摘要
为合理优化筛选土壤长期监测点位,对土壤监测区域开展均质性分区研究,以某山区金属矿下游耕地土壤中As、Cr、Pb、Zn、Cd、Cu、Ni、Hg 8种重金属元素含量作为研究变量,采用地统计分析方法对其开展空间结构特征分析,采用主成分-模糊聚类分析方法对土壤空间进行均质性分区研究。结果表明:研究区域8种重金属元素均具有空间自相关性,Hg、Cu、Ni、Cr为中度变异,As、Pb、Zn、Cd为高度变异;通过主成分-模糊聚类分析将8个重金属指标转化为3个综合指标,最终将研究区域合理划分为3个近似均质性的分区,As、Pb、Zn、Cd、Cu、Ni、Hg平均含量为分区1>分区2>分区3,Cr平均含量为分区3>分区2>分区1。通过主成分-模糊聚类分析方法得到的分区与实际土壤重金属含量空间分布情况相吻合,该方法可用于空间变异特征较复杂的土壤重金属多变量均质性分区。
To select long-term monitoring sites for soil,this study used the farmland downstream of a mine in a mountainous area,applied a geostatistical analysis method to analyze the spatial structural characteristics of eight heavy metal elements(As,Cr,Pb,Zn,Cd,Cu,Ni,and Hg),and carried out research on the homogeneity zoning of the soil space using the principal component-fuzzy cluster analysis(PCFCA)method.The results showed that all eight heavy metals had spatial autocorrelation.Additionally,Cr,Cu,Ni,and Hg elements were moderately variable,whereas As,Pb,Zn,and Cd elements were highly variable;eight heavy metal indicators were transformed into three comprehensive indicators,and the studied area was divided into three approximate homogeneous zones(Zone 1,Zone 2,and Zone 3)using the PCFCA method.The average contents of As,Pb,Zn,Cd,Cu,Ni,and Hg followed the order of Zone 1>Zone 2>Zone 3,whereas those of Cr were in the order of Zone 3>Zone 2>Zone 1.Moreover,the three zones obtained by the PCFCA method were consistent with the actual spatial distribution of soil heavy metal content,verifying that this method can be used for the multivariate homogeneous division of soil heavy metals with complex spatial variability.
作者
杨楠
连雅
夏新
张明顺
李宗超
YANG Nan;LIAN Ya;XIA Xin;ZHANG Mingshun;LI Zongchao(China National Environmental Monitoring Centre,Beijing 100012,China;School of Environment and Energy Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《农业环境科学学报》
CAS
CSCD
北大核心
2021年第7期1451-1459,共9页
Journal of Agro-Environment Science
基金
国家重点研发计划项目(2018YFC1800204)。
关键词
土壤重金属
均质性分区
空间自相关
空间异质性
主成分-模糊聚类分析
soil heavy metals
homogeneity zoning
spatial autocorrelation
spatial heterogeneity
principal component-fuzzy cluster analysis