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Global analysis and prediction of fluoride in groundwater

Author

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  • Joel Podgorski

    (Swiss Federal Institute of Aquatic Science and Technology)

  • Michael Berg

    (Swiss Federal Institute of Aquatic Science and Technology)

Abstract

The health of millions of people worldwide is negatively impacted by chronic exposure to elevated concentrations of geogenic fluoride in groundwater. Due to health effects including dental mottling and skeletal fluorosis, the World Health Organization maintains a maximum guideline of 1.5 mg/L in drinking water. As groundwater quality is not regularly tested in many areas, it is often unknown if the water in a given well or spring contains harmful levels of fluoride. Here we present a state-of-the-art global fluoride hazard map based on machine learning and over 400,000 fluoride measurements (10% of which >1.5 mg/L), which is then used to estimate the human population at risk. Hotspots indicated by the groundwater fluoride hazard map include parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. Of the approximately 180 million people potentially affected worldwide, most reside in Asia (51–59% of total) and Africa (37–46% of total), with the latter representing 6.5% of the continent’s population. Africa also contains 14 of the top 20 affected countries in terms of population at risk. We also illuminate and discuss the key globally relevant hydrochemical and environmental factors related to fluoride accumulation.

Suggested Citation

  • Joel Podgorski & Michael Berg, 2022. "Global analysis and prediction of fluoride in groundwater," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31940-x
    DOI: 10.1038/s41467-022-31940-x
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    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Joel Podgorski & Ruohan Wu & Biswajit Chakravorty & David A. Polya, 2020. "Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling," IJERPH, MDPI, vol. 17(19), pages 1-17, September.
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