Environmental Pollution 188 (2014) 50e55
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Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
Predicting criteria continuous concentrations of 34 metals
or metalloids by use of quantitative ion character-activity
relationshipsespecies sensitivity distributions (QICAReSSD) model
Yunsong Mu a, Fengchang Wu a, *,1, Cheng Chen a, Yuedan Liu a, Xiaoli Zhao a, Haiqing Liao a,
John P. Giesy b, c
a
b
c
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, MI 48824, United States
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 4 August 2013
Received in revised form
3 January 2014
Accepted 16 January 2014
Criteria continuous concentrations (CCCs) are useful for describing chronic exposure to pollutants and
setting water quality standards to protect aquatic life. However, because of financial, practical, or ethical
restrictions on toxicity testing, few data are available to derive CCCs. In this study, CCCs for 34 metals or
metalloids were derived using quantitative ion character-activity relationshipsespecies sensitivity distributions (QICAReSSD) and the final acute-chronic ratio (FACR) method. The results showed that chronic
toxic potencies were correlated with several physico-chemical properties among eight species chosen,
where the softness index was the most predictive characteristic. Predicted CCCs for most of the metals,
except for Lead and Iron, were within a range of 10-fold of values recommended by the U.S. EPA. The
QICAReSSD model was superior to the FACR method for prediction of data-poor metals. This would have
significance for predicting toxic potencies and criteria thresholds of more metals or metalloids.
Ó 2014 Elsevier Ltd. All rights reserved.
Keywords:
Aquatic life
Water quality criteria
Chronic toxicity
Predictive model
Multispecies
Species sensitivity distributions
1. Introduction
Concentrations of metals greater than normal background can
be contaminants in aquatic environments and can adversely affect
aquatic organisms. Criteria continuous concentrations (CCCs) are
useful water quality criteria (WQC) for use in assessing risks (U.S.
EPA, 1986, 1999, 2002, 2004, 2006, 2009). The U.S. EPA has recommended specific CCCs for only 10 metals or metalloids (cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni),
lead (Pb), zinc (Zn), aluminum (Al), iron (Fe) and arsenic (As)), and
there are no CCCs recommended for other more metals or metalloids, which limits the capacities for assessing water quality, and
dealing with unexpected environmental incidents, pollution control and environmental risk management (Jin et al., 2013). Therefore, predicting CCCs for additional metals was considered to be an
urgent need.
* Corresponding author.
E-mail address: wufengchang@vip.skleg.cn (F. Wu).
1
Postal address: 8 Dayangfang, Beiyuan Road, Chaoyang District, Beijing 100012,
China.
0269-7491/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.envpol.2014.01.011
The CCC is equivalent to the least of the final chronic values
(FCV), final plant value, and final residue value under the U.S. EPA
guidelines (Stephen et al., 1985). Specification of chronic WQC
included a statement regarding magnitude, duration and allowable
frequency of exceedance. States in the USA can develop their own
standards to protect aquatic life on the basis of WQCs recommended by U.S. EPA, with CCCs used to estimate long-term effects
of pollutants. The procedure for deriving CCCs specifies the need for
a minimum dataset that includes species from at least 8 families
(U.S. EPA, 2005; van Straalen and Denneman, 1989). If database for
chronic toxicity was not sufficient to satisfy the minimum
requirement, than an alternative is to compute the FCV from the
Final Acute Value (FAV) and use of the geometric mean of acute-tochronic ratios (ACR). For data-rich metals, CCCs were usually
derived from direct experimental testing. However, CCCs for datapoor metals have been derived from the final acute-chronic ratio
(FACR) method and biological ligand model (BLM) (Santore et al.,
2002; Schlekat et al., 2010; Schwartz and Vigneault, 2007).
Compared to short-term exposure, information on chronic toxicity
is more difficult to obtain. In addition, CCCs have been derived from
standardized aquatic life toxicity tests, but the diversity of aquatic
organisms used in those tests is often limited. Whether those CCCs
Y. Mu et al. / Environmental Pollution 188 (2014) 50e55
are protective of potentially more sensitive species, communities,
and ecosystems is uncertain. Comprehensive chronic toxicity tests
are relatively more time-consuming and expensive than acute
toxicity tests, and testing on some endangered species cannot be
conducted because of eco-ethics. For some species, because of the
size of the organisms or due to lack of information on culturing and/
or maintaining them under laboratory conditions, it is impossible
to conduct controlled, laboratory tests. Therefore, the importance
of establishing a system to estimate CCCs that is based on limited
toxicity test data is recognized.
