Model for Predicting Toxicities of Metals and Metalloids in Coastal

Subscriber access provided by Service des bibliothèques | Université de Sherbrooke

Environmental Modeling

Model for Predicting Toxicities of Metals and Metalloids in Coastal Marine Environments Worldwide Yunsong Mu, Zhen Wang, Fengchang Wu, Buqing Zhong, Mingru Yang, Fuhong Sun, Chenglian Feng, xiaowei jin, Kenneth M.Y. Leung, and John P. Giesy Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06654 • Publication Date (Web): 14 Mar 2018 Downloaded from http://pubs.acs.org on March 14, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 31

Environmental Science & Technology

1 2 3

Submitted to ES&T

Model for Predicting Toxicities of Metals and Metalloids in Coastal Marine Environments Worldwide

4 5 6 7 8 9

Yunsong Mu†,┴, Zhen Wang‡,┴, Fengchang Wu†,*, Buqing Zhong†, Mingru Yang†, Fuhong ∥ ﹟ Sun†, Chenglian Feng†, Xiaowei Jin§, Kenneth M.Y. Leung‡, ,* and John P. Giesy‡,

10 11

†

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China ‡ The Swire Institute of Marine Science and School of Biological Sciences, the University of Hong Kong, Pokfulam, Hong Kong 999077, China § China National Environmental Monitoring Center, Beijing 100012, China

12 13 14 15 16

∥

17 ﹟

18 19

School of Biology, University of Hong Kong, Hong Kong 999077, China

Department of Veterinary Biomedical Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon S7N 5B3, Canada

20 21 22

┴

Joint first author

23 24 25 26 27 28 29

*

To whom correspondence should be addressed. Tel: +86-10-84915312 Fax: +86-10-84915312 Email: [email protected] (F.C. Wu) Postal address: 8 Dayangfang, Beiyuan Road, Chaoyang District, Beijing 100012, China

30 31 32 33 34

Tel: (852) 22990607 Fax: (852) 25176082 Email: [email protected] (K.M.Y. Leung) Postal address: The University of Hong Kong, Pokfulam, Hong Kong, China

35 36 37

TITLE RUNNING HEAD: Predicting site-specific marine water quality criteria for metals

1

ACS Paragon Plus Environment


38

Abstract–Metals can pose hazards to marine species and can adversely affect structures and

39

functions of communities of marine species. However, little is known about how structural

40

properties of metal atoms combined with current geographical and climatic conditions affect

41

their toxic potencies. A mathematical model, based on quantitative structure-activity

42

relationships and species sensitivity distributions (QSAR-SSD) was developed by use of

43

acute toxicities of six metals (Cd, Cr, Cu, Hg, Ni, and Zn) to eight marine species and

44

accessory environmental conditions. The mode was then used to predict toxicities of 31

45

metals and metalloids then investigating relationships between acute water quality criteria

46

(WQC) and environmental conditions in coastal marine environments. The model was also

47

used to predict WQC in the coastal areas of different countries. Given global climate change,

48

the QSAR-SSD model allows development of WQC for metals that will be protective of

49

marine ecosystems under various conditions related to changes in global climate. This

50

approach could be of enormous benefit in delivering an evidence-based approach to support

51

regulatory decision making in management of metal and metalloids in marine waters.

52 53

Keywords–Predictive model; Water quality criteria; Quantitative structure-activity

54

relationships; Species sensitivity distributions; Temperature; Salinity

55

ACS Paragon Plus 2 Environment

Page 2 of 31

Page 3 of 31


56

Introduction

57

Increasing contamination of marine ecosystems by metals is a critical, environmental issue 1.

58

Annual increases in production and use of metals have resulted in uneven amounts of metals

59

accumulating in estuaries and coastal marine environments worldwide 2. Persistence and

60

stabilities of metals in the environment can have negative effects on marine species and can,

61

in some cases, indirectly affect health of humans through biological magnification. Toxic

62

potencies of metals to marine species are mainly determined not only by their

63

physiochemical characteristics

64

temperature, acidity and ionic strength as well as concentrations of specific inorganic salts

65

and dissolved organic matter 9-13.

3-8

, but also by environmental conditions, such as

66

Inter- and intra-species sensitivities to metals have been observed and need to be

67

considered when assessing hazards to populations or communities. Some countries employ

68

species sensitivity distributions (SSD) to derive hazardous concentrations that protect 95%

69

of species from chemical contaminants (HC5) 14, which are then used in developing water

70

quality criteria (WQC) and in environmental risk assessments

71

framework in North America, the European Union, and Australia mainly follow standard

72

test protocols, which mean that they are commonly conducted under specified, controlled

73

laboratory conditions with temperature (T), salinity (S), and pH held constant 17-19. However,

74

if conditions under which tests are conducted do not match conditions in various

75

environments, such standardization also potentially introduces bias into assessments of

76

hazards posed by metals to marine organisms or in development of appropriate WQC. For

77

example, ecological hazards posed by metals identified from laboratory-based datasets can

78

be a thousand-fold greater than hazards in the field

79

Protection Agency (U.S. EPA) revised its procedures for deriving site-specific WQC to

80

protect aquatic life, with the intention of encouraging state governments to tailor their

20

15, 16

. The current WQC

. In 2013, the U.S. Environmental



Page 4 of 31

21, 22

81

criteria to be more site-specific to protect aquatic communities at particular sites

82

Several countries, including the European Union

83

have developed or are currently developing national WQC with site-specific toxicity data.

84

To establish effective environmental regulations for metals, results of the SSD method

85

should be corrected to account for background environmental characteristics and specific

86

site information. Toward that end, temperature- and pH-dependent SSD have been

87

developed

88

site-specific bioavailability and toxicity of metals and metalloids in freshwater based on

89

their speciation. Investigations pertaining to saltwater are currently ongoing by

90

characterizing bioavailable effects from bulk, saltwater chemistry 29, 30.

23

, Canada

24

, Australia

25

and China

.

