Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches

Uddin, Md Galal; Imran, Md Hasan; Sajib, Abdul Majed; Hasan, Md Abu; Diganta, Mir Talas Mahammad; Dabrowski, Tomasz; Olbert, Agnieszka I.; Moniruzzaman, Md

doi:10.1016/j.jconhyd.2024.104307

Journal of Contaminant Hydrology

2024

DOI: 10.1016/j.jconhyd.2024.104307

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Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches

Md Galal Uddin,

Md Hasan Imran,

Abdul Majed Sajib

et al.

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2024

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Cited by 11 publications

References 70 publications

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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

Maleky,

Faraji,

Hashemi

et al. 2024

Appl Water Sci

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Assessing water quality is essential for acquiring a better understanding of the importance of water in human society. In this study, the quality of groundwater resources in Jiroft city, Iran, using artificial intelligence methods to estimate the groundwater quality index (GWQI) was evaluated. The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R2 = 0.986) was the best prediction model, while logistic regression (R2 = 0.98), decision tree (R2 = 0.979), K-nearest neighbor (R2 = 0.968), artificial neural network (R2 = 0.955), and support vector machine (R2 = 0.928) predicted GWQI with lower accuracy. The non-carcinogenic risk assessment revealed that children had the highest hazard quotient for oral and dermal intake, with values ranging from 0.47 to 13.53 for oral intake and 0.001 to 0.05 for dermal intake. The excess lifetime cancer risk of arsenic for children, adult females, and males was found to be from 2.5 × 10–4 to 7.2 × 10–3, 1.2 × 10–4 to 3.6 × 10–3, and 4.3 × 10–5 to 1.2 × 10–3, respectively. This study suggests that any effort to reduce the arsenic levels in the Jiroft population should take into account the health hazards associated with exposure to arsenic through drinking water.

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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

Maleky,

Faraji,

Hashemi

et al. 2024

Appl Water Sci

View full text Add to dashboard Cite

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Mobilization mechanisms and spatial distribution of arsenic in groundwater of western Bangladesh: Evaluating water quality and health risk using EWQI and Monte Carlo simulation

Rahman,

Reza,

Sattar

et al. 2024

Chemosphere

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Evaluation of groundwater quality indices using multi-criteria decision-making techniques and a fuzzy logic model in an irrigated area

Abidi,

Elzain,

Sabarathinam

et al. 2024

Groundwater for Sustainable Development

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Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches

Cited by 11 publications

References 70 publications

Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms

Mobilization mechanisms and spatial distribution of arsenic in groundwater of western Bangladesh: Evaluating water quality and health risk using EWQI and Monte Carlo simulation

Evaluation of groundwater quality indices using multi-criteria decision-making techniques and a fuzzy logic model in an irrigated area

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