Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

Mohammad Shahid Gulgundi,Amba Shetty

doi:10.1007/s13201-018-0684-z

Abstract

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Highlights

The dependence on groundwater has gone up over the years in most of the urban areas due to inadequacy of surface water resources to meet the water requirements
Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA) can interpret complex data matrices for improved understanding of water quality and other environmental systems by allowing the identification of possible factors/sources serving as a worthy tool for quickly solving pollution problems (Vega et al 1998; Lee et al 2001; Wunderlin et al 2001; Reghunath et al 2002; Simeonov et al 2003, 2004; Ravikumar and Somashekar 2017)
Principal component analysis (PCA) has been utilized to take out the noise from huge data matrix and classify the variables into measurable components, discriminant analysis (DA) recognizes the most segregating measurable element/variable according to goodness and cluster analysis (CA) chooses the identical group inside a specific data set

Summary

Introduction

The dependence on groundwater has gone up over the years in most of the urban areas due to inadequacy of surface water resources to meet the water requirements. Multivariate statistical techniques can be employed for analyzing huge water quality datasets with minimal loss of important information (Juahir et al 2011; Samson and Elangovan 2017; Shrestha and Kazama 2007) Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA) can interpret complex data matrices for improved understanding of water quality and other environmental systems by allowing the identification of possible factors/sources serving as a worthy tool for quickly solving pollution problems (Vega et al 1998; Lee et al 2001; Wunderlin et al 2001; Reghunath et al 2002; Simeonov et al 2003, 2004; Ravikumar and Somashekar 2017). Characterization and evaluation of surface and freshwater quality performed by multivariate statistical techniques has proved to be useful in verifying spatial and temporal variations caused naturally and due to human induced factors (Helena et al 2000; Singh et al 2004, 2005; Hassen et al 2016)

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Results