oEPA
United States
Environmental Protection
Agency
Office of Water
4304T
EPA-822-R-22-001
March 2022
Metals Cooperative Research and Development
Agreement (CRADA) Phase I Report:
Development of an Overarching Bioavailability Modeling
Approach to Support
US EPA's Aquatic Life Water Quality Criteria for Metals
March 2022
Developed by the US Environmental Protection Agency
in collaboration with the Metals CRADA Partners
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Acknowledgements
EPA Lead:
Christine Bergeron, Ph.D., Office of Water, Office of Science and Technology, Health and
Ecological Criteria Division, Washington, DC
EPA Reviewers:
Kathryn Gallagher, Ph.D., and Elizabeth Behl, Office of Water, Office of Science and
Technology, Health and Ecological Criteria Division, Washington, DC
CRADA Partner Collaborators (alphabetical order):
William Adams, Red Cap Consulting, Lake Point, Utah, USA
David Boyle, Cobalt Institute, Guilford, UK
M. Jasim Chowdhury, International Lead Association, Durham, North Carolina, USA
Carrie Claytor, Copper Development Association, Inc., Washington, D.C., USA
Robert Dwyer (retired), International Copper Association, Cape Cod, Massachusetts, USA
Emily Garman, NiPERA Inc., Durham, North Carolina, USA
Yamini Gopalapillai, Copper Development Association, Inc., Washington, D.C., USA
Elizabeth Middleton, NiPERA, Durham, North Carolina, USA
Eirik Nordheim, European Aluminium, Brussels, Belgium
Adam Ryan, International Zinc Association, Durham, North Carolina, USA
Christian Schlekat, NiPERA Inc., Durham, North Carolina, USA
William Stubblefield, Oregon State University, Corvallis, Oregon, USA
Curt Wells, The Aluminum Association, Arlington, VA, USA
Eric Van Genderen, International Zinc Association, Durham, North Carolina, USA
External Peer Reviewers:
David Buchwalter, Ph.D, Department of Biological Sciences, North Carolina State University,
Durham, NC, USA
Claude Fortin, Ph.D, Institut National de la Recherche Scientifique (INRS), Quebec, Canada
Erin M. Leonard, Ph.D, Integrative Biology, University of Guelph, Ontario, Canada
Christopher A. Mebane, U.S. Geological Survey, Boise, ID, USA
Wilhelmus Peijnenburg, Ph.D, National Institute of Public Health and the Environment (RIVM),
Centre for Safety of Substances and Products, The Netherlands
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Table of Contents
I. Overview of the Metals CRADA Project 1
II. Metal Toxicity Modifying Factors (TMFs) and their relative importance 3
III. Discussion of bioavailability modeling approaches examined 5
IV. Case Studies of Modeling Approach Comparisons 13
V. Conclusions: Discussion and recommendations of modeling approach 15
VI. References 17
List of Tables
Table 1. Toxicity modifying factors that have been demonstrated to be important in various
BLM and MLR published models and their relative importance within each metal 5
Table 2: Comparisons of bioavailability models currently available or in development for the six
metals represented by the CRADA 7
Table 3: Acute and chronic performance scores for each metal based on the recommended MLR
models and BLMs in the cases studies 13
List of Appendices
A. Publications based on the SET AC Technical Workshop, Bioavailability-Based Aquatic
Toxicity Models for Metals, December 2017 (available at
https://setac.onlinelibrary.wilev. com/doi/toc/10.1002/(ISSN)1552-8618. metal-bioavailability-
modeling).
B. Explanation of How Toxicity Modifying Factors (TMFs) Affect Individual Metals developed
by CRADA Partners
C. Table 1: Bioavailability Model Comparisons, Table 2: Supporting Information, and
References developed by CRADA Partners. See supplemental materials section at
https://www.epa.gov/wqc/metals-crada-phase-l-report.
D. Aluminum and Copper model comparisons: Brix et al. 2020b peer reviewed internal report.
The copper portion of this analyses has been published and is available at
https://setac.onlinelibrarv.wilev.com/doi/10.1002/etc.5Q12. The publication for the aluminum
MLR portion of this analyses has been published and is available at
https://setac.onlinelibrarv.wiley.com/doi/epdf/10.1002/etc.4796. Any subsequent publications
will be listed when available in the supplemental materials section at
https://www.epa.gov/wqc/metals-crada-phase-l-report.
in
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E. Lead model comparison: DeForest et al. 2020b peer reviewed internal report. The publication
for the lead analyses is in preparation and will be listed when available in the supplemental
materials section at https://www.epa.gov/wqc/metals-crada-phase-1 -report.
F. Nickel model comparison: Santore et al. 2020 peer reviewed internal summary report.
Santore et al. 2021 and Croteau et al. 2021 have been published and are available at
https://setac.onlinelibrarv.wilev.com/doi/10.1002/etc.51Q9 and
https://setac.onlinelibrarv.wilev.com/doi/abs/10.1002/etc.5Q63, respectively.
G. Biotic Ligand Models and Multiple Linear Regression models provided by the CRADA
Partners for comparison with artificial and natural waters (internal appendix containing
proprietary modeling information which includes the software needed to run the models for
aluminum, copper, lead, and nickel and databases with sample water quality parameters and
answer keys).
Addendum: Summary of How Toxicity Modifying Factors (TMFs) Affect Metals Listed in
Table 1. Information provided through the peer review by Christopher A. Mebane (USGS).
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I. Overview of the Metals CRADA Project
In December 2017, the U.S. Environmental Protection Agency (EPA) signed a Cooperative
Research and Development Agreement (CRADA) with eight metals associations (Aluminum
Association, Aluminum REACH Consortium, Cobalt Institute, International Copper Association,
Copper Development Association, International Lead Association, International Zinc
Association, NiPERA Inc.) in order to leverage the knowledge and resources of scientists inside
and outside of the agency to better protect aquatic life. EPA's Office of Science and Technology
within the Office of Water (OW) is the Agency's technical lead on this CRADA which supports
EPA's FY 2018-2022 Strategic Plan Goal: Provide for Clean and Safe Water: Protect and
Restore Water Quality. EPA is using a two-phased approach to address the CRADA. In the first
phase, EPA has worked with external technical experts from the metals associations to develop a
proposed modeling approach to predict the bioavailability and toxicity of metals under the range
of water chemistry conditions found in aquatic environments common in freshwaters of the
United States. Subsequently, in the second phase, EPA will work with the metals associations to
develop bioavailability models for individual metals using the overarching modeling approach.
Using the resulting peer-reviewed models, EPA plans to develop updated, externally-peer
reviewed Aquatic Life Ambient Water Quality Criteria for metals to better support states,
territories and tribes with criteria that reflect the latest science and are easier to implement than
more complex, previous approaches using metals bioavailability modeling for criteria
development.
a. Brief overview of metals bioavailability
As summarized in Adams et al. (2020), metal toxicity to aquatic organisms is variable depending
on the physicochemical characteristics of the water in which they reside. Adverse effects occur
when the metal binds to or accumulates on biotic ligands (surface binding sites leading to
internalization and effect, for example, on the gill surface) and reaches a critical toxic threshold.
Common water chemistry parameters that are known to affect the toxicity of one or more metals
include pH, alkalinity, hardness, temperature, sodium, chloride, suspended solids, and colloidal
or dissolved organic carbon (DOC). The bioavailability of metals to aquatic organisms is
influenced by these parameters as they control the rate and extent to which the metal reaches the
site of action by affecting the solubility, sorption, or partitioning of the metal. The variability in
the toxicity of metals as a result of different water chemistries was recognized as early as the
1930s. Since then, research has led to the development of models to describe and predict the
toxicity of metals and the response of aquatic organisms at differing water chemistries. Current
bioavailability-based models often used to predict metal toxicity include: 1) empirically-based
linear regression equations based on single parameters, like hardness, 2) the mechanistically-
based Biotic Ligand Model (BLM), and 3) empirically-based multiple linear regression (MLR)
models.
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b. Overview of EPA's metals criteria and historic use of bioavailability-based
approaches
EPA develops Aquatic Life Ambient Water Quality Criteria (AWQC) for metals pursuant to
Clean Water Act Section 304(a)(1). AWQC are intended to protect aquatic organisms from the
toxic effects of metals in the aquatic environment and thereby the aquatic life designated use.
For most metals, the AWQC is not a single number to be applied uniformly across all surface
waters. Early AWQC for metals published in the 1980's were developed to take into account the
effects of ambient water hardness on toxicity. Hardness-based criteria are based on simple linear
regression models where the numeric magnitude of the AWQC is normalized to be protective at
a given site-specific hardness. Currently, EPA has developed recommended AWQC for 9 metals
(aluminum, cadmium, chromium (III and IV), copper, iron, lead, nickel, silver, and zinc). Most
of these metals criteria were developed in the 1980's and 1990's
(https://www.epa.gov/wqc/national-recommended-water-qualitv-criteria-aquatic-life-criteria-
table). Recent updated criteria efforts address bioavailability using different modeling
approaches. For example, in 2007, EPA revised the AWQC for Copper (US EPA 2007) to
incorporate an acute BLM to account for bioavailability as a function of water chemistry. In
2016, EPA updated the acute and chronic hardness slopes for cadmium with data for several new
species in the AWQC for Cadmium (US EPA 2016a) and determined that a more complex
modeling approach was not necessary for the criteria update. Lastly, in 2018, EPA revised the
Final AWQC for Aluminum (US EPA 2018) which uses MLR models to incorporate three
parameters (pH, total hardness, and DOC) to normalize acute and chronic toxicity data to water
quality conditions. EPA is now working to update the older metals criteria to reflect the latest
scientific knowledge on bioavailability using modeling approaches to incorporate water
chemistry parameters in addition to hardness, that can modify the bioavailability and toxicity of
metals.
c. Goal of project
The goal of the CRADA project is to develop a simplified, overarching modeling framework to
predict the bioavailability of metals considering a common model parameter set, modeling
approach and platform to update the remaining metals AWQC. This report provides a review of
models that are available to predict the toxicity of metals with respect to the factors that modify
toxicity as a function of water chemistry. The report focuses on the performance of BLMs and
MLR models for existing data sets for aluminum, copper, lead, and nickel. These datasets were
developed to meet the criteria established in the 1985 Guidelines (US EPA 1985) for AWQC
development and the models were evaluated using the criteria established in the Society of
Environmental Toxicology and Chemistry (SETAC) Technical Workshop, Bioavailability-Based
Aquatic Toxicity Models for Metals, December 2017 (SETAC 2017). The workshop resulted in a
series of articles on "Metal Bioavailability Modeling" that evaluated the performance of models
and recommend best practices in the development and use of bioavailability-based values for
protection of aquatic life (Adams et al. 2020; Brix et al. 2020a; Garman et al. 2020; Mebane et
al. 2020; Schlekat et al. 2020; Van Genderen et al. 2020; see Appendix A for references).
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II. Metal Toxicity Modifying Factors (TMFs) and their relative importance
In the aquatic environment, the toxicity of metals is dependent on many factors including the
individual metal and its chemical speciation, and the duration, magnitude, and route of exposure.
