Model for Predicting Toxicities of Metals and Metalloids in Coastal

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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

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Submitted to ES&T

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

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

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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

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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

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Joint first author

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*

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

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

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

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

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

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

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

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

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

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

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metals and metalloids then investigating relationships between acute water quality criteria

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

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

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

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

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

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

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

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relationships; Species sensitivity distributions; Temperature; Salinity

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Introduction

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

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

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

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

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

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

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physiochemical characteristics

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

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and dissolved organic matter 9-13.

3-8

, but also by environmental conditions, such as

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

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

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

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

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quality criteria (WQC) and in environmental risk assessments

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

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

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

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

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environments, such standardization also potentially introduces bias into assessments of

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

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

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be a thousand-fold greater than hazards in the field

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

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

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15, 16

. The current WQC

. In 2013, the U.S. Environmental

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

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Several countries, including the European Union

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

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

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should be corrected to account for background environmental characteristics and specific

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

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developed

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

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their speciation. Investigations pertaining to saltwater are currently ongoing by

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

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, Canada

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, Australia

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and China

.

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,

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

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

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

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

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

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

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

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

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

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

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Materials and methods

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Modeling datasets

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

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

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

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that met selection

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

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

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

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

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

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

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

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

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(Table S1).

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

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

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

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

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

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potential (∆E0)

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density (AR/AW)

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

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

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parameters (Z/AR and Z/AR2)

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supporting environmental parameters.

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, first hydrolysis constants (|logKOH|)

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, atomic weight (AW), electron

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, 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

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

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

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

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

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worldwide 38.

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Modeling and internal validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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than 0.3

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QSAR results

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. 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.,

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MO, United States) for calculations.

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SSD analyses to predict sensitivities of eight marine species

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

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

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

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

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

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

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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).

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[2]

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

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

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

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to examine significant differences among species.

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

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

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

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

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

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

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

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

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

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China, Japan, and India).

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Statistical Analysis and Model Validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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analysis.

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Results and Discussion

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

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marine species

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

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

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

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

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

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

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

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

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

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

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

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multiple regression analyses, predictive relationships were developed for sensitivities of

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

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

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

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

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

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

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

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cause variable toxic effects.

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

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

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

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

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

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

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

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

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trace metals than tropical species

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

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

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

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

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biochemical activities, and resistance to xenobiotic chemicals 47.

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. However, some

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. These results indicate that species have different

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

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

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

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

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

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

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from which to derive WQC for marine systems.

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Construction of temperature-based and salinity-based SSDs

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

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

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

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

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

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

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to marine species.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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± 2 (Fig. S4).

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

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

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

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

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

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

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

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which meals were least toxic.

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Temperature-dependent SSDs were constructed for temperatures controlled at intervals

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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while salinity affects gradients of fitted curves.

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

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quality criteria of metals

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

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

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

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

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

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

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

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

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

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

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

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metals or metalloids in multiple locations in various geographic locations.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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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

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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

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395

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

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

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

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

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

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533

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

534

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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).

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Fig. 1

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Fig. 2

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Fig. 3

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Fig. 4

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Fig. 5

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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

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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

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