摘要
高氟地下水是全球广泛分布的环境地质问题,本研究基于结构方程模型(SEM)和随机森林(RF)算法开展新疆和田绿洲区浅层高氟地下水水质主控因素及氟浓度分布预测研究。结果表明:干旱气候条件下矿物溶解(β=0.99)及离子交换作用(β=0.68)对地下水水质具有重要贡献,与地下水矿物饱和指数、氯碱指数及Gibbs模型分析结果一致;基于RF算法构建的预测模型指示浅层高氟地下水主要分布于绿洲区中部,特征变量贡献度分析表明蒸发浓缩作用以及碱性pH值条件是高氟地下水形成的重要调控因素。研究结果可为和田绿洲区浅层高氟地下水的分布预测及环境调控机制提供新认识,也可为区域安全供水战略提供指导。
High fluoride groundwater poses an environmental geological problem worldwide.This study conducted research on the main controlling factors of groundwater quality and predicts the distribution of fluoride groundwater in Hotan Oasis region of Xinjiang province using structural equation modeling(SEM)and the random forest(RF)algorithm.The results indicate that mineral dissolution(β=0.99)and ion exchange(β=0.68)under arid climate conditions significantly contribute to groundwater quality,which is consistent with the analysis results of the groundwater mineral saturation index,chlor-alkali index,and Gibbs diagram.The predicting model based on RF indicates that shallow high-fluoride groundwater is mainly distributed in the central region of Hotan Oasis.The analysis of the degree of contribution of characteristic variables indicates that evaporation and concentration effects and alkaline pH conditions play important roles in the formation of high-fluoride groundwater.The present study supplies new insights into the prediction of distribution and environmental regulation mechanism of high fluoride groundwater in Hotan Oasis region and also provides guidance for the regional safe drinking-water supply strategies.
作者
蒋悦
郑天亮
李景吉
杨晴雯
黄振富
王双成
JIANG Yue;ZHENG Tianliang;LI Jingji;YANG Qingwen;HUANG Zhenfu;WANG Shuangcheng(College of Ecology and Environment,Chengdu University of Technology,Chengdu 610059,China;Tianfu Yongxing Laboratory,Chengdu 610213,China;The Second Hydrogeological and Engineering Geological Brigade of Xinjiang Bureau of Geology and Mineral Exploration and Development,Changji 831100,China)
出处
《安全与环境工程》
北大核心
2025年第2期264-272,共9页
Safety and Environmental Engineering
基金
新疆维吾尔自治区地质矿产勘查开发局地质勘查项目(XGMB202356)
国家自然科学基金项目(42107094)
四川省自然科学基金项目(2023NSFSC0806)
成都市科技局技术创新研发项目(2022-YF05-01166-SN)。
