Department of Geology and Mining, University of Rajshahi, Rajshahi, Bangladesh.
Asia Arsenic Network (AAN), Jashore, Bangladesh.
Environ Sci Pollut Res Int. 2024 Jul;31(33):46023-46037. doi: 10.1007/s11356-024-34148-2. Epub 2024 Jul 9.
Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO), nitrate (NO), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO), phosphate (PO), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.
孟加拉国西北部，特别是在乔普纳格甘杰地区，地下水已受到砷的污染。本研究利用机器学习技术记录了砷浓度的地理分布。本研究旨在通过准确识别地下水砷的发生情况来提高模型预测的准确性，从而采取有效的缓解措施并取得更有益的结果。砷富集的铁氧化物/氢氧化物的还原溶解被确定为将砷从沉积物释放到地下水中的主要机制。研究表明，在所研究的区域中，除了砷浓度升高外，还存在大量的钠（Na）、铁（Fe）、锰（Mn）和钙（Ca）。统计分析用于特征选择，确定与砷迁移密切相关的 pH 值、电导率（EC）、硫酸盐（SO）、硝酸盐（NO）、Fe、Mn、Na、K、Ca、Mg、碳酸氢盐（HCO）、磷酸盐（PO）和 As。随后，使用了各种机器学习模型，包括朴素贝叶斯、随机森林、支持向量机、决策树和逻辑回归。这些模型利用归一化的砷浓度分为高浓度（HC）或低浓度（LC），以及理化性质作为特征，来预测砷的发生。在所有机器学习模型中，逻辑回归和支持向量机模型基于准确性和混淆矩阵分析表现出较高的性能。在本研究中，生成了一个空间分布预测图，以识别砷易发生的区域。预测图还显示，乔普纳格甘杰市的 Baroghoria 联盟和 Rajarampur 地区是高风险地区，而 Maharajpur 联盟和 Baliadanga 联盟是研究区域的低风险地区。该地图将有助于研究人员和立法者实施缓解策略。逻辑回归（LR）和支持向量机（SVM）模型将用于连续监测砷浓度值。
