In the 40 years since the relation between arsenic (As) toxicity and groundwater extraction was first documented from the Holocene alluvial basin of West Bengal, India, (1) we have become more aware that groundwater contamination with naturally occurring (geogenic) As poses a serious health threat of global proportions. (2) With the aim of implementing effective and sustainable mitigation strategies, research into the occurrence and location of toxic As levels in drinking and irrigation water and in the food chain provided insight into all aspects of the As-contamination issue, including (a) geogenic As provenance in volcanic and metamorphic rocks, hydrothermal additions to groundwater and hot springs, and weathering of rocks in orogenic mountain belts, (b) its accumulation in sedimentary-basin aquifers, (c) the mobilization and transport of the contaminant into the groundwater, and (d) the associated health risks of sustained As ingestion for >200 million people around the world. (3,4) A wide range of potential As-mitigation measures have been proposed over the years, ranging from in situ chemical and biological oxidative processes for immobilizing As to subsequent filtration methods and social awareness programs for the affected population. (5−7)@en

Groundwater contamination with naturally-occurring arsenic (As) poses a serious health threat of global proportions. Implementation of focused and sustainable As-mitigation measures is hampered by the inability to pinpoint enigmatic As-hotspots in the vast area of continental alluvial basins with elevated toxicity risk. The catalyser role of invasive vegetation in anoxic fluvial oxbow lakes in combination with microbial metabolism processes are key in mobilizing As from its solid state into the shallow aquifer domain and accumulation in porous and permeable fluvial point-bar sediment. This insight opens up the opportunity for a cross-disciplinary approach to construct predictive machine learning-based object-based geospatial models to locate As-hotspots by the analyses of (1) satellite imagery of alluvial geomorphology, (2) oxbow-lake vegetation density, and (3) ground-truth databases of oxbow-lake/aquifer biogeochemistry and fluvial sedimentology.@en

Meandering-river geomorphology, forming abandoned channels/lakes with organic carbon-burial and microbial reductive dissolution, play many crucial roles in controlling arsenic (As) fluxes in sinks such as contaminated aquifers of riverine alluvial plains across the world. Suhiya oxbow-lake in the middle alluvial plain of the River Ganga, was selected as the natural laboratory. A top-down multidisciplinary approach was chosen employing satellite imagery to analyse the annual oxbow-lake surface vegetation dynamics (Eichhornia and Hydrilla). Side-scan sonar profiles across two oxbow lakes along with River Ganga core data and vintage topographical maps, estimated the lake-sedimentation rate of 9.6 cm/yr. Organic carbon [amino acids, aromatics, lingo-phenols and lipids hydrocarbons] infiltration-based on hydrophobicity and molecular-mass was detected at different depths along the water and sedimentary column. Elemental analysis showed lake surface to groundwater the As conc. varied from (0.37 to 185 μg/l). A microbial diversity based study showed that large sized photoautotrophs Nostoc, Anabaena are replaced by Fe-oxido-reducing As-metabolizing bacteria e.g. Acidovorax, Dechloromonas and enteric organisms e.g. Enterobacter, Salmonella at bottom of water column. Based on these inferences, a conceptual organic carbon transport model was constructed to understand the preferential preservation and microbial diagenesis resulting in mobilization of As and other geogenic elements.

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