Groundwater quality assessment using SPSS based on multivariate statistics and water quality index of Gaya, Bihar (India).

Abstract

Groundwater is a valuable resource for developmental activities, and its demand is growing as surface water becoming scarce. Groundwater demand is increasing, resulting in reduction in water level and deterioration in water quality. A total of 156 groundwater samples were taken from Gaya, a district in Bihar (India), to check the safety of drinking water. The quality of groundwater was assessed using a water quality index (WQI). Analysed samples were assessed using a variety of physicochemical characteristics, and statistical methods principal component analysis (PCA) and cluster analysis (CA) were used as they areeffective and efficient. As per the Gibbs, plot majority of the sample falls in the rock-water interaction and some evaporation dominance field. The domination of major cation in the order of Ca2+ > Mg2+ > Na+ and the major anions followed the order of HCO3- > [Formula: see text]>[Formula: see text]>[Formula: see text]>[Formula: see text]. The KMO's sample adequacy value of 0.703 and the significance level of Bartlett's test of sphericity (0.0001) were indicating that PCA may be implemented. Using the PCA, the three components recovered explained 69.58% of the total variation. Cluster analysis classified the groundwater sample into three cluster based on the similarities among chemical parameters involved in groundwater quality. HCA exhibit less, intermediate, and heavily mineralized groundwater characteristics of groups I, II, and III, respectively. The major parameters affecting the water quality in the study region are TDS, Ca2+, Mg2+, HCO3-, [Formula: see text]. WQI indicates 17% of the sample were found to be of very poor quality and not consumable. The study's findings offer insights and understanding into groundwater pollution regimes. These results used for water quality assessment leading to improved environmental management and planning and in decision-making for water quality management.