Multivariate Statistical Analysis for Assessment of Ground Water Quality in Hussainsagar Catchment Area, Hyderabad, India

Abstract

Groundwater quality mapping is often a complex problem due to uneven distribution of monitoring stations, variation in sampling frequency, hydrology, water quality, soil chemistry, etc. Numerical modeling is commonly used for mapping groundwater quality. As an alternative, in the present study, multivariate statistical approach is used to interpret ground water quality of Hussainsagar catchment area of Hyderabad, India. Ground water quality data was collected from Telangana State Ground Water Department which consists of analytical results from 2010 to 2016. Fourteen quality parameters are considered for 25 sampling sites for both pre monsoon and post monsoon period separately. The dataset was analysed using Cluster Analysis (CA) and Principle Component Analysis (PCA). CA formed three groups of similarity among the sampling sites representing the different physicochemical characteristics of the study area. Results of PCA showed that, four factors were responsible for the data structure explaining 78% of the total variance of the dataset in pre monsoon period and are identified as soil leaching, industrial, natural and anthropogenic Pollution. Similarly, principle components were identified as responsible for the data structure explaining 80% of the total variance in the post monsoon period and are described as soil leaching, industrial, natural pollution and weathering.