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Fluoride contamination in African groundwater: Predictive modeling using stacking ensemble techniques.

Usman Sunusi Usman1, Yousif Hassan Mohamed Salh1, Bing Yan1

  • 1School of Environmental Studies, China University of Geosciences Wuhan, 388 Lumo Road, Wuhan 430074, China.

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|November 22, 2024
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Groundwater fluoride contamination in Africa affects millions, posing severe health risks like dental and skeletal fluorosis. An advanced ensemble learning model predicts high-risk areas, identifying sodium and bicarbonate as key contributors to this public health crisis.

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AfricaFluorideGroundwaterPhysiochemical parametersStacking ensemble learning

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Area of Science:

  • Environmental Science
  • Public Health
  • Hydrogeology

Background:

  • Groundwater fluoride contamination is a significant public health issue in Africa, exacerbated by natural geological and climatic factors, as well as anthropogenic activities.
  • Elevated fluoride levels, particularly in the East African Rift Valley, surpass the World Health Organization's (WHO) recommended limit of 1.5 mg/L, leading to serious health conditions such as dental and skeletal fluorosis.
  • Millions across various African nations, including South Africa, Nigeria, and Ethiopia, are affected by this widespread contamination.

Purpose of the Study:

  • To forecast the distribution of fluoride (F-) in African groundwater using a sophisticated stacking ensemble learning model.
  • To identify the key physiochemical variables influencing fluoride contamination and assess the population at risk.
  • To provide guidance for health risk assessment in areas with limited groundwater data.

Main Methods:

  • An advanced stacking ensemble learning model was developed, integrating 11 groundwater physiochemical variables and 6270 observed fluoride concentration data points.
  • The model employed multiple base learners, including randomized trees, Tree-Bag, Random Forest (RF), Decision Trees (DT), XGBoost (XGB), and Extra Trees (ET), with Naïve Bayes as the meta-analyzer.
  • The model achieved an Area Under the Curve (AUC) score of 0.86, indicating its accuracy in representing the heterogeneous distribution of groundwater fluoride.

Main Results:

  • The study predicts that 20-35% of eastern Africa and 10% of western Africa face risks of exceeding WHO fluoride limits, potentially impacting approximately 80 million people.
  • Significant regional variations in fluoride contamination were observed, with ranges varying widely across West, East, South, North, and Central Africa.
  • Sodium (Na+) and bicarbonate (HCO3-) were identified as the primary drivers of fluoride contamination across the continent, with calcium (Ca2+) and chloride (Cl-) also contributing in certain regions.

Conclusions:

  • The developed ensemble model effectively forecasts groundwater fluoride distribution and identifies high-risk zones across Africa.
  • The findings highlight the substantial public health burden associated with fluoride contamination and underscore the need for targeted interventions.
  • Understanding the key chemical drivers and spatial distribution of fluoride is crucial for effective groundwater management and public health protection in affected regions.