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阳朔典型铅锌矿区流域土壤重金属空间分布特征及来源解析
引用本文:陈盟,潘泳兴,黄奕翔,王櫹橦,张睿东.阳朔典型铅锌矿区流域土壤重金属空间分布特征及来源解析[J].环境科学,2022,43(10):4545-4555.
作者姓名:陈盟  潘泳兴  黄奕翔  王櫹橦  张睿东
作者单位:桂林理工大学环境科学与工程学院, 桂林 541004;桂林理工大学岩溶地区水污染控制与用水安全保障协同创新中心, 桂林 541004;桂林理工大学地球科学学院, 桂林 541004
基金项目:广西自然科学基金项目(2020JJB150016);广西科技基地和人才专项项目(桂科AD19110046);桂林理工大学科研启动费项目(RD18100525)
摘    要:以广西阳朔县典型铅锌矿为研究对象,对其流域内水系表层土壤中10种金属元素(Cr、Mn、Ni、Cu、Zn、As、Cd、Sb、Hg和Pb)含量进行分析测定.综合运用Pearson相关性分析、主成分分析(PCA)和正定矩阵因子分解法(PMF)等多种方法,识别和定量解析污染源及贡献.在168个表层土壤样本分析数据中,土壤中Zn、Cd、Hg和Pb的含量平均值高于国家土壤环境质量标准,Sb、Cd、Cu、Pb和Zn的含量平均值分别比当地背景值高出约1.01、5.50、3.29、9.11和10.67倍,表明重金属在表层土壤中已经富集.土壤重金属地累积指数(Igeo)结果显示,研究区污染最严重的是Hg,其次为Pb、Zn和Mn.相关性分析和主成分分析结果表明,区内表层土壤中金属污染来源较为复杂,且明显受到人为活动的影响.Cu、Zn、Cd、Sb、As和Pb主要来源于采矿活动;Hg、Cr和Ni由土壤母质控制;Mn和Cd主要来源于工矿与农业活动.PMF模型分析结果表明,表层土壤中金属来源分为3种,工矿活动源、自然源和工矿及农业活动复合源的综合贡献率分别为58.0%、13.5%和28.6%.Ni、Cu、Zn、As、Sb、Hg和Pb以工矿活动源为主,贡献率分别为46.9%、89.6%、45.5%、67.8%、99.3%、58.3%和96.8%;母岩矿物风化和降雨冲刷等自然源对Cr的相对贡献率达到44.6%,对Ni和Hg也分别有23.2%和21.0%的相对贡献率;工矿及农业活动的复合源对Mn和Cd的相对贡献率分别达到75.4%和70.4%.

关 键 词:铅锌矿  土壤重金属  空间分布  来源解析  正定矩阵因子分解法(PMF)
收稿时间:2022/1/14 0:00:00
修稿时间:2022/2/17 0:00:00

Spatial Distribution and Sources of Heavy Metals in Soil of a Typical Lead-Zinc Mining Area, Yangshuo
CHEN Meng,PAN Yong-xing,HUANG Yi-xiang,WANG Xiao-tong,ZHANG Rui-dong.Spatial Distribution and Sources of Heavy Metals in Soil of a Typical Lead-Zinc Mining Area, Yangshuo[J].Chinese Journal of Environmental Science,2022,43(10):4545-4555.
Authors:CHEN Meng  PAN Yong-xing  HUANG Yi-xiang  WANG Xiao-tong  ZHANG Rui-dong
Institution:College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China;Collaborative Innovation Center for Water Pollution Control and Water Security in Karst Area, Guilin University of Technology, Guilin 541004, China;College of Earth Sciences, Guilin University of Technology, Guilin 541004, China
Abstract:Taking a typical lead-zinc mining area in Yangshuo county, Guangxi as the research object, the contents of 10 metal elements (Cr, Mn, Ni, Cu, Zn, As, Cd, Sb, Hg, and Pb) in the surface soil of Sidihe River basin in Yangshuo were analyzed and determined. Pearson correlation analysis, principal component analysis (PCA), positive definite matrix factorization (PMF), and other methods were comprehensively used to quantitatively analyze their contributions and identify pollution sources. In total, 168 surface soil samples were collected across the study area. The mean concentrations of Zn, Cd, Hg, and Pb in the soils were higher than the National Environmental Quality Standards for Soils in China. The mean contents of Sb, Cd, Cu, Pb, and Zn were higher than their corresponding local background values by approximately 1.01, 5.50, 3.29, 9.11, and 10.67 times, respectively, indicating that heavy metals have been enriched in topsoil. The Igeo showed that the major pollutant element in the soils was Hg, followed by Pb, Zn, and Mn. Correlation analysis and principal component analysis showed that the sources of metal pollution in surface soil in the study area were complex and mainly from human activities. Cu, Zn, Cd, Sb, As, and Pb were mainly derived from mining activities; Hg, Cr, and Ni were controlled by soil parent material sources; and Mn and Cd were mainly derived from mining activities and agricultural activities. PMF model analysis results showed that the metal pollution sources in the surface soil were jointly affected by these three sources. Mining activities, natural sources, and a mixed source of mining activities and agricultural activities were the main sources of heavy metal pollution in the soils, accounting for 58.0%, 13.5%, and 28.6% of the total heavy metal accumulation, respectively. Ni, Cu, Zn, As, Sb, Hg, and Pb were derived mainly from mining activities. Cr, Ni, and Hg were mainly attributed to natural sources, such as soil parent materials and rainfall erosion (44.6%, 23.2%, and 21.0%, respectively), and Mn and Cd were associated with a mixed source of mining activities and agricultural activities (75.4% and 70.4%).
Keywords:lead-zinc mine  heavy metal in soil  spatial distribution  source apportionment  positive matrix factorization (PMF)
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