Groundwater is an important freshwater resource in the world and serves as the main source of water for mining areas in Northern China. Coal mining may cause changes in water quality. As such, to identify ways to prevent water contamination, this study investigates the hydrogeochemical processes and transport paths of a complex aquifer system in the Sunan mining area in Northern China. Using the APFS-MLR model, a geographic information system (GIS) spatial analysis, and a hydrochemical correlation analysis method, this study identifies the potential mineral phases in groundwater, the spatial distribution of mineral reactions, and the contribution rate of these reactions to hydrochemical variables. Inverse modeling is used to verify hydrogeochemical process. The study reveals the relationship between multiple aquifers and four hydrological transport paths. Here, Path 1 and Path 2 show that the Quaternary aquifer, Carboniferous aquifer, and Ordovician aquifer are recharging the Permian aquifer through mineral dissolution and precipitation, cation exchange, and sulfate reduction. On the other hand, Path 3 and Path 4 show that tthe connections of Carboniferous and Ordovician limestone aquifers are dominated by the dissolution and precipitation of minerals and cation exchange, and that they are mainly recharged by the Quaternary aquifer. In the future, the water level of the Permian aquifer may rise somewhat after mining ends, and the mixing of water from the Permian aquifer, Quaternary aquifer, Carboniferous aquifer, and Ordovician aquifer could cause cross-pollution. In addition, sewage produced by human activities may recharge the deep water through the shallow water, polluting the deep karst water. As such, measures should be taken to reduce the hydraulic connection between Permian mine water and karst aquifers. The results of this study may benefit water quality predictions and treatment approaches in other complex multi-layer aquifer areas in the world.
Keywords: APFS-MLR model; hydrogeochemical; mining area; multi-layer groundwater; multivariate statistics; reverse model.