State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Environ Pollut. 2024 Nov 15;361:124920. doi: 10.1016/j.envpol.2024.124920. Epub 2024 Sep 7.
Per- and polyfluoroalkyl substances (PFASs) are severely polluted in aquatic environments and can harm aquatic organisms. Due to the limitation of conducting toxicity experiments directly on threatened and endangered (T&E) species, their toxicity data is scarce, hindering accurate risk assessments. The development of computational toxicology makes it possible to assess the risk of pollutants to T&E fishes. This study innovatively combined machine learning models, including random forest (RF), artificial neural network (ANN), and XGBoost, and the QSAR-ICE model to predict chronic developmental toxicity data of PFASs to T&E fishes. Among these, the XGBoost model exhibited superior performance, with R of 0.95 and 0.81 for the training and testing sets, respectively. Internal and external validation further confirmed that the XGBoost model is robust and reliable. Subsequently, it was used to predict chronic developmental toxicity data for seven priority PFASs to T&E fishes in the Yangtze River. Acipenseridae fishes (e.g., Acipenser dabryanus and Acipenser sinensis) showed high sensitivity to PFASs, possibly due to their unique lifestyle and physiological characteristics. Based on these data, the predicted no-effect concentration (PNEC) of individual PFASs was calculated, and the risk for T&E fishes in the Yangtze River was assessed. The results indicated that the risk of PFASs to T&E fishes is low (3.85 × 10∼8.20 × 10), with perfluorohexanoic acid (PFHxA) and perfluorooctanoic acid (PFOA) as the high-risk pollutants. The risk in the middle and lower reaches of the river is higher than in the upper reaches. This study provides a new approach for obtaining chronic toxicity data and conducting risk assessments for T&E species, advancing the protection of T&E species worldwide.
全氟和多氟烷基物质（PFASs）在水生环境中受到严重污染，会对水生生物造成危害。由于直接对受威胁和濒危（T&E）物种进行毒性实验受到限制，它们的毒性数据稀缺，阻碍了准确的风险评估。计算毒理学的发展使得评估污染物对 T&E 鱼类的风险成为可能。本研究创新性地将机器学习模型（包括随机森林（RF）、人工神经网络（ANN）和 XGBoost）和 QSAR-ICE 模型结合起来，预测 PFASs 对 T&E 鱼类慢性发育毒性数据。其中，XGBoost 模型表现出优异的性能，训练集和测试集的 R 分别为 0.95 和 0.81。内部和外部验证进一步证实了 XGBoost 模型的稳健性和可靠性。随后，它被用于预测长江 7 种优先 PFASs 对 T&E 鱼类的慢性发育毒性数据。鲟科鱼类（如达氏鲟和中华鲟）对 PFASs 表现出较高的敏感性，这可能是由于它们独特的生活方式和生理特征。基于这些数据，计算了单个 PFASs 的预测无效应浓度（PNEC），并评估了长江 T&E 鱼类的风险。结果表明，PFASs 对 T&E 鱼类的风险较低（3.85×10∼8.20×10），其中全氟己酸（PFHxA）和全氟辛酸（PFOA）为高风险污染物。河流中下游的风险高于上游。本研究为获取 T&E 物种的慢性毒性数据和进行风险评估提供了一种新方法，推进了全球 T&E 物种的保护。
