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基于多变量统计分析和水化学特征的海水入侵特征研究

刚什婷 吕明荟 卢茜茜 高帅 赵志强 陈奂良 彭同强 王玺 邢立亭 李莉霞

刚什婷,吕明荟,卢茜茜,等. 基于多变量统计分析和水化学特征的海水入侵特征研究−以青岛市崂山区为例[J]. 中国岩溶,2023,42(5):982-994 doi: 10.11932/karst20230509
引用本文: 刚什婷,吕明荟,卢茜茜,等. 基于多变量统计分析和水化学特征的海水入侵特征研究−以青岛市崂山区为例[J]. 中国岩溶,2023,42(5):982-994 doi: 10.11932/karst20230509
GANG Shenting, LYU Minghui, LU Qianqian, GAO Shuai, ZHAO Zhiqiang, CHEN Huanliang, PENG Tongqiang, WANG Xi, XING Liting, LI Lixia. Characterization of seawater intrusion based on multivariate statistical analysis and water chemistry characteristics: A case study of Laoshan district, Qingdao City[J]. CARSOLOGICA SINICA, 2023, 42(5): 982-994. doi: 10.11932/karst20230509
Citation: GANG Shenting, LYU Minghui, LU Qianqian, GAO Shuai, ZHAO Zhiqiang, CHEN Huanliang, PENG Tongqiang, WANG Xi, XING Liting, LI Lixia. Characterization of seawater intrusion based on multivariate statistical analysis and water chemistry characteristics: A case study of Laoshan district, Qingdao City[J]. CARSOLOGICA SINICA, 2023, 42(5): 982-994. doi: 10.11932/karst20230509

基于多变量统计分析和水化学特征的海水入侵特征研究——以青岛市崂山区为例

doi: 10.11932/karst20230509
基金项目: 山东省地矿局八〇一水文地质工程地质大队(暨山东省地下水环境保护与修复工程技术研究中心)基金项目(801KY2021-4)
详细信息
    作者简介:

    刚什婷(1990-),女,硕士研究生,主要从事地下水污染控制与数值模拟研究。E-mail:gangshenting@163.com

    通讯作者:

    吕明荟(1982-),女,工程师,主要从事水文地质研究工作。E-mail:lmhyoxiang@163.com

  • 中图分类号: P641.3

Characterization of seawater intrusion based on multivariate statistical analysis and water chemistry characteristics: A case study of Laoshan district, Qingdao City

  • 摘要: 沿海地区地下水环境问题日益突出,进行地下水水化学特征及演化规律的研究,能够更有效地开展地下水环境的监测和保护。以青岛市崂山区地下水为研究对象,综合运用统计分析、主成分分析、Piper图解法、HFE-D图解法、Chadha’s矩形图法等方法,对研究区海水入侵特征与地下水化学特征演化进行分析,探究地下水水化学特征及演化规律,并进一步评价了海水入侵现状。结果表明,研究区地下水以Na+、Ca2+、Cl、${\rm{SO}}_4^{2-}$为主要优势离子,地下水化学类型多为Cl·SO4-Na型和SO4·Cl-Ca·Mg型。地下水中Cl浓度变化幅度较大,且其均值超出了有无海水入侵的分界值(250 mg·L−1),地下水可能发生一定程度的海水入侵;青岛市崂山区地下水呈中性至弱碱性(pH均值=7.0~8.0),是沿海地区长期的水文地球化学过程的影响;地下水化学变化主要受自然因素(岩石与水的相互作用)或人为因素(农业和家庭活动)的控制;采用反距离加权(IDW)方法,结合地理信息系统(GIS),进行海水入侵位置的空间映射,研究结果表明崂山区海水入侵主要分布于江家土寨东−浦里社区北入侵段,王哥庄−港西−港东入侵段、仰口湾入侵段、登瀛村−栲栳岛入侵段。

     

  • 图  1  研究区位置图

    Figure  1.  Location of the study area

    图  2  青岛崂山区水文地质略图

    Figure  2.  Hydrogeological sketch of Laoshan district, Qingdao

    图  3  土寨河流域水文地质剖面图A-A′

    Figure  3.  Hydrogeological profile(A-A′) of the Tuzhai river basin

    图  4  取样监测点位置图

    Figure  4.  Locations of sampling and monitoring sites

    图  5  GQISWI对Piper图的分区

    Figure  5.  Domains of GQISW in piper diagram

    图  6  主成分分析结果图:PC1、PC2

    Figure  6.  Results of principal component analysis: PC1 and PC2

    图  7  地下水水化学Piper图

    Figure  7.  Piper diagram of groundwater hydrochemsitry

    图  8  采样点地下水七大离子浓度占比图

    Figure  8.  Percentages of concentrations of the seven ions in groundwater of the sampling sites