26

,

27, 28

. The biotic ligand model (BLMs) has been widely used to estimate

91

The goal of the present project was to use the model, based on quantitative ion

92

characteristic relationships (QICAR) and species sensitive distributions (SSDs) to estimate

93

acute toxicities and derive WQC for 31 metals and metalloids in coastal marine

94

environments. This approach is useful to reduce the need for site-specific toxicity testing

95

and to predict toxicities of metals for which few data are available. Relationships between

96

WQC for metals and metalloids to current geographic and climatic conditions were also

97

investigated. The analysis involved evaluation of effects of accessory factors such as

98

temperatures and salinity projected by the most recent report from a real-time geotropic

99

oceanography database (Argo, http://www.argo.org.cn/) 31.

100 101

Materials and methods

102

Modeling datasets

103

Toxicities of metals to five phyla and eight families of marine species

104

criteria were selected from the ECOTOX database (http://www.epa.gov/ecotox/) and

105

peer-reviewed literature from the last decade. Criteria for selection were: (1) results for six


32

that met selection

Page 5 of 31


106

or more metals were available for each species and the data were suitable for calculating a

107

geometric mean of the same type of toxicity endpoints; (2) datasets contained acute toxicity

108

endpoints (EC50) based on survival with exposure durations of 48 or 96 h; (3) toxicity data

109

included detailed information about experimental conditions, including T, pH, hardness, and

110

S that were between 10 and 30 °C, 5.5 and 8 mg/L, 20 and 5000 mg/L, and 10 and 30 ‰,

111

respectively. At least three parallel trials were conducted and the results were investigated

112

statistically. Training datasets were obtained from the ECOTOX database, which were

113

generated for all the species across all the phys-chemical conditions for at least five metals

114

(Table S1).

115

Twenty-three QSAR parameters of six metal ions (Cd, Cr, Cu, Hg, Ni and Zn), which

116

represented physicochemical, electronic, polarity, and thermodynamic properties, were

117

calculated, resulting in 138 data points. Parameters considered for use to predict toxic

118

potencies included: atomic number (AN), hydrated radius (AR), Pauling ionic radius (r),

119

ionic charge (Z), softness index (σp), softness index per ion charge (σp/Z) 33, electrochemical

120

potential (∆E0)

121

density (AR/AW)

122

ionization potentials between the O(N) state (IP(N)) and O(N-1) state (IP(N-1)) of the ion

123

(∆IP), atomic ionization potential (AN/∆IP) 34, electronegativity (Xm), covalent index (Xm2r)

124

36

125

parameters (Z/AR and Z/AR2)

126

supporting environmental parameters.

34

, first hydrolysis constants (|logKOH|)

35

, atomic weight (AW), electron

36

, actual electronegativity (x), relative softness (Z/rx), difference in

, polarization force parameters (Z/r, Z/r2 and Z2/r) and similar polarization force 37

. Accessory factors considered included T, pH, and S as

127

Five phyla and eight taxonomic families, two chordates, five arthropods, and a species

128

of algae, namely Acartia tonsa, Americamysis bahia, Eurytemora affinis, Palaemonetes

129

pugio, Penaeus merguiensis, Cyprinodon variegatus, Priopidichthys marianus,and

130

Isochrysis galbana, were selected. These species are widely distributed throughout oceans



131

worldwide 38.

132 133

Modeling and internal validation

134

Pearson coefficients of determination (r2) were calculated between toxicity endpoints of

135

eight species and QSAR parameters and between pairs of QSAR parameters. Parameters

136

with r2 greater than 0.6 were selected for describing toxic potency. Pairs of QSAR

137

parameters were examined for auto-correlation. Principal component analysis (PCA) was

138

used to extract features from datasets for each species. To be represented in the QSAR

139

model, the optimal parameters made a contribution of more than 70% to the first component.

140

Multiple linear regressions (MLR) between the softness index (σp), T, and S, were used to

141

develop predictive, QSAR models. Model coefficients were estimated by use of the least

142

squares regression method. Potentials to predict by use of multivariate QSAR models were

143

evaluated by coefficient of determination (R2) and the root mean square deviation (RMSE).

144

The magnitude of the association of each parameter with toxicity was tested with the F-test

145

statistic at a significance level of α = 0.05 (Supplementary methods in modeling).

146

To reduce the probability of over fitting the model to the training data, and to assess

147

robustness of the model depending on presence/absence of particular metals in the training

148

set, models were internally validated by use of the cross-validated, leave-one-out technique

149

(CVLOO) 39, 40. Following the CVLOO algorithm, metals were removed, one at a time, from the

150

training set. The cross-validated correlation coefficient, QCV2, and cross-validated root mean

151

square error of prediction, RMSECV, were calculated from the sum of the squared differences

152

between the observed and estimated toxicity. The recommended reference criteria stated that

153

R2 should be greater than 0.6 and that the difference between R2 and QCV2 should be less

154

than 0.3

155

QSAR results

41

. It also met requirement of a framework in Europe in assessing adequacy of 42

. We used the QSAR toolbox in the SYBYL X1.1 program (Tripos, Inc.,


Page 6 of 31

Page 7 of 31


156

MO, United States) for calculations.

157 158

SSD analyses to predict sensitivities of eight marine species

159

Toxic potencies of the six metals to the eight species in each training set were predicted by

160

use of each of the three-fitting variable, predictive models. The σp values of Hg, Cd, Cu, Zn,

161

Ni, and Cr were 0.065, 0.081, 0.104, 0.115, 0.126, and 0.142, respectively. Six temperatures

162

(10, 14, 18, 22, 26 and 30 °C) and six salinities (10, 15, 20, 25, 30, and 35‰) were

163

considered. The data were ranked from least to greatest, and plotting positions (proportions)

164

used in a cumulative probability distribution were calculated (Equation 1).

165 166 167

Proportion = (Rank−0.5) / Number of Species

[1]

The SSD curve was fitted with the Sigmoid-logistic model to obtain the logarithm of the values for the fifth centile (HC5) (Equation 2).

