Global threat of arsenic in groundwater

By J. Podgorski, M. Berg. Science, 2020

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Dowsing for danger Arsenic is a metabolic poison that is present in minute quantities in most rock materials and, under certain natural conditions, can accumulate in aquifers and cause adverse health effects. Podgorski and Berg used measurements of arsenic in groundwater from ∼80 previous studies to train a machine-learning model with globally continuous predictor variables, including climate, soil, and topography (see the Perspective by Zheng). The output global map reveals the potential for hazard from arsenic contamination in groundwater, even in many places where there are sparse or no reported measurements. The highest-risk regions include areas of southern and central Asia and South America. Understanding arsenic hazard is especially essential in areas facing current or future water insecurity. Science, this issue p. 845; see also p. 818 A global model for predicting groundwater arsenic levels suggests that 94 million to 220 million people are at risk of exposure. 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.

Subfields: Environmental Chemistry, Pollution

View Paper: https://www.semanticscholar.org/paper/7f64533ee1dd8a6f57242bcfff456b61d43aa1cb

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