Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling.

Existence of wide spread elevated concentrations of groundwater arsenic (As) across South Asia, including India, has endangered a huge groundwater-based drinking water dependent population. Here, using high-spatial resolution As field-observations (~3 million groundwater sources) across India, we have delineated the regional-scale occurrence of elevated groundwater As (≥10 μg/L), along with the possible geologic-geomorphologic-hydrologic and human-sourced predictors that influence the spatial distribution of the contaminant. Using statistical and machine learning method, we also modeled the groundwater As concentrations probability at 1 Km resolution, along with probabilistic delineation of high As-hazard zones across India. The observed occurrence of groundwater As was found to be most strongly influenced by geology-tectonics, groundwater-fed irrigated area (%) and elevation. Pervasive As contamination is observed in major parts of the Himalayan mega-river Indus-Ganges-Brahmaputra basins, however it also occurs in several more-localized pockets, mostly related to ancient tectonic zones, igneous provinces, aquifers in modern delta and chalcophile mineralized regions. The model results suggest As-hazard potential in yet-undetected areas. Our model performed well in predicting groundwater arsenic, with accuracy: 82% and 84%; area under the curve (AUC): 0.89 and 0.88 for test data and validation datasets. An estimated ~90 million people across India are found to be exposed to high groundwater As from field-observed data, with the five states with highest hazard are West Bengal (28 million), Bihar (21 million), Uttar Pradesh (15 million), Assam (8.6 million) and Punjab (6 million). However it can be much more if the modeled hazard is considered (>250 million). Thus, our study provides a detailed, quantitative assessment of high groundwater As across India, with delineation of possible intrinsic influences and exogenous forcings. The predictive model is helpful in predicting As-hazard zones in the areas with limited measurements.