Spatiotemporal variability of PAHs and PAEs in sediments driven by hydrological rhythms in Danjiangkou Reservoir, China: Multisource analysis to machine-learning prediction | Synapse
March 3, 2026
Spatiotemporal variability of PAHs and PAEs in sediments driven by hydrological rhythms in Danjiangkou Reservoir, China: Multisource analysis to machine-learning prediction
Key Points
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.