Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

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 NO 3 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.

Use of Heavy Metal Content and Modified Water Quality Index to Assess Groundwater Quality in a Semiarid Area

“…Many methods such as multivariate statistical techniques (e.g., cluster analysis, principal component analysis, and factor analysis) [13,14], hydro-geochemical evaluation [15,16], heavy metal indices (e.g., heavy metal pollution index, degree of contamination, heavy metal evaluation index, contamination factor, and health risk assessment) [17][18][19], and water evaluation indices [13] have been developed for assessing water quality considering physicochemical parameters. Grading water quality indicators largely depends on indicator concentration and the rate of relative toxicity.…”

Identification of the Hydrogeochemical Processes and Assessment of Groundwater Quality, Using Multivariate Statistical Approaches and Water Quality Index in a Wastewater Irrigated Region

“…However, it is a challenge to evaluate the complex, highly dimensional groundwater hydrogeochemical datasets. Multivariate statistical approaches, such as principal component analysis (PCA) and K-means clustering are robust tools for groundwater resources management [17][18][19][20][21]. They have been successfully used to define and understand the hydrogeochemical processes that dominate groundwater quality and identify pollution sources [22,23].…”

Ensemble modelling framework for groundwater level prediction in urban areas of India

“…While, deep aquifers of yield ranged from 2 to 8 litter per second are located in parts of Bengaluru north and Anekal taluks. Transmissivity ranged from 10 to 280 m 2 /day(CGWB, 2008;Gulgundi and Shetty, 2018).…”