Predicting the Site-Specific Toxicity of Metals to Fishes Using a New Machine Learning-Based Approach.

Journal: Environmental science & technology
Published Date: Jul 14, 2025

Abstract

Fishes of various trophic levels play an important role in the stability and balance of aquatic ecosystems. Metal contaminants can impair the survival and population fitness of fish at elevated concentrations. When universal water quality criteria (WQC) of metals are adopted to protect different species in different geographic regions, they may not adequately protect all fish due to a lack of consideration for site-specific environmental conditions and species assemblages. Additionally, obtaining credible toxicity data for rare and endangered species is challenging. Therefore, this study aims to develop a robust, machine learning-based method to predict the toxicity of metals to various fish species, including rare and endangered species, and combine it with the non-parametric kernel density estimation of the species sensitivity distribution (NPKDE-SSD) model to derive site-specific WQC for better ecosystem protection. We show that this machine learning-based approach, with consideration of physicochemical properties of metals, hydrochemical conditions, biological characteristics of fishes, and metal toxicities, as well as their relationships, can well predict the toxicity of 19 metals to various fish species. The method is applied to derive site-specific WQC (based on the hazardous concentration of 5%) of these metals for the Eastern Plain lake region in China. The study provides a novel, alternative approach to supplement the insufficient toxicity information for site-specific WQC derivation and potentially improve the protection of fish species.

Authors

  • Yinghao Cheng
    School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
  • Ying Wang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Zihan Xu
    Shenzhen Sixcarbon Technology, Shenzhen 518106, China.
  • Chenglian Feng
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese research academy of environmental sciences, Beijing 100012, China.
  • Zhaomin Dong
    School of Space and Environment, Beihang University, Beijing 100191, China. Electronic address: dongzm@buaa.edu.cn.
  • Wenhong Fan
    School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
  • Kenneth M Y Leung
    State Key Laboratory of Marine Pollution, and Department of Chemistry, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, P. R. China.
  • Fengchang Wu
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China.

Keywords

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