Article
pubs.acs.org/est
Predicting Water Quality Criteria for Protecting Aquatic Life from
Physicochemical Properties of Metals or Metalloids
Fengchang Wu,*,† Yunsong Mu,† Hong Chang,† Xiaoli Zhao,† John P. Giesy,‡,§,∥,⊥ and K. Benjamin Wu#
†
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences,
Beijing 100012, China
‡
Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan,
Canada
§
Zoology Department and Center for Integrative Toxicology, Michigan State University, East Lansing, Michigan 48824, United States
∥
Department of Biology & Chemistry and State Key Laboratory in Marine Pollution, City University of Hong Kong, Kowloon, Hong
Kong, SAR, China
⊥
School of Biological Sciences, University of Hong Kong, Hong Kong, SAR, China
#
HDR-HydroQual, 1200 MacArthur Blvd, Mahwah, New Jersey 07430, United States
S Supporting Information
*
ABSTRACT: Metals are widely distributed pollutants in water and can
have detrimental effects on some aquatic life and humans. Over the past
few decades, the United States Environmental Protection Agency (U.S.
EPA) has published a series of criteria guidelines, which contain specific
criteria maximum concentrations (CMCs) for 10 metals. However,
CMCs for other metals are still lacking because of financial, practical, or
ethical restrictions on toxicity testing. Herein, a quantitative structure
activity relationship (QSAR) method was used to develop a set of
predictive relationships, based on physical and chemical characteristics of
metals, and predict acute toxicities of each species for five phyla and
eight families of organisms for 25 metals or metalloids. In addition,
species sensitivity distributions (SSDs) were developed as independent
methods for determining predictive CMCs. The quantitative ion
character−activity relationships (QICAR) analysis showed that the softness index (σp), maximum complex stability constants
(log −βn), electrochemical potential (ΔE0), and covalent index (Xm2r) were the minimum set of structure parameters required to
predict toxicity of metals to eight families of representative organisms. Predicted CMCs for 10 metals are in reasonable
agreement with those recommended previously by U.S. EPA within a difference of 1.5 orders of magnitude. CMCs were
significantly related to σp (r2 = 0.76, P = 7.02 × 10−9) and log −βn (r2 = 0.73, P = 3.88 × 10−8). The novel QICAR-SSD model
reported here is a rapid, cost-effective, and reasonably accurate method, which can provide a beneficial supplement to existing
methodologies for developing preliminarily screen level toxicities or criteria for metals, for which little or no relevant information
on the toxicity to particular classes of aquatic organisms exists.
■
the latest water quality guideline,9 in which specific criteria
maximum concentrations (CMCs) are recommended for 10
metals. The CMCs for other metals are still lacking. This
deficiency limits the capacities of assessing water quality and
dealing with unexpected environmental incidents, pollution
control and environmental risk management. The present
CMCs were mainly derived from standardized aquatic life
toxicity tests. Thus, environmental behavior in specific ambient
media and different end points could lead to differences in
toxicity of metals.10 Comprehensive toxicity tests are time-
INTRODUCTION
Metals can become contaminants in aquatic environments. The
water quality criteria (WQC) for some metals were developed
in the middle of the 20th century. Some acute toxicity data for
aquatic organisms were used as the scientific basis for
development of WQC and also for use in the environmental
management.1,2 To sustain the reproduction and survival of
aquatic organisms, in 1976, the United States Environmental
Protection Agency (U.S. EPA) published the first WQC
guideline commonly referred to as the “Red Book”, which
recommended WQC for 12 metals or metalloids. Currently 167
ambient WQC for priority and nonpriority toxicants have been
developed, and these WQC have been updated seven times.3−9
However, there are only 12 aquatic life criteria values for
priority metals and 4 criteria values for nonpriority metals in
© 2012 American Chemical Society
Received:
Revised:
Accepted:
Published:
446
August 16, 2012
November 29, 2012
November 30, 2012
November 30, 2012
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Table 1. Two-Variable Regression Models Based on Seven Characteristics for Metal Ionsa
species
phyla
predicting equations
r2
RSS
RMSE
F
P
Chironomus tentans
Arthropoda
0.769
3.749
0.790
9.98
0.012
Crangonyx
pseudogracilis
Daphnia magna
Arthropoda
0.791
8.520
1.103
13.26
0.004
Arthropoda
0.869
1.360
0.583
13.25
0.017
Lymnaea acuminata
Mollusca
0.827
1.453
0.696
Cyprinus carpio
Chordata
0.960
0.226
0.274
35.55
0.008
Brachionus
calycif lorus
Bufo melanostictus
Rotifera
0.823
1.478
0.702
6.97
0.070
0.902
1.814
0.673
18.35
0.009
Lemna minor
Angiosperms
log 48 h − EC50 = (28.136 ± 18.459)σp + (−0.150 ± 0.112) log −βn +
(0.814 ± 3.625)
log 96 h − LC50 = (39.716 ± 25.627)σp + (−0.254 ± 0.136) log −βn +
(1.678 ± 4.533)
log 48 h − EC50 = (−0.272 ± 18.674)σp + (−0.360 ± 0.136) log −βn +
(6.604 ± 4.093)
log 96 h − EC50 = (−2.160 ± 0.821)Xm2r + (0.237 ± 0.222) AN/ΔIP +
(5.557 ± 1.399)
log 96 h − LC50 = (33.441 ± 6.256)σp + (0.412 ± 0.137)Z/r + (−3.159 ±
0.559)
log 24 h − LC50 = (−0.297 ± 0.082) log −βn + (−0.111 ± 0.106)|log KOH|
+ (6.375 ± 2.058)
log 96 h − LC50 = (6.955 ± 20.353)σp + (−1.569 ± 0.474)Xm2r + (5.014
± 3.156)
log 96 h − EC50 = (24.984 ± 9.959)σp + (1.494 ± 0.439)ΔE0 + (−2.046 ±
1.140)
0.824
0.728
0.427
9.40
0.030
Chordata
7.191
0.070
a 2
r is the coefficient of determination, RSS is the residual sum of squares, RMSE is root mean square deviation, and p is the statistical significance
level.
