Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning.

Autor: Cao W; The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China; Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Shijiazhuang 050061, China., Zhang Z; Tianjin Center (North China Center for Geoscience Innovation), China Geological Survey, Tianjin 300170, China. Electronic address: hydro_zhangzhuo@163.com., Fu Y; North China University of Water Resources and Electric Power, Zhengzhou 450046, China., Zhao L; Hebei Provincial academy of water resources, Shijiazhuang 050057, China., Ren Y; The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China; Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Shijiazhuang 050061, China., Nan T; The Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China; Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Shijiazhuang 050061, China., Guo H; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Beijing 100083, China. Electronic address: hmguo@cugb.edu.cn.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2024 Aug 01; Vol. 259, pp. 121848. Date of Electronic Publication: 2024 May 29.
DOI: 10.1016/j.watres.2024.121848
Abstrakt: Chronic exposure to elevated geogenic arsenic (As) and fluoride (F - ) concentrations in groundwater poses a significant global health risk. In regions around the world where regular groundwater quality assessments are limited, the presence of harmful levels of As and F - in shallow groundwater extracted from specific wells remains uncertain. This study utilized an enhanced stacking ensemble learning model to predict the distributions of As and F - in shallow groundwater based on 4,393 available datasets of observed concentrations and forty relevant environmental factors. The enhanced model was obtained by fusing well-suited Extreme Gradient Boosting, Random Forest, and Support Vector Machine as the base learners and a structurally simple Linear Discriminant Analysis as the meta-learner. The model precisely captured the patchy distributions of groundwater As and F - with an AUC value of 0.836 and 0.853, respectively. The findings revealed that 9.0% of the study area was characterized by a high As risk in shallow groundwater, while 21.2% was at high F - risk identified as having a high risk of fluoride contamination. About 0.2% of the study area shows elevated levels of both of them. The affected populations are estimated at approximately 7.61 million, 34.1 million, and 0.2 million, respectively. Furthermore, sedimentary environment exerted the greatest influence on distribution of groundwater As, with human activities and climate following closely behind at 29.5%, 28.1%, and 21.9%, respectively. Likewise, sedimentary environment was the primary factor affecting groundwater F - distribution, followed by hydrogeology and soil physicochemical properties, contributing 27.8%, 24.0%, and 23.3%, respectively. This study contributed to the identification of health risks associated with shallow groundwater As and F - , and provided insights into evaluating health risks in regions with limited samples.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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