Molla Mohammad Omar Faruk, Kabir Md Anisul, Hossain Md Kamrul, Jahan Md Saikoth, Khatun Most Suria, Kumar Sazal, Islam Rafiquel
Department of Environmental Science and Geography (ESG), Islamic University, Kushtia, 7003, Bangladesh.
School of Environmental and Life Sciences, The University of Newcastle (UoN), Callaghan, NSW, 2308, Australia.
Environ Geochem Health. 2025 Jul 20;47(8):324. doi: 10.1007/s10653-025-02627-1.
Groundwater is a vital source of drinking water in Bangladesh, with tubewells commonly used, particularly in schools. This study assessed the quality of tubewell water in the southwest region, focusing on iron (Fe), arsenic (As), pH, electrical conductivity (EC), and total dissolved solids (TDS). Using artificial neural network (ANN) modeling, daily safe water intake limits for children were estimated based on Fe and As levels. A total of 75 school-based water samples were collected. Fe and As were measured using a Hanna Iron Checker and Hach Arsenic Test Kit. Findings showed that 68% and 48% of samples exceeded WHO and USEPA limits for Fe and As, respectively. In contrast, only 32% and 26.7% of samples surpassed the Bangladesh Water Quality Standards (BDWS) for Fe and As, respectively. Spatial mapping identified Mahajanpur, Bagoan, and Dariapur as hotspots for contamination. TDS showed a strong positive correlation with conductivity (r = 0.92) and a moderate one with salinity (r = 0.7), indicating their interdependence. As and Fe had a weak positive correlation (r = 0.22). PC1 and PC2 explained 56.74% of variance, with Fe and As weakly correlated in PC1 but negatively loaded in PC2, suggesting distinct behaviours influenced by TDS, conductivity, and salinity. Further, Linear regression showed Fe (r = -0.34) and As (r = -0.71) decreased with depth. High health risks were identified for Fe in Mahajanpur and As in Baguan and Dariapur. ANN and Decision Tree Regression models showed 87% accuracy in estimating safe water intake, highlighting Monakhali Union as the most vulnerable area. To avoid health impacts in children, safe consumption levels in Baguan and Monakhali were notably low at 0.59 L/day and 0.39 L/day, respectively. These findings underscore the urgent need for regular monitoring and limiting tubewell water consumption by schoolchildren in the region.
地下水是孟加拉国重要的饮用水源，管井被广泛使用，尤其是在学校。本研究评估了该国西南部地区管井水的质量，重点关注铁（Fe）、砷（As）、pH值、电导率（EC）和总溶解固体（TDS）。通过人工神经网络（ANN）建模，根据铁和砷的含量估算了儿童每日安全饮水量。共采集了75份学校水样。使用Hanna铁离子检测仪和哈希砷检测试剂盒测定铁和砷的含量。结果表明，分别有68%和48%的样本中铁和砷的含量超过了世界卫生组织（WHO）和美国环境保护局（USEPA）的限值。相比之下，分别只有32%和26.7%的样本超过了孟加拉国水质标准（BDWS）中铁和砷的限值。空间映射确定Mahajanpur、Bagoan和Dariapur为污染热点地区。总溶解固体与电导率呈强正相关（r = 0.92），与盐度呈中度正相关（r = 0.7），表明它们之间相互依存。砷和铁呈弱正相关（r = 0.22）。主成分1（PC1）和主成分2（PC2）解释了56.74%的方差，铁和砷在PC1中弱相关，但在PC2中呈负载荷，表明它们受总溶解固体、电导率和盐度影响的行为不同。此外，线性回归显示铁（r = -0.34）和砷（r = -0.71）随深度降低。在Mahajanpur地区，铁对健康构成高风险；在Baguan和Dariapur地区，砷对健康构成高风险。人工神经网络和决策树回归模型在估算安全饮水量方面的准确率达87%，突出显示Monakhali联盟是最脆弱的地区。为避免对儿童健康产生影响，Baguan和Monakhali地区的安全饮水量极低，分别为每天0.59升和0.39升。这些研究结果强调，该地区迫切需要定期监测并限制学童的管井水消费量。
