Spatial–temporal variations and multi-statistical analysis of contaminants in the waters affected by acid-mining drainage in Karst area: a case of coal-mining area in Zhijin County

• Published in: Environmental earth sciences 2022-05, Vol.81 (10), Article 289
• Main Authors: Qin, Shichan, Li, Xuexian, Wu, Pan, Li, Qingguang
• Format: Article
• Language: English
• Subjects: Biogeosciences; Coal; Coal mining; Coefficients; Contaminants; Correlation coefficient; Correlation coefficients; Data structures; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Geochemistry; Geology; Groundwater; Hydrology/Water Resources; Identification; Karst; Metal concentrations; Metals; Original Article; Principal components analysis; Seasonal variation; Seasonal variations; Spatial analysis; Statistical analysis; Statistical methods; Statistics; Surface water; Surface-groundwater relations; Temporal variations; Terrestrial Pollution; Water analysis; Water flow; Water quality; Water quality assessments; Water sampling
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-022-10368-y

Due to the adverse effects of coal-mining activities in Karst areas, it is necessary to monitor the contaminants, analyze their fate in the transport and identify the sources. Therefore, concentrations of contaminants were measured in both groundwater and surface water samples collected from coal-mining area in Karst, located in Zhijin County, Guizhou Province (China). The analysis of spatial–temporal variations of contaminants indicates that (1) the concentrations of metal elements (except Ba) and SO 4 2− tend to decrease along the water flow direction, while the concentrations of Ba and nitrate still show an upward trend and (2) the concentrations of metal elements and SO 4 2− have seasonal variations. Furthermore, the results of multi-statistical analysis which combine Pearson correlation coefficients (PCC) with the principal component analysis (PCA), identify five factors for the data structure of contaminants. What is more, the assessment of the water quality was evaluated by Nemerow index method to better understand the status. The results indicate that water quality in the area was poor, while 52.6% of water samples were identified into Grade IV and 16.7% were identified into Grade V by the Nemerow index method.