Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China.

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated.

Impact of upstream river inputs and reservoir operation on phosphorus fractions in water-particulate phases in the Three Gorges Reservoir.

The impoundment of the Three Gorges Reservoir (TGR) has changed water-sand transport regime, with inevitable effects on phosphorus transport behavior in the TGR. In this study, we measured phosphorus fractions in water and suspended particles transported from upstream rivers of the TGR (the Yangtze River, the Jialing River and the Wu River) to reservoir inner region over the full operation schedule of the TGR. The aim was to determine how phosphorus fractions in water and particulate phases varied in response to natural hydrological processes and reservoir operations.