and predictive relationships could be developed for additional
aquatic species. The purpose of this study was to relate
characteristics of data-rich metal ions with metal toxicities of
each species of several representative aquatic organisms that
were necessary for getting WQC, to predict the toxicities of
additional data-poor metals and ultimately to obtain the CMCs
for these additional metals.
The species sensitivity distribution (SSD) analysis is a
promising method to determine CMCs, based on cumulative
probability distributions of toxicity values for multiple species.
For derivation of CMCs, the concentration of a chemical that
can be used as a hazard level can be extrapolated from a SSD
such that 95% of species would not be affected by a particular
concentration of a specific metal.30 The SSD can be used to
estimate the concentration at which 5% of species would be
affected. The concentration associated with the fifth percentile
has been referred to as the 5% hazard concentration (HC5). In
the semiprobabilistic approach developed by Stephan,31 the
minimum data set required for derivation of a WQC for
freshwater was at least three phyla and eight different
taxonomic families.32,33 As a result, the diversity and the
sensitivities of a range of aquatic life are represented in the
criteria values in order to estimate a concentration to protect
organisms against small effects. The present study was
conducted to compile the relative toxicity data of 25 metals
or metalloids and to examine the relationships between selected
physicochemical parameters and corresponding toxicities (LC50
or EC50) in eight families of representative organisms. This
information was used to determine CMCs of each metal by
SSD analysis and obtain a predictive relationship for CMCs.
consuming and expensive, and testing on some endangered
species cannot be conducted due to eco-ethics challenges. For
other species, sometimes it is not possible to conduct
controlled laboratory tests because of size, lack of information
on culturing or maintaining them under laboratory conditions.
Therefore, the importance of establishing a system to estimate
CMCs that is based on limited toxicity test data is recognized.
One of the commonly used models to predict criteria of metals
is the recently developed biotic ligand model (BLM).11−15 The
U.S. EPA adopted the copper BLM for establishing WQC and
extended the use of the BLM to WQC of silver and zinc.16,17
The European Union also recently applied the BLM to assess
chronic toxicity of nickel (Ni) and developed WQC for Ni.18
The BLM is used to estimate the bioavailable fraction of metals
for ascertaining the role of water chemistry on toxicity of metals
to aquatic organisms. A model, based only on physicochemical
properties to predict toxicity, has also been developed.19
However, that model was of limited scope.
Quantitative structure activity relationships (QSARs) establish intrinsic relationships between characteristics of a
compound to bioactivity or toxicity of metals by use of
statistical analysis. Most QSARs have been developed for
organic chemicals, with inorganic chemicals, such as metals
being under-represented in the environmental toxicology
literature.20−22 Because of metal speciation, complexation,
interactions in biological systems and formation/degradation
of metal−ligand bond, correlation of toxicity with physical or
chemical properties of metals is still challenging. It is known
that most metals exist in biological system as cations and
toxicity of metals depends mainly on cationic activity.23 At
present, ion characteristics have been used to predict toxicity or
sublethal effects of metal ions, and the quantitative ion
character−activity relationships (QICAR) models based on
metal−ligand binding have recently been developed.19,23−28
More than twenty ion characteristics including hydrolysis,
ionization, covalent binding and spatial characteristics were
used in a study conducted by Walker et al.,29 to predict binding
of soft ligands. However, there are still challenging issues to
solve. First, existing QICAR models contained different metal
toxicity data, which varied largely in exposure times, organisms,
effects and effect levels. Second, most of QICAR models relate
certain ion characteristics with toxicity of metals. Thus, novel
characteristics of metals or metalloids could also be considered
■
MATERIALS AND METHODS
Modeling Data Sets. Toxicity data used in the present
study were selected based on data collection and requirement
described in 1985 U.S. EPA WQC guidelines where minimum
eight species (three phyla) were required.31 Those data have
also been used in conjunction with metal criteria derivation and
recent risk assessment.34,35 For better comparison and
consistence, toxicity data were further selected based on the
following standards: (1) Toxicities of metals to each species
were required from the same data source and the same research
team under the same experimental conditions. (2) Results for
six or more metals were investigated for each species. (3) Data
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Figure 1. Regression models of log −EC50 or log −LC50 and two most predictive characteristics of ions for eight model organisms.
as parameters in predictive relationships models were obtained
from a variety of sources. The basic characteristics considered
in developing the relationships were atomic number (AN),
atomic radius (AR), Pauling ionic radius (r), ionic charge (Z),
and ionization potential (ΔIP).44 In total, 14 parameters were
considered to characterize the metals in the QICAR model.