The effect of a number of metals on aquatic organisms is not well predicted by the total metal
concentration (except for aluminum). Metal bioavailability is a function of many modifying
factors that affect the speciation, bioavailability, and toxicity of metals. These factors include
pH, water hardness (primarily Ca and Mg ions), alkalinity, temperature, sodium, chloride,
fluoride, suspended solids, and DOC. However, the toxicity modifying factors (TMFs) that have
received the most attention in terms of bioavailability models are pH, hardness, and DOC
(Adams et al. 2020).
Meyer et al. (2007) described two ways in which these modifying factors can affect whether
metals result in bioavailable concentrations that can cause toxicity by affecting the physiological
responses of aquatic organisms. The first is by complexing or sorbing to metal ions (e.g., DOC,
carbonates, chloride, and hydroxide) which decreases the concentration of the free metal ion and
negatively affects the interaction with binding sites on the organism. The second way is by
competing with metal ions for binding sites on organisms (e.g., competition from H+, Ca2+, and
Mg2+).
Specifically, the effects of the most commonly studied TMFs are described below (see Meyer et
al. 2007 for more information and Appendix B for more detailed information on how TMFs
affect aluminum, copper, lead, nickel and zinc:
a. pH
There are several mechanisms by which changes in H+ ion concentrations (reflected by changes
in pH) can affect metal bioavailability, including speciation, solubility, and competitive
interactions between the metal and biotic ligands. The relative effect of H+ ions depends on the
binding strength of the metal to carbonate, bicarbonate and hydroxide ions. Generally, metals
dissociate at low pH (less than pH 6 to 7) which increases their solubility and thus bioavailability
and toxicity. However, as pH increases above pH 6 to 7, alkalinity often increases as well and
many metals become less bioavailable and less toxic because they form complexes with
carbonates and hydroxides, and subsequently may precipitate as oxides and hydroxides.
Complexation and precipitation reactions, mediated by changes in pH, can therefore affect the
concentration of the free metal ions available to bind to the biotic ligand (Meyer et al. 2007).
b. Hardness
In freshwater, hardness is dominated by Ca2+ and Mg2+ions which compete with divalent metal
ions for binding to the biotic ligand. As a result, increased water hardness generally leads to less
metal accumulation by aquatic organisms and lower toxicity. There are differences in the
protective effects of Ca2+ and Mg2+ ions: generally, Ca2+ is more protective in fish than Mg2+
(Meyer et al. 2007).
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c. Dissolved Organic Carbon [DOC]
Dissolved organic matter, typically quantified as DOC, is a heterogeneous mix of organic matter
of natural and anthropogenic origin that is impermeable to biological membranes. Generally, an
increase in DOC decreases metal bioavailability and toxicity by complexing with free metal ions,
thereby reducing metal binding at the biotic ligand. The protective effects of DOC depend on its
concentration, composition, and the binding affinity of the metal (Meyer et al. 2007, Wood et al.
2011).
d. Other
Although the TMFs pH, hardness, and DOC have been studied the most, other factors are known
also to modify the bioavailability and toxicity of metals. Temperature is potentially an important
TMF for some metals, but this is dependent on the species as well as the metal in the exposure
scenario. For example, the kinetics underlying aluminum bioavailability has a strong dependency
on temperature (Santore et al. 2018). Existing data for other metals such as nickel, copper, and
zinc do not show the same magnitude of correlations between temperature and chronic toxicity
(Pereira et al. 2017), but more information is needed. Ultimately, this factor has not received
enough attention in toxicity testing (Brix et al. 2020a; Mebane et al. 2020) to incorporate this
parameter into many models. Another parameter that is potentially important is total suspended
solid (TSS). Generally, toxicity decreases as TSS concentration increases because the free metal
ion binds to or sorbs to particles. Another parameter that has been investigated is sodium (Na+).
An increase in Na+ cations generally decreases toxicity by competition at metal binding sites;
however, based on the few comparative studies for Cu and Zn toxicity to freshwater invertebrate
and algal species, Na+ appears to provide less protection than Ca2+ and Mg2+ (Meyer et al. 2007).
As mentioned above, metals respond differently to the effects of various TMFs which, in part, is
dependent on the type and strength of bonds (ionic or covalent) formed with the binding sites
(Meyer et al. 2007). Table 1 illustrates the relative importance of the most studied TMFs for
several metals within a given metal (not across metals). This table is a general guideline as these
trends may be variable depending on the species, life stage, test duration, and other factors that
are considered within bioavailability models.
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Table 1. Toxicity modifying factors that have been demonstrated to be important in various
BLM and MLR published models and their relative importance within each metal.
Metal
Type
Most Important Parameters1
Hardness
pH
DOC
Other
Aluminum
Freshwater
+
+++
++
temperature
Cadmium
Freshwater
+++
+
+
Cobalt
Freshwater
++
+
+
Copper
Freshwater
+
++
+++
sodium
Copper
Marine
+
+
salinity
Lead
Freshwater
+
+
+++
Nickel
Freshwater
+
+
Silver
Freshwater
+
chromium
reducible sulfur,
sodium, chloride
Zinc
Freshwater
+++
++
+
1 Since it is difficult to separate the effects of alkalinity and pH, alkalinity is not listed as a separate factor but is
considered as a contribution to the overall effects of pH.
See Appendix B for a detailed summary of the how TMFs affect some of the metals (aluminum,
copper, lead, nickel and zinc) listed in Table 1. See also Appendix H for a high-level summary
of how TMFs affect the metals listed in Table 1.
It is important that high quality TMF data be collected for the use in bioavailability model
development or as input parameters into the model. Data should be collected using good
sampling and measurement practices, particularly in regard to pH and DOC collection (e.g.,
Balistrieri et al. 2012, Nimick et al. 2011, and Yoro et al. 1999).
III. Discussion of bioavailability modeling approaches examined
Bioavailability-based models have been developed to take the influence of water chemistry into
account when evaluating aqueous metal toxicity to aquatic organisms. Diet is another route of
metal exposure that is generally not considered within bioavailability models because of a lack of
available data and mechanistic complexity. Currently, for most metals, data indicate that
respiratory organs are more sensitive to cationic metals via water exposure than exposure
through the gut. Furthermore, these models have been validated with long-term mesocosm
studies in which the dietary route of exposure is an operational pathway (Roussel et al. 2007;
Schlekat et al. 2010; Versteeg et al. 1999). Additionally, in a dietary zinc toxicity study, De
Schamphelaere et al. (2004) concluded that "the zinc BLM predicts chronic reproductive zinc
bioavailability and toxicity in synthetic and field surface waters with reasonable accuracy even
without explicitly directly considering the dietary toxicity pathway." For many metals, toxicity
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stemming from the waterborne pathway has been shown to occur at similar or lower
concentrations than the dietary route (e.g., Evens et al. 2009 for nickel, De Schamphelaere et al.
2007 for copper, Nys et al. 2013 and Alsop et al. 2016 for lead),indicating that AWQC which are
protective of aqueous metal exposure are also protective of dietary exposures. Mebane et al.
(2020) also suggested there is currently "insufficient evidence to conclude that bioavailability
models would be under-protective if based on waterborne-only exposures" and recommended
that researchers conduct concurrent exposures to strengthen the literature surrounding dietary
exposure and support the development of a biodynamic modeling framework that is able to
incorporate the dietary exposure route (Mebane et al. 2020). Lastly, when it is well-established
that the diet is an important exposure route, EPA has considered this information in their criteria
development. For example, the freshwater selenium water quality criteria (US EPA 2016b) are
based on fish tissue concentrations since diet is the primary route of exposure.
The approaches used to develop bioavailability-based models fall within a continuum between
empirical (e.g., hardness equations) and mainly mechanistic (e.g., biokinetic BLM) (see Textbox
3 in Adams et al. 2020 and Figure 1 in Brix et al. 2020a). In the middle of the continuum are the
empirically-based MLR and mechanistically-based BLM. Adams et al. (2020) and Mebane et al.
(2020) provide overviews of the history of the science resulting in the development of the BLM
and later MLR models, as well as other bioavailability models not under consideration as an
overarching approach at this time as they are either not as scientifically robust and/or practical as
the BLM and MLR models (e.g., hardness-based equations, WER, generalized bioavailability
models [gBAMs] and biodynamic models). In addition, after reviewing bioavailability-based
toxicity models in terms of use, refinement, and application to protection values, Mebane et al.
(2020) lays out a series of recommendations for developing mechanistically-based models.
Similarly, Brix et al. (2020a) describe best practices for the development and evaluation of
empirical models.
In this section, we describe the BLM and MLR approaches and discuss the advantages and
disadvantages of each, which can depend on the complexity of the environmental chemistry, data
availability and intended use or policy decisions for a given metal. Table 2 highlights the
different models in this category that are currently available or in development for the six metals
represented by the CRADA (Al, Co, Cu, Pb, Ni and Zn). Bioavailability models encompassed in
this table span across fresh- and marine waters and, in total, include 17 BLMs and 13 MLR
models developed across different global jurisdictions. Simplified bioavailability look-up tools
(e.g., Bio-Met, M-BAT), which have been designed for regulatory ease-of-use, are also included
in this comparison framework. More information is provided in Appendix C where the
comparative metrics have been divided into two tables. The "Primary" comparison table, similar
to Table 2, summarizes major details of each model including the user-interface, primary
toxicity modifying factors and chemistry inputs required for each model, the output value
generated and the source/references from which the model can be obtained. The "Supplemental"
comparison table describes specific details surrounding the development of the models such as
applicable chemistry ranges, validation datasets, and the use in regulatory frameworks. The
"References" table contains full references for all information included in the primary and
supplemental tables.
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Table 2: Comparisons of bioavailability models currently available or in development for the six metals represented by the CRADA.
More information is provided in Appendix C which also summarizes other major details of each model including the user-interface,
the output value generated and the source/references from which the model can be obtained. An additional table describes specific
details surrounding the development of the models such as applicable chemistry ranges, validation datasets, and the use in regulatory
frameworks. Reference list provided in Appendix C.
