Although there are an increasing number of artificial intelligence/machine learning models of various hazardous chemicals (e.g. As, F, U, NO 3 − , radon) in environmental media (e.g. groundwater, soil), these most commonly use arbitrarily selected cutoff criteria to balance model specificity and sensitivity. This results in models of hazard distribution that, whilst often of considerable interest and utility, are not designed...

Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions of people globally. Notwithstanding the particular risks to exposed populations in the Indian sub-continent, at the time of writing, there was a paucity of geostatistically based models of the spatial distribution of groundwater hazard in India. In this study, we used logistic regression models of secondary groundwater arsenic dat...