These parameters included: softness index (σp);45 maximum
complex stability constants (log −βn), which was derived from
maximum strength of complexes formed between metals and
EDTA, CN−, or SCN−; electrochemical potential (ΔE0);19 first
hydrolysis constants (|log KOH|);46 electronegativity (Xm);47
electron density (AR/AW);47 relative softness (Z/rx), where x
represents electronegativity values; atomic ionization potential
(AN/ΔIP); covalent index (Xm2r); polarization force parameters (Z/r, Z/r2, and Z2/r); and similar polarization force
considered included the assessment end points of survival and
growth. (4) Results of acute tests conducted in unusual dilution
water, for example, dilution water in which total organic carbon
or particulate matter exceeded 5 mg/L, should not be used. All
toxicity data of different species were from the ECOTOX
Database and literatures.36−43 In the present study, five phyla
and eight different taxonomic families (three chordates, two
arthropods, a rotifer, a mollusk and an aquatic plant) were
selected (Table 1). Twenty-five metals or metalloids (e.g.,
mono-, di-, trivalent- and hexavalent metals) were chosen.
Those included 16 metals recommended by U.S. EPA in the
latest WQC guideline, in which 10 metals had their own CMCs
for protecting aquatic life.
Characteristics of Metals and Development of
Predictive Relationships. Characteristics of the metals used
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parameters (Z/AR and Z/AR2). For each species, measures of
acute toxicity were correlated to every characteristic of ions by
use of linear regression. The magnitude of association of each
characteristic with toxicity was tested by F-test statistic, with the
level of significance at α = 0.05. Because the two-variable model
provided better fits and contained sufficient information on
structure, the two most predictive characteristics were selected
based on the rank of adjusted correlation coefficients,. As a
result, multiple linear regressions were performed between the
logarithm of LC50 or EC50 and the two most highly correlated
characteristics of ions in Table S1, Supporting Information
(SI). The predictive potentials of QICAR models were
evaluated by use of the coefficient of determination (r2),
residual sum of squares (RSS), root-mean-square deviation
(RMSE), F value using multiple analysis of variance (ANOVA),
and the level of Type I error (P).
SSD Construction and HC5 Derivation. On the basis of
the QICAR equations developed for each of the representative
organisms, predicted acute toxicity values were derived for each
metal. After ranking these data from least to greatest, plotting
positions (proportions) for use in a cumulative probability
distribution were calculated (eq 1).
proportion = (rank − 0.5)/number of species
were Xm2r (r2 = 0.7620, F = 12.809, P = 0.023) and AN/ΔIP (r2
= 0.4293, F = 3.009, P = 0.158). Values of Xm2r were
significantly and negatively correlated with potency of metals.
However, the AN/ΔIP ratio was not significantly correlated
with toxicity of metals. Xm2r, which qualifies covalent
interactions relative to ionic interactions, is an index of stability
of metal ions in water.48 Absorption of cadmium ions in
mussels has been reported to involve covalent interactions with
sulfhydryl groups on proteins 49 and that parameter has been
used to successfully predict bioaccumulation of metals. This
observation suggests that Xm2r was a characteristic that was
useful for predicting the potency of metals to cause toxicity in
mollusks.48,50 For two organisms of the chordate, C. carpio and
B. melanostictus, σp was correlated with the potency of metals
with r2 = 0.8373 (F = 20.578, P = 0.011) and 0.6325 (F = 8.605,
P = 0.033), respectively. Although the Z/r ratio also provided
adequate fits, it was not statistically significant (r2 = 0.5739, F =
5.388, P = 0.081). Therefore, σp was the only characteristic that
was used to predict the potency of metals to cause toxicity to
fish. For B. calycif lorus, only log −βn was significantly associated
with log-LC50 (r2=0.7587, F = 12.580, P = 0.024), while |
logKOH| was not significantly correlated (r2 = 0.051, F = 0.213,
P = 0.668). This is consistent with the findings of other
researchers, who found that |log KOH| was not a unique
characteristic for predicting the potency of toxicity of metals to
rotifer.27 The two-variable model that best predicted potency of
metals was a combination of σp and ΔE0. The predictive
relationships for these two parameters were significant and
positive with r2 = 0.5484 (F = 6.071, P = 0.057) and 0.3173 (F
= 2.324, P = 0.188), respectively. In conclusion, four
characteristics of ions, σp, log −βn, Xm2r, and ΔE0, were
statistically significantly associated with potencies of metals to
the reference species studied here. All these characteristics of
ions except for log −βn had been previously reported to be
useful in developing QICAR models to predict toxicity of
metals. The other three characteristics (Z/r, |log KOH|, and AN/
ΔIP) could be used as additional factors to improve the
predictability.
Eight two-variable linear regression models were developed
(Table 1). Among three arthropods, the model for D. magna
had the best relationships (r2 = 0.869, F = 13.25, P = 0.017),
while the model for C. tentans exhibited the poorest coefficient
of determination (r2 = 0.769, F = 9.98, P = 0.012). The QICAR
models for L. acuminata, B. calycif lorus, and L. minor had
coefficients of determination, which were 0.827, 0.823, and
0.824, respectively. Chordates, compared to other species in the
present study, exhibited the best regressions. RSS and MSE
were used to evaluate indicators to assess the robustness of the
predictive relationships. Because of the diversity of training sets
with the maximum number of metal ions (n = 10) and valence
types (+1 to +3), RSS and MSE were greater for C.
pseudogracilis, than for other species. On the basis of the
results of the multiple regression analysis of variance these
characteristics of ions exhibited a statistically significant
relationship to log −EC50 (P < 0.05). Alternatively, equations
to predict potency of toxicity of metals to both L. acuminata
and B. calycif lorus were better (0.05 < P < 0.1), which were
accepted for statistical significance at the 90% confidence level.
