Geochemical analysis is a useful tool in hydrogeological assessment, particularly in constructing a conceptual model of a hydrogeological system. In this study, major ion concentrations of 53 groundwater samples from the coal-bearing aquifer in the Qidong coal mine, northern Anhui Province of China have been processed by statistical analysis for understanding either hydro-chemical characteristics or hydrological evolution, which will be useful for the safety of coal mining. The results suggest that most of the samples are Na–SO₄ and Na–HCO₃ types, and their hydro-chemical compositions are mainly controlled by dissolution of more soluble minerals (e.g. calcite) and weathering of silicate minerals (e.g. plagioclase). Two groups of samples have been subdivided by quantile and scatter plots of factor scores, one is related to different degrees of water–rock interactions and another is related to groundwater mixing. Moreover, four end members have been identified and the mixing calculation suggests that the groundwater samples affected by mixing have 20–100 % contribution from the loose layer aquifer (LA), and therefore, groundwater from the LA in the coal mine should be taken seriously during coal mining. The study demonstrated that statistical analysis is useful for connecting the hydrochemistry of groundwater with hydrological evolution of the aquifer.

Water source identification is important for water hazard controlling in coal mines. In this study, major ion concentrations of the groundwater collected from four representative aquifer systems in the Baishan coal mine, northern Anhui Province, China, have been analysed by a series of statistical methods. The results indicate that the major ion concentrations of the groundwater from different aquifer system are different with each other, and provided the possibility of water source identification based on hydrochemistry. Factor analysis indicates that these differences are controlled by different types of water rock interactions. The analysis based on US Environmental Protection Agency (EPA) Unmix model identified three sources (weathering of silicate minerals, dissolution of carbonate and evaporate minerals) responsible for the hydrochemical variations of the groundwater. Also, it shows that their contributions for the groundwater in different aquifer systems vary considerably. Based on these variations and on step by step analysis, the source aquifer system for the groundwater samples with unknown source has been determined and, similar to the result obtained by the cluster and discriminant analysis. Therefore, EPA Unmix model can be applied for water source identification in coal mine, as it can provide information about water rock interaction and water source identification simultaneously.

Hydrochemistry of groundwater is important in coal mines because it can be used for understanding water–rock interaction and inrush water source identification. In this study, major ion concentrations of groundwater samples from the loose layer aquifer (LA), coal-bearing aquifer (CA) and Taiyuan Formation limestone aquifer (TA) in the Qingdong coal mine, northern Anhui province, China, have been analyzed by a series of statistical methods for identifying the source of chemical constituents in groundwater and the source of inrush water. The results indicate that the mean concentration of the major ions in the LA were ordered as follows: HCO₃⁻ > SO₄²⁻ > Na⁺ + K⁺ > Cl⁻ > Ca²⁺ > Mg²⁺ > CO₃²⁻, whereas average values of the CA in decreasing order are SO₄²⁻, Na⁺ + K⁺, HCO₃⁻, Cl⁻, Ca²⁺, Mg²⁺ and CO₃²⁻, and the major ion concentrations of the TA have the following order: SO₄²⁻ > Na⁺ + K⁺ > Ca²⁺ > HCO₃⁻ > Cl⁻ > Mg²⁺ > CO₃²⁻, and most of the samples are Na-SO₄ and Ca-SO₄ types. TDS content in water increases with aquifer depth, whereas the pH values ranged from 7.1 to 8.9, indicating a weak alkaline environment. Two sources (weathering of silicate minerals and dissolution of evaporate minerals) have been identified by principal component analysis responsible for the chemical variations of the groundwater, and their contribution ratios have been quantified by Unmix model. Moreover, based on the Q-mode cluster and discriminant analyses, the samples with known sources have been identified correctly to be 95.7% and 97.6%, respectively, and the samples with unknown sources have been determined with high probability (78–100%).