Metal
Model
Type
Primary toxicity modifying factors
Taxa model is
applicable to
Chemistry Inputs needed
Aluminum
BLM
v3.18.2.42
Full BLM
DOC, Hardness, pH, Temperature
A, I, F
Temperature, pH, DOC, Al, Ca, Mg,
Na, K, SO4, CI, Alkalinity
MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness
Cobalt
BLM
v3.15.2.41
Full BLM
DOC, Hardness, pH
A, I, F
Temperature, pH, Co, DOC, Humic
acid %, Ca, Mg, Na, K, S04, CI,
Alkalinity, S
MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness (Ca, Mg)
Bio-met
v5.1
Simplified BLM
Lookup Tool
DOC, Ca, pH
A, I, F
pH, DOC, Ca, Co
USEPA BLM
Full BLM
Alkalinity, DOC, Hardness, pH
I, F
Temperature, pH, Cu, DOC, Humic
acid %, Ca, Mg, Na, K, S04, CI,
Alkalinity, S
Copper
ECCC BLM
vl.10
Full BLM
Alkalinity, DOC, Hardness, pH
A, P, I, F
Required: Temperature, pH, Cu, DOC,
Hardness;
Optional: Humic acid %, Ca, Mg, Na,
K, S04, CI, Alkalinity, S
BC BLM
vl.ll
Full BLM
Alkalinity, DOC, Hardness, pH
A, P, I, F, Am
Required: Temperature, pH, Cu, DOC,
Hardness;
Optional: Humic acid %, Ca, Mg, Na,
K, S04, CI, Alkalinity, S
Windward
Required: Temperature, pH, Cu, DOC,
Hardness;
Optional: Humic acid %, Ca, Mg, Na,
K, S04, CI, Alkalinity, S
BLM
v3.4L2.45
Full BLM
Alkalinity, DOC, Hardness, pH
I, F
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Metal
Model
Type
Primary toxicity modifying factors
Taxa model is
applicable to
Chemistry Inputs needed
BLM/gBAM
Mixed
regression +
speciation model
Alkalinity, DOC, Hardness, pH
I, F
Temperature, pH, Cu, DOC, Humic
acid %, Ca, Mg, Na, K, SO4, CI,
Alkalinity, S
Bio-met
v5.0
Simplified BLM
Lookup Tool
DOC, Ca, pH
A, I, F
pH, DOC, Ca, Cu
M-BAT
v30.0
Simplified BLM
Lookup Tool
DOC, Ca, pH
A, I, F
pH, DOC, Ca, Cu
PNEC-Pro
v6.0
[M]LR
DOC
A, I, F
Required: DOC;
Optional: pH, Mg, Ca, Na, Cu
Copper
WHAM-Ftox
Toxicity model
linked to
speciation
Not specified
P
Temperature, pH, Cu, DOM (fulvic and
humic acids), Ca, Mg, Na, K, SO4, CI,
Alkalinity, metals
MLR
MLR
DOC, Hardness, pH
I, F
pH, Hardness, DOC
Windward
Marine BLM
v3.4L2.45
Full BLM
DOC, pH, salinity
I, F
Required: Temperature, pH, Cu, DOC,
Salinity;
Optional: Ca, Mg, Na, K, SO4, CI, PO4,
DIC
Marine MLR
[M]LR
DOC
I
(Mytilus sp.)
DOC
Unified/North
America BLM
Full BLM
DOC, Hardness, pH
I, F
Temperature, pH, Pb, DOC, Humic acid
%, Ca, Mg, Na, K, SO4, CI, Alkalinity,
S
Lead
EU Risk
Assessment
BLM/gBAM
Full BLM
DOC, Hardness, pH
A, I, F
Temperature, pH, Pb, DOC, Ca, Mg,
Na, K, S04, CI, C03
EU Risk
Assessment
Lead EQS
Screening Tool
vl.O
DOC Equation
DOC
A, I, F
DOC, Pb
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Metal
Model
Type
Primary toxicity modifying factors
Taxa model is
applicable to
Chemistry Inputs needed
Bio-met
v5.0
Simplified BLM
Lookup Tool
pH, DOC, Ca
A, I, F
pH, DOC, Ca, Pb
Lead
PNEC Pro
v6.0
[M]LR
DOC
A, I, F
Required: DOC;
Optional: pH, Mg, Ca, Na, Pb
Canadian
WQG
MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness
MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness
EU Risk
Assessment
BLM
Full BLM
DOC, Hardness, pH
A, I, F
Temperature, pH, Ni, DOC, Humic acid
%, Ca, Mg, Na, K, SO4, CI, Alkalinity,
S
Bio-met
v5.0
Simplified BLM
Lookup Tool
pH, DOC, Ca
A, I, F
pH, DOC, Ca, Ni
M-BAT
20150206
Simplified BLM
Lookup Tool
pH, DOC, Ca
A, I, F
pH, DOC, Ca, Ni
Nickel
Best Overall
Pooled
Full BLM
DOC, Hardness, pH
A, I, F
Temperature, pH, Ni, DOC, Humic acid
%, Ca, Mg, Na, K, SO4, CI, Alkalinity,
S
North
American C.
dubia BLM
Full BLM
DOC, Hardness, pH, Alkalinity
I
Temperature, pH, Ni, DOC, Humic acid
%, Ca, Mg, Na, K, SO4, CI, Alkalinity,
S
PNEC Pro
v6.0
[M]LR
DOC, Hardness, pH
A, I, F
Required: DOC;
Optional: pH, Mg, Ca, Na, Ni
MLR
MLR
DOC, Hardness
A, I, F
Hardness (Ca/Mg), DOC, pH, Ni
Marine BLM
Full BLM
DOC
I
Temperature, pH, Ni, DOC, Humic acid
%, Ca, Mg, Na, K, SO4, CI, Alkalinity,
S
Zinc
Unified/North
America BLM
Full BLM
DOC, Hardness, pH
I, F
Temperature, pH, Zn, DOC, Humic
acid %, Ca, Mg, Na, K, S04, CI,
Alkalinity, S
EU Risk
Assessment
BLM/gBAM
Full BLM
DOC, Hardness, pH
A, I, F
Temperature, pH, Zn, DOC, Humic
acid %, Ca, Mg, Na, K, S04, CI,
Alkalinity, S
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Metal
Model
Type
Primary toxicity modifying factors
Taxa model is
applicable to
Chemistry Inputs needed
M-BAT
v30.0 -
20150206
Simplified BLM
Lookup Tool
pH, DOC, Ca
A, I, F
pH, DOC, Ca, Zn
Bio-met
v5.0
Simplified BLM
Lookup Tool
pH, DOC, Ca
A, I, F
pH, DOC, Ca, Zn
Zinc
PNEC Pro
v6.0
[M]LR
DOC
A, I, F
Required: DOC;
Optional: pH, Mg, Ca, Na, Zn
Canadian
WQG MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness
MLR
MLR
DOC, Hardness, pH
A, I, F
pH, DOC, Hardness
Marine BLM
Full BLM
DOC, pH, salinity
I, F
Required: Temperature, pH, Zn, DOC,
Salinity;
Optional: Ca, Mg, Na, K,, CI, PO4,
DIC
Model name, Version/Identification, and Type abbreviations: BC - British Columbia; BLM - Biotic ligand model; ECCC - Environment and Climate Change
Canada; EU - European Union; gBAM - Generalized bioavailability model; M-BAT - Metal bioavailability assessment tool; MLR - Multiple linear regression;
[M]LR - Multiple linear regression and/or simple linear regression; PNEC - Predicted no effect concentration; USEPA - United States Environmental Protection
Agency; WHAM - Windermere humic aqueous model; WQG - Water quality guideline.
Taxa model applicable to abbreviations: A - algae; I - invertebrates; F - fish; P - plants; Am - amphibians
Chemistry inputs needed abbreviations: DIC - Dissolved inorganic carbon; DOC - Dissolved organic carbon
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a. Biotic Ligand Models
Biotic Ligand Models are mechanistically-based and the most complex of the models considered.
As summarized in Adams et al. (2020), the BLM uses sub-models to account for 1) chemical
speciation, 2) the competition of metal and non-metal ions and complexes for binding to the
biotic ligand (which is assumed to be the gill or respiratory mechanism) and 3) the metal
accumulation and toxicity. BLMs require several inputs of water parameters for chemical
speciation calculations including: temperature, pH, DOC, major ions (Ca, Mg, Na, K, CI, SO4),
and alkalinity. Once the water parameters are entered into the model, the BLM predicts the
concentration of the different metal species (complexes and free metal ion) associated with a
critical accumulation (i.e., an accumulation level at the biotic ligand that corresponds to a certain
effect level). BLMs assume that equilibrium is reached immediately and there are no changes in
reaction rates over time.
Acute and/or chronic BLMs have been developed for several metals, including all six of the
metals represented by the CRADA (see Table 2, Appendix C, and Table 2 in Mebane et al.
2020). However, currently only four regulatory jurisdictions have adopted the BLM approach to
develop aquatic life protective values (EPA's Cu AWQC [US EPA 2007], British Columbia's Cu
Water Quality Guideline for Protection of Freshwater Aquatic Life [B.C. Ministry of
Environment and Climate Change Strategy 2019], Environment and Climate Change Canada's
draft Cu Federal Environmental Quality Guidelines [ECCC 2019], and European Commission's
Ni Environmental Quality Standard [EQS; European Commission 2010]). BLMs are also under
consideration by others (Canada and Australia/New Zealand) (Adams et al. 2020). One of the
primary advantages of the BLM approach is that it is based on the premise that bioavailability is
linked to chemical speciation, which supports its application to a wide range of conditions and
media. However, a barrier to adoption and implementation is the complexity of the approach
which can be technically demanding, transparency of the algorithms, and the large number of
water chemistry parameters required, some of which are costly and not routinely collected. For
example, in 2007, EPA finalized its recommended Cu AWQC, however only five states
(Delaware, Idaho, Iowa, Kansas, and Oregon) and the Commonwealth of the Northern Mariana
Islands have adopted the Cu BLM statewide and nine states (California, Colorado, Georgia,
Maryland, Massachusetts, New Hampshire, North Carolina, South Carolina, and Texas) have the
ability to develop site-specific Water Quality Standards to be submitted to EPAfor review and
approval.
b. Simplified Biotic Ligand Models
To address the issues of complexity, transparency, and water chemistry requirements which have
hindered the adoption and implementation of the BLM, several simplified or abbreviated tools
have been developed based on the full BLM. Most of these "user-friendly" tools have been
developed under the European Union's Water Framework Directive (Adams et al. 2020).
Compared to the full BLM, these tools require fewer water chemistry input parameters (but
typically still require DOC, pH, and hardness [or Ca as an estimator of hardness]) by restricting
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input parameters or relying on default values and require less training. Examples of simplified
BLMs include Bio-met (an Excel-based "look-up" table with EQS values for thousands of
combinations of water chemistries that have been calculated using the full BLM), PNEC-pro and
MBAT (both "algorithm"-based tools). Adams et al. (2020) provides an overview of these
simplified tools and how they perform compared to the BLM. In addition, Appendix C provides
a summary of the comparison of these tools (e.g., inputs, outputs) to developed BLMs for the six
metals represented in the CRADA.
c. Multiple Linear Regressions Models
Multiple linear regression models have more recently been developed (e.g., Brix et al. 2017,
DeForest et al. 2018 and 2020a, Brix et al. 2020a) partially in response to the complexity and
high water quality data input requirements of the BLM. As summarized in Adams et al. (2020),
MLR models are empirically-based, statistically-derived approaches to incorporating TMFs to
predict metal toxicity across a range of water chemistries where there are direct measurements of
the influence of water chemistry on metal toxicity. This approach is similar to the simple linear
regression hardness-based models, but MLRs take into account multiple TMFs like hardness,
pH, and DOC (and their interactions, if necessary) and rely on large empirical toxicity data sets
covering wide ranges of water chemistry parameters and ecotoxicology endpoints. Unlike the
BLM, MLR models often use hardness as a parameter rather than the concentrations of specific
ions (e.g., Ca and Mg). One of the main reasons MLR models use hardness is because most end-
users monitor hardness rather than Ca and Mg. One line of evidence that validates the use of
hardness instead of Ca and/or Mg concentrations in MLR models is the consistency in the result
from cross-validation exercises comparing the BLM and MLR predictions (see Appendices D, E
and F). In addition, models may be fitted to acute or chronic toxicity data and for single species
or pooled into a single model for multiple species.