The QICAR models presented herein were an improvement on
the contributions reviewed by Wolterbeek and Walker et al.29,47
First, the present study further extended the application to 25
metals or metalloids with various valences. Second, more than
20 physicochemical parameters reviewed by Walker et al. and
(1)
To obtain the logarithm of HC5 values, the SSD was fitted by
use of the sigmoidal-logistic model (eq 2), where a was
represented as an amplitude, Xc was a center value, and k was a
coefficient. The CMCs were defined as (HC5)/2. Multiple
linear regression and SSD fitting were performed by use of the
OriginPro 8 software package, with three fitting parameters (a,
Xc, and k) and their standard errors (a-SE, Xc-SE, and k-SE).
Significant differences among species were examined by use of
ANOVA.
a
y=
−k(x − xc)
(2)
1+e
■
RESULTS AND DISCUSSION
Quantitative Ion Character−Activity Relationships
(QICARs) to Predict Toxicity of Metals. Statistically
significant, positive or negative relationships between log
−EC50 or log −LC50 and the 14 ion characteristics were
observed. Characteristics with the greatest r2 values for each
organism were obtained. Seven characteristics, σp, log −βn,
Xm2r, AN/ΔIP, Z/r, |log KOH|, and ΔE0 exhibited statistically
significant associations with toxicities to representative
organisms (Figure 1). The two parameters with the greatest
r2 were used to predict the toxicity of each metal for each
representative species. The parameter σp was significantly and
positively correlated with log −EC50 of three arthropods (C.
tentans, C. pseudogracilis, and D. magna), with coefficients of
determination (r2) of 0.6994 (F = 16.289, P = 0.005), 0.6872 (F
= 17.575, P = 0.003), and 0.6403 (F = 8.900, P = 0.03),
respectively (Figure 1a, b, and c). These results are consistent
with those reported previously where σp was the characteristic
that exhibited the strongest association with metal−ligand
quantifying binding constant of seven metals for D. magna.28
Log −βn was strongly correlated with acute toxicities of
arthropods. Values of log −βn were derived from maximum
strength of complexes formed between each metal and EDTA,
CN−, or SCN−, which is a measure of binding affinity of each
with the O-donor group. The two characteristics that were
most predictive of toxicity of metals to mollusks (L. acuminata)
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their potential to produce toxic effects were examined29and
then acute toxicities of five phyla and eight species were
predicted, including a wide range of aquatic life with different
trophic levels. Finally, the two-variable QICAR models for
different species were established with indication of which ion
characteristic was the most relevant for each species. Base on
the analyses of QICAR models for eight species, two-variable
correlations appear to be species-specific and also provide
major and minor ion characteristics for each species. The
presented results demonstrated that all the QICAR models
presented herein were capable of predicting potencies of
toxicity of metals (RSS < 8.520, MSE < 1.103) that could be
used in development of CMCs for these metals.
Species Sensitivity to Metals and HC5 Derivation. The
predicted acute toxicities of each species were calculated based
on eight two-variable linear regression models using seven ion
characteristics (see Table S1, SI). Potencies of 25 metals or
metalloids to each species, which were derived from QICAR
models, varied among species (see Table S2, SI). Planktonic
arthropods were most sensitive to Al, As, Fe, Hg, La, Ni, and
Pb, with log −EC50 values ranging from −1.252 to 1.487.
Arthropods were more sensitive to trivalent ions (Al, As, and
Fe) in contrast to group IVA, IIB, IIIB and VIII metals. In the
present study, it was demonstrated that C. pseudogracilis was the
most sensitive species to Hg, which agrees with the report of
Zhang.51 However, B.calycif lorus was among the most sensitive
species to the effects of metals in groups IA, IIA, VIIB and IIB,
including Ba, Be, Ca, Cd, K, Li, Mg, Mn, Sr, and Zn, with log
−EC50 values ranging from −2.225 to 0.751.
C. pseudogracilis instead of D. magna was the most sensitive to
the effects of Cd, a result that is in compliance with the findings
that D. magna was not the most sensitive species to Cd.52 For
Zn, C. pseudogracilis and D. magna were equally sensitive with
similar log −EC50 values of 0.594 and 0.669. This finding also
agrees with reports that proposed that freshwater crustaceans
were the most sensitive to Zn.53 In conclusion, zooplankton
were most vulnerable to the toxic effects of metals, which is also
consistent with previous conclusions based on empirical
measures of toxic potency.54−56 Among the three arthropods,
C. tentans, C. pseudogracilis, and D. magna, D. magna was the
most sensitive species to major metals. This conclusion
corresponds to the results of Song et al.,57,58 who found that
D. magna was more sensitive to triphenyltin than C. tentans,
with 24 h-LC50 values of 13.3 and 287.7 μg/L. The mollusk, L.
acuminate, was the most sensitive organism to Ag, Co, Cu, Sb,
and Tl, which are group IIIA, VA, VIII, and IB metals. This was
also in compliance with the results of a previous study.59
Compared to other organisms, the vertebrates, C. carpio and B.
melanostictus, were the least sensitive to metals except Na. L.
minor was the representative of aquatic plants, since metals
affected its microstructures and cell growth. L. minor was
among the most sensitive organisms to Cr(III) and Cr(VI) (see
Table S2, SI), which was consistent with previous studies in
which exposure to Cr resulted in lesser production of biomass
of L. minor.60 In conclusion, five phyla and eight families of
representative organisms were used as minimum set of species
to establish models of sensitivity to metals. In accordance with
requirements of the U.S. EPA, all of the species were found to
be sensitive to the toxic effects of main group metals (IA−VA)
and metals in groups IIB, IIIB, VIIB, and VIII.
