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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False Colour Composites (FCC's) of IRS-1A LISS-II sensor pertaining to the dates 9th April 1989 and 7th December 1989 are used to delineate the pre-monsoon and post-monsoon surface waterlogged areas in a region around Habibpur sub-distributary bounded by Vaishali branch canal and Gandak river in North Bihar, India for the year 1989 using visual interpretation technique. Also, digital data of IRS-1C LISS-III sensor pertaining to the dates 7th December 1998 and 6th April 1999 are analyzed in a digital image processing software -ERDAS Imagine 8.3.1, to delineate the pre-monsoon and post-monsoon surface waterlogged areas for the year 1998-1999. Further, for the study area, the waterlogging conditions are delineated for the year 1991-1992 using the groundwater flow modeling software package, MODFLOW. The results obtained using satellite remote sensing data and groundwater flow modeling are integrated in a GIS environment in ERDAS Imagine for assessment of the waterlogging areas.

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