Global threat of arsenic in groundwater

Abstract

Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling

“…It is difficult to state possible reasons for these differences as the methods used to calculate these other estimates are not clear. Our range of potentially affected population is also toward the lower end of the range of 18–90 million estimated for India as part of a global groundwater arsenic prediction model [ 16 ], which highlights how a separate study, such as this one, concentrated on a single area can lead to more precise results.…”

“…Despite the hazard model presented here being very effective in predicting high groundwater arsenic concentrations, it is unable to account for the depth dependency of arsenic in an aquifer, which, for example, can vary according to sediment age and/or redox conditions as well as 3-D heterogeneous permeability structures [ 76 ]. As such, it can be assumed that the predictions become less accurate with greater depth, as was demonstrated in a recent global study [ 16 ]. Although ~3/4 of the modeled data points used here have an associated well depth, essentially all of these data were confined to just a few areas in West Bengal and Bihar.…”

“…In total, 26 different spatially continuous parameters were used as predictor variables in modeling ( Table 2 ). These variables were selected based on their known or perceived function as proxies for the accumulation of arsenic in groundwater [ 16 ]. Most of the variables are related to climate or the surface geology, which includes metamorphic and sedimentary rocks in the Himalayas in the north, volcanic and metamorphic units in Deccan plateau in the south, and extensive unconsolidated sediments along the Ganges and Brahmaputra rivers in between ( Figure 1 b).…”

“…The first step of this procedure was to determine the areas at high risk of having arsenic concentrations greater than 10 µg/L. This was done using two approaches that have been described in more detail by Podgorski and Berg [ 16 ]. The sensitivity and specificity, that is, the correct classification of high and low values, respectively, were plotted for 100 probabilities between 0 and 1.…”

“…This should indicate where in the country high groundwater arsenic concentrations are likely to be found where no arsenic measurements currently exist. Such an approach has been previously been carried out for the states of Gujarat [ 14 ] and Uttar Pradesh [ 15 ], as well as the entire world [ 16 ], but never solely for all of the country of India or with the substantially larger dataset of arsenic concentrations as in this study. All other factors being equal, a modeling study conducted on a smaller area allows the model to focus on, and better characterize, the arsenic occurrences in that area and thereby produce a more accurate model.…”