Predicting geogenic groundwater fluoride contamination throughout China

被引:28
|
作者
Cao, Hailong
Xie, Xianjun [1 ]
Wang, Yanxin
Liu, Hongxing
机构
[1] China Univ Geosci, Sch Environm Studies, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Fluoride; Groundwater; Prediction; Artificial neural network; China; SPATIAL-DISTRIBUTION; DRINKING-WATER; BASIN; REMOVAL; GENESIS; PERSPECTIVE; PROVENANCE; PARAMETERS; EXPOSURE; AQUIFERS;
D O I
10.1016/j.jes.2021.07.005
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 x 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. (c) 2021 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.
引用
收藏
页码:140 / 148
页数:9
相关论文
共 50 条
  • [1] Predicting geogenic groundwater fluoride contamination throughout China
    Hailong Cao
    Xianjun Xie
    Yanxin Wang
    Hongxing Liu
    [J]. Journal of Environmental Sciences, 2022, 115 (05) : 140 - 148
  • [2] Prediction modeling of geogenic iodine contaminated groundwater throughout China
    Liu, Hongxing
    Li, Junxia
    Cao, Hailong
    Xie, Xianjun
    Wang, Yanxin
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 303
  • [3] Groundwater Arsenic Contamination Throughout China
    Rodriguez-Lado, Luis
    Sun, Guifan
    Berg, Michael
    Zhang, Qiang
    Xue, Hanbin
    Zheng, Quanmei
    Johnson, C. Annette
    [J]. SCIENCE, 2013, 341 (6148) : 866 - 868
  • [4] Geogenic contamination of groundwater with fluoride in the Rift Valley of Ethiopia and its mitigations
    Feleke, Z.
    Mulugeta, E.
    Alemu, A.
    Chandravanshi, B. S.
    [J]. GEOCHIMICA ET COSMOCHIMICA ACTA, 2009, 73 (13) : A363 - A363
  • [5] Prediction Modeling and Mapping of Groundwater Fluoride Contamination throughout India
    Podgorski, Joel E.
    Labhasetwar, Pawan
    Saha, Dipankar
    Berg, Michael
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2018, 52 (17) : 9889 - 9898
  • [6] Predicting Geogenic Arsenic Contamination in Shallow Groundwater of South Louisiana, United States
    Yang, Ningfang
    Winkel, Lenny H. E.
    Johannesson, Karen H.
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (10) : 5660 - 5666
  • [7] Effect of hydrogeological structure on geogenic fluoride contamination of groundwater in granitic rock belt in Tanzania
    Nakayama, Hiroyuki
    Yamasaki, Yasumasa
    Nakaya, Shinji
    [J]. JOURNAL OF HYDROLOGY, 2022, 612
  • [8] Effect of hydrogeological structure on geogenic fluoride contamination of groundwater in granitic rock belt in Tanzania
    Nakayama, Hiroyuki
    Yamasaki, Yasumasa
    Nakaya, Shinji
    [J]. JOURNAL OF HYDROLOGY, 2022, 612
  • [9] Comparison of machine learning models for predicting fluoride contamination in groundwater
    Rahim Barzegar
    Asghar Asghari Moghaddam
    Jan Adamowski
    Elham Fijani
    [J]. Stochastic Environmental Research and Risk Assessment, 2017, 31 : 2705 - 2718
  • [10] Comparison of machine learning models for predicting fluoride contamination in groundwater
    Barzegar, Rahim
    Moghaddam, Asghar Asghari
    Adamowski, Jan
    Fijani, Elham
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2017, 31 (10) : 2705 - 2718