168

[2]

169

where a represented the amplitude, Xc was a center value, and k was a coefficient 43. MLR

170

and SSD fitting were performed with three fitting parameters (a, Xc, and k) and their

171

standard errors (a-eer, Xc-eer, and k-eer). One-way analysis of variance (ANOVA) was used

172

to examine significant differences among species.

173

Quantitative correlations were constructed between three variables (σp, T, and S) and

174

SSD fitting parameters (a, Xc, and k) to obtain site-specific SSDs. HC5, to protect 95% of

175

species, and their respective corresponding 95% confidence intervals (CIs) were determined

176

from the best-fit model. Criteria maximum concentrations (CMCs) were defined as half of

177

the HC5 value. Global data for annual average T and S in sea surface areas were collected

178

from a real-time geotropic oceanography database (Argo, http://www.argo.org.cn/)

179

Marine CMCs at various locations were derived from mean surface temperature and salinity


31

.


180

data collected in 2015, the latest available statistics, at 15 field sites in coastal marine

181

environments worldwide (e.g. the United States, Canada, Australia, the European Union,

182

China, Japan, and India).

183 184

Statistical Analysis and Model Validation

185

Accuracies of QSAR models were estimated by use of one-way ANOVA. Means and

186

variances were compared with Bonferroni and Tukey’s multiple tests. Values of HC5 and

187

95% CIs were computed by use of Monte Carlo simulations, with a repeated sampling

188

frequency of 5,000. Statistical analyses were completed with Statistical Analysis System 9.4

189

(SAS Institute, NC, United States), SPSS Statistics 17.0 (IBM Inc., NY, United States),

190

G*Power 3.1.9.2 (Program written by Franz Faul, Kiel University, Germany), and Origin

191

Pro 8.0 (OriginLab Inc., MA, United States).

192

True predictive power of a model can be estimated by comparing predicted with

193

observed HC5s, based on external testing datasets that were not used to develop the model.

194

These testing data should meet requirements for constructing SSDs of the same predicted

195

species and deriving hazard concentrations. As a result, only 2 out of 31 metals (Cd and Cr

196

(III)) could be used to externally validate the derived model. However, the results of those

197

validations, based on two different data sources, model prediction and toxicity testing in the

198

field, were convincing. Temperature-dependent HC5 for two metals were derived from the

199

temperature-dependent SSDs at different temperatures of 15, 20, and 25˚C, and salinities of

200

10, 20, and 30‰ 27, while HC5 values were predicted under the same conditions by use of

201

the QSAR-SSD model. Agreements between predicted and observed HC5 values were

202

estimated by use of concordance regression analysis, paired-sample t-test and power of

203

analysis.

204


Page 8 of 31

Page 9 of 31


205

Results and Discussion

206

Relationships between structural properties of metals, temperature, salinity, and toxicity to

207

marine species

208

Pearson, product-moment correlations between the toxicity endpoints (log-EC50) of eight

209

species and 23 physicochemical properties were calculated (Table S1). By calculating and

210

sorting r2, the softness index σp was identified as the parameter with the greatest power to

211

predict toxic potencies of metals. All species except E. affinis had r2 values that exceeded

212

0.64. The optimal parameter σp was significantly correlated with the log-EC50 of metals to

213

the eight species, and water chemistry influenced toxicities of metals in saltwater (Fig. S1).

214

After PCA, we retained the first three principal components with cumulative contributions

215

of greater than 95% (Fig. S2). The contribution of the optimal parameter σp to the first

216

principal component was more than 60%. Examination of toxicities of the six metals to the

217

eight test species showed that physicochemical properties of metal ions combined with T

218

and S were significantly correlated, either positively or negatively, with log-EC50. After

219

multiple regression analyses, predictive relationships were developed for sensitivities of

220

species in three phyla and eight families (Table 1). Multivariate R2 ranged from 0.617 and

221

0.946, which showed that the selected properties were significantly correlated with acute

222

toxicities of selected marine species (RMSE > 0.721, p > 0.05). Based on the principle of

223

“hard” and “soft” acid-base (HSAB), the structural parameter σp, used to predict potencies

224

of metals to marine species, can quantitatively characterize trends in formation of ionic and

225

covalent bonds 44. Therefore, σp was selected as the primary predictor for evaluating hazards

226

of metals to marine species. T and S might also affect bioavailability of metal ions and might

227

cause variable toxic effects.

228

Predicted toxic potencies of these metals to the eight sensitive species were directly

229

proportional to temperatures between 10 and 30 °C, and were expressed as the log-EC50 for



Page 10 of 31

230

each species. This result is consistent with results of a recent study that investigated

231

toxicities of Cu and Zn to Exosphaeroma gigas and the influence of temperature on toxicity

232

and bioaccumulation kinetics. That study also reported that temperature did not have a

233

significant effect on rates of uptake or efflux rate constants of Cu

234

researchers, by comparing relative sensitivities in temperate and tropical areas of coastal

235

marine environments, have shown that temperate species are more susceptible to effects of

236

trace metals than tropical species

237

physiological mechanisms that are adapted to fluctuating environmental conditions. As with

238

temperature, effects of salinity on toxicities also varied among studied species. Salinity

239

affected toxicities of metals to E. affinis and P. marianus, and affected toxicities of metals to

240

the other six species, which most likely is due to the influence of normal physiological or

241

biochemical activities, and resistance to xenobiotic chemicals 47.

45

. However, some

46

. These results indicate that species have different

242

Of the eight species, QSAR models for C. variegatus and P. marianus had the strongest

243

(r2 = 0.946, F= 54.0, p > 0.001) and weakest correlations (r2 = 0.617, F= 15.0, p > 0.001),

244

respectively. The LOOCV of each model showed that predicted QSAR models were robust

245

(R2－Q2cv> 0.3, RMSECV> 0.745), and demonstrated that accuracies of QSAR models could

246

be improved if corrected for T and S. In this study relationships between three parameters

247

(σp, T, and S) and toxicities of metals to marine species, were developed as a novel approach

248

from which to derive WQC for marine systems.