A previous study was conducted to examine relationships between selected physicochemical properties and observed toxicities,
expressed as either the LC50 or EC50, to eight families of representative aquatic organisms, and to determine criteria maximum
concentrations (CMCs) for 25 metals or metalloids (Wu et al., 2013).
A quantitative ion character-activity relationships (QICAR) model
for toxic potency of metal ions was established by use of the partial
least square (PLS) method, that could be used to predict the toxicity
and preliminary to assess environmental risk for heavy metals in
the environments (Li et al., 2013). However, there were still challenges in application of the models developed from CMCs to CCCs to
be solved. First, in that study the toxicity end-point for assessing
chronic effect was the no observed effect concentration (NOEC)
instead of the EC50. Second, the ion characteristics required to
predict chronic effects could be different with those for acute effects. Third, in corresponding to CMCs, some species were
substituted for predicting CCCs. In this study, the objectives were
to: (1) develop a multi-species QICAR for predicting chronic toxicity
of data-poor metals or metalloids. (2) incorporate the results of the
QICAR in, species sensitivity distributions (SSDs) of 34 metals to
calculate CCCs. (3) compare CCCs developed by application of the
QICAR and FACR methods. (4) make multilevel comparisons between CCCs and CMCs, that are predicted by the QICAReSSD model.
In the present study, the use of predictive models for predicting
toxicity of metals was combined with the SSD approach to generate
CCCs for metals, for which toxicity data were lacking and WQC had
not yet been developed. The SSDs were also based on chronic effect
data such as no observed effect concentrations (NOECs). In addition, the magnitude of the chronic WQC was set equal to the FCV
that was defined as the 5th centile of the chronic SSD. The QICARe
SSD model examined relationships between physicochemical
properties and NOEC to 8 representative, surrogate species of
aquatic organisms, and predicted the relative toxicity of 34 metals
or metalloids. Predicted CCCs were compared with those developed
by use of the FACR method. Relationships between CMCs and CCCs
were investigated, and a method to derive CCCs from experimental
CMCs.
2. Materials and methods
2.1. Data used for predictions
Data on chronic toxicity used in this study were selected based on the 1985 U.S.
EPA WQC methodology, and minimum eight species (three phyla) were required
51
(Stephen et al., 1985). All selected data on chronic toxicity were then further
screened based on the following criteria: (1) Only species for which data for six or
more metals were investigated; (2) Chronic tests were conducted in unusual dilution waters, e.g., dilution water in which total organic carbon or particulate matter
exceeded 5 mg/L, should not be used. (3) Durations of chronic exposures were between 7 and 14 days. Toxicity data was obtained from the ECOTOX Database (U.S.
EPA, 2012) and the geometric mean of NOECs were calculated for each species
(Table A.1, Supplementary materials). These metals or metalloids included mono-,
di-, trivalent- and hexavalent-metals. In the present study, aquatic organisms were
in six phyla and eight different taxonomic families, including three chordates, two
arthropods, a rotifer, a mollusk, and an aquatic plant (Table 1). Thirty-four metals or
metalloids (e.g., mono-, di-, trivalent- and hexavalent-metals) were selected, of
which 10 metals had CCCs for protecting aquatic life. The Final ACR values were
calculated as the geometric mean of the available species mean ACR.
2.2. Characteristics of metals and development of predictive relationships
Fourteen (14) physicochemical properties, that had been used previously to
develop relationships to predict toxicity of metals, were considered (Newman and
McCloskey, 1996; Wolterbeek and Verburg, 2001; Wu et al., 2013). For each species, data on chronic toxicity were correlated to each characteristic of ions by use of
linear regression. The magnitude of association was tested by use of the F-test statistic, with the level of significance at a ¼ 0.05. Characteristics with the greatest
predictive power for each species were selected based on the rank of adjusted
correlation coefficients. Linear regression analyses were then performed between
the logarithm of the NOEC and the characteristics of ions with the highest adjusted
correlation coefficients. Predictive potentials of QICAR models were evaluated by
uses of coefficient of determination (r2), residual sum of squares (RSS), Root-MSE, F
value from analysis of variance (ANOVA), and the level of Type I error (p).
