Integrating Suspect and Nontarget Screening with Spatial Autocorrelation to Prioritize Emerging Contaminants in a Coastal Urban River

Emerging contaminants (ECs) in coastal urban rivers threaten marine ecosystems, yet existing prioritization strategies focus on the physicochemical properties of ECs, neglecting the contribution of environmental conditions (e.g., pollution sources and hydrological dynamics) to the transport behavior of ECs. This study integrates suspect and nontarget screening with spatial autocorrelation analysis to establish a monitoring-driven prioritization strategy. In this study, 276 ECs were identified in an urban river discharging into Laizhou Bay, Bohai Sea, including five newly detected per- and polyfluoroalkyl substances (PFAS). The source-tracing analysis, incorporating multidimensional indicators, revealed three representative pollution patterns of ECs in rivers: single-point, composite-point, and mixed sources. Spatial autocorrelation derived from monitoring data was evaluated for 161 ECs with source assignment and intrinsic properties, such as mobility, demonstrating the potential of spatial autocorrelation/connectivity to capture the combined effects of chemical characteristics and actual environmental conditions on ECs’ transport behavior. A quantitative measure of spatial connectivity, correlation length, was applied to prioritize ECs. PFAS from mixed sources exhibited high priority. Persistence, mobility, and toxicity (PMT) evaluation highlighted the need for further investigation into the ecological risks posed by prioritized PFAS. This approach guides targeted monitoring and in-depth risk assessments of ECs, supporting efficient coastal ecosystem management.