https://www.selleckchem.com/products/kenpaullone.html From the perspective of the mechanism of soil pollution, it is difficult to explain the process of predicting the spatial distributions of soil heavy metal pollution using traditional geostatistical methods at a regional scale. Furthermore, few methods are available to proactively identify potential risk areas for preventing soil contamination. In this study, we selected 13 environmental factors related to the accumulation of soil heavy metals based on the source-sink theory. Then, the fuzzy k-means method in combination with the random forest (RF) method was used to classify potential risk areas. The concentrations and spatial distributions of the heavy metals were well predicted by RF, and the average values of the root mean square error of the prediction and R2 were 4.84 mg kg-1 and 0.57, respectively. The results indicated that the soil pH, fine particulate matter, and proximity to polluting enterprises significantly influenced the heavy metal pollution in soils, and the environmental variables varied significantly across the identified subregions. This study provides a theoretical basis for the sustainable management and control of soil pollution at the regional scale. In this study, six antibiotic resistance genes (ARGs), one mobile genetic element (int1), and their relation with microbial communities, antibiotics, and water quality were investigated in and around of an agriculturally disturbed lake, namely, Lake Honghu. The ARGs and int1 in the research area had a 100 % detection frequency in each sample during two sampling times. The ARGs were higher in the rivers and inlets than in Lake Honghu. Sul1 was the main ARG in this area. Antibiotics, nutrients, and dissolved oxygen were significantly, positively, and negatively correlated with nearly all of the ARGs, respectively. This finding suggests that reducing antibiotics and the eutrophication level could reduce the risk of ARGs. Microbial community shift