    图  9  崂山区地下水HFE−D

    Figure  9.  Evolution of hydrochemical facies of groundwater in Laoshan district

    图  10  Chadha矩形水化学类型图

    Figure  10.  Chadha rectangle hydrochemical diagram

    图  11  青岛市崂山区海水入侵空间分布

    Figure  11.  Spatial distribution of seawater intrusion in Laoshan district, Qingdao City

    表  1  地下水水化学参数统计特征值(单位:mg·L−1,pH除外)

    Table  1.   Statistics of hydrochemical parameters of groundwater (unit: mg·L−1, except for pH)

    分区项目pHTDSTHCa2+Mg2+K+Na+${\rm{HCO}}_3^{-}$${\rm{SO}}_4^{2-}$Cl${\rm{NO}}_3^{-}$
    基岩裂
    隙水
    Mean7.34998.81380.6094.2334.711.78199.04104.33135.62385.9435.94
    SD0.272032.16670.59200.6846.612.49512.0182.42218.811103.4532.57
    Cv0.042.031.762.131.341.402.570.791.612.860.91
    Min6.9067.2146.1212.933.360.132.177.653.0521.530
    Max8.208 877.643 064.85910.97191.908.822 125.00306.08778.464 812.71101.35
    第四系
    孔隙水
    Mean7.201 435.83554.2688.3581.0412.92277.95108.77235.23560.8369.69
    SD0.383 211.69802.7263.12163.2736.36927.5675.61403.371 722.6367.98
    Cv0.052.241.450.712.012.813.340.701.713.070.98
    Min6.5074.8339.208.314.480.176.6715.3010.6813.130.36
    Max8.5017 138.103 597.12263.18728.01200.005 000.00369.841 617.989 160.70244.75
    地表水Mean7.30301.99138.7140.888.904.4330.6985.0756.1140.9123.25
    SD0.32302.26127.0635.809.185.7541.8290.2662.8153.3833.27
    Cv0.041.000.920.881.031.301.361.061.121.301.43
    Min6.8091.6948.5415.432.310.164.4327.9819.217.185.52
    Max7.60814.84351.78100.8024.3112.59103.60243.46165.70132.7882.60
    注:Min为最小值,Max为最大值,Mean为平均值,SD为标准差,Cv为变异系数。
    Note: Min represents minimum value; Max represents maximum value; Mean represents average value, SD represents standard deviation; Cv represents variation coefficient.
    下载: 导出CSV

    表  2  主成分分析法组成矩阵

    Table  2.   Matrix formed by principal component analysis

    PC1PC2
    pH0.007 700.641 80
    Ca2+0.270 27−0.106 40
    Mg2+0.431 21−0.056 97
    K+0.396 81−0.004 19
    Na+0.444 050.006 05
    ${\rm{HCO}}_3^{-}$0.099 170.482 26
    ${\rm{SO}}_4^{2-}$0.414 96−0.072 80
    Cl0.447 43−0.016 36
    ${\rm{NO}}_3^{-}$−0.063 99−0.579 08
    下载: 导出CSV

    表  3  海水入侵指标的等级划分 (单位/mg·L−1

    Table  3.   Classification of indexes of seawater intrusion (unit/mg·L−1)

    特征因子IIIIIIIV
    无入侵轻度入侵中度入侵严重入侵
    Cl ≤250 ≤600 ≤1 500 >1 500
    ${\rm{SO}}_4^{2-}$ ≤200 ≤450 ≤1 200 >1 200
    M ≤1 000 ≤2 000 ≤3 000 >3 000
    SAR ≤2 ≤3.55 ≤10 >10
    γCl/γHCO3 ≤0.5 ≤1.0 ≤6.6 >6.6
    下载: 导出CSV
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    KOU Xinyue, DING Junjun, LI Yuzhong, MAO Lili, LI Qiaozhen, XU Chunying, ZHENG Qian, ZHUANG Shan. Identifying the sources of groundwater NO3 -N in agricultural region of Qingdao[J]. Environmental Science, 2021, 42(7): 3232−3241.
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出版历程
  • 收稿日期:  2023-04-20
  • 录用日期:  2023-08-11
  • 修回日期:  2023-08-07
  • 刊出日期:  2023-10-01

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