强降水作用下府城遗址块段浅层地下水水质时空响应  

Spatial and temporal response of shallow groundwater quality in the Fucheng site block under the strong precipitation

作　　者：许亚东 张萌 王心义[1,2,3] 李洁祥 XU Ya-dong;ZHANG Meng;WANG Xin-yi;LI Jie-xiang(Department of resources&Environment,Henan polytechnic university,Jiaozuo 454000,Henan,China;Collaborative innovation center of coalbed methane and shale gasfor central plains economic region,Jiaozuo 454000,Henan,China;Collaborative innovation center of coal work safety and clean high-efficiency utilization,Jiaozuo 454000,Henan,China)

机构地区：[1]河南理工大学资源环境学院,河南焦作454000 [2]中原经济区煤层(页岩)气河南省协同创新中心,河南焦作454000 [3]煤炭安全生产与清洁高效利用省部共建协同创新中心,河南焦作454000

出　　处：《地下水》2023年第6期5-9,共5页Ground water

摘　　要：以焦作中站区朱村镇府城遗址为研究区,根据收集的多年降水量资料及2019年自建雨量站实测的降水量数据,结合2017年以来18个长观点逐月监测的地下水水位和水质信息,采用综合性分析方法研究了日降水量高于和低于25mm/day阈值时的浅层地下水水质变化及其响应。结果表明:浅层地下水水质时空响应特征主要体现在两个方面:水位时空响应和水质时空响应。年内浅层地下水位变化曲线呈山谷形状,呈现先下降再上升的趋势。年际变化趋势为先缓慢下降后急剧上升。典型离子F^(-)、Cl^(-)、SO_(4)^(2-)、NO_(3)^(-)、Na^(+)、TDS年际变化总体上呈震荡变化趋势,不同离子的增减趋势不一致;年内变化表现为枯水期离子含量普遍高于丰水期。基于BP神经网络模型对2017年至2021年枯水期和丰水期的浅层地下水水质进行了评价,实现了评价指标因子权重和阈值的自主识别和自动调整,评价结果符合实际。2017年至2021年同年度下丰水期浅层地下水质均好于枯水期,降水量对研究区的浅层地下水水质影响较大。Taking the Fucheng site in Zhucun town,Zhongzuo district,Jiaozuo as the study area,and based on the precipitation data collected from and the precipitation data measured by self-built rainfall stations in 2019,combined with the groundwater level and water quality information monitored month by month from 18 long viewpoints since 2017,a comprehensive analysis method was used to study the shallow groundwater when the daily precipitation was above and below the 25mm/day threshold water quality changes and their responses.The result shows:The spatiotemporal response characteristics of shallow groundwater quality were mainly reflected in two aspects:the spatiotemporal response of water level and the spatiotemporal response of water quality.The interannual variation of shallow groundwater level showed a trend of slow decline followed by sharp rise;the intraannual shallow groundwater level variation curve was valley shaped,showing a trend of first decline and then rise.The inter-annual variation trend was a slow decline at first and then a sharp rise.Typical ions F^(-),Cl^(-),SO_(4)0^(2-),NO_(3)^(-),Na^(+),and TDS showed an overall oscillating trend of interannual variation,with inconsistent trends of increase and decrease of different ions;the intraannual variation showed that the ion content was generally higher in the dry period than in the abundant period.Based on the BP neural network model,the shallow groundwater quality in the dry and wet seasons from 2017 to 2021 was evaluated,and the self-identification and automatic adjustment of the evaluation index factor weights and thresholds were realized,and the evaluation results were in line with reality.The quality of shallow groundwater was better than that in the dry period under the same year from 2017 to 2021,and precipitation had a greater influence on the quality of shallow groundwater in the study area.

关 键 词：强降水阈值 典型离子 水质评价 水质响应 BP神经网络 

分 类 号：P641.2[天文地球—地质矿产勘探]