Multivariate statistical approach for the assessment of groundwater quality in Ujjain City, India

Groundwater quality assessment is an essential study which plays important role in the rational development and utilization of groundwater. Groundwater quality greatly influences the health of local people. The variations of water quality are essentially the combination of both anthropogenic and natural contributions. In order to understand the underlying physical and chemical processes this study analyzes 8 chemical and physical-chemical water quality parameters, viz. pH, turbidity, electrical conductivity, total dissolved solids, total alkalinity, total hardness, chloride and fluoride recorded at the 54 sampling stations during summer season of 2011 by using multivariate statistical techniques. Hierarchical clustering analysis (CA) is first applied to distinguish groundwater quality patterns among the stations, followed by the use of principle component analysis (PCA) and factor analysis (FA) to extract and recognize the major underlying factors contributing to the variations among the water quality measures. The first three components were chosen for interpretation of the data, which accounts for 72.502% of the total variance in the data set. The maximum number of variables, i.e. turbidity, EC, TDS and chloride were characterized by first component, while second and third were characterized by total alkalinity, total hardness, fluoride and pH respectively. This shows that hydro chemical constituents of the groundwater are mainly controlled by EC, TDS, and fluoride. The findings of the cluster analysis are presented in the form of dendrogram of the sampling stations (cases) as well as hydro chemical variables, which produced four major groupings, suggest that groundwater monitoring can be consolidated.