The combined effect of pollution source discharge and sediment adsorption leads to the rapid enrichment of heavy metals and other pollutants in lake sediments, which poses a serious threat to the lake ecosystem. Accurately identifying the risk areas of heavy metals in sediments is the key to lake sediment pollution control. Taking Taihu Lake as the study area, combined with the ecological risk status of heavy metals in sediments, the spatial clustering characteristics of pollution sources and the clustering information of sediment attributes, a potential toxic risk area identification method based on sediment source aggregation class (SLISA-SCA) was established. Through the source analysis of heavy metals in sediments, heavy metals such as Cr, Mn, Cu and Zn in Taihu Lake sediments were identified to have originated from natural sources and were subsequently disturbed by human activities to a certain extent. Cd was found to be strongly affected by human activities, and almost all Taihu Lake sediments were affected to varying degrees. In addition, the anthropogenic sources of heavy metals show high concentration clustering characteristics in the lake bay. By K-means cluster analysis of sediment attributes, three significant differences were obtained, which were determined as potential high pollution risk areas, potential medium risk areas and potential low risk areas, and the proportions were 5.6%, 27.6% and 66.8%, respectively. The SLISA-SCA model established in this study, from the perspective of source sinks, comprehensively considers the risks caused by pollution sources and sediment attributes to sediments and divides Taihu Lake into five different risk control areas (high-risk control area, potential high-risk control area, potential risk control area, potential low-risk control area and low-risk control area). This study identified areas with different levels of heavy metal pollution in Taihu Lake sediments, proposes corresponding treatment measures, and provides a scientific and systematic method and technology for the pollution management of other river and lake sediments in the world.
Keywords: Heavy metals; Risk identification; Sediment; Source‒sink; Spatial clustering.
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