249 250

Construction of temperature-based and salinity-based SSDs

251

Toxicities of six metals were predicted for 216 combinations of T and S (6 × 6 × 6). The

252

sigmoidal-logistic model was used to construct temperature- and salinity-based SSDs, all of

253

which had r2 greater than 0.8368 (χ2> 0.0005, F > 56.2, p > 0.0001) (Table S2). Metals with

254

lesser σp exhibited greater toxic potencies toward the eight species (Fig. 1). HC5 values and


Page 11 of 31


255

their 95% CIs (Table S3) for all conditions were ranked in decreasing order: Hg > Cd >

256

Cu > Zn > Ni >Cr. Physiochemical properties of metals significantly affected their toxicities

257

to marine species.

258

Three fitting parameters of the 216 SSD curves (a, Xc, and K) were first tested to

259

identify outliers. Six samples with abnormal K values (6.10, 6.15, 6.54, 7.43, 7.60, 10.68,

260

13.77, and 21.88) were tested to determine whether they were statistical deviations (Fig. S3).

261

Multiple statistical analyses showed that the three independent variables (σp, T, and S) were

262

quantitatively correlated with the fitting parameters (Xc and K) (Table 2). The fitting results

263

showed that parameter a was not significantly correlated with σp, T, or S (R2 = 0.123, F =

264

10.7, and p = 0.0001). Further testing of paired means showed that variations in temperature

265

and salinity had no significant effect on a (Table S4). The softness index σp affected a only

266

when the value of σp was 0.142. The parameter a, was therefore assigned as a constant.

267

There were linear correlations between Xc and the three independent variables (R2 = 0.987,

268

RMSE = 0.119, F = 142, and p = 0.0001). The standard residual of the predicted Xc was

269

within ± 0.05. A nonlinear regression equation (R2 = 0.506, RMSE = 0.0521, F = 117, and p

270

= 0.0001), rather than a linear relationship (R2 = 0.0625, F = 5.78, and p = 0.0008), was

271

established between K and σp, T, and S. The standard residual of the predicted K was within

272

± 2 (Fig. S4).

273

Within realistic ranges of temperature and salinity, SSDs were represented by S-type

274

surfaces. Fitting parameters, a, Xc, and K, represented the amplitude, median, and slope of

275

the curve, respectively. Fitting results for Xc suggested that temperature could change the

276

median of the SSD model. The ratio of Xc and T was greater than that of Xc and S,

277

indicating that temperature had a greater effect on Xc than salinity. Similarly, temperature

278

can have a linear effect on slopes of SSD models. Fitting results identified K as a parabolic

279

function to the independent parameter S. The inflection point corresponds to salinity in



280

which meals were least toxic.

281

Temperature-dependent SSDs were constructed for temperatures controlled at intervals

282

of 4 °C between 10 and 30 °C. The predicted log-EC50 of Hg to each species was inversely

283

proportional to temperature from 10 °C (purple) to 30 °C (red), such that the SSD curve

284

shifted leftward along the X-axis (Fig. 2a). In other words, toxic potencies of Hg to marine

285

species were directly proportional to temperature. Temperature-based SSDs of the five other

286

metals exhibited the same pattern (Fig. S5a), and might represent a mismatch of energy

287

demand and supply at higher temperatures because of the metal-mediated reduction of the

288

aerobic scope for movement 48. Slopes of SSD curves first increased and then decreased as a

289

function of salinity, which resulted in a non-monotonic V-shape relationship. There might

290

be an optimum salinity, where the marine organisms use less energy for osmoregulation and

291

thus have greater reserves of energy to resist effects of metals. Under these conditions,

292

marine organisms might be most resistant to adverse effects of metals (Fig. 2b). The

293

influence of salinity on SSDs for the other five metals exhibited the same non-monotonic

294

tendency. Optimum salinities, however, varied among metals (Fig. S5b). We can therefore

295

conclude that temperature and salinity affect toxicities of metals to marine species, and thus

296

determine shapes and slopes of SSD curves. Temperature mainly affects horizontal positions,

297

while salinity affects gradients of fitted curves.

298 299

Prediction, validation, and application of the QSAR-SSDs model for site-specific water

300

quality criteria of metals

301

In the present study, eight representative species that were distributed in oceans worldwide

302

were selected. They included five arthropods (A. tonsa, A. bahia, E. affinis, P. pugio, and P.

303

merguiensis), two chordata (C. variegatus and P. marianus), and one haptophyta (I.

304

galbana). A. tonsa, A. bahia, C. variegatus, and E. affinis were identified as the species that


Page 12 of 31

Page 13 of 31


305

were most sensitive to the six metals (Fig. 3). The amphipod crustacean A. bahia has been

306

recommended as a test organism by the U.S. EPA and the Organization for Economic

307

Co-operation and Development 49. While it was the most sensitive to Hg under conditions

308

found in most parts of the oceans, it was not sensitive to the five other metals. Consistent

309

with results of previous studies 50, A. tonsa was sensitive to Cd, Cu, and Zn. C. variegatus

310

and A. tonsa were most sensitive to Ni and Cr in tropical and temperate areas, respectively.

311

When combined with Argo real-time data, sensitivities of marine species to additional

312

metals or metalloids in multiple locations in various geographic locations.

313

The perquisite of application is external model validation on basis of the site-specific

314

toxicity data. Temperature-dependent HC5 for two metal ions Cd and Cr (III) were derived

315

from the temperature-dependent SSD analysis at different temperatures of 15, 20, and 25˚C,

316

and salinities of 10, 20, and 30‰ 27 (Table S5, S6). Predicted HC5 values for Cd and Cr (III)

317

were consistent with HC5 that have been previously derived based on empirical data (Table

318

S7). After statistical testing by concordance regression analysis (r2 = 0.959, F = 189, and p

319

< 0.001), the predicted HC5 indicated a significant correlation with observed value at the

320

0.05 level (Fig. 4). External validation would make the QSAR-SSD model more credible.