Brix et al. (2020a) recommend a pooled MLR modeling approach, if feasible, because the pooled
version may increase the confidence of applying the model to different species as it is based on
more data and it often includes a wider range of TMFs than species-specific models. In a pooled
MLR modeling approach, species-specific intercepts account for the variances in species
sensitivity. However, determining whether to use a species-specific or pooled model depends on
the available data for the metal, metal-specific characteristics and interactions1 with TMFs, and
performance over a broad range of water chemistries. For example, EPA decided to use the
individual fish and invertebrate models in the final recommended Al AWQC (US EPA 2018)
rather than a pooled model because the chronic toxicity of Al differed considerably between
species depending on water chemistry conditions.
Acute and/or chronic MLR models have been developed for several metals, including all six of
the metals represented by the CRADA (see Table 2, Appendix C, and Table 2 in Brix et al.
[2020a]). As noted, EPA adopted vertebrate and invertebrate (unpooled) MLRs for the chronic
Al AWQC (US EPA 2018). The MLR approach has also been adopted or is under consideration
1 MLR models can explicitly evaluate the interactive effects of how TMFs influence each other. For example, pH
may influence the speciation of a metal, while the influence of hardness on the bioavailability of the metal varies
depending on the pH-dependent speciation of the metal.
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by others for water quality standards (Canada and Australia/New Zealand) (Adams et al. 2020,
ECCC 2020). Some advantages of the MLR approach are the relative simplicity and
transparency of the model, decreased number of input parameters (in comparison to the BLM)
resulting in easier data collection, and ease of use while maintaining comparable output (see
Section IV). The primary disadvantage of the MLR is that it does not explicitly address the
effects of metals speciation and the binding affinity of the metal for the biotic ligand receptor
within the model, but instead these effects are taken into account in the models based on
empirical observations. MLRs can be informed by mechanistic analyses by evaluating MLR
models against existing BLMs.
IV. Case Studies of Modeling Approach Comparisons
This section provides a comparative evaluation of BLM and MLR models for several metals.
While there are many variations of these models available (see Table 2 and Appendix C), the
current analyses start with the same underlying toxicity data sets to facilitate model comparisons.
Table 3 reports the model performance scores which form the basis for the evaluation of the
model comparisons from methods developed in the 2017 SET AC Metals Bioavailability
Modelling workshop (see publications in Appendix A; specifically, Garman et al. 2020) and
modified by Brix et al. (2020b; see Appendix D). Most MLR models include DOC, hardness,
and pH as TMFs with the exception of Ni (that only considers DOC and hardness). In addition,
these case studies followed EPA guidelines (US EPA 1985) to generate estimated AWQC based
on the output of the differing modeling approaches to assess how the criteria respond to changing
water quality characteristics.
Table 3: Acute and chronic performance scores for each metal based on the recommended MLR
models and BLMs in the cases studies (Appendices D, E and F). Performance score is the
arithmetic mean of individual scores for adjusted R2 (for MLR) or R2 (for BLM), RFx,2.o, and
residuals (see Garman et al. 2020 and Brix et al. 2020b for details). NA - no model available.
Metal
Acute Score
Chronic Score
Reference
MLR
BLM
MLR
BLM
Aluminum
NA
NA
0.91
0.75
Brix et al. 2020b
Copper
0.71
0.71
0.87
0.55
Brix et al. 2020b
Lead
0.79
0.76
0.81
0.62
DeForest et al. 2020b
Nickel
0.90
0.93
0.89
0.88
Croteau et al. 2021
a. Aluminum
Aluminum is the metal that EPA has most recently updated (US EPA 2018) and the only metal
for which EPA used the MLR approach to develop the AWQC. Compared to some other metals
(e.g., Cu), Al has a relatively small toxicity data set as well as complex environmental chemistry
that can strongly influence bioavailability and toxicity (Brix et al. 2020b). A full analysis of the
comparison of the chronic Al MLR and BLM is provided in Appendix D (Brix et al. 2020b) and
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describes the toxicity dataset used for the models. This dataset was an update from the dataset
used by Santore et al. (2018) and DeForest et al. (2018 and 2020a; which formed the basis of the
MLR used in the EPA Al AWQC). Briefly, chronic MLR models were developed for a
representative invertebrate (Ceriodaphnia dubia) and fish (Pimephalespromelas) which both
included interactions among TMFs (the C. dubia model included a term for interactions between
pH and hardness and the P. promelas model included terms for interactions between pH and
hardness and between pH and DOC. The data used to develop the two species-specific models
were pooled to develop a pooled MLR model for the comparison to the BLM. The analysis
indicates that the MLR model performs considerably better than the BLM across a range of
performance metrics (Table 3) and resulted in differences in estimated AWQC as a function of
water chemistry.
b. Copper
Copper is the only metal for which the EPA has adopted the BLM approach to develop AWQC
(US EPA 2007). Copper has a large toxicity data set over a range of water quality conditions and
the environmental chemistry is comparatively simpler than some other metals. A full analysis of
the comparison of the acute and chronic Cu MLR and BLM is provided in Appendix D (Brix et
al. 2020b)2. Briefly, six acute species-specific MLR models (four daphnid and two fish) and two
chronic models (a daphnid and a fish) were developed and then pooled without interactions for
comparison to the BLM. The BLM is the same for both acute and chronic with only the
sensitivity adjusted; the MLR models are separate for acute and chronic effects. The analysis
indicates that the acute Cu MLR and BLM performance is comparable (Table 3), however there
are differences in performance on a species-specific basis. In contrast, the chronic Cu MLR
performs better than the BLM (Table 3). It is important to note that the Cu BLM is optimized for
measured Cu accumulations on the biotic ligand and not for toxicity observations (neither
chronic nor acute). In contrast, the chronic Cu MLR is based explicitly on chronic Cu toxicity
data and so it is not surprising that it performs better than the Cu BLM. For both the acute and
chronic modeling approaches, there are differences in the estimated AWQC as a function of
water chemistry.
c. Lead
The existing EPA AWQC for Pb are based on a hardness equation (US EPA 1984). A full
analysis of the comparison of the acute and chronic Pb MLR models and the BLM is provided in
Appendix E (DeForest et al. 2020b). Briefly, two acute species-specific MLR models (a daphnid
and a fish) and three chronic models (two invertebrates and a fish) were developed and then
pooled (separate acute and chronic pooled models were developed) for comparison to the BLM
(MLR models without TMF interaction terms were recommended because MLR models with
interaction terms resulted in toxicity predictions under some water chemistry conditions that
were not mechanistically supported). DeForest et al. (2020b) explains that only the pooled MLR
models were compared to the BLM as this approach is most similar where model parameters are
2 The copper portion of this analysis has been published and is available via open access at:
https://setac.onlinelibrarv.wilev.com/doi/10.1002/etc.5Q12
14
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applied to all species. To account for species-specific sensitivity, the sensitivity term varies in the
BLM which is similar to the intercept of the pooled MLR model. The analysis indicates that the
acute Pb MLR and BLM performance were similar, however the chronic Pb MLR model
performs considerably better than the chronic Pb BLM (Table 3).
d. Nickel
The existing EPA AWQC for Ni is based on a hardness equation (US EPA 1995).
The BLM approach for Ni, described by Santore et al. (2021) in Appendix F, provides a critical
review of the importance of TMFs (including hardness cations, DOC, and pH) on acute and
chronic toxicity to aquatic organisms. The authors also propose a refined BLM that incorporates
the conclusions of the critical review of TMFs. The analysis of the "Best Overall Pooled" model
clearly shows the broad importance of hardness cations and DOC across all taxonomic groups. A
second nickel BLM was developed following the observation that C. dubia exhibit poor
reproduction at pH > 8, in which the authors propose that these organisms are experiencing
combined effects of nickel and bicarbonate toxicities under these circumstances. To address this
observation, Santore et al. (2021) developed a species-specific C. dubia model which considers
both Ni and bicarbonate toxicities. Although this model has only been calibrated and validated
with C. dubia data, there is preliminary evidence that these effects may be present in other
organisms. The refined BLM software contained both the "Best Overall Pooled" BLM and the
"North American C. dubia BLM".
The MLR approach, described by Croteau et al. (2021) in Appendix F, empirically seeks to
explain the influence of TMFs on acute and chronic Ni toxicity. The MLRs account for a similar
set of TMFs as the BLMs. Croteau et al. (2021) compares the performance of the BLMs versus
the MLRs using a recently published approach for quantifying model performance (Garman et al.
2020; Appendix A) and aNi toxicity and chemistry database consisting of 1498 toxicity
observations in 64 studies. The outcome of this comparison is that both models perform
similarly, and that both can serve as the basis for normalizing Ni ecotoxicity data for the purpose
of developing bioavailability-based AWQC for Ni.
V. Conclusions: Discussion and recommendations of modeling approach
There are now several approaches for modeling metals bioavailability in freshwater. The SETAC
Technical Workshop, Bioavailability-Based Aquatic Toxicity Models for Metals held in
December 2017 sought to assess and provide recommendations on approaches for model
development, evaluation, selection, and use. Schlekat et al. (2020) summarized the main
recommendations from the workshop and resulting publications: 1) the mechanistic
understanding of metal toxicity and speciation should inform all bioavailability models, 2) it is
possible to develop simplified tools (including MLR models) that are mechanistically-informed,
3) models should be validated with qualitative and quantitative methods and appropriately
applied within a range of water chemistries, and 4) communication regarding the choice of
appropriate models, which may be different depending on the situation, needs to be clear. For
example, Brix et al (2020b) describe that the selection of the most appropriate model for a given
situation requires consideration of several factors including data needs and availability, proposed
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model use, model performance, and practical and policy decisions.
In this report, we explored and compared performance of the BLM and MLR approaches for
several metals by applying procedures developed as part of the Technical Workshop. Model
performance evaluations were conducted for four of the six metals represented by the CRADA.
For each metal case study, the BLM and MLR approaches were applied to the same toxicity
dataset. In most cases, the empirically-based MLR models performed as well as or better than the
mechanistically-based BLM (see Table 3 and Appendices D, E, and F). While there may be
metal-specific advantages and disadvantages of using the BLM or MLR approach, it is
advantageous, if feasible, for EPA to choose one overarching approach for updating AWQC for
all metals. Given the similarities in performance between the BLM and MLR approaches for
several metals, with the MLR generally showing somewhat to significantly better performance
scores across the acute and chronic metals models examined, and as a practical and policy
decision, EPA intends to use MLR models as the overarching metals bioavailability-modeling
approach with pH, hardness, and DOC as the core set of TMFs to consider in model
development. Additional reasons to recommend the MLR modeling approach are its relative
simplicity, transparency, decreased number of input parameters and data collection requirements,
ease of use, and reduced need for ongoing maintenance of the models compared to the BLM.
