Environmental Chemistry Laboratory, Department of Environmental Science, The University of Burdwan, India.
Department of Geography, The University of Burdwan, India.
J Contam Hydrol. 2023 May;256:104195. doi: 10.1016/j.jconhyd.2023.104195. Epub 2023 May 3.
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.
地下水质量恶化是一个长期事件，导致地下水不断脆弱。本研究在印度西孟加拉邦默尔希达巴德区进行，旨在评估由于该地区砷（As）和其他重金属污染升高而导致的地下水脆弱性。进行了砷和其他重金属的地理分布以及包括地下水理化参数（在季风前和季风后季节）和不同物理因素在内的不同物理因素的研究。本研究使用了 GIS-机器学习模型，如支持向量机（SVM）、随机森林（RF）和支持向量回归（SVR）。结果表明，默尔希达巴德区的地下水砷浓度在季风前为 0.093 至 0.448mg/L，季风后为 0.078 至 0.539mg/L；这表明默尔希达巴德区的所有水样均超过世界卫生组织（WHO）的允许限值（0.01mg/L）。GIS-机器学习模型的结果表明，SVR、RF 和 SVM 的曲线下面积（AUC）值在训练数据集和验证数据集上分别为 0.923、0.901 和 0.897（0.910、0.899 和 0.891）。因此，“支持向量回归”模型最适合预测默尔希达巴德区的砷脆弱区。然后，通过三维地下水流路径和砷运移模型（MODPATH）评估地下水流动路径和砷运移。排放趋势的粒子清楚地表明，全新世含水层是 As 的主要贡献者，而更新世含水层则不是，这可能是默尔希达巴德区东北部和西南部地区 As 脆弱性的主要原因。因此，应特别关注预测的脆弱区域，以保障公众健康。此外，本研究可以为可持续地下水管理提供适当的框架。
