Water quality assessment of the Jinshui River (China) using multivariate statistical techniques

Multivariate statistical techniques have been widely utilized to assess water quality and evaluate aquatic ecosystem health. In this study, cluster analysis, discriminant analysis, and factor analysis techniques are applied to analyze the physical and chemical variables in order to evaluate water quality of the Jinshui River, a water source area for an interbasin water transfer project of China. Cluster analysis classifies 12 sampling sites with 22 variables into three clusters reflecting the geo-setting and different pollution levels. Discriminant analysis confirms the three clusters with nine discriminant variables including water temperature, total dissolved solids, dissolved oxygen, pH, ammoniacal nitrogen, nitrate nitrogen, turbidity, bicarbonate, and potassium. Factor analysis extracts five varifactors explaining 90.01% of the total variance and representing chemical component, oxide-related process, natural weathering and decomposition processes, nutrient process, and physical processes, respectively. The study demonstrates the capacity of multivariate statistical techniques for water quality assessment and pollution factors/sources identification for sustainable watershed management.