However, EPA agrees with Mebane et al. (2020) and Brix et al (2020b) that the development of
empirical models like MLR can be informed by mechanistic models like the BLM by helping to
identify the key TMFs and expected mechanistic patterns and by evaluating MLR models against
existing BLMs.
While MLR models may require lower maintenance than BLMs (Mebane et al. 2020), as EPA
moves forward with updating the metals AWQC, it is desirable to have a single software
platform. This user-friendly platform would incorporate the updated bioavailability modeling
information for all metals so that the user could enter the core set of TMFs once and receive
output information on multiple metal criteria values.
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Appendix B
Explanation of How Toxicity Modifying Factors (TMFs)
Affect Individual Metals
Aluminum, Copper, Lead, Nickel and Zinc
Developed by CRADA Partners
A. Aluminum
Hardness, pH and DOC
To evaluate how water chemistry affects toxicity, aluminum (Al) HC5 values (hazard
concentrations affecting 5% of the population) were calculated by varying DOC, pH, and
hardness concentrations. HC5 values were calculated as a function of one parameter being varied
and the other 2 held constant. In these examples, HC5s were calculated using the MLR EC20
models and following the USEPA approach. The most noticeable observations are that the HC5
values consistently increase with increasing DOC (Figure 1A-C) and with increasing pH (Figure
1D-F). The influence of hardness on HC5 values is variable depending on pH. Overall, HC5
values increase with increasing hardness at pH 6, remain essentially constant at pH 7, and show a
variable pattern at pH 8 (Figure 1G-I). These trends generally follow the empirical data, where
available, which is not unexpected given that the MLR models were derived solely from those
data. However, fewer empirical toxicity data are available to evaluate the HC5 trends at pH 8.
For example, the observation that HC5 values at pH 8 decrease with increasing hardness appears
to be consistent with data fori5, subcapitata but less clearly so for C. dubia based on more
limited data and insufficient data are available for P. promelas. See DeForest et al. 2018 and
2020 for more details.
B-l
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A pH 6
1200
1000
_J
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a.
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1200
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400
200
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2 3 4
DOC, mg/L
1400
1200
1000
800
600
400
200
0 I
6 0
B pH 7
2 3 4
DOC, mg/L
E H = 75 mg/L
1400
G
DOC = 1 mg/L
1400
1200
1200
1000
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O
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400
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H DOC = 3 mg/L
50 100
Hardness, mg/L
1400
1200
1000
800
600
400
200
0
6
1400
1200
1000
800
600
400
200
0
8.5 5.5
C pH8
0 1
F H = 125 mg/L
2 3 4
DOC, mg/L
7.0
PH
1400
1200
1000
800
600
400
200
0
I DOC = 5 mg/L
50 100
Hardness, mg/L
50 100
Hardness, mg/L
Figure 1: Total A1 5% hazardous concentrations as a function of dissolved organic carbon (DOC) concentration (A-
C), pH (D-F). and hardness (G-I). (A-C) Hardness of 10. 50. and 125 mg/L (blue, red, and green symbols,
respectively). (D-F) Dissolved organic carbon of 1, 3, and 5 mg/L (blue, red, and green symbols, respectively). (G-
I) pH of 6,7, and 8 (blue, red. and green symbols, respectively). H = hardness; HC5 = 5% hazardous concentration.
Aluminum References
DeForest D. Brix K, Tear L, Adams W. 2018. Multiple linear regression models for predicting
chronic aluminum toxicity to freshwater organisms and developing water quality criteria. Environ
Toxicol and Chem 37(1): 80-90.
B-2
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DeForest D. Brix K, Tear L, Cardwell A, Stubblefield W, Nordheim E, Adams W. 2020.
Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to
Freshwater Aquatic Organisms and Developing Water Quality Guidelines. Environ Toxicol and
Chem 39 (9): 1724-1736.
B. Copper
Hardness
Many studies have reported a protective effect of water hardness on Cu toxicity in acute and
chronic exposures to fish and invertebrates (e.g., Waiwood and Beamish 1978; Miller and
Mackay 1980; Birge et al. 1983; Winner 1985; Erickson et al. 1996, 1997; Collyard 2002; De
Schamphelaere and Janssen 2002; Gensemer et al. 2002; Meyer et al. 2002; Long et al. 2004;
Sciera et al. 2004; Van Genderen et al. 2005; Ryan et al. 2009). However, inconsistent results or
no protection have been reported in some other studies (e.g., Chapman et al. 1980, Richards and
Playle 1999, De Schamphelaere and Janssen 2004b, Hyne et al. 2005, Markich et al. 2005, Wang
et al. 2009). Details are provided in Meyer et al. (2007). In terms of the Cu BLM, the hardness
effect is characterized by competitive interactions between Cu and hardness cations (i.e., Ca and
Mg) at the biotic ligand. For example, logio values of the biotic ligand binding constants (i.e., log
K values) for both Ca and Mg are 3.60 in the Windward (formerly HydroQual) acute Cu BLM,
which is the basis for the U.S. EPA's current acute Cu water quality criteria; and they are 4.40 in
the chronic Cu BLMs for fish and invertebrates that were recently proposed by Environment and
Climate Change Canada (ECCC) and by the Province of British Columbia (BC). As a
complementary approach to the BLM for calculating acute and chronic water quality criteria for
Cu, Brix et al. (2017) recently recommended multiple linear regression (MLR) models that
included the protective effect of hardness (i.e., represented by a positive regression coefficient
for hardness). In the update of these models described in Brix et al. (2020), hardness was also
identified as a TMF in all of the MLRs developed.
DOC
In freshwaters, dissolved organic matter (DOM) - quantified as dissolved organic carbon (DOC)
- decreases Cu bioavailability and toxicity (e.g., Brown et al. 1974; Lind et al. 1978; Buckley
1983; Winner 1984, 1985; Flickinger 1984; Meador 1991; Oikari et al. 1992; Welsh et al. 1993;
Erickson et al. 1996; Hollis et al. 1997; Kim et al. 1999; Ma et al. 1999; De Schamphelaere et al.
2002, 2004, 2006; McGeer et al. 2002; De Schamphelaere and Janssen 2004a, 2004b; Kramer et
al. 2004; Schwartz et al. 2004; Sciera et al. 2004; Tusseau-Vuillemin et al. 2004; Van Genderen
et al. 2005; Rogevich et al. 2008; Ryan et al. 2009). Details are provided in Meyer et al. (2007).
As with other metals, DOC effects are characterized in the Windward, ECCC, and BC Cu BLMs
by using a set of discrete binding sites and reactions calibrated in the Windermere Humic
Aqueous Model (WHAM; Tipping 1994) in which Cu competes with other metals and cations
for binding, thereby decreasing the ability of Cu to bind at the biotic ligand. As a complementary
approach to the BLM for calculating acute and chronic water quality criteria for Cu, Brix et al.
B-3
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(2017) recently recommended multiple linear regression (MLR) models that included the
protective effect of DOC (i.e., represented by a positive regression coefficient for DOC). In the
update of these models described in Brix et al. (2020), DOC was also identified as a TMF in all
of the MLRs developed.
pH
There are several mechanisms by which pH can affect Cu bioavailability, including via
speciation, solubility, and competitive interactions between Cu and biotic ligands. Additionally,
pH and alkalinity (another water chemistry parameter that can protect against Cu toxicity; Fulton
and Meyer 2014, and review in Meyer et al. 2007) usually are positively correlated at pH values
exceeding approximately 6.0. Thus, in some experiments with some species, the toxicity of
dissolved or total Cu increased as pH was increased; but in other experiments with some species,
the toxicity of dissolved or total Cu decreased as pH was increased (e.g., see details in Meyer et
al. 2007). However, Cu toxicity to fish and invertebrates, expressed on the basis of Cu2+,
increases with increasing pH (e.g., Howarth and Sprague 1978; Meador 1991; Erickson et al.
1996; Collyard 2002; De Schamphelaere and Janssen 2002; De Schamphelaere et al. 2002;
Meyer et al. 2002; Ryan et al. 2004, 2009), suggesting the importance of competition between
protons and Cu at the biotic ligand. All Cu BLMs incorporate Cu2+ speciation in the exposure
water (usually via WHAM calculations) and competition with protons at the biotic ligand when
predicting Cu toxicity, thus reconciling the otherwise apparently contradictory toxicity results if
only dissolved or total Cu concentrations is used to predict toxicity. A generalized bioavailability
model (gBAM) can incorporate both the fundamental pH-related speciation effects and the
positive relationship between Cu2+ toxicity and pH (e.g., Van Regenmortel et al. 2015; Nys et al.
2020). As a complementary approach to the BLM for calculating acute and chronic water quality
criteria for Cu, Brix et al. (2017) recently recommended multiple linear regression (MLR)
models that included the protective effect of pH [i.e., represented by a positive regression
coefficient for pH when calculating dissolved Cu (not Cu2+) criteria]. In the update of these
models described in Brix et al. (2020), pH was also identified as a TMF in all of the MLRs
developed, with the only exceptions being the species-specific acute D. pulex and 0. mykiss
MLRs.
Copper References
Birge WJ, Benson WH, Black JA. 1983. The induction of tolerance to heavy metals in natural
and laboratory populations of fish. Research Report Number 141. Lexington, KY, USA:
University of Kentucky, Water Resources Research Institute. 26 p.
Brix KV, DeForest DK, Tear L, Grosell M, Adams WJ. 2017. Use of multiple linear regression
models for setting water quality criteria for copper: A complementary approach to the biotic
ligand model. Environmental Science and Technology 51:5182-5192.
Brix KV, Tear L, Santore RC, Croteau K, DeForest DK. 2020. Comparative Performance of
Multiple Linear Regression and Biotic Ligand Models for Estimating the Bioavailability of
B-4
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Aluminum and Copper. Technical Report prepared for Aluminum Association, Arlington, VA,
USA; Aluminum REACH Consortium, Brussels, Belgium; International Copper Association,
Washington D.C., USA: Copper Development Association, McLean, VA, USA.
Brown VM, Shaw TL, Shurben DG. 1974. Aspects of water quality and the toxicity of copper to
rainbow trout. Water Research 8:797-803.
Buckley JA. 1983. Complexation of copper in the effluent of a sewage treatment plant and an
estimate of its influence on toxicity to coho salmon. Water Research 17:1929-1934.
Chapman, G.A., S. Ota, and F. Recht. 1980. Effects of water hardness on the toxicity of metals to
Daphnia magna. U.S. EPA, Office of Research and Development, Corvallis, Oreg. 23 pp.
Collyard SA. 2002. Bioavailability of copper to the amphipod Hyalella azteca. MS thesis.
Laramie, WY, USA: University of Wyoming. 60 p.
De Schamphelaere KAC, Heijerick DG, Janssen CR. 2002. Refinement and field validation of a
biotic ligand model predicting acute copper toxicity to Daphnia magna. Comparative
Biochemistry and Physiology Part C Toxicology and Pharmacology 133:243-258.