321

Hazards of adverse effects of metals in saltwater depend not only on structural or

322

physicochemical property of metals, but also on spatial and temporal variations in external

323

environmental conditions. In this study, site-specific WQC were determined for 15 coastal

324

regions, including the United States, Canada, Australia, the European Union, China, Japan,

325

and India. CMCs for 31 metals and metalloids ranging from 0 and 400 µg/L were predicted,

326

from mean surface temperatures and salinities in 2015, by use of the QSAR-SSD model

327

(Fig. 5). Hazards posed by metals to marine organisms were ranked as follows: Hg > Ag >

328

Be > Cd > Al > Cr(III) > Mg > Ga(III) > Cu > V(III) > Ti(III) > As(III) > Zn > Mn >

329

Sc(III) > Fe(II) > Ni > V(II) > Co > In(III) > Sc(II) > Ti(II) > Ge > Tl > Sb > Pb > Y(III) >



330

Sr > Sn > Bi(III) > La. Because of differences in temperature and salinity, CMCs predicted

331

for all metals in the Arabian Sea were 100 µg/L less than those site-specific CMCs for the

332

Baltic Sea. Site-specific CMCs for the western coasts of the United States, Canada, and

333

Japan were less than those for waters along the eastern coasts. Hazards posed by metals

334

were greater in tropical areas, such as Hong Kong and the South China Sea, than in

335

temperate areas (Shanghai and East China Sea). To date, there have been few studies of

336

WQC for metals in saltwater. CMCs have been recommended for eight metals in the United

337

States

338

revised to 7.5 µg/L 52. Some other countries have developed acute benchmarks by referring

339

to guidelines recommended by the U.S. EPA and the National Oceanic and Atmospheric

340

Administration of the United States. The approach can be applied to derive hazard

341

concentrations that account for differences in areas of the marine environment across wide

342

geographic areas. Recommended CMCs listed for seven metals in the current U.S. EPA

343

guidelines are close to the thresholds of predicted values (Fig. 5). Differences in T and S

344

among regions can now be considered in decision making related to national or regional

345

water quality standards in saltwater.

51

. In Canada, the acute reference value for silver in saltwater has recently been

346

Models developed not only have important scientific value, but also carry positive

347

socioeconomic impacts. The approach for deriving site-specific hazard criteria can greatly

348

reduce the number of toxicity tests and use of marine animals for these tests, and minimize

349

the cost for running these tests. Within the process of environmental management, the

350

adoption of the site-specific water quality criteria or standards that reflect different

351

geographical features can better protect marine biodiversity and associated fisheries

352

resources. The model can help us to understand the ecological hazards of different metals,

353

and provide a scientific basis for improved land use planning (e.g. relocation of coastal

354

industries) and implementation of effective pollution control measures.


Page 14 of 31

Page 15 of 31


355

Results of the study, results of which are presented here, support a 10-fold

356

differentiated scenario of site-specific WQC to protect 95% of marine organisms from acute

357

effects of individual metals. This approach might be useful for constructing the SSDs of

358

various metals or metalloids (e.g. alkali or alkaline earth, transition, or inner transition

359

metals) under different environmental conditions, predicting site-specific WQC in saltwater,

360

and assessing hazards of metal pollution to marine species. This novel integrated approach

361

can be potentially adapted for use with non-metal pollutants. However, the model is based

362

on exposures of 48 or 96 h, and so has limited capacity to predict effects over longer

363

exposure times. Additional important factors, such as life cycles of species, metal speciation,

364

or metals associated with algae and fine particles, should also be considered when the

365

current model is being developed and applied to real situations.

366 367

Acknowledgements

368

The authors acknowledge support from the National Natural Science Foundation of China

369

(No. 41521003, 41630645, and 21507120) and the General Research Fund of Research

370

Grants Council of the Hong Kong SAR Government (No. 17305715).

371 372

Author contributions

373

Y.S.M. and F.C.W. conceived the project and designed the model. Z.W., X.W.J., and

374

K.M.Y.L. collected and analyzed the data. M.R.Y., C.L.F., B.Q.Z., and F.H.S. performed

375

statistical analysis and validated the QSAR-SSD model. Y.S.M. and Z.W. wrote the paper.

376

K.M.Y.L. and J.P.G. revised the paper and gave constructive suggestions.

377 378

Supporting Information

379

The supporting information (SI) provides details of supplementary methods, five figures



380

showing Pearson correlations between σp and the log-EC50 values of five or six metals to

381

eight species, scree plot from PCA, outlier testing of the parameters (a, Xc, and K), residual

382

plot of predicted Xc and K, and the temperature- and salinity-dependent SSDs of five metals

383

(Cd, Cu, Zn, Ni, and Cr (III)). The SI also contains eight tables with toxicity data of eight

384

species in the training sets, pearson correlation coefficients (r2) between the log-EC50 of

385

eight species and physiochemical properties, SSD fitting results of six metals at six different

386

temperatures and salinities, HC5 values under different T, S, and σp conditions, linear

387

regression analysis between three fitting variables (a, Xc, and K) and QSAR parameters (σp,

388

T, and S), datasets used for the construction of temperature-dependent SSDs and

389

salinity-dependent SSDs for Cd and Cr (III), and results for the model validation.

390 391

Conflict of interest

392

The authors declare no competing financial interest.

393 394

References

395

1.

396

mini review. J Appl. Sci. 2004, 4, (1), 1-20.

397

2.

398

Aquatic Environment. Springer Study Edition 1983.

399

3.

400

divalent metal ions: Microtox® bioluminescence assay. Environ. Toxicol. Chem. 1996, 15,

401

(3), 275-81.

402

4.

403

for predicting toxicity of metals. Environ. Toxicol. Chem. 2003, 22, (8), 1916-35.

404

5.

405

relationships (QICARs): Using metal-ligand binding characteristics to predict metal toxicity.

