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Non-parametric kernel density estimation of species sensitivity distributions in developing water quality criteria of metals

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Abstract

Due to use of different parametric models for establishing species sensitivity distributions (SSDs), comparison of water quality criteria (WQC) for metals of the same group or period in the periodic table is uncertain and results can be biased. To address this inadequacy, a new probabilistic model, based on non-parametric kernel density estimation was developed and optimal bandwidths and testing methods are proposed. Zinc (Zn), cadmium (Cd), and mercury (Hg) of group IIB of the periodic table are widespread in aquatic environments, mostly at small concentrations, but can exert detrimental effects on aquatic life and human health. With these metals as target compounds, the non-parametric kernel density estimation method and several conventional parametric density estimation methods were used to derive acute WQC of metals for protection of aquatic species in China that were compared and contrasted with WQC for other jurisdictions. HC5 values for protection of different types of species were derived for three metals by use of non-parametric kernel density estimation. The newly developed probabilistic model was superior to conventional parametric density estimations for constructing SSDs and for deriving WQC for these metals. HC5 values for the three metals were inversely proportional to atomic number, which means that the heavier atoms were more potent toxicants. The proposed method provides a novel alternative approach for developing SSDs that could have wide application prospects in deriving WQC and use in assessment of risks to ecosystems.

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Acknowledgments

The present study was supported by the Environmental Public Welfare Program (201409037) and the National Natural Science Foundation of China (Nos. 41130743 and 41473109).

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Authors and Affiliations

  1. College of Water Sciences, Beijing Normal University, Beijing, 100875, China

    Ying Wang

  2. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing, 100012, China

    Ying Wang, Fengchang Wu, John P. Giesy, Chenglian Feng, Ning Qin & Yujie Zhao

  3. Department of Veterinary Biomedical Science and Toxicology Centre, University of Saskatchewan, 44 Campus Drive, Saskatoon, SK, Canada

    John P. Giesy

  4. The Key Laboratory of Water and Air Pollution Control of Guangdong Province, South China Institute of Environmental Sciences, The Ministry of Environment Protection of PRC, Guangzhou, 510065, China

    Yuedan Liu

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  1. Ying Wang
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Correspondence to Fengchang Wu.

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Responsible editor: Thomas Braunbeck

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Wang, Y., Wu, F., Giesy, J.P. et al. Non-parametric kernel density estimation of species sensitivity distributions in developing water quality criteria of metals. Environ Sci Pollut Res 22, 13980–13989 (2015). https://doi.org/10.1007/s11356-015-4602-8

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