De Schamphelaere KAC, Heijerick DG, Janssen CR. 2006. Cross-phylum comparison of a
chronic biotic ligand model to predict chronic toxicity of copper to a freshwater rotifer,
Brachionus calyciflorus (Pallas). Ecotoxicology and Environmental Safety 63:189-195.
De Schamphelaere KAC, Janssen CR. 2002. A biotic ligand model predicting acute copper
toxicity for Daphnia magna: The effects of calcium, magnesium, sodium, potassium, and pH.
Environmental Science and Technology 36:48-54.
De Schamphelaere KAC, Janssen CR. 2004a. Development and field validation of a biotic ligand
model predicting chronic copper toxicity to Daphnia magna. Environmental Toxicology and
Chemistry 23:1365-1375.
De Schamphelaere KAC, Janssen CR. 2004b. Effects of dissolved organic carbon concentration
and source, pH, and water hardness on chronic toxicity of copper to Daphnia magna.
Environmental Toxicology and Chemistry 23:1115-1122.
De Schamphelaere KAC, Vasconcelos FM, Tack FMG, Allen HE, Janssen CR. 2004. Effect of
dissolved organic matter source on acute copper toxicity to Daphnia magna. Environmental
Toxicology and Chemistry 23:1248-1255.
Erickson RJ, Benoit DA, Mattson VR, Nelson Jr HP, Leonard EN. 1996. The effects of water
chemistry on the toxicity of copper to fathead minnows. Environmental Toxicology and
Chemistry 15:181-193.
B-5
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Erickson RJ, Kleiner CF, Fiandt JT, Highland TL. 1997. Effect of acclimation period on the
relationship of acute copper toxicity to water hardness for fathead minnows. Environmental
Toxicology and Chemistry 16:813-815.
Fulton BA, Meyer JS. 2014. Development of a regression model to predict copper toxicity to
Daphnia magna and site-specific copper criteria across multiple surface-water drainages in an
arid landscape. Environmental Toxicology and Chemistry 33:1865-1873.
Flickinger AL. 1984. Chronic toxicity of mixtures of copper, cadmium and zinc to Daphnia
pulex. PhD dissertation. Oxford, OH, USA: The Miami University of Ohio. 135 p.
Gensemer RW, Naddy RB, Stubblefield WA, Hockett JR, Santore R, Paquin P. 2002. Evaluating
the role of ion composition on the toxicity of copper to Ceriodaphnia dubia in very hard waters.
Comparative Biochemistry and Physiology Part C Toxicology and Pharmacology 133:87-97.
Hollis L, Muench L, Playle RC. 1997. Influence of dissolved organic matter on copper binding,
and calcium on cadmium binding, by gills of rainbow trout. Journal of Fish Biology 50:703-720.
Howarth RS, Sprague JB. 1978. Copper lethality to rainbow trout in waters of various hardness
and pH. Water Research 12:455-462.
Hyne RV, Pablo F, Julli M, Markich SJ. 2005. Influence of water chemistry on the acute toxicity
of copper and zinc to the cladoceran Ceriodaphnia CF dubia. Environmental Toxicology and
Chemistry 24(7): 1667-1675.
Kim SD, Ma H, Allen HE, Cha DK. 1999. Influence of dissolved organic matter on the toxicity
of copper to Ceriodaphnia dubia: Effect of complexation kinetics. Environmental Toxicology
and Chemistry 18:2433-2437.
Kramer KJM, Jak RG, van Hattum B, Hooftman RN, Zwolsman JJG. 2004. Copper toxicity in
relation to surface water-dissolved organic matter: Biological effects to Daphnia magna.
Environmental Toxicology and Chemistry 23:2971-2980.
Lind D, Alto K, Chatterton S. 1978. Regional copper-nickel study: Aquatic toxicology study.
Minnesota Environmental Quality Board. 54 p.
Long KE, Van Genderen EJ, Klaine SJ. 2004. The effects of low hardness and pH on copper
toxicity to Daphnia magna. Environmental Toxicology and Chemistry 23:72-75.
Ma H, Kim SD, Cha DK, Allen HE. 1999. Effect of kinetics of complexation by humic acid on
toxicity of copper to Ceriodaphnia dubia. Environmental Toxicology and Chemistry 18:828-837.
Markich, S.J., G.E. Batley, J.L. Stauber, N.J. Rogers, S.C. Apte, R.V. Hyne, K.C. Bowles, K.L.
Wilde, and N.M. Creighton. 2005. Hardness corrections for copper are inappropriate for
protecting sensitive freshwater biota. Chemosphere. 60(1): 1-8. https://doi.ore/
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McGeer JC, Szebedinszky C, McDonald DG, Wood CM. 2002. The role of dissolved organic
carbon in moderating the bioavailability and toxicity of Cu to rainbow trout during chronic
waterborne exposure. Comparative Biochemistry and Physiology Part C Toxicology and
Pharmacology 133:147-160.
Meador JP. 1991. The interaction of pH, dissolved organic carbon, and total copper in the
determination of ionic copper and toxicity. Aquatic Toxicology 19:13-32.
Meyer JS, Boese CJ, Collyard SA. 2002. Whole-body accumulation of copper predicts acute
toxicity to an aquatic oligochaete (Lumbriculus variegatus) as pH and calcium are varied.
Comparative Biochemistry and Physiology Part C Toxicology and Pharmacology 133:99-109.
Meyer JS, Clearwater SJ, Doser TA, Rogaczewski MJ, Hansen JA. 2007. Effects of Water
Chemistry on the Bioavailability and Toxicity of Waterborne Cadmium, Copper, Nickel, Lead,
and Zinc to Freshwater Organisms. SET AC Press, Pensacola, Florida, USA.
Miller TG, Mackay WC. 1980. The effects of hardness, alkalinity and pH of test water on the
toxicity of copper to rainbow trout (Salmo gairdneri). Water Research 14:129-133.
Nys C, Vlaeminck K, Van Sprang P, De Schamphelaere KAC. 2020. A generalized
bioavailability model (gBAM) for predicting chronic copper toxicity to freshwater fish.
Environmental Toxicology and Chemistry. 39: 2424-2436, . https://doi.ore/10.1002/etc.4806 .
Oikari A, Kukkonen J, Virtanen V. 1992. Acute toxicity of chemicals to Daphnia magna in
humic waters. Science of the Total Environment 117/118:367-377.
Richards, J.G. and R.C. Playle. 1999. Protective effects of calcium against the physiological
effects of exposure to a combination of cadmium and copper in rainbow trout (Oncorhynchus
mykiss). Canadian Journal of Zoology. 77(7): 1035-1047. https://doi.org/doi jz-77-7-
1035.
Rogevich EC, Hoang TC, Rand GM. 2008. The effects of water quality and age on the acute
toxicity of copper to the Florida apple snail, Pomacea paludosa. Archives of Environmental
Contamination and Toxicology 54:690-696.
Ryan AC, Van Genderen EJ, Tomasso JR, Klaine SJ. 2004. Influence of natural organic matter
source on copper toxicity to larval fathead minnows (.Pimephales promelas): Implications for the
biotic ligand model. Environmental Toxicology and Chemistry 23:1567-1574
Ryan AC, Tomasso JR, Klaine SJ. 2009. Influence of pH, hardness, dissolved organic carbon
concentration, and dissolved organic matter source on the acute toxicity of copper to Daphnia
magna in soft waters: Implications for the biotic ligand model. Environmental Toxicology and
Chemistry 28:1663-1670.
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Schwartz ML, Curtis PJ, Playle RC. 2004. Influence of natural organic matter source on acute
copper, lead, and cadmium toxicity to rainbow trout (Oncorhynchus mykiss). Environmental
Toxicology and Chemistry 23:2889-2899.
Sciera KL, Isley JJ, Tomasso Jr JR, Klaine SJ. 2004. Influence of multiple water-quality
characteristics on copper toxicity to fathead minnows {Pimephalespromelas). Environmental
Toxicology and Chemistry 23:2900-2905.
Tipping E. 1994. WHAM-A chemical equilibrium model and computer code for waters,
sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by humic
substances. Computers and Geosciences 20(6):973-1024.
Tusseau-Vuillemin M-H, Gilbin R, Bakkaus E, Garris J. 2004. Performance of diffusion gradient
in thin films to evaluate the toxic fraction of copper to Daphnia magna. Environmental
Toxicology and Chemistry 23:2154-2161.
Van Genderen EJ, Ryan AC, Tomasso JR, Klaine SJ. 2005. Evaluation of acute copper toxicity
to larval fathead minnows (Pimephales promelas) in soft surface waters. Environmental
Toxicology and Chemistry 24:408-414.
Van Regenmortel T, Janssen CR, De Schamphelaere KAC. 2015. Comparison of the capacity of
two biotic ligand models to predict chronic copper toxicity to two Daphnia magna clones and
formulation of a generalized bioavailability model. Environmental Toxicology and Chemistry
34(7): 1597-1608.
Wang, N., C.A. Mebane, J.L. Kunz, C.G. Ingersoll, T.W. May, W.R. Arnold, R.C. Santore, T.
Augspurger, F.J. Dwyer, and M.C. Barnhart. 2009. Evaluation of acute copper toxicity to
juvenile freshwater mussels (fatmucket, Lampsilis siliquoidea) in natural and reconstituted
waters. Environmental Toxicology and Chemistry. 28(11): 2367-2377.
https://doi.org/10.1897/Q8-
Waiwood KG, Beamish FWH. 1978. The effect of copper, hardness and pH on the growth of
rainbow trout, Salmo gairdneri. Journal of Fish Biology 13:591-598.
Welsh PG, SkidmoreJF, Spry DJ, Dixon DG, Hodson PV, Hutchinson NJ, Hickie BE. 1993.
Effect of pH and dissolved organic carbon on the toxicity of copper to larval fathead minnow
(Pimephales promelas) in natural lake waters of low alkalinity. Canadian Journal of Fisheries
and Aquatic Sciences 50:1356-13 62.
Winner RW. 1984. The toxicity and bioaccumulation of cadmium and copper as affected by
humic acid. Aquatic Toxicology 5:267-274.
Winner RW. 1985. Bioaccumulation and toxicity of copper as affected by interactions between
humic acid and water hardness. Water Research 19:449-455.
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C. Lead
Hardness
The effect of hardness on Pb toxicity is variable among species and depending on whether acute
or chronic exposures were evaluated. In acute Pb exposures, Mager et al. (201 la) observed that
hardness (calcium specifically) was protective against Pb toxicity to the fathead minnow
(Pimephalespromelas) but not the cladoceran Ceriodaphnia dubia. In contrast, Nys and De
Schamphelaere (2013) observed that hardness (calcium specifically) did protect against acute Pb
toxicity to C. dubia. Despite the conflicting data for C. dubia, hardness was retained by the
Akaike information criterion (AIC) and Bayesian information criterion (BIC) in the MLR models
for both C. dubia and P. promelas, with the hardness slope for P. promelas being about two-fold
greater than for C. dubia (Table l).3 Hardness was likewise retained by AIC and BIC in the final
pooled MLR model for these two species (Table 1).