Ansari, T. M.; Marr, I. L.; Tariq, N., Heavy metals in marine pollution perspective–A

Foerstner, U.; Wittmann, G. T. W.; Prosi, F.; Lierde, J. H. V., Metal Pollution in the

Newman, M. C.; McCloskey, J. T., Predicting relative toxicity and interactions of

Walker, J. D.; Enache, M.; Dearden, J. C., Quantitative cationic-activity relationships

Ownby, D. R.; Newman, M. C., Advances in quantitative ion character-activity


Page 16 of 31

Page 17 of 31


406

QSAR Comb. Sci. 2003, 22, (2), 241-6.

407

6.

408

quality criteria for protecting aquatic life from physicochemical properties of metals or

409

metalloids. Environ. Sci. Technol. 2013, 47, (1), 446-53.

410

7.

411

Predicting criteria continuous concentrations of 34 metals or metalloids by use of

412

quantitative

413

(QICAR-SSD) model. Environ. Pollut. 2014, 188, (5), 50-5.

414

8.

415

marine water quality criteria for metals based on a novel QICAR-SSD model. Environ. Sci.

416

Pollut. Res. Int. 2015, 22, (6), 4297-304.

417

9.

418

Influence of ionic strength, pH, and cation valence on aggregation kinetics of titanium

419

dioxide nanoparticles. Environ. Sci. Technol. 2009, 43, (5), 1354-9.

420

10. Hall, L. W., Jr.; Anderson, R. D., The influence of salinity on the toxicity of various

421

classes of chemicals to aquatic biota. Crit. Rev. Toxicol. 1995, 25, (4), 281-346.

422

11. Matar, Z.; Soares Pereira, C.; Chebbo, G.; Uher, E.; Troupel, M.; Boudahmane, L.; Saad,

423

M.; Gourlay-France, C.; Rocher, V.; Varrault, G., Influence of effluent organic matter on

424

copper speciation and bioavailability in rivers under strong urban pressure. Environ. Sci.

425

Pollut. Res. Int. 2015, 1-12.

426

12. Sanchez-Marin, P.; Lorenzo, J. I.; Blust, R.; Beiras, R., Humic acids increase dissolved

427

lead bioavailability for marine invertebrates. Environ. Sci. Technol. 2007, 41, (16), 5679-84.

428

13. Xu, F.; Hu, B.; Li, J.; Cui, R.; Liu, Z.; Jiang, Z.; Yin, X., Reassessment of heavy metal

429

pollution in riverine sediments of Hainan Island, China: sources and risks. Environ. Sci.

430

Pollut. Res. Int. 2018, 25, (2), 1766-72.

Wu, F. C.; Mu, Y. S.; Chang, H.; Zhao, X. L.; Giesy, J. P.; Wu, K. B., Predicting water

Mu, Y. S.; Wu, F. C.; Chen, C.; Liu, Y. D.; Zhao, X. L.; Liao, H. Q.; Giesy, J. P.,

ion

character-activity

relationships-species

sensitivity

distributions

Chen, C.; Mu, Y. S.; Wu, F. C.; Zhang, R. Q.; Su, H. L.; Giesy, J. P., Derivation of

French, R. A.; Jacobson, A. R.; Kim, B.; Isley, S. L.; Penn, R. L.; Baveye, P. C.,



431

14. Posthuma, L.; Suter, G. W.; Traas, T. P., Species Sensitivity Distributions in

432

Ecotoxicology. Lewis Publishers: Boca Raton, Fla., 2002; p 587.

433

15. Solomon, K. R., Overview of recent developments in ecotoxicological risk assessment.

434

Risk Anal. 1996, 16, (5), 627–33.

435

16. Straalen, N. M. V.; Rijn, J. P. V., Ecotoxicological risk assessment of soil fauna recovery

436

from pesticide application. Rev. Environ. Contam. Toxicol. 1998, 154, 83–141.

437

17. American Society for Testing Materials, Standard guide for conducting acute toxicity

438

tests on test materials with fishes, macroinvertebrates, and amphibians. In Annual Book of

439

ASTM Standards, ASTM: West Conshohocken, PA, 1996; pp 175-96.

440

18. Organisation for Economic Cooperation and Development Guidelines for the Testing of

441

Chemicals: Freshwater Alga and Cyanobacteria, Growth Inhibition Test; OECD: Paris,

442

2011; p 25.

443

19. U.S. EPA. Guidelines for Ecological Risk Assessment; Risk Assessment Forum:

444

Washington, D.C., 1998; p 61.

445

20. Smetanová, S.; Bláha, L.; Liess, M.; Schäfer, R. B.; Beketov, M. A., Do predictions

446

from species sensitivity distributions match with field data? Environ. Pollut. 2014, 189, (12),

447

126-33.

448

21. Janssen, C. R.; Heijerick, D. G.; De Schamphelaere, K. A.; Allen, H. E., Environmental

449

risk assessment of metals: tools for incorporating bioavailability. Environ. Int. 2003, 28, (8),

450

793-800.

451

22. U.S. EPA. Revised Deletion Process for the Site-Specific Recalculation Procedure for

452

Aquatic Life Criteria; Office of Water: Washington, DC, 2013; p 12.

453

23. Le Croizier, G.; Lacroix, C.; Artigaud, S.; Le Floch, S.; Raffray, J.; Penicaud, V.;

454

Coquille, V.; Autier, J.; Rouget, M. L.; Le Bayon, N.; Lae, R.; Tito De Morais, L.,

455

Significance of metallothioneins in differential cadmium accumulation kinetics between two


Page 18 of 31

Page 19 of 31


456

marine fish species. Environ. Pollut. 2018, 236, 462-476.

457

24. Canadian Council of Ministers of the Environment., Canadian environmental quality

458

guidelines. CCME: Hull, QC, 1999.

459

25. Anzecc, A. Australian and New Zealand Guidelines for Fresh and Marine Water

460

Quality, Vol. 1, The Guidelines; Department of the Environment, 2000; p 103.

