Combining source identification and risk assessment to uncover spatial risk patterns in an agricultural lake.

Pollutant source identification and risk assessment underpin environmental management, necessitating innovative methods for both pollution source identification and comprehensive evaluation to enhance management efficiency. In this study, we developed a novel integrated framework that combines Bayesian isotope mixing, positive matrix factorization (PMF), random forest, and spatial autocorrelation for multi-pollutant source identification and risk assessment. The Bayesian isotope mixing model revealed that fertilizers accounted for 61 % of the nitrate in the lake and 46 % of the nitrate in the river. Furthermore, PMF analysis indicated that polycyclic aromatic hydrocarbons (PAHs) in sediments and soil were primarily sourced from vehicular emissions (32 %), while heavy metals (40 %) were mainly from vehicular emissions and agricultural activities. Using a comprehensive pollution assessment framework for water and sediment quality, we found that water quality ranged from "medium" to "excellent", and sediment quality ranged from "good" to "excellent". Among various evaluation indices, COD_Mn, As, F^-, TP, Pb, and Zn were pivotal in determining comprehensive water quality. Key indices for sediment quality evaluation included Flua, BaP, BaA, Pyr, Ant, Pb, and As, primarily sourced from automobile emissions and agricultural activities. Spatial autocorrelation analysis demonstrated a spatial relationship between water quality and sediment quality, covering 43 % of the area. High-pollution areas (13 %) were concentrated around natural river inlets, while low-pollution zones (17 %) were located near ecological water replenishment river inlets. This underscores the significant influence of inflowing water quality on sediment conditions. This study highlights the development of a comprehensive pollution assessment framework to evaluate sediment and soil pollution, as well as to identify high-risk zones of compound pollution in water and sediment. Furthermore, the framework's universal applicability for agricultural lake systems enables the identification of high-risk zones through water-sediment interaction analysis.