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Mitigating the Risk of Arsenic and Fluoride Contamination of Groundwater Through a Multi-model Framework of Statistical Assessment and Natural Remediation Techniques

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Emerging Issues in the Water Environment during Anthropocene

Abstract

The present book chapter discusses the mechanism of Arsenic and Fluoride release in the groundwater and its related toxicity. The use of spatial modelling techniques involving machine learning classification algorithms can help predict the concentration in binary outputs. The variability in the cause of occurrence of Arsenic forces researchers to try different adsorption materials that could help decrease the concentration levels of contaminants to permissible levels. The co-occurrence of Fluoride and Arsenic at places complicates both prediction as well as remediation. Therefore a multi-model technique involving statistical assessment and natural remediation is required to be used in tandem.

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, India

    Ashwin Singh

  2. Department of Earth Sciences, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India

    Arbind Kumar Patel & Manish Kumar

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  1. Ashwin Singh
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  2. Arbind Kumar Patel
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  3. Manish Kumar
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Correspondence to Manish Kumar .

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Editors and Affiliations

  1. Earth Sciences, IIT Gandhinagar, Gandhinagar, Gujarat, India

    Manish Kumar

  2. Water Sciences Laboratory, University of Nebraska–Lincoln, Lincoln, NE, USA

    Daniel D. Snow

  3. RSET, Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanagawa, Japan

    Ryo Honda

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Singh, A., Patel, A.K., Kumar, M. (2020). Mitigating the Risk of Arsenic and Fluoride Contamination of Groundwater Through a Multi-model Framework of Statistical Assessment and Natural Remediation Techniques. In: Kumar, M., Snow, D., Honda, R. (eds) Emerging Issues in the Water Environment during Anthropocene. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-32-9771-5_15

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  • DOI: https://doi.org/10.1007/978-981-32-9771-5_15

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  • Publisher Name: Springer, Singapore

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  • Online ISBN: 978-981-32-9771-5

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