Global machine-learning model of naturally occurring fluoride in groundwater

Abstract

Chronic consumption of elevated concentrations of fluoride in groundwater can cause detrimental health effects including dental mottling and skeletal fluorosis. However, the concentration of fluoride is not known in many aquifers. To help address this, we have used machine learning to create a global fluoride prediction map based on the WHO drinking-water guideline of 1.5 mg/L. Over 400,000 data points of fluoride in groundwater (10% greater than1.5 mg/L) from 77 countries were used along with 12 predictor variables out of an initial set of 62 spatially continuous variables relating to geology, soil, climate and topography. The model performs very well, (e.g. AUC of 0.90) and was used to produce a global prediction map. This helps gauge the scope of the problem and identify potential hotspots that should receive the focus of more groundwater testing, including parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. This fluoride hazard model was also used to estimate the global at-risk human population at about 180 million people, most of whom live in Asia and Africa. Another model was created using additional physicochemical parameters measured in situ. Although this model (AUC of 0.95) could not be used to create a map, it helps to better understand the processes related to the dissolution and accumulation of fluoride. For example, both the spatially continuous and in-situ predictor variables confirm that arid conditions promote the dissolution of fluoride in groundwater.