Machine Learning-Assisted Tissue-Residue-Based Risk Assessment for Protecting Threatened and Endangered Fishes in the Yangtze River Basin
Rui Wang, Xiaolei Wang, Yuanpu Ji, Yuefei Ruan, Longfei Zhou, Jiayu Wang, Xiaoli Zhao, Fengchang Wu
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 R 2 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.