Species sensitivity distributions for 25 metals or metalloids
were constructed and ultimately used to predict CMCs. The
basic fitting parameters (a, Xc, k, a-SE, Xc-SE, and k-SE) and
statistical indexes (Adj.r2, RSS, F and P) used to develop the
predictive relationships are shown in Table S3, SI. Coefficients
of determination (r2) of the 25 fitting equations were greater
than 0.9 (RSS < 0.0437, P < 0.0001), which suggests that all
SSD models provided adequate fits to the data. However, the
predicted toxicities for every species in the Al-SSD model were
similar, which was insufficient to provide a reasonable fit. The
SSD for Al that contains more sensitive species needs to be
further investigated.
Distributions of 25 Metal Criteria and Correlation
Analyses. On the basis of the SSD curves of 25 metals or
metalloids in Figure 2, the toxicity profiles were classified as
Figure 2. Species sensitivity distributions analysis and derivation of the
predicted log −HC5 based on the QICAR regressions for 25 metals or
metalloids. The predicted toxicities were derived from minimum eight
species (three phyla), including C. tentans, C. pseudogracilis, D. magna,
L. acuminata, C. carpio, B. calycif lorus, B. melanostictus, and L. minor.
highly toxic, moderately toxic, low toxic and lesser toxic within
the whole concentration thresholds between −1.89 and 3.11.
Cr (VI), Ag, Hg, and Tl were classified as highly toxic metals,
with log −HC5 values between −1.89 and −1.0. As (III), Cd,
Cu, and Sb were classified as moderately toxic metals, with log
−HC5 values ranging from −1.0 to 0. Al, Co, Fe, La, Mn, Ni,
Pb, and Zn were classified as low toxic metals with log −HC5
values from 0 to 1.0. The rest of the metals caused lesser acute
toxicity at the concentrations tested, with log −HC5 values
from 1.0 and 3.11.
Correlations between log −HC5 and seven characteristics of
metal ions were good indicators for prediction of the toxicity of
free ions. The coefficient of determination ranked in the
descending order was σp > log −βn>ΔE0 > Xm2r > |log KOH| >
Z/r > AN/ΔIP (See Table S4, SI). In the present study, σp and
log −βn were significantly correlated with log-HC5, with
adjusted correlation coefficients of 0.7638 (F = 78.626, P =
7.02 × 10−9) and 0.7265 (F = 64.760, P = 3.88 × 10−8),
respectively. The relationships of actual and model predicted
log −HC5 with softness index (σp) were shown in Figure 3.
The softness index σp separated metal ions into three groups
based on their solubility constants with sulfur and oxygencontaining anions: (1) hard ions, which preferentially bind to
oxygen or nitrogen (e.g., Li, Na, Ca, and Mg); (2) soft ions,
which preferentially bind to sulfur (e.g., Cd, Hg, Ag, and
As(III)); and (3) borderline ions, which form complexes with
oxygen, nitrogen, and sulfur to varying degrees (e.g., Co, Ni,
Cu, and Zn). Most soft ions were classified as having high and
intermediate toxic potency (σp < 0.10); borderline ions were
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Zn < Cr (III)<Cu < Hg < Pb < Ni < Ag < Cd < Al < Cr (VI) <
As (III). The predicted CMC for Zn was 120 μg/L and the
CMC recommended by the U.S. EPA was 110.9 μg/L. The
model provided the poorest prediction of the potency of As
(III). The predicted CMC was 12.16 μg/L, and the CMC value
by U.S. EPA was 340 μg/L. In summary, the comparison results
indicate that values of log −CMCs predicted by this study and
log −CMCs recommended by U.S. EPA for Zn, Cr (III), Cu,
and Hg were in the same order of magnitude. The differences
of values for Pb, Ni, Ag, and Cd were within 1 order of
magnitude, and for the rest metals or metalloids, the differences
were within 1.5 orders of magnitude. Of these metals, the
CMCs recommended by U.S. EPA for Hg, Cu, Cr (III), Zn, Ni,
and Pb were well reproduced by the models with differences of
less than 0.20-fold. The model provided reasonable predictions
for Ag and Cd with standard error between 0.20 and 1.00. For
Ag, the toxicity prediction equation for the sensitive species L.
acuminata contains two sensitive characteristics of metal ions,
Xm2r and AN/ΔIP, which resulted in greater toxicity prediction
error and SSD fitting errors. For Cd, it water hardness can
influence aquatic toxicity. When acute toxicity of Cd to D.
magna was determined at three different harnesses, it was found
that the toxicity in softer water was 5 times greater than that
harder water.4 However, the QICAR-SSD model only focuses
on the influence of physicochemical properties of metals and
neglects site-specific geochemical conditions such as hardness,
pH, temperature, dissolved oxygen, and dissolved organic
matter. Indeed, site-specific geochemical conditions influence
the degree to which organisms take up metals and exhibit
adverse effects. It is critical to consider bioavailability in
extended QICAR-SSD model, as geographically distinct ecoregions and sites will show distinctive geochemical characteristics.
