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Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area

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Abstract

Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking. In this study, stacking of five base learners, including eXtreme gradient boosting, random forest, feed-forward neural networks, generalized linear models with Lasso or Elastic Net regularization, and support vector machines, was used to study the spatial variation of Mn, Cd, Pb, and nitrate in Qom-Kahak Aquifers, Iran. The stacking strategy proved to be an effective substitute predictor for existing machine learning approaches due to its high accuracy and stability when compared to individual learners. Contrarily, there was not any best-performing base model for all of the involved parameters. For instance, in the case of cadmium, random forest produced the best results, with adjusted R2 and RMSE of 0.108 and 0.014, as opposed to 0.337 and 0.013 obtained by the stacking method. The Mn and Cd showed a tight link with phosphate by the redundancy analysis (RDA). This demonstrates the effect of phosphate fertilizers on agricultural operations. In order to analyze the causes of groundwater pollution, spatial methodologies can be used with multivariate analytic techniques, such as RDA, to help uncover hidden sources of contamination that would otherwise go undetected. Lead has a larger health risk than nitrate, according to the probabilistic health risk assessment, which found that 34.4% and 6.3% of the simulated values for children and adults, respectively, were higher than HQ = 1. Furthermore, cadmium exposure risk affected 84% of children and 47% of adults in the research area.

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Acknowledgements

The authors of this paper would like to express their gratitude to Iranian Department of Environment and the Qom Province Water Authority for providing the data applied in the current research. Additionally, we would like to thank the USGS for providing the freely available products employed for LULC classification.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Environmental Sciences, Shahid Rajaee Teacher Training University, Lavizan, 1678815811, Tehran, Iran

    Mohamad Sakizadeh

  2. International Network for Environment and Health (INEH), School of Geography, Archaeology and Irish Studies, University of Galway, Galway, Ireland

    Chaosheng Zhang

  3. Department of Geology, University of Georgia, Athens, USA

    Adam Milewski

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  1. Mohamad Sakizadeh
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  2. Chaosheng Zhang
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  3. Adam Milewski
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Dr. Mohamad Sakizadeh makes the main contribution in writing this paper. Prof.Chaosheng Zhang provides research ideas for revision of the initial version of the paper and Prof. Adam Milewski read and revised the paper to improve its quality before final submission.

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Correspondence to Mohamad Sakizadeh.

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Sakizadeh, M., Zhang, C. & Milewski, A. Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area. Environ Geochem Health 46, 80 (2024). https://doi.org/10.1007/s10653-023-01845-9

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  • DOI: https://doi.org/10.1007/s10653-023-01845-9

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