Aswal R S, Prasad Mukesh, Singh Jaswinder, Singh Hakam, Shrivastava Utpal, Wadhwa Manoj, Pandey Om Prakash, Egbueri Johnbosco C
Department of Environmental Sciences, H.N.B. Garhwal University, Badshahi Thaul Campus, Tehri Garhwal, 249 199, India.
Department of Medical Physics, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Jolly Grant, Dehradun, 248 016, India.
Sci Rep. 2024 Oct 26;14(1):25473. doi: 10.1038/s41598-024-76147-w.
Swiftly increasing population and industrial developments of urban areas has accelerated the worsening of the water quality in recent years. Groundwater samples from different locations of the Doon valley, Garhwal Himalaya were analyzed to measure concentrations of six potential toxic elements (PTEs) viz. chromium (Cr), nickel (Ni), arsenic (As), molybdenum (Mo), cadmium (Cd), and lead (Pb) using Inductively Coupled Plasma Mass Spectrometer (ICP-MS) with the aim to study the spatial distribution and associated hazards. In addition, machine learning algorithms have been used for prediction of water quality and identification of influencing PTEs. The results inferred that the mean values (in the units of µg L) of analyzed PTEs were observed in the order of Mo (1.066) > Ni (0.744) > Pb (0.337) > As (0.186) > Cr (0.180) > Cd (0.026). The levels and computed risks of PTEs were found below the safe limits. The radial basis function neural network (RBF-NN) algorithms showed high level of accuracy in the predictions of heavy metal pollution index (HPI), heavy metal evaluation index (HEI), non-carcinogenic (N-CR) and carcinogenic (CR) parameters with determination coefficient values ranged from 0.912 to 0.976. However, the modified heavy metal pollution index (m-HPI) and contamination index (CI) predictions showed comparatively lower coefficient values as 0.753 and 0.657, respectively. The multilayer perceptron neural network (MLP-NN) demonstrated fluctuation in precision with determination coefficient between 0.167 and 0.954 for the prediction of computed indices (HPI, HEI, CI, m-HPI). In contrast, the proficiency in forecasting of non-carcinogenic and carcinogenic hazards for both sub-groups showcased coefficient values ranged from 0.887 to 0.995. As compared to each other, the radial basis function (RBF) model indicated closer alignments between predicted and actual values for pollution indices, while multilayer perceptron (MLP) model portrayed greater precision in prediction of health risk indices.
近年来，城市地区人口的迅速增长和工业发展加速了水质的恶化。对来自加瓦尔喜马拉雅山杜恩山谷不同地点的地下水样本进行了分析，以使用电感耦合等离子体质谱仪（ICP-MS）测量六种潜在有毒元素（PTEs），即铬（Cr）、镍（Ni）、砷（As）、钼（Mo）、镉（Cd）和铅（Pb）的浓度，目的是研究其空间分布和相关危害。此外，机器学习算法已被用于水质预测和影响PTEs的识别。结果推断，所分析的PTEs的平均值（以µg/L为单位）按以下顺序排列：钼（1.066）＞镍（0.744）＞铅（0.337）＞砷（0.186）＞铬（0.180）＞镉（0.026）。PTEs的水平和计算出的风险低于安全限值。径向基函数神经网络（RBF-NN）算法在预测重金属污染指数（HPI）、重金属评价指数（HEI）、非致癌（N-CR）和致癌（CR）参数方面显示出较高的准确性，决定系数值在0.912至0.976之间。然而，修正后的重金属污染指数（m-HPI）和污染指数（CI）预测显示系数值相对较低，分别为0.753和0.657。多层感知器神经网络（MLP-NN）在预测计算指数（HPI、HEI、CI、m-HPI）时，精度存在波动，决定系数在0.167至0.954之间。相比之下，两个亚组在预测非致癌和致癌危害方面的熟练程度显示系数值在0.887至0.995之间。相互比较而言，径向基函数（RBF）模型表明污染指数的预测值与实际值之间的一致性更高，而多层感知器（MLP）模型在预测健康风险指数方面表现出更高的精度。