Trivalent and hexavalent metals (Al (III), As (III), and Cr
(VI)) provided weak prediction because of the average
deviation of 1.4 orders of magnitude. Of the seven characteristics of metal ions, σp exhibited the greatest statistical
significance to predict log-HC5, with the linear regression
slope of 26.408 (F = 78.626, P = 7.02 × 10−9). Therefore, σp of
more charged metals need to be revised by increasing the value
by a factor of 0.05 (1.387/26.408). However, valences of metal
ions affected the magnitude of σp, which is the reason that the
ratio of coordinate bond energy of iodide to coordinate bond
energy of fluoride will increase after highly charged metals
combine covalently to bioligands.47 In QICAR-SSD model, the
σp per unit charge was used as a criterion for evaluation with
the cumulative contribution of charge neglected. In further
investigation, the QICAR-SSD model for highly charged metals
should be developed to raise accuracy of predicted WQCs.
To provide reasonable predictions of the toxicity of metals to
various organisms, one needs to consider many factors since
each metal has unique physicochemical property that will make
differences in its environmental behavior and toxic mechanism
to sensitive species. How to predict toxicity of metals to all
species under various conditions from limited information is
the key issue to the WQC study. Although the models
presented can reasonably predict toxicity of metals from limited
information, they do need to be further developed in five areas.
These include (1) QICAR models for additional representative
species in order to expend the protection range of WQC; (2)
modeling metal toxicity separately by metal valence might
improve the prediction accuracy of the model; (3) the need to
account the effect of site-specific geochemical conditions, such
Figure 3. Model for log −HC5 and softness index (σp) at 95%
prediction level.
less toxic (0.10 < σp < 0.163); and hard ions exhibited lesser
potency (0.163<σp < 0.250). These findings support the
conclusion that σp is the best predictor of toxic potency of
metals or metalloids to aquatic organisms. Alternatively, log
−βn had the greatest negative correlation with toxicity, which
reflected the binding affinities of metal−ligand complexes.
Values of log −βn of more toxic metals, such as Hg, Ag, and Tl,
were greater than 18.0, while those for metals of intermediate
toxicity were between 11.0 and 18.0. Both ΔE0 and Xm2r were
weakly correlated (P < 0.001) and the rest of the descriptive
parameters were not significantly (P > 0.05) correlated with
toxic potency. Thus, the minimum set of characteristics of
metal ions required to reasonably predict WQC of 25 metals
were σp, log −βn, ΔE0, Xm2r, |log KOH|, Z/r, and AN/ΔIP, while
not strongly predictive, can be used as complementary
parameters to discriminate among potencies of metals to
aquatic organisms.
Validation and Applicability of the QICAR-SSD Model.
Values of log −HC5 for 10 metals derived from CMCs
recommended by U.S. EPA in 2009 are shown in Figure 4. The
QICAR-SSD model herein demonstrated a good performance
to predict log-HC5. The order of standard errors between
predicted values and values recommended by U.S. EPA were
Figure 4. Relationships between predicted log −HC5 and recommended log −HC5 derived from WQC.
451
dx.doi.org/10.1021/es303309h | Environ. Sci. Technol. 2013, 47, 446−453
Environmental Science & Technology
Article
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as hardness, pH, temperature, dissolved oxygen and dissolved
organic matter; (4) optimization can be further generalized
throughout the entire predictive framework; and (5)
bioavailability of multiple metal speciation due to transformation should also be considered. These predictive models
could be useful when data on metal toxicity are lacking or
incomplete. While further development of such models is
necessary to determine their range of applicability, the QICARSSD model is a promising screen level tool that can be used to
rapidly predict aquatic toxicity and criteria of metals.
■
ASSOCIATED CONTENT
* Supporting Information
S
Tables with seven metal ion characteristics used in regression
models, predicted log −EC50 values of minimum eight species,
SSD fitting parameters and correlations between metal ion
characteristics, and predicted log −HC5. This material is
available free of charge via the Internet at http://pubs.acs.org.
■
AUTHOR INFORMATION
Corresponding Author
*Phone: +86-10-84915312. Fax: +86-10-84931804. E-mail:
wufengchang@vip.skleg.cn.
Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS
The present study was supported by the National Basic
Research Program of China (973 Program) (No.
2008CB418200), the National Natural Science Foundation of
China (No. U0833603 and 41130743), and the National Water
Pollution Control and Management Technology Major
Projects of China (2012ZX07503-003). J.P.G. was supported
by the program of 2012 “High Level Foreign Experts” (no.
GDW20123200120) funded by the State Administration of
Foreign Experts Affairs, the P. R. China. J.P.G. was also
supported by the Canada Research Chair program, an at large
Chair Professorship at the Department of Biology and
Chemistry and State Key Laboratory in Marine Pollution,
City University of Hong Kong, and the Einstein Professor
Program of the Chinese Academy of Sciences.
