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山东省地下水氟富集规律及其驱动机制
投稿时间:2020-02-28  修订日期:2020-06-15  点此下载全文
引用本文:
DOI:10.19762/j.cnki.dizhixuebao.2020280
摘要点击次数: 179
全文下载次数: 200
作者单位地址
刘春华 山东省地质调查院 山东省济南市历下区二环东路5948号
王威 山东省第一地质矿产勘查院 济南市历城唐冶街道敬德街521号
杨丽芝 山东省地质调查院 
朱恒华 山东省地质调查院 
郭艳 山东省地质调查院 
马瑜宏 山东省地质调查院 
郭晶 山东省地质调查院 
刘柏含 山东省地质调查院 
基金项目:国家重点研发计划项目(2017YFC0406106)、国家自然科学基金(41902265)、中国地质调查局(1212010634603)和山东省地勘基金联合资助
中文摘要:本文以生命必需元素氟为研究对象,选择地方性氟病分布典型、地下水类型分布全面的山东省全境为研究区,依托2006~2016年间采集的4321件地下水无机分析数据,综合运用数理统计分析、离子比值分析、水化学平衡体系分析,详细研究了山东省高氟地下水的分布特征和富集机制。结果表明:山东省浅层高氟地下水集中连片分布于胶莱盆地和鲁西南平原地区地势低洼地带,地下水氟含量超过1mg/L的分布面积约13227km2,最大值22mg/L;深层孔隙地下水高氟区集中分布于平原盆地中心的德州、滨州、菏泽深层地下水位降落漏斗区,深层孔隙水氟含量超过2mg/L的分布面积约15086km2,最大值7.5mg/L,地下水开采是驱动深层孔隙水氟富集的主要因素;不同类型地下水氟平均含量从大到小依次是深层孔隙水、浅层孔隙水、基岩裂隙水、裂隙岩溶水、孔隙裂隙水;深层孔隙水F–含量与Ca2+含量呈明显的负相关,其他类型地下水F–含量与Ca2+含量相关关系不明显。综合得出:山东省高氟地下水形成受地貌与地质构造部位、含水介质地球化学特性、人类地下水开采等三方面因素共同驱动,含氟矿物的溶解是地下水中氟的物质来源,淋滤、蒸发浓缩、水岩相互作用使得地下水氟含量进一步升高,氟-钙拮抗作用机制最终决定地下水中氟含量。此研究揭示了控制不同类型地下水氟富集的关键因素,深化了氟在地下水中化学行为的认识。
中文关键词:山东省  高氟地下水  分布特征  富集  驱动机制  
 
Fluorine Enrichment Characteristics of Groundwater and its Driving Mechanisms in Shandong Province
Author NameAffiliationAddress
LIU Chunhua Shandong Institute of Geological Survey 山东省济南市历下区二环东路5948号
WANG Wei NO.1 Institute of Geology and Mineral Resources of Shandong Province 济南市历城唐冶街道敬德街521号
YANG Lizhi Shandong Institute of Geological Survey 
ZHU Henghua Shandong Institute of Geological Survey 
GUO Yan Shandong Institute of Geological Survey 
MA Yuhong Shandong Institute of Geological Survey 
GUO Jing Shandong Institute of Geological Survey 
LIU Bohan Shandong Institute of Geological Survey 
Abstract:This article took the essential elemental fluorine as the research object, and selected the entire area of Shandong Province, which had typical endemic fluorine disease distribution and comprehensive groundwater type distribution, as the research area. Based on 4,321 pieces of groundwater inorganic analysis data collected from 2006 to 2016, the distribution characteristics and driving mechanisms of fluorine enrichment in groundwater in Shandong Province were studied in detail, using mathematical statistical analysis, ion ratio analysis, and water chemical equilibrium system analysis methods. The results show that: the distribution of shallow high fluoride groundwater was contiguously distributed in the low-lying areas of the Jiaolai Basin and the Southwestern Plain of Shandong. The distribution area where shallow groundwater fluorine concentration exceeding 1mg / L is about 13227km2, with a fluorine concentration maximum of 22mg/L. The distribution of deep pore groundwater with high fluoride is concentrated in the deep groundwater depression funnel areas of Dezhou, Binzhou and Heze. The distribution area where deep pore groundwater fluorine concentration exceeding 2mg / L is about 15086 km2, with a fluorine concentration maximum of 7.5 mg/L. Groundwater extraction is the main factor driving fluorine enrichment in deep pore water. The average concentration of fluorine of different types of groundwater in descending order is deep pore water, shallow pore water, bedrock fissure water, fissured karst water, and pore fissure water. The fluoride concentration of deep pore water has a significant negative correlation with Ca2+ concentration, and the correlation is not obvious in other types of groundwater. It is concluded that the formation of high fluoride groundwater in Shandong Province is driven by three factors, including geomorphology and geological structural parts, the geochemical characteristics of the aquifer medium, and human groundwater extraction. Leaching, evaporation and water-rock interactions further increase the groundwater fluorine content, and the fluorine-calcium antagonistic mechanism ultimately determines the fluorine content in groundwater. This study revealed the key factors controlling the fluorine enrichment of different types of groundwater and deepened the understanding of the chemical behavior of fluorine in groundwater.
keywords:Shandong Province  high fluoride groundwater  distribution characteristics  enrichment  driving mechanisms
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