Machine Learning-Assisted Tissue-Residue-Based Risk Assessment for Protecting Threatened and Endangered Fishes in the Yangtze River Basin.

Journal: Environmental science & technology
Published Date: Jul 28, 2025

Abstract

Assessing pollutant risks to threatened and endangered (T&E) species is crucial for their conservation. However, traditional risk assessment methods for bioaccumulative pollutants to T&E fishes is challenging due to uncertainties in exposure-based toxicity relationships and data gaps. Tissue-residue concentration-response relationships provide a more reliable approach. This study employed machine learning (ML) algorithms to predict tissue-residue toxicity of bioaccumulative pollutants to T&E fishes, and found the extreme gradient boosting (XGBoost) model performed best, with an external validation of 0.85 and a root-mean-squared error of 0.81. It was then used to predict the developmental toxicity of 22 bioaccumulative flame retardants to 98 T&E fishes from the Yangtze River basin, across four life stages. Results showed embryonic and juvenile stages were most sensitive, with organophosphate flame retardants (OPFRs), particularly (4-methylphenyl) diphenyl phosphate (CDPP) and isodecyl diphenyl phosphate (IDPP), exhibiting higher toxicity than novel brominated flame retardants (NBFRs) and polybrominated diphenyl ethers (PBDEs). Ecological risk assessment for T&E fishes revealed that aryl-OPFRs posed the highest risks, with CDPP exhibiting a risk quotient (RQ = 4.07) four times higher than the safety threshold, significantly exceeding the risks associated with NBFRs and PBDEs. This study established a novel ML-assisted tissue-residue-based risk assessment method for bioaccumulative pollutants to T&E fishes, which is significant for global T&E species conservation.

Authors

  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Xiaolei Wang
    Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering of Nankai University, Tianjin 300350, China.
  • Yuanpu Ji
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
  • Yuefei Ruan
    State Key Laboratory of Marine Pollution (SKLMP), and Department of Chemistry, City University of Hong Kong, Hong Kong SAR 999077, China.
  • Longfei Zhou
    From the Department of Radiology, Division of Neuroradiology, Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and Neurocognitive Disorders Program, Departments of Psychiatry and Medicine (P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808; and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and Computer Engineering, Department of Computer Science, Department of Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham, NC.
  • Jiayu Wang
    Department of Cardiology, the Second Hospital of Shandong University, 250033 Jinan, Shandong, China.
  • Xiaoli Zhao
    College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China. Electronic address: xlee_zhao@njucm.edu.com.
  • 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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