that
many
of
the
test
results
presented
in
Appendix
C
were
conducted
in
laboratory
dilution
water
with
low
levels
of
DOC,
the
appropriate
comparison
would
be
to
the
criteria
derived
from
low
DOC
waters.
Comparing
many
of
the
values
in
Appendix
C
to
the
example
criteria
presented
in
this
document,
it
appears
that
a
large
proportion
of
Appendix
C
values
are
above
these
concentration
levels.
This
is
a
broad
generalization
though
and
as
stated
previously,
all
important
water
chemistry
variables
that
affect
toxicity
of
copper
to
aquatic
organisms
should
be
considered
before
making
these
types
of
comparisons.

Studies
not
considered
suitable
for
criteria
development
were
placed
in
Appendix
I,
Unused
Data.

9.0
NATIONAL
CRITERIA
STATEMENT
The
procedures
described
in
the
"
Guidelines
for
Deriving
Numerical
National
Water
Quality
Criteria
for
the
Protection
of
Aquatic
Organisms
and
Their
Uses"
indicate
that,
except
where
a
locally
important
species
is
very
sensitive,
freshwater
aquatic
organisms
and
their
uses
should
not
be
affected
unacceptably
if
the
4­
day
average
concentration
of
dissolved
copper
does
not
exceed
the
BLM­
derived
sitewater
LC50
(
i.
e.,
FAV)
divided
by
the
FACR
more
than
once
every
3
years
on
the
average
(
i.
e.,
the
CCC)
and
if
the
24­
hour
average
dissolved
copper
concentration
does
not
exceed
the
BLM­
derived
site­
LC50
(
or
FAV)
divided
by
two,
more
than
once
every
3
years
on
the
average
(
i.
e.,
the
CMC).

The
procedures
described
in
the
"
Guidelines
for
Deriving
Numerical
National
Water
Quality
Criteria
for
the
Protection
of
Aquatic
Organisms
and
Their
Uses"
indicate
that,
except
where
a
locally
important
species
is
very
sensitive,
saltwater
aquatic
organisms
and
their
uses
should
not
be
affected
unacceptably
if
the
4­
day
average
concentration
of
dissolved
copper
does
not
exceed
1.9

g/
L
more
than
once
every
3
years
on
the
average
and
if
the
24­
hour
average
concentration
does
not
exceed
3.1

g/
L
more
than
once
every
3
years
on
the
average.

A
return
interval
of
3
years
continues
to
be
EPA's
general
recommendation.
However,
the
resilience
of
ecosystems
and
their
ability
to
recover
differ
greatly.
Therefore,
a
site­
specific
return
interval
for
the
criteria
may
be
established
if
adequate
justification
is
provided.

10.0
IMPLEMENTATION
The
use
of
criteria
in
designing
waste
treatment
facilities
requires
selection
of
an
appropriate
wasteload
allocation
model.
Dynamic
models
are
preferred
for
application
of
these
criteria.
Limited
data
or
other
factors
may
make
their
use
impractical,
in
which
case
one
should
rely
on
a
steady­
state
model.
EPA
recommends
the
interim
use
of
1Q5
or
1Q10
for
criterion
maximum
concentration
design
flow
and
7Q5
or
7Q10
for
the
criterion
continuous
concentration
design
flow
in
steady­
state
models
for
unstressed
and
stressed
systems,
respectively.
These
matters
are
discussed
in
more
detail
in
the
Technical
Support
Document
for
Water
Quality­
Based
Toxics
Control
(
U.
S.
EPA
1991).

With
regard
to
BLM­
derived
freshwater
criteria,
to
develop
a
site­
specific
criterion
for
a
stream
reach,
one
is
faced
with
determining
what
single
criterion
is
appropriate
even
though
a
BLM­
calculated
"
instantaneous
criterion"
(
i.
e.,
a
criterion
value
appropriate
for
specific
water
chemistry
conditions
at
a
particular
instant)
will
be
time­
variable.
This
is
not
a
new
problem
unique
to
the
BLM
 
hardnessdependent
metals
criteria
are
also
time­
variable
values.
Although
the
variability
of
hardness
over
time
can
be
characterized,
EPA
has
not
provided
guidance
on
how
to
calculate
site­
specific
criteria
considering
this
variability.
Multiple
input
parameters
for
the
BLM
complicate
the
calculation
of
site­
specific
criteria
because
of
their
combined
effects
on
variability.
EPA
is
currently
in
the
process
of
developing
guidance
on
how
to
address
these
factors.
Presently,
EPA
expects
that
few
sites
have
sufficient
data
for
all
the
input
parameters
to
enable
adequate
characterization
of
the
inherent
variation
at
a
site.
Therefore,
EPA
is
25
currently
evaluating
probabilistic
techniques
(
Monte
Carlo
techniques)
and
statistical
analyses
to
address
this
issue
and
anticipates
publishing
separate
BLM
implementation
guidance.
58
11.0
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Appendices
Appendix
A.
Ranges
in
Calibration
and
Application
Data
Sets
Appendix
B.
Biotic
Ligand
Model
(
BLM)
User's
Guide
Appendix
C.
Other
Data
on
Effects
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Freshwater
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Saltwater
Organisms
Appendix
D.
Estimation
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Water
Chemistry
Parameters
for
Acute
Copper
Toxicity
Tests
Appendix
E.
Saltwater
Conversion
Factors
for
Dissolved
Values
Appendix
F.
BLM
Input
Data
and
Notes
Appendix
G.
Hardness
Slopes
Appendix
H.
Regression
Plots
Appendix
I.
Unused
Data
