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Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

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Abstract

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 μg/L, with an average of 11.64 μg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 μg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3− and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock–water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F−, Fe2+, K+, Mg2+, Ca2+, Cl−, SO42−, NO3−, HCO3−, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

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Acknowledgements

The authors gratefully acknowledge the help of anonymous peer reviewers and the editors for their valuable comments.

Funding

The research work was supported by the National Key R&D Program of China (2022YFC2503001) and the National Natural Science Foundation of China (Grant 42177078).

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Authors and Affiliations

  1. School of Environmental Studies, China University of Geosciences, Wuhan, 430074, People’s Republic of China

    Javed Iqbal, Chunli Su, Abdur Rashid, Zahid Ullah, Hasnain Abbas & Asmat Ali

  2. State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, China University of Geosciences, Wuhan, 430074, China

    Javed Iqbal, Chunli Su, Abdur Rashid, Zahid Ullah, Hasnain Abbas & Asmat Ali

  3. School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China

    Maqsood Ahmad

  4. College of New Energy and Environment, Jilin University, Changchun, 130021, China

    Muhammad Yousuf Jat Baloch

  5. School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China

    Anam Nigar

  6. Institute of Geological Survey, China University of Geosciences, 388 Lumo Road, Wuhan, 430074, China

    Arif Ullah

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  1. Javed Iqbal
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Contributions

JI: wrote the main manuscript. CS: Supervision. MA: data analysis. AR: conceptualization. ZU: made Fig. 1. HA: made Fig. 2. AN and MY, JB material preparation. AA and AU: reviewed the article.

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Correspondence to Chunli Su.

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Iqbal, J., Su, C., Ahmad, M. et al. Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models. Environ Geochem Health 46, 14 (2024). https://doi.org/10.1007/s10653-023-01782-7

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