Stream sediments from mine area are a converging source of water and soil pollution. The risk and development trends of metal(loid)s pollution in sediments from an abandoned arsenic-containing mine were studied using modelling techniques. The results showed that the combined techniques of geographic information system (GIS), random forest (RF), and numerical simulation (NS) could identify risk sources and diffusion trends of metal(loid)s in mine sediments. The median values of As, Cd, Hg, and Sb in sediments were 5.01, 3.02, 5.67, and 3.20 times of the background values of stream sediments in China, respectively. As (14.09%) and Hg (18.64%) pollution in mine stream sediments were severe while As is the main potential risk source with a strong spatial correlation. High-risk blocks were concentrated in the landfill area, with the surrounding pollution shows a decreasing trend of "step-type" pollution. The risk correlation between Hg and As (55.37%) in the landfill area is high. As a case of arsenic, the diffusion capacity of As within 500m is strong and stabilizes at 1 km when driven by the flows of 0.05, 0.5, and 5 m3/s, respectively. With the worst-case scenario flow (86 m3/s), it would take only 147 days for the waters within 3 km to become highly polluted. The high pollution levels in a stream under forecast of different distance intervals (500, 1500, 2000 m) within 6.5 km is arrived at approximate 344, 357, and 384 days, respectively. The study suggested the combined technique of GIS, RF, and NS can serve the risk source identification of contaminated sites and risk forecast of toxic element diffusion in emergency situations.
Keywords: Arsenic mine; Diffusion forecast; Modeling techniques; Source identification; Stream sediment.
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