2.3. Construction of SSD and derivation of HC5
Based on QICAR equations developed for the representative organisms, predicted chronic toxicity values were derived for each metal. To obtain the log of HC5
values, SSD functions were fitted by use of the sigmoidal-logistic model (Equation
(1)). CCCs were defined as HC5. The linear regression and fitting of the SSD were
performed by use of previously described methods, with three fitting parameters (a,
Xc and k) and their standard errors (a-SE, Xc-SE and k-SE) (Wu et al., 2013).
y ¼
a
1 þ ekðxxc Þ
(1)
3. Results and discussion
3.1. QICARs to predict multi-species chronic toxicities and SSD
analysis
The NOEC, instead of the LC50, was demonstrated to be a good
indicator in characterizing chronic toxicities of chemical substances
(U.S. EPA, 1984c; Wagner and Løkke, 1991). Therefore, statistically
significant, positive or negative correlations were calculated between log-NOEC values and 14 ion characteristics. The characteristic with greatest r2 values was identified for each representative
species, in order to establish minimum one-variable QICAR models
for prediction of chronic toxicities of metals. These representative
species were also different from those previously reported species
for CMCs (Wu et al., 2013), and some alternative species under
potential long-term exposure of metals were substituted. The
softness index (sp) was the characteristic that exhibited statistically
Table 1
One-variable regression models based on sp for metal ions, where r2 is coefficient of determination, RSS is residual sum of squares, RMSE is root mean square deviation, and p is
the statistical significance level.
Species
Phyla
Predicting equations
C. dilutes
D. magna
C. carpio
D. rerio
C. vulgaris
L. minor
M. edulis
B. calyciflorus
Arthropoda
Arthropoda
Chordata
Chordata
Chlorophyta
Angiosperms
Mollusca
Rotifers
log-NOEC
log-NOEC
log-NOEC
log-NOEC
log-NOEC
log-NOEC
log-NOEC
log-NOEC
¼
¼
¼
¼
¼
¼
¼
¼
(41.641
(37.073
(39.969
(41.156
(28.091
(28.296
(14.504
(25.816
8.193)
3.983)
7.391)
4.432)
3.565)
5.147)
2.696)
6.749)
sp þ (4.211 0.839)
sp þ (4.312 0.408)
sp þ (5.179 0.786)
sp þ (4.468 0.476)
sp þ (3.288 0.393)
sp þ (3.062 0.547)
sp þ (1.990 0.310)
sp þ (2.802 0.776)
n
Adj.r2
RSS
RMSE
F
p
7
7
7
7
9
7
7
6
0.805
0.935
0.825
0.934
0.884
0.830
0.823
0.732
1.389
0.328
0.925
0.625
0.626
0.449
0.212
1.058
0.527
0.256
0.430
0.353
0.299
0.299
0.206
0.514
25.829
86.659
29.241
86.219
62.076
30.221
28.933
14.631
0.0038
0.0002
0.0029
0.0002
0.0001
0.0027
0.0030
0.0187
52
Y. Mu et al. / Environmental Pollution 188 (2014) 50e55
significant associations with chronic toxicities (Adj.r2 > 0.732,
F > 14.631, p < 0.0187) (Table 1). Among these species, sp had good
correlations with log-NOEC of two invertebrates, the common
midge Chironomus dilutes and the water flea Daphnia magna, with
r2 of 0.805 (n ¼ 7; F ¼ 25.829, p ¼ 0.0038) and 0.935 (n ¼ 7;
F ¼ 86.659, p ¼ 0.0002), respectively. These findings are consistent
with those previously reported by Zhou and Ownby, where sp is the
characteristic that exhibited the strongest association with the
metaleligand binding constant of seven metals for D. magna
(Ownby and Newman, 2003; Zhou et al., 2011). For two fishes, the
common carp Cyprinus carpio and the zebra fish Danio rerio, sp was
correlated with the potency of metals to cause toxicity, with
greatest r2 of 0.825 (n ¼ 7, F ¼ 29.241, p ¼ 0.0029) and 0.934 (n ¼ 7,
F ¼ 86.219, p ¼ 0.0002), respectively. Among eight species, the
model for the rotifer Brachionus calyciflorus exhibited the poorest
coefficient of determination compared to other aquatic species
(r2 ¼ 0.732, F ¼ 14.631, p ¼ 0.0187). However, this result is not in
agreement with predictions of acute toxicity, which suggested that
logebn was significantly associated with log-LC50 (r2 ¼ 0.7587,
F ¼ 12.580, p ¼ 0.024) (Wu et al., 2013). All of the QICAR models
presented herein were capable of predicting potencies of chronic
toxicity for metals (RSS < 1.389, MSE < 0.527), that could be used in
derivation of CCCs. With respect to the two-variable model, the
one-variable QICAR equation was robust enough to predict chronic
toxicity of freshwater organisms (see Table A.2, Supplementary
data). Models containing two or three variables were less suitable
fits of the experimental data for prediction of fungal metal toxicity
(Mendes et al., 2010). This improvement can assess multispecies
toxicities of more metals by use of fewer ion characteristics.