461

26. Wu, F. C.; Meng, W.; Zhao, X.; Li, H.; Zhang, R.; Cao, Y.; Liao, H., China embarking

462

on development of its own national water quality criteria system. Environ. Sci. Technol.

463

2010, 44, (21), 7992-3.

464

27. Zhou, G. J.; Wang, Z.; Lau, E. T. C.; Xu, X. R.; Leung, K. M. Y., Can we predict

465

temperature-dependent chemical toxicity to marine organisms and set appropriate water

466

quality guidelines for protecting marine ecosystems under different thermal scenarios? Mar.

467

Pollut. Bull. 2014, 87, (1-2), 11-21.

468

28. Wang, Z.; Meador, J. P.; Leung, K. M. Y., Metal toxicity to freshwater organisms as a

469

function of pH: A meta-analysis. Chemosphere 2016, 144, 1544-52.

470

29. Afonso, A.; Gutierrez, A. J.; Lozano, G.; Gonzalez-Weller, D.; Lozano-Bilbao, E.;

471

Rubio, C.; Caballero, J. M.; Revert, C.; Hardisson, A., Metals in Diplodus sargus cadenati

472

and Sparisoma cretense-a risk assessment for consumers. Environ. Sci. Pollut. Res. Int. 2018,

473

25, (3), 2630-42.

474

30. Reguera, P.; Couceiro, L.; Fernandez, N., A review of the empirical literature on the use

475

of limpets Patella spp. (Mollusca: Gastropoda) as bioindicators of environmental quality.

476

Ecotoxicol. Environ. Saf. 2018, 148, 593-600.

477

31. Li, H.; Liu, Z. H.; Xu, J. P.; Sun, C. H. User manual of global ocean Argo gridded

478

datasets (BOA_Argo); Second Institute of Oceanography,SOA: Zhejiang, P.R. China, 2015;

479

p 21.

480

32. Stephen, C. E.; Mount, D. I.; Hansen, D. J.; Gentile, J. R.; Chapman, G. A.; Brungs, W.



481

A. Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of

482

Aquatic Organisms and Their Uses; Office of Research and Development: Washington, DC,

483

1985; p 45.

484

33. Pearson, R. G.; Mawby, R. J., The nature of metal-halogen bonds. Phys. Rev. D 1967, 66,

485

(5), 55-84.

486

34. Kaiser, K. L. E., Correlation and prediction of metal toxicity to aquatic biota. Can. J.

487

Fish Aquat. Sci. 1980, 37, (2), 211-8.

488

35. Base, C. F.; Mesmer, R. E., The Hdrolysis of Cations. John Wiley and Sons Inc.: New

489

York, USA, 1976; p 2385-7.

490

36. Wolterbeek, H. T.; Verburg, T. G., Predicting metal toxicity revisited: general properties

491

vs. specific effects. Sci. Total Environ. 2001, 279, (1-3), 87-115.

492

37. Mccloskey, J. T.; Newman, M. C.; Clark, S. B., Predicting the relative toxicity of metal

493

ions using ion characteristics: Microtox® bioluminescence assay. Environ. Toxicol. Chem.

494

1996, 15, (10), 1730-7.

495

38. Holland, G., Ocean Biogeographic Information System (OBIS). Unesco 2009, 1-8.

496

39. Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y. D.; Lee, K. H.; Tropsha, A., Rational

497

selection of training and test sets for the development of validated QSAR models. J. Comput.

498

Aid. Mol. Des. 2003, 17, (2-4), 241-53.

499

40. Tropsha, A.; Gramatica, P.; Gombar, V. K., The importance of being earnest: validation

500

is the absolute essential for successful application and interpretation of QSPR models.

501

QSAR Comb. Sci. 2003, 22, (1), 69-77.

502

41. Eriksson, L.; Jaworska, J.; Worth, A. P.; Cronin, M. T.; McDowell, R. M.; Gramatica, P.,

503

Methods for reliability and uncertainty assessment and for applicability evaluations of

504

classification- and regression-based QSARs. Environ. Health Perspect. 2003, 111, (10),

505

1361-75.


Page 20 of 31

Page 21 of 31


506

42. European Chemicals Agency, Guidance on information requirements and chemical

507

safety assessment: QSARs and grouping of chemicals. In 2008; p 134.

508

43. Wheeler, J. R.; Grist, E. P.; Leung, K. M.; Morritt, D.; Crane, M., Species sensitivity

509

distributions: Data and model choice. Mar. Pollut. Bull. 2002, 45, (1-12), 192-202.

510

44. Pearson, R. G., Hard and Soft Acids and Bases. Dowden, Hutchinson & Ross: 1973; p

511

1-52.

512

45. Lewis, A.; King, C. K.; Hill, N. A.; Cooper, A.; Townsend, A. T.; Mondon, J. A.,

513

Seawater temperature effect on metal accumulation and toxicity in the subantarctic

514

Macquarie Island isopod, Exosphaeroma gigas. Aquat. Toxicol. 2016, 177, 333-42.

515

46. Kwok, K. W.; Leung, K. M.; Lui, G. S.; Chu, S. V.; Lam, P. K.; Morritt, D.; Maltby, L.;

516

Brock, T. C.; Van den Brink, P. J.; Warne, M. S.; Crane, M., Comparison of tropical and

517

temperate freshwater animal species' acute sensitivities to chemicals: implications for

518

deriving safe extrapolation factors. Integr. Environ. Assess Manag. 2007, 3, (1), 49-67.

519

47. Dube, A.; Jayaraman, G.; Rani, R., Modelling the effects of variable salinity on the

520

temporal distribution of plankton in shallow coastal lagoons. J. Hydro-Environ. Res. 2010, 4,

521

(3), 199-209.

522

48. Li, A. J.; Leung, P. T.; Bao, V. W.; Yi, A. X.; Leung, K. M., Temperature-dependent

523

toxicities of four common chemical pollutants to the marine medaka fish, copepod and

524

rotifer. Ecotoxicology 2014, 23, (8), 1564-73.

