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1
Exploration of Methods for
Characterizing Effects of Chemical
Stressors to Aquatic Animals
December 1, 2010
United States Environmental Protection Agency
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2
Acknowledgement
• Project team• Charles Delos
• Russell Erickson
• Matthew Etterson
• Kristina Garber
• Dale Hoff
• David Mount
• Sandy Raimondo
• Keith Sappington
• Charles Stephan
• Patti TenBrook
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Outline
• Introduction
• Species Sensitivity Distributions (SSDs)
• Extrapolation Factors (EFs)
• Potential applications of SSDs and EFs for OPP and OW
• Proposed Analysis
• Case Study
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Introduction
• Assessment Endpoints
• an explicit expression of the
environmental value to be protected
• OPP and OW = survival, growth and
reproduction of aquatic animals
• OPP = taxa (fish and invertebrates are
separated)
• OW = community (fish + invertebrates)
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5
Introduction
• Measures of Effect
• Results from acute and chronic toxicity
tests
• OPP = lowest endpoints for fish and
invertebrates
• EC50s, NOECs
• OW = HC5
• EC50s, MATCs or EC20s
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Introduction
• The objective of this project is to
examine how available toxicity data
can best be used to characterize
adverse effects on aquatic animals
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Sensitivity Distributions
• Definition
• Cumulative distribution of responses of
different biological taxa to the same
stressor
• Based on similar endpoints (e.g.,
EC50s from acute toxicity tests)
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Sensitivity Distributions
• Regulatory uses
• OPP – SWAMP, ecological risk
assessments
• OW – 1985 guidelines
• Europe
• OECD
• Australia, New Zealand
• UC-Davis methodology
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Sensitivity Distributions
• Distributions with large data sets
• Toxicity test results are log-
transformed
• Normal, logistic, triangular, Burr,
Gompertz
• Cumulative distribution functions used
to derive 5th percentile (aka HC5) and
other percentiles
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Sensitivity Distributions
Figure 1. Fitted cumulative distribution functions of log-transformed SMAVs (ppb) for diazinon.
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Sensitivity Distributions
• Distributions with small data sets
• 2 Approaches are available to account
for uncertainty in deriving HC5
• Approach 1:
Mean (x) and variance (s) are based on
sample data (log-transformed data)
Use extrapolation constants (k)
• Based on distribution shape and level of
confidence
Equation:ksx
HC 105
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Sensitivity Distributions
• Distributions with small data sets
• 2 Approaches are available to account
for uncertainty in deriving HC5 (cont.)
• Approach 2:
Mean (α) is based on sample, variance (β) is
known (de Zwart 2002)
• variance based on MOA
• Equation:94.2
5 10HC
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Sensitivity Distributions
• Distributions with small datasets
• Methods available to derive chronic
HC5 values from acute toxicity data
• De Zwart 2002
MOA is known, variance is known
• Duboudin et al. 2004
Separate regressions for fish and
invertebrates
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Extrapolation Factors
• Definition
• set values that are applied to available
toxicity test results to account for
various sources of uncertainty in
extrapolating from individual species
toxicity data to measures of effect
• available toxicity data are identified for
a chemical and the lowest toxicity test
result is divided by the EF
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Extrapolation Factors
• Great Lakes Water Quality Guidance Tier II values
• EFs based on the number of MDRs
• EFs intended to be below the FAV (5th
percentile of triangular distribution)
• Lowest GMAV is divided by EF
• Several similar approaches in use by regulatory agencies
• Michigan DEQ, Ohio EPA, USDOE, UC-Davis method
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Extrapolation Factors
• Scientific Literature
• Pennington 2003 derived EFs based on
de Zwart (2002)
• Variability is known and based on MOA
• Can be used to approximate HC5 values
for normal, logistic and triangular
distributions
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Extrapolation Factors
• OPP Aquatic Benchmarks
• Based on OPP’s ERA process
• Lowest toxicity test result is multiplied
by LOC
• 4 animal benchmarks (FW)
• Acute fish
• Acute invertebrate
• Chronic fish
• Chronic invertebrate
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Potential Applications of SSDs and EFs
• SSDs in ERA
• Characterization of monitoring data and
EECs
• Characterization of measures of effect
• EFs can be used to derive various
percentiles of SSD to accomplish above
• Aquatic Life Screening Value (ALSV)
• ≤ 5th percentile
• Derive scientifically defensible water quality
standards
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Acute ALSV
Figure 2. Conceptual framework for deriving acute ALSV.
