Mitigating the risk of arsenic and fluoride contamination of groundwater through a multi-model framework of statistical assessment and natural remediation techniques

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.