525

49. Wortham Neal, J. L.; Price, W. W., Marsupial developmental stages in Americamysis

526

Bahia (mysida: mysidae). J. Crustacean Biol. 2002, 22, (1), 98-112.

527

50. Bielmyer, G. K.; Grosell, M.; Brixti, K. V., Toxicity of silver, zinc, copper, and nickel to

528

the copepod Acartia tonsa exposed via a phytoplankton diet. Environ. Sci. Technol. 2006, 40,

529

(6), 2063-8.

530

51. U.S. EPA. National Recommended Water Quality Criteria; Office of Science and



531

Technology: Washington D.C., 2009; p 21.

532

52. Canadian Council of Ministers of the Environment. Canadian Water Quality Guidelines

533

for the Protection of Aquatic Life; Guidelines and Standards Division: Hull, QC, 2007.

534


Page 22 of 31

Page 23 of 31

535


Figure Captions

536 537

Fig. 1. Predicted SSDs of six metals (Hg (red), Cd (yellow), Cu (green), Zn (cyan), Ni

538

(blue), and Cr (purple)) from the QSAR model.

539 540

Fig. 2. Temperature-dependent (a) and salinity-dependent (b) SSDs of Hg. Temperature was

541

from 10 °C (purple) to 30 °C (red) with 4 °C intervals, and the salinity is from 10‰ (red) to

542

35‰ (purple) with intervals of 5‰.

543 544

Fig. 3. Most sensitive species to six different metals (Hg, Cd, Cu, Zn, Ni, and Cr) in global

545

oceans, include A. tonsa (red), A. bahia (yellow), C. veriegatus (cyan), and E. affinis (blue).

546

Temperatures and salinities at each sampling point were obtained from the Argo real-time

547

assay.

548 549

Fig. 4. Predicted CMCs for 31 metals and metalloids in 15 different geographic areas vs.

550

CMCs recommended in the current U.S. EPA guidelines (µg/L). The predicted CMCs were

551

derived from mean surface temperatures and salinities in 2015. The 15 coastal areas

552

belonged to the United States, Canada, Australia, the European Union, China, Japan, and

553

India. The solid red line represents the CMCs recommended by the U.S. EPA.

554 555

Fig. 5. Predicted hazardous concentrations 5% (HC5) generated by the QSAR-SSD model vs.

556

observed site-specific HC5 of cadmium and chromium at temperatures of 15, 20 and 25 °C.

557

All HC5 values are expressed as µg/L, with their 95% confidence intervals (95% CIs).



558 559

Fig. 1


Page 24 of 31

Page 25 of 31


560

561 562

Fig. 2



563 564

Fig. 3


Page 26 of 31

Page 27 of 31


565 566

Fig. 4



567 568

Fig. 5


Page 28 of 31

Page 29 of 31


569

Table 1. Toxicities predicted for eight marine species based on the structure parameter (σp)

570

and environmental conditions (T & S). Species

Predicting equations

n

R2

F

p

RMSE

QCV2

RMSECV

Acartia tonsa

log-EC50=(-2.1135±1.2098)+(-0.0588±0.0591)T + (-0.0139±0.0133)S+(37.8829±4.9666)σp

25

0.760

26.4

2.56×10-7

0.440

0.582

0.518

Americamysis log-EC50=(-3.1631±0.5728) + (-0.0626±0.0197)T bahia + (-0.0066±0.0089)S + (50.4065±2.9291)σp

37

0.899

108

1.00×10-3

0.365

0.884

0.375

Cyprinodon variegatus

log-EC50=(5.4539±2.8414) + (-0.2109±0.0709)T + (-0.0308±0.0078)S + (13.7692±10.3215)σp

10

0.946

54.0

9.82×10-5

0.194

0.877

0.294

Eurytemora affinis

log-EC50=(-3.1216±1.0185) + (-0.0109±0.0412)T + (0.0776±0.0081)S + (20.8087±6.6848)σp

18

0.794

22.8

1.18×10-5

0.486

0.688

0.556

Isochrysis galbana

log-EC50=(-1.4077±0.3629) + (-0.0305±0.0120)T + (-0.0014±0.008)S + (31.0580±2.3966)σp

50

0.773

56.6

1.8×10-15

0.369

0.754

0.342

Palaemonetes log-EC50=(-1.9872±1.5252) + (-0.0570±0.0338)T pugio + (-0.0176±0.0224)S + (41.0724±7.1153)σp

22

0.761

23.3

1.99×10-6

0.721

0.687

0.745

log-EC50=(-1.5913±0.5575) + (-0.0483±0.0155)T + (-0.0177±0.0096)S + (35.6574±3.2020)σp

23

0.852

43.2

1.10×10-8

0.361

0.807

0.384

Priopidichthys log-EC50=(-1.0168±0.8775) + (-0.0059±0.0218)T marianus + (0.0055±0.0132)S + (30.1768±4.6303)σp

27

0.617

15.0

1.29×10-5

0.534

0.511

0.464

Penaeus merguiensis

571



Page 30 of 31

572

Table 2. Fitting parameters of the SSD for integrating the structural property, temperature,

573

and salinity. Mode

Parameters fitting equations

Statistics

Constant

a=0.9195±0.0465

No sig. in means comparison and ANOVA

Linear

Xc = (-1.77 ± 0.068) - (0.0501 ± 0.0012)T - (0.0022

R2 = 0.987, RMSE = 0.119, F = 142, p = 0.0001

± 0.0001)S + (53.0 ± 0.311)σp Non-linear

574

K = -681σp2 + 119σp - 0.704T/S - 0.005S/σp - 1.65

R2 = 0.506, RMSE = 0.052, F = 117, p = 0.0001

Note: a was represented as an amplitude, Xc was a median value, and K was a coefficient


Page 31 of 31

575


SYNOPSIS TOC

576


Model for Predicting Toxicities of Metals and Metalloids in Coastal

Recommend Documents