A Copula-based spatiotemporal probabilistic model for heavy metal pollution incidents in drinking water sources.

Water pollution incidents pose a significant threat to the safety of drinking water supplies and directly impact the quality of life of the residents when multiple pollutants contaminate drinking water sources. The majority of drinking water sources in China are derived from rivers and lakes that are often significantly impacted by water pollution incidents. To tackle the internal mechanisms between water quality and quantity, in this study, a Copula-based spatiotemporal probabilistic model for drinking water sources at the watershed scale is proposed. A spatiotemporal distribution simulation model was constructed to predict the spatiotemporal variations for water discharge and pollution to each drinking water source. This method was then applied to the joint probabilistic assessment for the entire Yangtze River downstream watershed in Nanjing City. The results demonstrated a significant negative correlation between water discharge and pollutant concentration following a water emergency. The water quantity-quality joint probability distribution reached the highest value (0.8523) after 14 hours of exposure during the flood season, much higher than it was (0.4460) during the dry season. As for the Yangtze River downstream watershed, five key risk sources (N1-N5) and two high-exposure drinking water sources (W3-W4; AEI=1) should be paid more attention. Overall, this research highlights a comprehensive mode between water quantity and quality for drinking water sources to cope with accidental water pollution.