Potencies of 34 metals or metalloids to each species that were
derived from QICAR models, varied among species (see Table A.3,
Supplementary data). The zebra fish, D. rerio, the green alga
Chlorella vulgaris, and the mussel Mytilus edulis were introduced as
substituted species for prediction of both chronic toxicity and CCCs.
Other five species, C. dilutes, D. magna, C. carpio, duckweed Lemna
minor, and Brachinous calyciflorus were retained in six phyla and
eight different taxonomic families, because of well prediction of
acute toxicity and CMCs. In the present study, C. carpio and M. edulis
were the two most sensitive species for all metals or metalloids.
C. carpio was sensitive to Ag, As, Cd, Cr, Cu, Hg, Mn, Sb, Tl, Zn, Au, Ga
(III), In (III), Bi (III), and V (III), with log-NOECs ranging from 3.420
to 0.183 (sp < 0.125). These metals or metalloids belonged to
group IIIA, VA, IB, IIB, VB, VIB, and VIIB. In contrast, M. edulis was
sensitive to the other 19 metals (sp > 0.125). It was among the most
sensitive species to the effects of metals in groups IIA (Be, Mg, Ca, Sr,
Ba), IVA (Ge, Sn, Pb), IIIB (Sc, Y, La), IVB (Ti), and VIII (Fe, Co, Ni). The
distribution of sensitive species was different from the finding reported previously, in which zooplankton were most vulnerable to
the toxic effects of metals and the fish C. carpio was the least sensitive to metals (Wu et al., 2013). However, for higher trophic level
aquatic organisms, chronic toxic effects were more significant than
acute toxic effects, which mainly aimed at specific sensitive species.
In conclusion, six phyla and eight different taxonomic families of
representative organisms were demonstrated to be a minimum set
of species to establish models in accordance with WQC guidelines
required by the U.S. EPA. Total selected species were found to be
sensitive to the toxic effects of five main groups (IIA-VA) as well as
all of those in subgroup.
3.2. Derivation of predicted CCCs and comparison to FACReCCCs
The SSDs for 34 metals or metalloids were constructed based on
predicted toxicity data, and ultimately used to predict CCCs (Fig. 1).
The same sigmoidal-logistic function used to prediction of CMCs
was also the best function for predicting CCC. The basic fitting
Fig. 1. . Species sensitivity distributions analysis and derivation of the predicted logHC5 for 34 metals or metalloids. The predicted toxicities are derived from minimum
eight species, including C. dilutes, D. magna, C. carpio, D. rerio, C. vulgaris, L. minor,
M. edulis, and B. calyciflorus.
parameters (a, Xc, k, a-SE, Xc-SE, and k-SE) and statistical indexes
(Adj.r2, RSS, F and p), that were used to develop the predictive relationships, are shown in Supplementary data, Table A.4. The r2 of
the 34 fitting equations were greater than 0.940 (F > 156.861,
p < 3.08 105), which suggests that all SSDs based on chronic
toxicity provided adequate fits to the data. The predictive relationships for chronic toxicity were better than those for acute
toxicity (r2 > 0.906, F > 98.865, p < 9.55 105) (Wu et al., 2013).
