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Open AccessArticle

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling

Department of Earth and Environmental Sciences and Williamson Research Centre for Molecular Environmental Science, University of Manchester, Manchester M13 9PL, UK
Centre for Flood Management Studies, National Institute of Hydrology, Water and Land Management Institute Complex, Phulwarisharif, Patna, Bihar 801505, India
Authors to whom correspondence should be addressed.
Now at Department Water Resources and Drinking Water, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
Int. J. Environ. Res. Public Health 2020, 17(19), 7119;
Received: 7 September 2020 / Revised: 24 September 2020 / Accepted: 25 September 2020 / Published: 28 September 2020
(This article belongs to the Special Issue Arsenic Exposure in Environment and Human Health)
Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18–30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs. View Full-Text
Keywords: groundwater; arsenic; India; machine learning; geospatial modeling; random forest groundwater; arsenic; India; machine learning; geospatial modeling; random forest
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Podgorski, J.; Wu, R.; Chakravorty, B.; Polya, D.A. Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling. Int. J. Environ. Res. Public Health 2020, 17, 7119.

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