■
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1
The Authors: Fengchang Wu, Yunsong Mu, Hong Chang, Xiaoli Zhao, John P. Giesy
2
and K. Benjamin Wu
3
4
Manuscript ID es-2012-03309h entitled " Predicting Water Quality Criteria for
5
Protecting Aquatic Life from Physico-chemical Properties of Metals or Metalloids"
6
7
Number SI pages: 4
8
9
10
Number the tables: 4
11
Supporting Information
12
Table S1. Values of seven ion characteristics for all metals or metalloids
σp
log-βn
Xm2r
AN/∆IP
Z/r
|logKOH|
∆E0
Ag
0.074
20.6
4.284
6.209
0.87
12.4
0.80
Al
0.136
14.11
1.4
1.351
5.556
4.3
1.66
As(III)
0.106
19.3
2.756
3.395
5.172
2.2
0.68
Ba
0.183
7.78
1.069
11.69
1.481
13.4
2.9
Be
0.172
9.3
1.109
0.45
4.444
3.7
1.85
Ca
0.181
11
1
3.47
2.02
12.7
2.76
Cd
0.081
18.78
2.713
6.068
2.105
10.1
0.4
Co
0.13
10.2
2.65
2.94
2.685
9.7
0.28
Cr(III)
0.107
11.2
1.708
1.66
4.839
4.0
0.41
Cr(VI)
0.107
11.2
1.212
1.134
13.64
4.0
0.13
Cu
0.104
18.5
2.635
2.309
2.74
8.0
0.16
Fe(III)
0.103
15.77
1.842
1.798
5.455
2.2
0.77
Hg
0.065
21.7
4.08
9.62
1.96
3.4
0.91
K
0.232
1.6
0.93
4.38
0.725
14.5
2.92
La
0.171
15.5
1.27
7.36
2.828
8.5
2.37
Li
0.247
2.79
0.71
0.56
1.316
13.6
3.05
Mg
0.167
8.64
1.24
1.62
2.778
11.6
2.38
Mn
0.125
14.2
1.61
3.045
2.985
10.6
1.185
Na
0.211
1.66
0.88
2.14
0.98
14.2
2.71
Ni
0.126
11.33
2.517
2.662
2.899
9.9
0.23
Pb
0.131
18.3
6.46
10.78
1.681
7.7
0.126
Sb
0.119
10.9
3.194
6.439
3.947
0
0.66
Sr
0.174
8.8
1.02
7.12
1.786
13.2
2.89
Tl
0.097
18.47
3.557
5.133
2.67
2.6
0.502
Zn
0.115
16.4
2.015
3.501
2.703
8.2
0.76
Metals
13
S1
14
Table S2. Acute toxicities of 25 metals or metalloids to representative species from eight taxonomic
15
families (log-EC50)
Metals
C. tentans
C. pseudogracilis
D. magna
L. acuminata
C. carpio
B.calyciflorus
Ag
-0.194
-0.615
-0.832
-2.225
-0.326
-1.12
-1.193
0.998
Al
2.524
3.495
1.487
2.853
3.678
1.707
3.763
3.832
As(III)
0.901
0.986
-0.37
0.409
2.517
0.399
1.427
1.618
Ba
4.796
6.97
3.753
6.019
3.571
2.577
4.61
6.859
Be
4.258
6.147
3.209
3.268
4.424
3.202
4.47
5.015
Ca
4.257
6.073
2.595
4.219
4.888
1.698
4.704
6.6
Cd
0.276
0.125
-0.18
1.135
0.417
-0.32
1.321
0.575
Co
2.942
4.25
2.897
0.53
2.295
2.269
1.76
1.62
Cr(III)
2.145
3.083
2.543
2.261
2.413
2.605
3.078
1.24
Cr(VI)
0.653
0.978
2.557
3.208
4.267
2.605
3.488
-0.503
Cu
2.66
3.804
2.491
0.751
2.249
1.911
1.941
1.446
Fe(III)
1.347
1.763
0.899
2.004
2.533
1.447
2.84
1.678
Hg
-0.612
-1.252
-1.226
-0.976
-0.178
-0.447
-0.935
0.938
K
7.102
10.49
5.965
4.586
4.898
4.29
5.168
8.113
La
3.3
4.532
0.978
4.558
3.725
0.828
4.211
5.767
Li
7.345
10.78
5.532
4.156
5.643
4.037
5.618
8.682
Mg
4.217
6.116
3.448
3.263
3.57
2.521
4.23
5.682
Mn
2.201
3.036
1.458
2.801
2.251
0.981
3.357
2.855
Na
6.502
9.636
5.949
4.163
4.301
4.306
5.101
7.274
Ni
0.965
1.11
-0.08
0.413
1.448
-0.01
1.603
0.791
Pb
1.755
2.233
-0.02
1.396
5.663
0.085
1.791
1.421
Sb
2.527
3.636
2.648
0.184
2.447
3.138
0.83
1.913
Sr
4.39
6.353
3.389
5.041
3.396
2.296
4.624
6.619
Tl
0.773
0.839
-0.07
-0.91
1.185
0.601
0.108
1.127
Zn
1.59
2.08
0.669
2.034
1.8
0.594
2.652
1.963
S2
B. melanostictus
L. minor
16
17
Table S3. SSD fitting parameters and CMCs derivation for 25 metals or metalloids (µg/L), with coefficients, standard error, RSS, F and P values.