For chronic Pb exposures, hardness did not have an influence on Pb toxicity to the rotifer
Brachionus calyciflorus (Nys et al. 2016) nor C. dubia (Mager et al. 201 lb; Nys et al. 2014). For
the snail L. stagnalis, P. promelas, and the alga Raphidocelis subcapitata, the influence of
hardness on chronic Pb toxicity was less clear. For L. stagnalis and P. promelas, series of
chronic toxicity tests with only hardness varied were not available for these two species, while
fori?, subcapitata the influence of hardness was equivocal (De Schamphelaere et al. 2014).
Hardness was retained in the pooled chronic MLR model for invertebrates and fish and in the
pooled chronic MLR model for the two most sensitive invertebrates, C. dubia and L. stagnalis
(Table 1).4 Ultimately, the final recommended pooled model for chronic toxicity did include
hardness, but the influence of hardness was relatively minor compared to dissolved organic
carbon (DOC).
DOC
Increasing DOC concentrations consistently reduced both the acute and chronic toxicity of lead
(De Schamphelaere et al. 2014; Esbaugh et al. 2011, 2012; Mager et al. 201 la,b; Nys et al. 2016;
Parametrix 2010). The consistent and, in many cases, strong influence of DOC as a TMF for
algal and animal species has led to the development and adoption of a DOC-based bioavailable
EQS for Pb in the EU (EC 2010). DOC was retained by AIC and BIC in all MLR models,
including the acute individual C. dubia and P. promelas models and the pooled acute model, as
well as in the chronic individual B. calyciflorus, C. dubia, L. stagnalis, P. promelas, and R.
subcapitata models and the pooled chronic model (Table 1).
3 The MLR models referred to in this summary are those that considered hardness, DOC, and pH as individual
TMFs, but not interactions of these TMFs, as the final recommended MLR models for lead did not consider
interactions.
4 For the pooled C. dubia and L. stagnalis model, AIC retained hardness but BIC did not.
B-9
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pH
As for hardness, the effects of pH on lead toxicity is variable among species. For acute
exposures, increasing pH tends to have a protective effect on lead toxicity to P. promelas and a
lesser effect on lead toxicity to C. dubia (Mager et al. 201 la; Nys and De Schamphelaere 2013).
Nevertheless, pH was retained by AIC and BIC in the acute individual species MLR models for
C. dubia and P. promelas, as well as in the pooled acute model (Table 1).
For chronic exposures, from series of tests where only pH was varied, there is evidence that
chronic toxicity is reduced with increasing pH for B. calyciflorus and C. dubia (Nys et al. 2014,
2016). However, pH was not retained by AIC nor BIC in either the individual species MLR
models nor in the pooled MLR model (Table 1). Regardless, in selection of the final chronic
MLR model, the "full" model with pH included (along with DOC and hardness) was selected
based on (1) the empirical data for B. calyciflorus and C. dubia; and (2) mechanistic support
from the biotic ligand model (BLM).5 For the alga R. subcapitata, the influence of pH is the
opposite, with Pb toxicity increasing as pH increases (De Schamphelaere et al. 2014). This is
why a pooled MLR model that included both animals and algae was not considered.
Table 1. Summary of TMFs identified in lead MLR models
Exposure
Model
DOC
Hardness
pH
Acute
C. dubia
X
X
X
P. promelas
X
X
X
Pooled Acute
X
X
X
Chronic
B. calyciflorus
X
C. dubia
X
L. stagnalis
X
X
P. promelas
X
X
Pooled Chronic1
X
X
X
R. subcapitata
X
X
X
1 The pooled model was based on toxicity data for animals (C. dubia and L. stagnalis, specifically) because TMFs
influence Pb toxicity to algae differently. DOC was retained in the pooled model by both AIC and BIC; hardness
was retained by AIC; and pH was included based on considerations from empirical data and mechanisms supported
by the BLM.
5 As noted in Brix et al. (2020), selection of the final model should not be based solely on strict adherence to
statistical methods for model selection, but should also consider mechanistic and other information on how TMFs
influence toxicity and validity for other datasets.
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Lead References
Brix KV, DeForest DK, Tear L, Peijnenburg W, Peters A, Middleton ET, Erickson R. 2020.
Development of empirical bioavailability models for metals. Environ Toxicol Chem 39:85-100.
De Schamphelaere KAC, Nys C, Janssen CR. 2014. Toxicity of lead (Pb) to freshwater green
algae: Development and validation of a bioavailability model and inter-species sensitivity
comparison. Aquat Toxicol 155:348-359.
European Commission (EC). 2010. Lead and its compounds EQS Sheet. Prepared by UK,
Environment Agency on behalf of the European Union.
Esbaugh AJ, Brix KV, Mager EM, Grosell M. 2011. Multi-linear regression models predict the
effects of water chemistry on acute lead toxicity to Ceriodaphnia dubia and Pimephales
promelas. Comp Biochem Physiol Part C 154:137-145.
Esbaugh AJ, Brix KV, Mager EM, De Schamphelaere K, Grosell M. 2012. Multi-linear
regression analysis, preliminary biotic ligand modeling, and cross species comparison of the
effects of water chemistry on chronic lead toxicity in invertebrates. Comp Biochem Physiol Part
C 155:423-431.
Mager EM, Brix KV, Gerdes RM, Ryan AC, Grosell M. 201 la. Effects of water chemistry on
the chronic toxicity of lead to the cladoceran, Ceriodaphnia dubia. Ecotoxicol Environ Saf
74:238-243.
Mager EM, Esbuagh AJ, Brix KV, Ryan AC, Grosell M. 201 lb. Influences of water chemistry
on the acute toxicity of lead to Pimephales promelas and Ceriodaphnia dubia. Comp Biochem
Physiol PartC 153:82-90.
Nys C, De Schamphelaere KAC. 2013. Effect of Ca and pH on acute toxicity of Pb to
Ceriodaphnia dubia. Ghent University, Gent, Belgium. Prepared for the International Lead Zinc
Research Organization. 13 pp.
Nys C, Janssen CR, Mager EM, Esbaugh AJ, Brix KV, Grosell M, Stubblefield WA, Holtze K,
De Schamphelaere KAC. 2014. Development and validation of a biotic ligand model for
predicting chronic toxicity of lead to Ceriodaphnia dubia. Environ Toxicol Chem 33:394-403.
Nys C, Janssen CR, De Schamphelaere KAC. 2016. The effects of Ca, pH and dissolved organic
carbon on the chronic toxicity of Pb to the freshwater rotifer Brachionus calyciflorus:
development and validation of a bioavailability model. Environ Toxicol Chem 35:2977-2986.
Parametrix. 2010. Chronic toxicity of lead to the fathead minnow, Pimephales promelas: a
comparison of three different testing methodologies. Albany, OR.
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D. Nickel
Hardness
There is a consistent hardness effect on nickel toxicity in acute and chronic exposures to fish and
invertebrates (Deleebeeck et al., 2008; Kozlova et al., 2009). In terms of the nickel BLM, this
effect is quantified through binding constants (Log K values) of Ca and Mg with the biotic
ligand. In the nickel BLM, final Log K values for BL-Ca and BL-Mg were 4.25 and 3.60,
respectively. Hardness was identified as a TMF in all available nickel MLR models, with the
only exception being the species-specific acute C. dubia MLR.
DOC
Increased concentrations of dissolved organic carbon (DOC) consistently shows mitigation of the
toxic effects of nickel (Doig & Liber, 2006; Kozlova et al., 2009). DOC effects are simulated in
the nickel BLM by using a set of discrete binding sites and reactions calibrated in the WHAM
model (Tipping, 1994) in which nickel and other cations in the system can bind to DOC, thereby
reducing the ability of the metal to bind at the biotic ligand. DOC was identified as a TMF in all
three of the "Pooled" MLRs developed for nickel, in every chronic-species-specific MLR
spanning across fish, invertebrates and algae, and identified in both D. pulex and D. pulicaria
acute-species-specific MLRs.
pH
pH effects on nickel toxicity have been observed to be highly species-dependent. While some
studies (Deleebeeck et al., 2008; Kozlova et al., 2009; Pyle et al., 2002; Schubauer-Berigan et
al., 1993) have shown essentially no change in nickel toxicity to D. pulex, I). magna, and P.
promelas in acute exposures ranging from pHs around 5.5 through 8.7, Schubauer-Berigan et al
(1993) reported a 10-fold decrease in nickel EC50s between pH 7.3-8.7 in acute exposures to C.
dubia. The results of this study could be indicative of species-specific differences in pH
mechanisms of nickel bioavailability. For the pooled MLR models developed for nickel, only the
chronic model identified pH as a TMF. However, pH was identified as a TMF in 7 out of the 10
species-specific nickel MLR models.
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Table 1. Summary of TMFs identified in nickel MLR models (adapted from Croteau et al, 2021)
Model
Duration
Endpoint
Measure
DOC
Hardness
pH
Acute
C. dubia
48h
Survival
LC50
X
D. magna
48h
Survival
LC50
X
X
D. pulex
48h
Survival
LC50
X
X
D. pulicaria
48h
Survival
LC50
X
X
X
P. promelas
96h
Survival
LC50
X
X
Pooled Acute
-
-
-
X
X
Chronic
C. dubia
7d
Survival +
Reproduction
IC25
X
X
X
D. magna
21d
Reproduction
EC50
X
X
D. magna
21d
Survival
LC50
X
X
X
O. mykiss
17-26d
Survival
LC50
X
X
X
P.
subcapitata
72h
Growth
EC50
X
X
Pooled
Chronic
-
-
-
X
X
X
Acute +
Chronic
Pooled All
-
-
-
X
X
Nickel References
Deleebeeck, N.M.E., De Schamphelaere, K.A.C., Janssen, C.R. 2008. A novel method for
predicting chronic nickel bioavailability and toxicity to Daphnia magna in artificial and
natural waters. Environmental Toxicology and Chemistry, 27(10), 2097-2107.
Doig, L., Liber, K. 2006. Nickel partitioning in formulated and natural freshwater sediments.
Chemosphere, 62, 968-79.
Kozlova, T., Wood, C.M., McGeer, J.C. 2009. The effect of water chemistry on the acute
toxicity of nickel to the cladoceran Daphnia pulex and the development of a biotic ligand
model. Aquatic Toxicology, 91(3), 221-228.
Pyle, G., Swanson, S., Lehmkuhl, D. 2002. The influence of water hardness, pH, and suspended
solids on nickel toxicity to larval fathead minnows (Pimephales promelas). Water, Air,
and Soil Pollution, 133(1-4), 215-226.
Schubauer-Berigan, M.K., Dierkes, J.R., Monson, P.D., Ankley, G.T. 1993. pH-dependent
toxicity of Cd, Cu, Ni, Pb and Zn to Ceriodaphnia dubia, Pimephales promelas, Hyalella
azteca and Lumbriculus variegatus. Environmental Toxicology and Chemistry: An
International Journal, 12(7), 1261-1266.
Tipping, E. 1994. WHAM-A chemical equilibrium model and computer code for waters,
sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by
humic substances. Computers and Geosciences, 20(6), 973-1024.
