Groundwater is a vital source of freshwater, but its quality is often compromised by various physicochemical factors. Despite the importance of groundwater quality, there is a gap in understanding the spatial distribution of physicochemical factors controlling groundwater quality, particularly in regions with limited data. This study bridges the gap in groundwater quality assessment by applying advanced statistical and spatial analysis techniques. The research aims to assess groundwater quality in a data-limited region, identify physicochemical factors controlling quality, and analyze physicochemical parameters of samples, examining correlations and chemical characteristics. To achieve these objective groundwater samples were collected from 103 different sampling locations and their physiochemical parameters such as hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), nitrate, sulfate, phosphate, chloride, fluoride, total hardness (TH), calcium, magnesium, and bicarbonate were tested and analyzed. Correlation coefficient analysis revealed a strong correlation between TDS, chloride, and sulfate. Hierarchical cluster analysis (HCA) divided the collected samples into three clusters based on similarities in chemical characteristics related to groundwater quality. HCA showed groundwater properties similar to groups I, II, and III (less, moderate, and severely mineralized, respectively). In the spatial analysis, the physio-chemical variables were interpolated by the application of Empirical Bayesian Kriging (EBK). In this interpolation, the lowest root mean square error (RMSE) values for TDS (1.03) and EC (0.98) were calculated using the exponential model. Using the K-Bessel model of the EBK interpolation approach, the lowest RMSE values were determined for pH, HCO3−, F−, Cl−, Mg2+, TH, SO42−, PO43−, and DO. The Whittle model of the EBK interpolation determined the lowest RMSE for NO3− (0.98) and Ca2+ (1.22). Water quality index (WQI) value ranges from 31.77 to 131.76 in the study area. The spatial distribution of the WQI values showed that the southeastern, northern, and central regions of the research area have unsuitable groundwater quality. In 8% samples of the study area, the water is not suitable for drinking before proper treatment. These findings have significant implications for the government and society, highlighting the need to take notice of the possible health dangers to the people living in the study region.

Assessment of groundwater contamination in Aurangabad, Bihar using WQI and geostatistical modeling

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发表日期：

2025年1月19日

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Arun Prasun,
Anshuman Singh

摘要

Groundwater is a vital source of freshwater, but its quality is often compromised by various physicochemical factors. Despite the importance of groundwater quality, there is a gap in understanding the spatial distribution of physicochemical factors controlling groundwater quality, particularly in regions with limited data. This study bridges the gap in groundwater quality assessment by applying advanced statistical and spatial analysis techniques. The research aims to assess groundwater quality in a data-limited region, identify physicochemical factors controlling quality, and analyze physicochemical parameters of samples, examining correlations and chemical characteristics. To achieve these objective groundwater samples were collected from 103 different sampling locations and their physiochemical parameters such as hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), nitrate, sulfate, phosphate, chloride, fluoride, total hardness (TH), calcium, magnesium, and bicarbonate were tested and analyzed. Correlation coefficient analysis revealed a strong correlation between TDS, chloride, and sulfate. Hierarchical cluster analysis (HCA) divided the collected samples into three clusters based on similarities in chemical characteristics related to groundwater quality. HCA showed groundwater properties similar to groups I, II, and III (less, moderate, and severely mineralized, respectively). In the spatial analysis, the physio-chemical variables were interpolated by the application of Empirical Bayesian Kriging (EBK). In this interpolation, the lowest root mean square error (RMSE) values for TDS (1.03) and EC (0.98) were calculated using the exponential model. Using the K-Bessel model of the EBK interpolation approach, the lowest RMSE values were determined for pH, HCO₃⁻, F⁻, Cl⁻, Mg²⁺, TH, SO₄²⁻, PO₄³⁻, and DO. The Whittle model of the EBK interpolation determined the lowest RMSE for NO₃⁻ (0.98) and Ca²⁺ (1.22). Water quality index (WQI) value ranges from 31.77 to 131.76 in the study area. The spatial distribution of the WQI values showed that the southeastern, northern, and central regions of the research area have unsuitable groundwater quality. In 8% samples of the study area, the water is not suitable for drinking before proper treatment. These findings have significant implications for the government and society, highlighting the need to take notice of the possible health dangers to the people living in the study region.