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The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Transition metal pollution in rivers in South Asia is more serious than in other regions because of the lack of adequate freshwater management measures. Water quality criteria (WQC) for South Asia is urgently needed to protect regional aquatic environments because of the occurrence of transboundary rivers. The present study established non-parametric kernel density estimation species sensitivity distribution (NPKDE-SSD) models and then derived the acceptable hazardous concentration for protection of 95% of all aquatic species (HC5) and WQC of six typical transition metals in South Asia. The results showed that the order of acute and chronic WQC was Mn > Fe > Cd > Zn > Cu > Hg and Cu > Fe > Cd, respectively. A risk assessment of these metals in the Indus River, the Ganges River, the Brahmaputra River, the Meghna River, and the Bagmati River was also carried out. Based on the results, these major rivers in South Asia were highly polluted with transition metals, with significant ecological risks for a large number of aquatic species. This study can contribute to a better understanding of ecological risks in South Asia and provide a scientific basis for the updating of water quality standards and the increase in overall water quality.

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