Compile
empirical
toxicity
data.
Determine
adverse
outcome
pathway.
Can empirical toxicity
data be supplemented
with predicted data?
If yes, use appropriate
tools for chemical to
predicted toxicity data.
If no, acute toxicity
database contains only
empirical data.
Acute toxicity database
contains empirical and
predicted data.
Use extrapolation factor or sensitivity
distribution to derive ALSV.
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Chronic ALSV
Figure 3. Conceptual framework for deriving chronic ALSV.
Compile
empirical
toxicity data
Determine
adverse
outcome
pathway
Select option for
deriving HC5 (based
on available data)
Use sensitivity distribution
with empirical data to derive
ALSV
Apply ACR to acute HC5 to
derive ALSV
Use regression approach to
derive ALSV
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Proposed Analyses
• 3 Phases:
• Analyses with “data-rich” chemicals
• Analysis with “data-limited” chemicals
• Empirical data only
• Analysis with “data-limited” chemicals
• Empirical and predicted data
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Proposed Analyses
• Phase 1: Analyses with “data-rich” chemicals
• Use large data sets
• Acute: from Web-ICE empirical database
• Chronic: AquaChronTox database
• Compare fit of several distributions
• Derive reference percentile values (HCp) for each chemical
• including HC5
• Other percentiles will be considered
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Proposed Analyses
• Phase 2: Analysis with “data-limited”
chemicals
• EPA will explore the accuracy of HCp
estimates from limited subsets of the same
data
• Consider MOA
• Use EF and SSD approaches to derive HC5
values and other percentiles
• Compare to “known” HC5 values from full
distributions
• EPA will derive EFs
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Proposed Analyses
• Phase 3: Analysis with “data-limited”
chemicals and predicted toxicity data
• Evaluate various approaches described
in tools paper
• Acute and chronic methods
• Compare estimated HC5 values to
“known” HC5 values from phase 1
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Case Study: Diazinon
OPP Benchmarks Value (ppb) OW Criteria Value (ppb)
Acute FW Fish 45 Acute FW
(CMC)
0.17
(FAV = 0.3397)Acute FW inverts 0.105
Chronic FW fish <0.55Chronic FW
(CCC)0.17
Chronic FW inverts 0.17
• Organophosphate Insecticide
• MOA in animals: AChE inhibition
• Criteria and benchmarks are available
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Case Study- Acute
• Criterion derived from acute toxicity
test results for FW fish and
invertebrates
• 23 species
• 19 genera
• Invertebrates represent the top 9 most
sensitive species
• cladocerans are top 4
• SMAVs range 0.3773 – 11,640 ppb
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Case Study - Acute
HC5 values (ppb)
-Normal = 0.50
-Logistic = 0.54
-Triangular = 0.39
-Gompertz = 0.71
(FAV = 0.34)
Figure 1. Fitted cumulative distribution functions of log-transformed SMAVs (ppb) for diazinon (from criteria).
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Case Study - Acute
• Scenario: data are only available for
3 animal species
• Typical species tested to fulfill FIFRA
data requirements
• Daphnia magna EC50 = 1.05 ppb
• Rainbow trout LC50 = 426 ppb
• Bluegill Sunfish LC50 = 470 ppb
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Case Study - Acute
Figure 2. Conceptual framework for deriving acute ALSV.
Compile
empirical
toxicity data
Determine
adverse
outcome
pathway
Can empirical toxicity
data be supplemented
with predicted data?
If yes, use appropriate
tools for chemical to
predicted toxicity data.
If no, acute toxicity
database contains only
empirical data.
Acute toxicity database
contains empirical and
predicted data.
Use extrapolation factor or sensitivity
distribution to derive ALSV.
