Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

Factor analysis is applied to 28 groundwater samples collected from wells in the coastal blackfoot disease area of Yun-Lin, Taiwan. Correlations among 13 hydrochemical parameters are statistically examined. A two-factor model is suggested and explains over 77.8% of the total groundwater quality variation. Factor 1 (seawater salinization) includes concentrations of EC, TDS, Cl(-), SO(4)(2-), Na(+), K(+) and Mg(2+), and Factor 2 (arsenic pollutant) includes concentrations of Alk, TOC and arsenic. Maps are drawn to show the geographical distribution of the factors. These maps delineate high salinity and arsenic concentrations. The geographical distribution of the factor scores at individual wells does not reveal the sources of the constituents, which are instead, deduced from geological and hydrological evidence. The areas of high seawater salinization and arsenic pollution correspond well to the groundwater over-pumping area. Over-pumping of the local groundwater causes land subsidence and gradual salinization by seawater. The over-pumping also introduces excess dissolved oxygen that oxidizes the immobile minerals, releases arsenic by reductive dissolution of arsenic-rich iron oxyhydroxides and increases the arsenic concentration in water. The over-extraction of groundwater is the major cause of groundwater salinization and arsenic pollution in the coastal area of Yun-Lin, Taiwan.

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.