Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches

Abstract

Deterioration of groundwater quality is a long-term incident which leads unending vulnerability of groundwater. The present work was carried out in Murshidabad District, West Bengal, India to assess groundwater vulnerability due to elevated arsenic (As) and other heavy metal contamination in this area. The geographic distribution of arsenic and other heavy metals including physicochemical parameters of groundwater (in both pre-monsoon and post-monsoon season) and different physical factors were performed. GIS-machine learning model such as support vector machine (SVM), random forest (RF) and support vector regression (SVR) were used for this study. Results revealed that, the concentration of groundwater arsenic compasses from 0.093 to 0.448 mg/L in pre-monsoon and 0.078 to 0.539 mg/L in post-monsoon throughout the district; which indicate that all water samples of the Murshidabad District exceed the WHO's permissible limit (0.01 mg/L). The GIS-machine learning model outcomes states the values of area under the curve (AUC) of SVR, RF and SVM are 0.923, 0.901 and 0.897 (training datasets) and 0.910, 0.899 and 0.891 (validation datasets), respectively. Hence, "support vector regression" model is best fitted to predict the arsenic vulnerable zones of Murshidabad District. Then again, groundwater flow paths and arsenic transport was assessed by three dimensions underlying transport model (MODPATH). The particles discharging trends clearly revealed that the Holocene age aquifers are major contributor of As than Pleistocene age aquifers and this may be the main cause of As vulnerability of both northeast and southwest parts of Murshidabad District. Therefore, special attention should be paid on the predicted vulnerable areas for the safeguard of the public health. Moreover, this study can help to make a proper framework towards sustainable groundwater management.

Keywords: Arsenic transport models; Groundwater arsenic vulnerability; Machine learning models; Physical factors; Spatiotemporal distribution.