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 novel regional-scale human health risk assessment model for soil heavy metal(loid) pollution based on empirical Bayesian kriging.

Soil heavy metal(loid)s contamination caused by rapid urbanization and industrialization seriously affects human health and hinders the global sustainable development goals (SDGs). Currently, there is a lack of comprehensive human health risk assessment (HHRA) studies for multiple land use types at the regional scale. We propose a practical risk assessment framework that integrates empirical Bayesian kriging (EBK), pollution level analyses, and modified HHRA modeling.