ArticleInternational journal of environmental research and public health2020
Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Surface Variable-Based Machine Learning for Scalable Arsenic Prediction in Undersampled Areas.GeoHealth · 2026Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
- Predicting removal of arsenic from groundwater by iron based filters using deep neural network models.Scientific reports · 2024Article
- Machine-Learning Approach for Identifying Arsenic-Contamination Hot Spots: The Search for the Needle in the Haystack.ACS ES&T water · 2024Article
- Warming drives dissolved organic carbon export from pristine alpine soils.Nature communications · 2024Article
- Global analysis and prediction of fluoride in groundwater.Nature communications · 2022Article
- Article
- Surface Flooding as a Key Driver of Groundwater Arsenic Contamination in Southeast Asia.Environmental science & technology · 2022Article
- Application of Geostatistical Analysis and Random Forest for Source Analysis and Human Health Risk Assessment of Potentially Toxic Elements (PTEs) in Arable Land Soil.International journal of environmental research and public health · 2020Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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