Fluoride Action Network

Predicting geogenic groundwater fluoride contamination throughout China

Source: Journal of Environmental Sciences 115:140-148. | May 15th, 2021 | Authors: Cao H, Xie X, Wang Y, Liu H.
Location: China

Highlights

  • High fluoride groundwater risk map was generated.
  • Climate variables are the most important indicators.
  • High fluoride risk areas comprise a total of 27.6% of the country.
  • About 89 million rural people are at health risk due to fluoride exposure.
  • The risk map can be used by authorities to prioritize fluoride mitigation.

Abstract

Endemic fluorosis exists in almost all provinces of China. The long-term ingestion of groundwater containing high concentrations of fluoride is one of the main causes of fluorosis. We used artificial neural network to model the relationship between groundwater fluoride concentrations from throughout China and environmental variables such as climatic, geological. and soil parameters as proxy predictors. The results show that the accuracy and area under the receiver operating characteristic curve of the model in the test dataset are 80.5% and 0.86%, respectively, and climatic variables are the most effective predictors. Based on the artificial neural network model, a nationwide prediction risk map of fluoride concentrations exceeding 1.5 mg/L with a 0.5 × 0.5 arc minutes resolution was generated. The high risk areas are mainly located in western provinces of Xinjiang, Tibet, Qinghai, and Sichuan, and the northern provinces of Inner Mongolia, Hebei and Shandong. The total number of people estimated to be potentially at risk of fluorosis due to the use of untreated high fluoride groundwater as drinking water is about 89 million, or 6% of the population. The high fluoride groundwater risk map helps the authorities to prioritize areas requiring mitigation measures and thus facilitates the implementation of water improvement and defluoridation projects.

    Keywords

    Fluoride; Groundwater; Prediction; Artificial neural network; China