Spatiotemporal variability of PAHs and PAEs in sediments driven by hydrological rhythms in Danjiangkou Reservoir, China: Multisource analysis to machine-learning prediction

Puntos clave

• Sediments demonstrate significant spatiotemporal variability in the concentrations of polycyclic aromatic hydrocarbons and phthalate esters, indicating environmental impacts.
• Data shows that the levels of PAHs varied significantly with hydrological rhythms, suggesting a strong linkage between water flow and sediment composition.
• Machine-learning prediction models were utilized to analyze sediment data across different seasons, showing promising accuracy in forecasting pollutant levels.
• Findings highlight the need for continuous monitoring of hydrological effects on pollutant distribution in aquatic ecosystems.