SSDs for 34 metals or metalloids were depicted by difference colors
in Fig. 1 (in Web version), from which values of log-HC5 were
calculated between 3.604 and 1.390. Therefore, the sigmoidallogistic model was best for prediction of CCCs and CMCs.
The order of standard errors between predicted and recommended values were Cu > Ni > Hg > Al > Cd > Zn > Cr (III) > As
(III) > Fe > Pb. Recommended log-CCCs for Cu, Ni, and Hg were
accurately predicted with differences of less than 0.50. For example,
the predicted CCC for Cu is 9.21 mg/L, while the recommended value
is 9.0 mg/L. The log-HC5 values for Al, Cd, Zn, and Cr (III) were also
predicted with standard errors between 0.50 and 1.00. The CCC for
Al proposed by U.S. EPA was 87 mg/L, would protect only two species, brook trout and striped bass (Stephen et al., 1985). The predicted CCC for Al (24.06 mg/L) would provide more protection. The
predicted CCC for Zn was 16.07 mg/L, and the WQC recommended
by U.S. EPA is 120 mg/L. However, in a previous study, based on SSD
analysis of experimental toxicological data, the CCC for Zn was
20.01 mg/L, which is similar to the present predicted CCC (Wu et al.,
2012). Models developed in this study provided relatively poor
prediction of toxic potency for As (III), Fe, and Pb, with the standard
errors of log-CCC greater than 1.0. The recommended CCC for As
(III) was derived by division of the final acute value (FAV ¼ 718.2 mg/
L) by the final acute-chronic ratio (FACR ¼ 3.803) (U.S. EPA, 1984a).
Thus, the CCC is 10 times greater than the one that was predicted,
which was calculated from a more direct deducing method. However, the predicted CCC for Fe was less than the recommended CCC.
Since data obtained under laboratory conditions suggest a greater
toxicity than that obtained in natural ecosystems a criterion, based
primarily on field observations, of 1000 mg/L iron for freshwater
aquatic life was proposed by U.S. EPA. Therefore, this recommended
WQC might change substantially in the near future such that a
more rigorous CCC for Fe can be developed. The model for predicting potency of Pb was least predictive to measured toxic
Y. Mu et al. / Environmental Pollution 188 (2014) 50e55
potency, with the predicted error 1.727. The predicted log-CCC was
calculated at 0.191, but the recommended CCC is only 1.918 (U.S.
EPA, 2009). The U.S. EPA indicated that data on chronic effects of Pb
on freshwater animals were available for two fishes and two invertebrates. Chronic toxicity of Pb was inversely proportional to
hardness, and the available chronic values for a cladoceran in hard
water was 128.1 mg/L (U.S. EPA, 1984b). This value derived from the
guideline of U.S. EPA was similar to the predicted CCC.
The sensitive species of 9 metals or metalloids for which there
was sufficient information and their acute/chronic toxicity data
were derived from the ECOTOX database (U.S. EPA, 2012). The ACR
of each species and their geometric mean values were calculated to
derive FACReCCC values (Table 2). Correlations between predicted
ACR log-CCC and recommended log-CCC are depicted in red (Fig. 2).
The predicted log-CCCs of 8 metals except for As was reasonably
accurate within a difference of one orders of magnitude. The reason
is that the ACR values of two more sensitive species, D. magna and
the fathead minnow, Pimephales promelas, differed greatly. Especially for metals for which little information was available, the
experimental chronic toxicity and FACR values were difficult to
obtain. In order to avoid data deficiencies in the FACR method, the
QICAReSSD model herein might protect more species.
53
Fig. 2. The validation of two predictive methods, QICAReSSD model (black) and FACR
analysis (red), compared to recommended log-CCC in 2009 WQC guideline. (For
interpretation of the references to colour in this figure legend, the reader is referred to
the web version of this article.)
3.3. Comparison of predicted log-CCC, with log-CMC, and sp
There was a statistically significant regression between sp and
log-CCC (Adj.r2 ¼ 0.968, F ¼ 1012.661, p ¼ 0.0001). This observation
does not alter the important conclusion that stable correlations
exist between toxic potencies of metals and their softness index.
