地学前缘 ›› 2018, Vol. 25 ›› Issue (1): 267-275.DOI: 10.13745/j.esf.yx.2017-02-3

• 非主题来稿选登 • 上一篇    下一篇

地下水次要组分视背景值研究:以柳江盆地为例

廖磊,何江涛,彭聪,张振国,王磊   

  1. 1. 中国地质大学(北京) 水资源与环境学院, 北京 100083
    2. 核工业二九〇研究所, 广东 韶关 512026
  • 收稿日期:2016-06-06 修回日期:2017-02-06 出版日期:2018-01-15 发布日期:2018-01-15
  • 作者简介:何江涛(1974—),男,副教授,主要研究方向为土壤地下水污染控制与修复。E-mail:jthe@cugb.edu.cn
  • 基金资助:
    廖磊(1991—),男,硕士,主要研究方向为土壤地下水污染控制与修复。E-mail:Leiz91@163.com

Methodologies in calculating apparent background values of minor components in groundwater: a case study of the Liujiang Basin

LIAO Lei,HE Jiangtao,PENG Cong,ZHANG Zhenguo,WANG Lei   

  1. 1. School of Water Resources and Environment, China University of Geosciences(Beijing), Beijing 100083, China
    2. No.290 Research Institute, CNNC, Shaoguan 512026, China
  • Received:2016-06-06 Revised:2017-02-06 Online:2018-01-15 Published:2018-01-15

摘要: 在广泛调研总结国内外次要组分背景值研究的基础上,对比分析各方法的优缺点,提出水化学分析与数理统计法相结合的地下水次要组分视背景值研究体系。该方法体系首先在次要组分与主要组分之间进行因子分析,建立次要组分与主要组分的联系,运用三倍标准差准则识别主要组分粗大误差的异常值,利用Piper图水化学类型对异常值进行分析检验,从宏观上分析识别水化学影响明显异常的次要组分数据,再运用平均值加减2倍标准差迭代法和概率图法组合分析识别异常值。完成异常值剔除的数据取95百分位数作为次要组分视背景值的上限阈值。该方法有利于把握数据的整体统计特征,同时能避免主观确定阈值的误差,对异常数据的剔除高效充分。上述方法体系运用在柳江盆地表明,与4种常用数理统计学方法相比,该方法体系计算出的次要组分视背景值的可靠性与稳定性更高,同时能解释引起次要组分异常的原因及背景控制因素。运用水化学分析与数理统计法体系,较为科学合理地计算出了柳江盆地浅层地下水次要组分硝酸盐、偏硅酸、铝、氟和溴的视背景值阈值,分别为75.1、27.4、0.11、0.30和0.32 mg·L-1。硝酸盐视背景值阈值偏高,反映出柳江盆地广泛的农牧业养殖及生活污水排放已不可避免地对地下水产生了一定的影响。

关键词: 次要组分, 视背景值, 浅层地下水, 水化学, 数理统计, 柳江盆地

Abstract: Based on the extensive literature research of internationally adapted methodologies for the calculation of minor component background values, and after comparing and analyzing the relative strength of each method, a methodology combining hydrochemistry analysis and mathematical statistics was developed for the analysis of groundwater minor component apparent background values. Firstly, factor analysis was carried out on the minor and major components to establish their relationships. Next, the outliers of the major components were identified with the 3σ rule, and results were checked by using Piper graph, which was also used to determine macroscopically the obvious outliers of minor components related to hydrochemistry. Then the outliers of the minor components were identified by the iterative 2σ technique and probability graph method. Finally, the 95th percentiles for the outliereliminated data were taken as the apparent background values of the minor components. This methodology is useful in understanding the overall statistical characteristics, avoiding errors in threshold values due to subjectivity, and removing outliers efficiently. Comparing with the four frequentlyused mathematical statistics methods, this methodology, applied in the Liujiang Basin study, was shown to be more stable and reliable for calculating apparent background values of minor components, and able to explain the abnormal minor components and background constraints. The results show that the apparent background values of minor components in the Liujiang Basin shallow groundwater were 75.1, 27.4, 0.11, 0.30 and 0.32 mg/L for nitrate, metasilicic, acidaluminum, fluorine and bromine, respectively. The high nitrate value indicates that the extensive farming, husbandry and sewage discharge in the Liujiang Basin have inevitably influenced groundwater compositions in the area.

Key words: minor component, apparent background value, shallow groundwater, hydrochemistry, mathematical statistics, Liujiang Basin

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