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Case Study - Acute
• Example tool: Web-ICE
• Can estimate EC50 values for several
species
• Need to consider
• extrapolation outside of models
• df>3
• MSE <0.22
• close taxonomic relatedness
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Case Study - Acute
• Web-ICE estimates
+ logistic SSD
• HC5 = 29.8 ppb
• Limitations:
• All species are fish
• based on the MOA of
diazinon, invertebrates are
expected to be more
sensitive
• The HC5 may not be
conservative
(supported by fit data
where HC5 values were 2
orders of magnitude lower)
Species Surrogate EC50 (ppb)
Atlantic salmon B 225
Brook trout B 233
Apache trout R 350
Yellow perch B 367
Brown trout R 393
Lake trout R 396
Largemouth bass B 400
Cutthroat trout R 458
Spotfin chub R 542
Green throat darter R 557
Chinook salmon R 588
Coho salmon R 625
Cape fear shiner R 723
Green sunfish R 749
Razorback sucker R 917
B= bluegill; R = rainbow trout
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Case Study - Acute
• If no tools are available for this
chemical, SSDs or EFs can be
applied directly to empirical data
• Diazinon toxicity data subset used
for this case study
• Daphnia magna EC50 = 1.05 ppb
• Rainbow trout LC50 = 426 ppb
• Bluegill Sunfish LC50 = 470 ppb
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• SSDs for subset of diazinon toxicity data
• Mean and variance is estimated from
sample
• Equation:
• Mean (x) = 1.77 (log transformed)
• Standard deviation (s) = 1.52
33
Case Study - Acute
Distribution (method source)Extrapolation
constant (k)Median HC5
Log-normal (Aldenberg and Jaworska 2000) 1.94 0.0674
Log-triangular (Pennington 2003) 1.9 0.0775
Log-logistic (Aldenberg and Slob 1993) 2.05 0.0459
ksx
HC 105
HC5 - full
data set
0.50
0.39
0.54
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• SSDs for subset of diazinon toxicity
data
• Equation: (de Zwart 2002)
• Mean is estimated from sample
Mean (α) = 1.77 (log-transformed)
• Variance is known
For AChE inhibition (by OPs), β = 0.50
• HC5 = 2.01 ppb
34
Case Study - Acute
94.2
5 10HC
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Case Study - Acute
Description (method source)Extrapolation
factor
Estimated
HC5 (μg/L)
Great lakes guidance 8 0.131
Log-normal (Pennington 2003) 10 0.105
Log-logistic (Pennington 2003) 12 0.0875
Log-triangular (Pennington 2003) 9.4 0.112
• Extrapolation factors for subset of
diazinon toxicity data
• Lowest toxicity value is divided by
extrapolation factor
• Extrapolation factor for n = 3
• Lowest toxicity value = 1.05 ppb
HC5 - full
data set
0.39
0.50
0.54
0.39
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Case Study - Chronic
• ACRs (from criteria)
• range 1.112-1.586 for aquatic
invertebrates (2 species)
• Range 23.84 to >903.8 for fish
• None of these ACRs correspond to the
data subset used above (i.e., D.
magna, bluegill, rainbow trout)
• Scenario: no chronic data are available for
the example chemical
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Deriving the Chronic ALSV
Figure 3. Conceptual framework for deriving chronic ALSV.
Compile
empirical
toxicity data
Determine
adverse
outcome
pathway
Select option for
deriving HC5 (based
on available data)
Use sensitivity distribution
with empirical data to derive
ALSV
Apply ACR to acute HC5 to
derive ALSV
Use regression approach to
derive ALSV
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Case Study - Chronic
• Can use MOA-specific ACRs
(Raimondo et al 2007)
• For OPs, median and 90th percentile
ACRs are 6.2 and 77.8, respectively
• If acute ALSV were based on the great
lakes EFs (0.131 ppb), and the median
ACR is used
• the chronic ALSV would be 0.0211 ppb
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Case Study - Chronic
• Can use SSD based on acute toxicity data (de Zwart 2002)
• Regression equation:
• Mean of acute data (αacute) = 1.77
• Use AChE inhibition variance (0.50)
• HC5 equation:
• Chronic HC5 = 0.0930 ppb
430.1*053.1 acutechronic
94.2
5 10HC
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Summary
• EPA is currently considering 2
approaches for characterizing
available toxicity data for aquatic
animals
• These approaches can be used by
OPP and OW for ecological risk
assessment and criteria
development
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Summary
• EPA will evaluate available methods
and tools to derive a process that
OPP and OW can use to
characterize the effects of chemicals
with varying amounts of empirical
data
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Comments/Questions