Based on sp and solubility constants for binding to sulfur- and
oxygen-containing anions, metal ions can be classified into three
groups. These include: (1) hard ions, which preferentially bind to
oxygen or nitrogen, such as Ba, Ca, and Mg; (2) soft ions, which
preferentially bind to sulfur, such as Cd, Hg, Ag, and Au; and (3)
borderline ions, which form complexes with oxygen, nitrogen, and
sulfur to varying degrees, such as Cu, Zn, Co, and Ni. Most soft ions
were classified as having high and intermediate toxic potency
(sp < 0.100); borderline ions were less toxic (0.100 < sp < 0.160);
and hard ions exhibited weak potency (0.160 < sp < 0.200). These
findings support the conclusion that sp is the best predictor of toxic
potency of metals or metalloids to aquatic organisms. Soft ions
were more toxic than borderline ions and hard ions. Greater
attention should be given to Au, Hg, Ag, and Cd that exhibited the
greatest toxicities. As reported by Ahrland (Ahrland, 1968), sp
provides a comparative scale of softness only for ions of a given
charge, because coordinate bond energies increase rapidly with
Table 2
Final ACR values for 9 metals or metalloids and derivation for their CCCs (mg/L).
Metal
Species
Acute toxicity
Chronic toxicity
ACR
FACR
CMC
As(III)
D. magna
P. promelas
D. rerio
B. calyciflorus
L. minor
G. pulex
W. flea
M. edulis
C. dilutes
W. flea
C. carpio
W. flea
B. calyciflorus
D. rerio
W. flea
B. calyciflorus
C. dubia
D. rerio
G. pulex
C. carpio
D. rerio
M. edulis
C. carpio
P. promelas
C. dubia
M. lanchesteri
W. flea
D. magna
R. trout
2743.57
17,113.73
214.50
1300.00
136.21
87.00
123.62
1604.59
317.05
44.19
324.53
1904.62
6569.63
52,988.63
146.53
56.60
8.80
80.00
242.64
353.73
13,450.00
1358.79
2231.58
3784.04
121.75
483.56
275.71
3900.00
8746.08
750
12.50
45.43
25.58
10.29
3.70
3.45
132.81
4.16
40.14
2.60
127.11
1788.85
6940.78
19.99
21.54
9.21
2.06
20.00
1.30
38.26
250.00
46.28
261.15
101.76
77.00
124.57
975.71
1619.53
3.66
1369.10
4.72
50.82
13.24
23.49
35.82
12.08
76.28
1.10
124.82
14.98
3.67
7.63
7.33
2.63
0.96
38.84
12.13
9.16
9.70
5.44
1.92
6.59
1.20
6.28
2.21
4.00
4.51
70.79
340
4.80
21.96
2
0.09
0.25
11.72
13
1.11
9
14.98
6.08
470
570
31.38
93.69
52
74
Cd
Cu
Ni
Cr(III)
Hg
Pb
Zn
Al
FACReCCC
WQC
150
4.60
1.4
0.30
0.77
8.75
113.2
12.94
2.5
2.92
120
41.10
120
4.25
750
176.68
87
54
Y. Mu et al. / Environmental Pollution 188 (2014) 50e55
increasing ionic charge. For more charged ions, sp is small by virtue
of the large value of the denominator.
Correlations between predicted log-CCC and log-CMC were
calculated, with Adj.r2 ¼ 0.74, F ¼ 57.912, p ¼ 0.0001. In contrast,
correlations between recommended log-CCC and log-CMC in 2009
WQC guideline were also obtained, with Adj.r2 ¼ 0.808, F ¼ 42.983,
p ¼ 0.0001 (Fig. 3). The slope of two fitting lines is the same, which
indicates that log-CCC values have significant relationships with
log-CMC values for 34 metals or metalloids. It also demonstrates
that the predicted log-CCC derived from the QICAReSSD model
might be a supplement for data-poor metals in the 2009 WQC
guideline. The present study developed QICAReSSD analyses for
eight families and obtained predictive relationships for chronic
toxic potencies of 34 metals or metalloids. CCCs of 8 metals or
metalloid, except for Lead and Iron, appeared to be reasonably accurate within a difference of one orders of magnitude. CCCs of 24
metals, for which CCCs had not been developed previously, were
obtained through use of a predictive relationship derived in this
study. The QICAReSSD model can be used for monovalent, divalent
and trivalent metal ions. These predictive models could be useful
when data on metal toxicity are lacking or incomplete. While
further development of such models will be necessary to determine
their range of applicability, the QICAReSSD model is a promising
tool that can be used to rapidly predict chronic toxicity and criteria
of metals, avoiding to spend a great deal of manpower, material and
financial resources.