Metals
Ag
Al
As(III)
Ba
Be
Ca
Cd
Co
Cr(III)
Cr(VI)
Cu
Fe
Hg
K
La
Li
Mg
Mn
Na
Ni
Pb
Sb
Sr
Tl
Zn
a
0.9545
923.498
0.9758
0.9387
0.9935
1.0816
0.9146
0.9617
0.9147
1.539
1.2362
0.908
0.9273
0.8819
1.2924
0.8987
0.8873
1.7877
0.9391
0.9664
0.9648
1.367
0.9595
3.7912
1.1602
Xc
-0.7612
12.268
0.9257
4.655
4.1851
4.6755
0.2901
2.2409
2.4172
3.5298
1.0483
1.6699
-0.7808
5.5426
4.3943
5.7567
3.7525
3.2607
5.4775
2.0934
1.5224
2.8647
4.3693
2.0502
1.9188
k
2.573
0.8280
2.0628
1.04
1.6012
0.929
3.5246
1.8697
4.7783
0.6637
1.9225
3.477
3.3526
1.157
0.8022
0.9453
1.9811
1.2941
1.0534
2.4283
1.9592
1.1087
1.1481
1.334
2.237
a-SE
0.0441
0.0001
0.0659
0.1049
0.1234
0.1936
0.0409
0.0682
0.0557
0.6118
0.2293
0.0386
0.0593
0.0672
0.4173
0.0828
0.0548
0.9485
0.083
0.0487
0.0869
0.4263
0.0829
6.469
0.2848
Xc-SE
0.0552
1406.4
0.0959
0.3305
0.2234
0.479
0.042
0.1124
0.0404
1.2343
0.2242
0.0411
0.0647
0.2544
0.8948
0.3412
0.1118
0.7922
0.2785
0.0616
0.14
0.6144
0.2228
1.9196
0.2535
k-SE
0.3132
0.5155
0.3222
0.2747
0.409
0.2874
0.4779
0.3312
0.9387
0.1768
0.3665
0.482
0.5625
0.2983
0.2916
0.2919
0.4185
0.3552
0.2288
0.3178
0.5812
0.3247
0.2338
0.4097
0.8688
Adj.r2
0.9823
0.9418
0.9734
0.9546
0.9067
0.9437
0.981
0.9655
0.9699
0.9596
0.9776
0.9818
0.9594
0.9353
0.9237
0.9135
0.9518
0.9669
0.9343
0.9799
0.9148
0.9537
0.9632
0.9757
0.9061
S3
RSS
0.0083
0.0273
0.0125
0.0213
0.0437
0.0264
0.0089
0.0162
0.0141
0.019
0.0105
0.0085
0.019
0.0303
0.0358
0.0406
0.0226
0.0155
0.0308
0.0094
0.0399
0.0217
0.0173
0.0114
0.0465
F
531.434
160.494
352.93
206.452
99.542
166.21
494.711
272.42
312.564
231.794
420.478
517.632
231.176
144.28
122.172
107.513
194.072
283.517
141.974
468.199
109.203
202.104
254.99
386.453
98.865
P
log-HC5
AW
CMCs
WQC
-6
-1.8865
107
0.695
3.2
-5
0.4035
27
34.18
750
-6
-0.4892
75
12.16
340
-5
1.8879
137
5292
/
-5
2.3505
4
448.3
/
-5
1.4173
40
522.8
/
-6
-0.5186
112
16.97
2
-6
0.688
59
143.8
/
-6
1.1656
52
380.7
570
-5
-1.5837
52
0.678
16
-6
-0.5988
64
8.06
13
-6
0.8524
56
199.3
/
-5
-1.6352
201
2.328
1.4
-5
3.1124
39
25263
/
-5
0.3894
139
170.4
/
-5
2.7612
7
2019
/
-5
2.3300
24
2565
/
-6
0.5188
55
90.81
/
-5
2.7452
23
6396
/
-6
0.8957
59
232
470
-5
0.0388
207
113.2
65
-5
-0.0857
51
20.93
/
-6
1.8426
88
3063
/
-6
-1.1845
204
6.669
/
-5
0.5330
65
110.9
120
1.50×10
2.91×10
4.15×10
1.57×10
9.40×10
2.67×10
1.79×10
7.89×10
5.61×10
1.18×10
2.69×10
1.60×10
1.18×10
3.79×10
5.69×10
7.79×10
1.82×10
7.14×10
3.94×10
2.06×10
7.49×10
1.65×10
9.29×10
3.31×10
9.55×10
Table S4. Correlations between metal ion characteristics and predicted log-HC5, with
intercept, slope, coefficients values, F, P and Ranks.
Metals
Intercept
Intercept-SE
Slope
Slope-SE
Adj.R2
F
P
Ranks
σp
-3.0211
0.4358
26.408
2.9782
0.7638
78.626
7.02×109
1
log-βn
3.3881
0.3735
-0.2162
0.0269
0.7265
64.760
3.88×108
2
∆E0
-0.7015
0.3161
0.9998
0.1853
0.5394
29.104
1.76×105
3
S4
Xm2r
2.1214
0.4219
-0.6886
0.1663
0.4022
17.147
3.96×104
4
|logKOH|
-0.7487
0.5273
0.1694
0.0567
0.2481
8.918
0.0066
5
Z/r
1.2523
0.4457
-0.1914
0.1089
0.0801
3.089
0.0921
6
AN/∆IP
1.1577
0.4841
-0.1216
0.0918
0.0306
1.756
0.1981
7