Dissolved Heavy Metal Pollution and Assessment of a Karst Basin around a Mine, Southwest China

Risk source identification and diffusion trends of metal(loid)s in stream sediments from an abandoned arsenic-containing mine

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.

Impacts of mining activities on the spatial distribution and source apportionment of soil organic matter in a karst farmland

Worldwide mining activities produce vast quantities of mine tailings, which pose a threat to soil quality, crop yields, and the regional environment in the adjacent agricultural soil, but little is known about the impact of mining activities on the SOM source and migration. In this study, soil samples of the topsoil (0-15 cm) and soil profiles (0-50 cm), as well as the potential sources samples (C3 plants, C4 plants and mining tailings) were collected from mine-contaminated karst farmland of four different pollution levels (NP, non-polluted; SP, slightly polluted; MP, moderately polluted; and HP, heavily polluted). Total organic carbon (TOC), total organic nitrogen (TON), and stable isotopic compositions (δ¹³C_org and δ¹⁵N_org) of soil and potential sources samples were determined. In the topsoil, the concentrations of TOC (1.9 ± 1.4 %) and TON (0.1 ± 0.1 %), and the value of δ¹³C_org (-25.4 ± 0.9 ‰) and δ¹⁵N_org (-3.6 ± 3.6 ‰), were not significantly different among HP, MP and SP farmland (P > 0.05). C3 plants (42.1 %-49.9 %) and mine tailings (32.3 %-40.1 %) were identified as the dominant source of topsoil SOM. In the soil profile, TOC%, TON%, δ¹³C_org, and δ¹⁵N_org were affected by soil depth and pollution level. TOC% and TON% in the soil profiles of NP changed slightly with soil depth, while that in the other soil profiles was decreased with the increasing of soil depth. The δ¹⁵N_org value in the SP soil dropped sharply when the soil depth was >15 cm, while that in the HP and MP soil was fluctuated and no obvious vertical pattern. Our findings provide valuable information regarding the impact of mining activities on SOM distribution and source apportionment in karst farmlands. The effects of mine tailings on SOM should be considered when the soil quality was estimated in the mine-grain composite area.

Pollution Risk Prediction for Cadmium in Soil from an Abandoned Mine Based on Random Forest Model

It is highly uncertain as to the potential risk of toxic metal(loid)s in abandoned mine soil. In this study, random forest was used to predict the risk of cadmium pollution in the soils of an abandoned lead/zinc mine. The results showed that the random forest model is stable and precise for the pollution risk prediction of toxic metal(loid)s. The mean of Cd, Cu, Tl, Zn, and Pb was 6.02, 1.30, 1.18, 2.03, and 2.08 times higher than the soil background values of China, respectively, and their coefficients of variation were above 30%. As a case study, cadmium in the mine soil had "slope" hazard characteristics while the ore sorting area was the major source area of cadmium. The theoretical values of the random forest model are similar to the practical values for the ore sorting area, metallogenic belt, riparian zone, smelting area, hazardous waste landfill, and mining area. The potential risk of soil Cd in the ore sorting area, metallogenic belt, and riparian zone are extremely high. The tendency of pollution risk migrates significantly both from the ore sorting area to the smelting area and the mining area, and to the hazardous waste landfill. The correlation of soil pollution risk is significant between the mining area, the smelting area, and the riparian zone. The results suggested that the random forest model can effectively evaluate and predict the potential risk of the spatial heterogeneity of toxic metal(loid)s in abandoned mine soils.