Abstract
Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.
Highlights
Groundwater is a critical freshwater resource for billions of habitants around the world
This paper attempts to evaluate the potability of groundwater by the integrated use of WQIs, geostatistics and chemometric data analysis techniques
The study reveals that GWQI for the region varies from 21 to 278 with a mean value of 130, whereas the entropy weighted water quality index (EWQI) lies in the range of 104 to 276 with a mean value of 175
Summary
Groundwater is a critical freshwater resource for billions of habitants around the world. In light of global changes including meteoric growth of population, unplanned urbanization, industrialization, redundant use of agricultural chemicals and climate change, the groundwater extraction has steadily increased from 312 km3/year in the 1960s to 743 km3/year in 2000 (Wada et al 2010; Joarder et al 2008). During this time frame, the rate of global groundwater loss has increased mostly due to rises in India (23%), China (102%) and USA (31%) (Dalin et al 2017). A plethora of studies have assessed the groundwater quality by considering various state-of-the-art techniques for different regions across the world. The technique helps interpretation of complex water quality data into simple terms (Sadat-Noori et al 2014)
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