Assessment of heavy metal pollution in water using multivariate statistical techniques in an industrial area: a case study from Patancheru, Medak District, Andhra Pradesh, India.

Application of different multivariate statistical approaches for the interpretation of data obtained during a monitoring programme of surface and groundwater in Patancheru industrial town near Hyderabad (India) is presented in this study. A number of chemical and pharmaceutical industries have been established since past three decades. Effluents from these industries are reportedly being directly discharged onto surrounding land, irrigation fields and surface water bodies forming point and non-point sources of contamination for groundwater in the study area. Thirteen parameters including trace elements (B, Cr, Mn, Fe, Co, Ni, Zn, As, Sr, Ba and Pb) have been monitored on 53 sampling points from a hydrogeochemical survey conducted in surface and groundwater. Data set thus obtained was treated using R-mode factor analysis (FA) and principal component analysis (PCA). FA identified four factors responsible for data structure explaining 75% of total variance in surface water and two factors in groundwater explaining 85%, and allowed to group selected parameters according to common features. Sr, Ba, Co, Ni and Cr were associated and controlled by mixed origin with similar contribution from anthropogenic and geogenic sources whereas Fe, Mn, As, Pb, Zn, B and Co were derived from anthropogenic activities. This study indicates the necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of the data with a view to get better information about the water quality and design some remedial techniques to prevent the pollution caused by hazardous toxic elements in future.