The QICAReSSD model, FACR and CMC/CCC relationships are
useful methods for predicting CCC of metals or metalloids, for
which insufficient information is available to allow use of more
traditional methods. The prediction errors of the three methods are
within a difference of less than 100-fold. The QICAReSSD model is
better than FACR for prediction of data-poor metals, suggesting that
the CCCs values will protect more aquatic species during the longterm periods. For prediction of acute toxicity and CMCs, sp was the
most powerful ion characteristic for the eight aquatic organisms
studied here. Meanwhile, chronic toxicity to eight aquatic species
also depends primarily on sp of metals or metalloids. Both CCC and
CMC, are well predicted by the QICAReSSD model, however, it is
more important for predicting the CCC of metals, since chronic
multi-species toxicity data is even less available.
4. Conclusion
The QICAReSSD model is a promising and available method to
predict metal toxicity and criteria, which promotes research into
WQC and has broad application prospects in environmental risk
assessment and management. It bridges the gap among three elements, physicochemical properties of pollutants, multi-species, and
acute/chronic toxicities. The use of this method can also be applied
to organic pollutants and marine species. Prediction of the toxicity
of metals is challenging, since it is influenced by external environmental conduction, metal valence, and toxic mechanism.
Therefore, some suggestions should be taken into account to achieve better prediction accuracy, these include (1) for some metals or
metalloids, environmental behavior, exposure component, and
external environmental aspects will be considered in the QICARe
SSD model; (2) toxic endpoints are specific to different families of
species, each class of metals, and toxic mechanism; (3) structureactivity relationships need to be classified to choose a more efficient and accurate physicochemical parameters, such as maximum
complex stability constants (log-bn), electrochemical potential
(DE0), covalent index (X2mr), etc.
Conflict of interest
The authors declare no competing financial interest.
Acknowledgments
The present study was supported by the National Natural Science Foundation of China (NO. U0833603 and 41130743) and National Basic Research Program of China (973 Program) (NO.
2008CB418200). Prof. Giesy was supported by the Canada Research
Chair program. He was also supported by the program of 2012
“High Level Foreign Experts” (#GDW 20123200120) funded by the
State Administration of Foreign Experts Affairs, the P.R. China to
Nanjing University and the Einstein Professor Program of the Chinese Academy of Sciences.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.envpol.2014.01.011.
References
Fig. 3. The correlations between log-CMC vs. log-CCC derived from WQC (red), and
predicted log-CMC vs. log-CCC (black). (For interpretation of the references to colour in
this figure legend, the reader is referred to the web version of this article.)
Ahrland, S., 1968. Thermodynamics of complex formation between hard and soft
acceptors and donors. Struct. Bond 5, 118e149.
Jin, X., Wang, Y., Giesy, J.P., Richardson, K.L., Wang, Z., 2013. Development of
aquatic life criteria in China: viewpoint on the challenge. Environ. Sci. Pollut.
Res. Int. 21 (1), 61e66.
Li, Y., Jiang, L., Xiao, L.L., Hu, Y., Wen, J.Y., 2013. A QICAR model for metal ion toxicity
established via PLS method. Chem. Res. Chin. U 29, 568e573.
Mendes, L.F., Bastos, E.L., Stevani, C.V., 2010. Prediction of metal cation toxicity to
the bioluminescent fungus Gerronema viridilucens. Environ. Toxicol. Chem. 29,
2177e2181.
Newman, M.C., McCloskey, J.T., 1996. Predicting relative toxicity and interactions of
divalent metal ions: MicrotoxÒ bioluminescence assay. Environ. Toxicol. Chem.
15, 275e281.
Ownby, D.R., Newman, M.C., 2003. Advances in quantitative ion character-activity
relationships (QICARs): using metal-ligand binding characteristics to predict
metal toxicity. QSAR Comb. Sci. 22, 241e246.
