Groundwater and hot-spring interactions around Bakreswar geothermal spring

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中文题名Bakreswar地热泉周围地下水与温泉的相互作用
作者Susmita Goswami
作者单位Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, 721302, WB, India
刊名Sustainable Water Resources Management
2024
10
摘要
This study aims to evaluate the groundwater quality and geochemical vulnerability using an integrated approach of multi-criteria decision-making (MCDM), statistical, and GIS methods in Birbhum district, West Bengal. About 55% of the study area shows excellent-to-good water quality, suitable for irrigation and drinking purposes. Nearly 45% of the area shows moderate-to-very poor water quality which is unsuitable for domestic use. The US Salinity Diagram indicates that ~ 82% of groundwater samples fall in low salinity and low sodium hazard categories, whereas thermal water samples fall into medium salinity and medium sodium hazard zones. The freshwater types, i.e., Ca–HCO3 (~ 24.14%), and mixed water types such as Ca–Na–HCO3 (~  55.17 % ) and Ca–Mg–Cl (~  12.07 % ) are the most dominant groundwater types in the region. The thermal water samples were mostly of Na–Cl–HCO3 water types. Factor analysis reveals that approximately 52.092% of ions, attributed to Factor 1, primarily originate from natural sources. Factor 2 (13.785%) and Factor 3 (9.153%) are predominantly derived from anthropogenic sources. The ions, such as Ca2+, Mg2+, K+, Na+, HCO3−, SO42−, and Cl−, are mostly derived from natural sources such as water–rock interaction. Furthermore, Na+, K+, NO3−, SO42−, and Cl− may also be derived from anthropogenic sources such as infiltration of domestic wastewater, irrigation return flow, inorganic fertilizer, effluent from septic tanks, discharge of domestic trash, and industrial wastewater near the industrial area, etc. About 25% of the study area appears to be under high-to-very high geochemical vulnerability. The geochemical vulnerability model was cross-validated by the Receiver-Operating Characteristic (ROC) curve, which indicates a prediction accuracy of 0.73%.

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