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E. Zinc
Hardness
There is a consistent effect of hardness on Zn toxicity in acute and chronic exposures to fish and
invertebrates (e.g., Hyne et al. 2005; Clifford and McGeer 2009; De Schamphelaere and Janssen
2004; Heijerick et al. 2005). Similar ameliorative effects of hardness have been demonstrated in
tests with natural waters where pH was allowed to covary (Mebane et al. 2012). In terms of the
Zn BLM, the hardness effect is characterized by competitive interactions between Zn and
hardness cations (i.e., Ca and Mg) at the biotic ligand. The biotic ligand binding constants for Ca
and Mg for several Zn BLMs, representing multiple organisms and acute and chronic exposures,
are summarized in DeForest and Van Genderen (2012). The recently updated MLR-based
Canadian water quality guideline (WQG) for Zn, includes hardness as a toxicity modifying factor
(TMF) in both the short-term Zn benchmark and the long-term Zn WQG (CCME 2018).
DOC
In freshwaters, dissolved organic matter (DOM) - quantified as dissolved organic carbon (DOC)
- generally decreases Zn bioavailability (e.g., Clifford and McGeer 2009; Heijerick et al. 2003),
though the effect is not as strong as observed for copper (e.g., Hyne et al. 2005), and mainly at
high DOC concentrations (e.g., above 10 mg/L mg/L; Clifford and McGeer 2009; Bringolf et al.
2006). As with other metals, DOC effects are characterized in the Zn BLM by using a set of
discrete binding sites and reactions calibrated in the Windermere Humic Aqueous Model
(WHAM; Tipping 1994) in which Zn competes with other metals and cations for binding. In the
recently updated Canadian WQG, DOC was identified as a TMF for Zn and included as a term in
both the short-term benchmark and long-term WQG equations (CCME 2018).
pH
There are several mechanisms by which pH can affect Zn bioavailability, including via
speciation, solubility, and competitive interactions between Zn and biotic ligands. Generally, Zn
toxicity to fish and invertebrates, expressed on the basis of Zn2+, increases with increasing pH
(e.g., De Schamphelaere and Janssen 2004; Van Regenmortel et al. 2017), suggesting the
importance of competition between protons and Zn at the biotic ligand. On the basis of dissolved
Zn, toxicity generally increases marginally with increasing pH in acute exposures, but the effect
is inconsistent in chronic exposures (see summary in CCME 2018), potentially due to differences
in bulk solution chemistry characteristics. Santore et al. (2002) describes how differences in bulk
chemistry characteristics can influence the relative importance of competitive interactions and
speciation on Zn toxicity across a pH gradient. In the recently updated Canadian WQG, pH is
included as a TMF in the short-term benchmark, but not the long-term WQG (CCME 2018).
B-14
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Zinc References
Bringolf, R.B., B.A. Morris, C.J. Boese, R.C. Santore, H.E. Allen, and J.S. Meyer. 2006.
Influence of dissolved organic matter on acute toxicity of zinc to larval fathead minnows
(Pimephales promelas). Archives of Environmental Contamination and Toxicology. 51(3): 438-
444. https://doi.org/10.1007/s00244-005-0088-6
Canadian Council of Minsters of the Environment (CCME). 2018. Scientific criteria document
for the development of the Canadian water quality guidelines for the protection of aquatic life:
zinc. Canadian Council of Ministers of the Environment, Winnipeg, MB.
Clifford M, McGeer JC. 2009. Development of a biotic ligand model for the acute toxicity of
zinc to Daphnia pulex in soft waters. Aquatic Toxicology 91:26-32.
DeForest DK, Van Genderen EJ. 2012. Application of U.S. EPA guidelines in a bioavailability-
based assessment of ambient water quality criteria for zinc in freshwater. Environmental
Toxicology and Chemistry 31(6): 1264-1272.
De Schamphelaere KAC, Janssen CR. 2004. Bioavailability and chronic toxicity of zinc to
juvenile rainbow trout (iOncorhynchus mykiss): Comparison with other fish species and
development of abiotic ligand model. Environmental Science and Technology 38:6201-6209.
Heijerick DG, Janssen CR, De Coen WM. 2003. The combined effects of hardness, pH and
dissolved organic carbon on the chronic toxicity of Zn to D. magna: Development of a surface
response model. Archives of Environmental Contamination and Toxicology 44:210-217.
Heijerick DG, De Schamphelaere KAC, Van Sprang PA, Janssen CR. 2005. Development of a
chronic zinc biotic ligand model for Daphnia magna. Ecotoxicology and Environmental Safety
62:1-10.
Hyne RV, Pablo F, Julli M, Markich SJ. 2005. Influence of water chemistry on the acute toxicity
of copper and zinc to the cladoceran Ceriodaphnia CF dubia. Environmental Toxicology and
Chemistry 24(7): 1667-1675.
Mebane CA, Dillon FS, Hennessy DP. 2012. Acute toxicity of cadmium, lead, zinc, and their
mixtures to stream-resident fish and invertebrates. Environmental Toxicology and Chemistry
31(6): 1334-1348.
Santore RC, Mathew R, Paquin PR, Di Toro D. 2002. Application of the biotic ligand model to
predicting zinc toxicity to rainbow trout, fathead minnow, and Daphnia magna. Comparative
Biochemistry and Physiology Part C 133:271-285.
Tipping E. 1994. WHAM-A chemical equilibrium model and computer code for waters,
sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by humic
substances. Computers and Geosciences 20(6):973-1024.
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Van Regenmortel T, Berteloot O, Janssen CR, De Schamphelaere KAC. 2017. Analyzing the
capacity of the Daphnia magna and Pseudokirchneriella supcapitata bioavailability models to
predict chronic zinc toxicity at high pH and low calcium concentrations and formulation of a
generalized bioavailability model for D. magna. Environmental Toxicology and Chemistry
36(10):2781-2798.
B-16
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Addendum
Summary of How Toxicity Modifying Factors (TMFs)
Affect Metals Listed in Table 1
Information provided through the peer review by
Christopher A. Mebane
Water Quality Specialist, U.S. Geological Survey
Aluminum: Hardness has a moderate role in modifying A1 toxicity; pH has a strong role with
the greatest toxicity expressed at both low (pH 5) and elevated (pH >8.5) pH, and DOC
consistently reduced A1 toxicity (DeForest et al. 2018).
Cadmium: Hardness regressions predict acute and chronic toxicity well in natural waters
(Mebane 2006; USEPA 2016). pH effects appear weak and ambiguous (Niyogi et al. 2008;
Clifford and McGeer 2010). The threshold for a DOC effect appears to be >5 mg/L (Niyogi et al.
2008).
Cobalt: Hardness is clearly important (Diamond et al. 1992; Borgmann et al. 2005). pH at least
affected gill uptake, with uptake increasing with increasing pH up to 8.7. DOM reduced Co gill
binding, but Co-DOM affinity was much lower than that of Cd, Cu, or Ag (Richards and Playle
1998).
Copper, freshwater: DOC has a strong binding affinity to Cu and predictably reduces Cu
toxicity, even at low concentrations (Erickson et al. 1996; Welsh et al. 2008). pH has a strong
effect on Cu toxicity, with toxicity tending to decrease with increasing pH in alkaline conditions,
but toxicity decreasing with decreasing pH in acidic conditions (Cusimano et al. 1986; Erickson
et al. 1996). Hardness is a comparatively minor factor in natural waters (Markich et al. 2005).
Copper, marine: DOC and salinity tend to reduce Cu toxicity in marine and estuarine waters
(Grosell et al. 2007; Hall et al. 2008).
Lead: Similar to Cu, DOC and pH have strong effects on the bioavailability and toxicity of Pb
(DeForest et al. 2017) Hardness may be an important factor in natural waters, especially when
DOC is low (Mebane et al. 2012).
Nickel: Ni toxicity tends to decrease as hardness increased and decrease with increasing DOC.
pH has inconsistent influence on toxicity (Croteau et al. 2021; Santore et al. 2021).
Silver: DOC reduces toxicity but pH and hardness influences may be inconsistent (Naddy et al.
2018).
Addendum-1
-------
Zinc: Similar to Cd, hardness has a strong influence on Zn toxicity, with decreasing toxicity with
increasing hardness (Clifford and McGeer 2009; Mebane et al. 2012; CCME 2018); with fish,
toxicity generally increases with increasing pH but relations may be inconsistent in other taxa
(De Schamphelaere and Janssen 2004). DOC reduces Zn toxicity but some studies suggest
influences may be nonlinear, with a threshold of>«10 mg/L DOC required to substantially
reduce toxicity (Hyne et al. 2005; Bringolf et al. 2006; Ivey et al. 2019).
References:
Borgmann, U., Y. Couillard, P. Doyle, and D.G. Dixon. 2005. Toxicity of sixty-three metals and
metalloids to Hyalella azteca at two levels of water hardness. Environmental Toxicology
and Chemistry. 24(3): 641-652
Bringolf, R.B., B.A. Morris, C.J. Boese, R.C. Santore, H.E. Allen, and J.S. Meyer. 2006.
Influence of dissolved organic matter on acute toxicity of zinc to larval fathead minnows
(Pimephales promelas). Archives of Environmental Contamination and Toxicology. 51(3):
438-444. https://doi.org/10.1007/s00244-005-0088-6
CCME. 2018. Canadian Water Quality Guidelines: Zinc. Scientific Criteria Document. Canadian
Council of Ministers of the Environment, ISBN 978-1-77202-043-4 PDF, Winnipeg. 127
pp.
Clifford, M. and J.C. McGeer. 2009. Development of a biotic ligand model for the acute toxicity
of zinc to Daphnia pulex in soft waters. Aquatic Toxicology. 91(1): 26-32
https://doi.Org/10.1016/j.aquatox.2008.09.016
Clifford, M. and J.C. McGeer. 2010. Development of a biotic ligand model to predict the acute
toxicity of cadmium to Daphnia pulex. Aquatic Toxicology. 98(1): 1-7.
https://doi.Org/10.1016/j.aquatox.2010.01.001
Croteau, K., A.C. Ryan, R. Santore, D. DeForest, C. Schlekat, E. Middleton, and E. Garman.
2021. Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the
Toxicity of Nickel to Aquatic Freshwater Organisms. Environmental Toxicology and
Chemistry. n/a(n/a). https://doi.org/https://doi.org/10.1002/etc. 5063
Cusimano, R.F., D.F. Brakke, and G.A. Chapman. 1986. Effects of pH on the toxicities of
cadmium, copper, and zinc to steelhead trout (Salmo gairdneri). Canadian Journal of
Fisheries and Aquatic Sciences. 43(8): 1497-1503. https://doi.org/10.1139/f86-187
De Schamphelaere, K.A.C. and C.R. Janssen. 2004a. Bioavailability and chronic toxicity of zinc
to juvenile rainbow trout (Oncorhynchus mykiss): comparison with other fish species and
Addendum-2
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Addendum-3
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Mebane, C.A., F.S. Dillon, and D.P. Hennessy. 2012. Acute toxicity of cadmium, lead, zinc, and
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Addendum-4
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