Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

• Published in: Applied water science Vol. 8; no. 1; pp. 1 - 15
• Main Authors: Gulgundi, Mohammad Shahid, Shetty, Amba
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V
• Subjects: Anthropogenic factors; Aquatic Pollution; Aquifers; Cluster analysis; Comparative Law; Discriminant analysis; Drinking water; Earth and Environmental Science; Earth Sciences; Groundwater; Groundwater data; Groundwater quality; Hydrogeology; Identification; Industrial and Production Engineering; Interactions; International & Foreign Law; Mathematical analysis; Minerals; Monsoons; Nanotechnology; Original Article; Parameter identification; Parameters; Principal components analysis; Private International Law; Quality assessment; Quality control; Seasons; Spatial distribution; Spatial variations; Statistical analysis; Statistical methods; Temporal variations; Variance analysis; Variation; Waste Water Technology; Water Industry/Water Technologies; Water Management; Water pollution; Water Pollution Control; Water quality; Wind; Bengaluru India; India; Cluster analysis; Principal component analysis/factor analysis; Discriminant analysis; Groundwater quality; Multivariate statistical techniques
• ISSN: 2190-5487; 2190-5495; 2190-5495
• DOI: 10.1007/s13201-018-0684-z

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.