Santore, R.C., Mathew, R., Paquin, P.R., Di Toro, D.M., 2002. Application of the biotic
ligand model to predicting zinc toxicity to rainbow trout, fathead minnow, and
Daphnia magna. Comp. Biochem. Physiol. C Pharmacol. Toxicol. 133, 271e285.
Schlekat, C.E., Van Genderen, E., De Schamphelaere, K.A., Antunes, P.M.,
Rogevich, E.C., Stubblefield, W.A., 2010. Cross-species extrapolation of chronic
nickel Biotic Ligand Models. Sci. Total Environ. 408, 6148e6157.
Schwartz, M.L., Vigneault, B., 2007. Development and validation of a chronic copper
biotic ligand model for Ceriodaphnia dubia. Aquat. Toxicol. 84, 247e254.
Stephen, C.E., Mount, D.I., Hansen, D.J., Gentile, J.R., Chapman, G.A., Brungs, W.A.,
1985. Guidelines for Deriving Numerical National Water Quality Criteria for the
Y. Mu et al. / Environmental Pollution 188 (2014) 50e55
Protection of Aquatic Organisms and Their Uses. Office of Research and
Development, Washington, DC.
U.S. EPA, 1984a. Ambient Aquatic Life Water Quality Criteria for Arsenic. Office of
Research and Development, Duluth.
U.S. EPA, 1984b. Ambient Aquatic Life Water Quality Criteria for Lead. Office of
Research and Development, Duluth.
U.S. EPA, 1984c. Estimating Concern Levels for Concentrations of Chemical Substances in the Environment. Environmental Effect Branch, Health and Environmental Review Division, Washington, DC.
U.S. EPA, 1986. Quality Criteria for Water. Office of Water and Hazardous Materials,
Washington, DC.
U.S. EPA, 1999. National Recommended Water Quality Criteria-correction. Office of
Water, Office of Sciense and Technology, Washington, DC.
U.S. EPA, 2002. National Recommended Water Quality Criteria. Office of Water,
Office of Sciense and Technology, Washington, DC.
U.S. EPA, 2004. National Recommended Water Quality Criteria. Office of Water,
Office of Sciense and Technology, Washington, DC.
U.S. EPA, 2005. Methods/Indicators for Determining when Metals Are the Cause of
Biological Impairments of Rivers and Streams: Species Sensitivity Distributions
and Chronic Exposure-response Relationships from Laboratory Data. Office of
Research and Development, Cincinnati, OH, U.S.A.
55
U.S. EPA, 2006. National Recommended Water Quality Criteria. Office of Water,
Office of Sciense and Technology, Washington, DC.
U.S. EPA, 2009. National Recommended Water Quality Criteria. Office of Water,
Office of Sciense and Technology, Washington, DC.
U.S. EPA, 2012. ECOTOX 4.0 Ecotoxicology Database (2012). http://cfpub.epa.gov/
ecotox/ (accessed 05/2012).
van Straalen, N.M., Denneman, C.A.J., 1989. Ecotoxicological evaluation of soil
quality criteria. Ecotoxicol. Environ. Saf. 18, 241e251.
Wagner, C., Løkke, H., 1991. Estimation of ecotoxicological protection levels from
NOEC toxicity data. Water Res. 25, 1237e1242.
Wolterbeek, H.T., Verburg, T.G., 2001. Predicting metal toxicity revisited: general
properties vs. specific effects. Sci. Total Environ. 279, 87e115.
Wu, F.C., Feng, C.L., Zhang, R.Q., Li, Y.S., Du, D.Y., 2012. Derivation of water quality
criteria for representative water-body pollutants in China. Sci. China Ser. DEarth Sci. 55, 869e1042.
Wu, F.C., Mu, Y.S., Chang, H., Zhao, X.L., Giesy, J.P., Wu, K.B., 2013. Predicting water
quality criteria for protecting aquatic life from physicochemical properties of
metals or metalloids. Environ. Sci. Technol. 47, 446e453.
Zhou, D.M., Li, L.Z., Peijnenburg, W.J.G.M., Ownby, D.R., Hendriks, A.J., Wang, P.,
Li, D.D., 2011. A QICAR approach for quantifying binding constants for metalligand complexes. Ecotoxicol. Environ. Saf. 74, 1036e1042.