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 fluoro-sis.We used artificial neural network to model the relationship between groundwater fluo-ride 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 concen-trations 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 flu-oride 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 defluorida-tion projects.

Hailong Cao;Xianjun Xie;Yanxin Wang;Hongxing Liu

School of Environmental Studies&State Key Laboratory of Biogeology and Environmental Geology,China University of Geosciences,Wuhan 430074,China

环境科学学报（英文版）

[1]Hailong Cao;Xianjun Xie;Yanxin Wang;Hongxing Liu-.Predicting geogenic groundwater fluoride contamination throughout China)[J].环境科学学报（英文版）,2022(05):140-148

Endemic,oride,defluorida

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