摘要： 为了解三江平原农田土壤重金属含量并摸清其来源,通过野外调查取样,结合地统计学和经典相关分析方法,分析了三江平原农田土壤Cd、Cr、Pb、Cu、Zn、Ni、Hg及As含量的空间变异与来源,结果表明:研究区Hg的变异系数为100%,属于高度的空间异质性,其他7种重金属含量的变异系数为34.68%～51.93%,具有中等空间异质性。土壤重金属含量呈斑块状、条带状、大斑块状零星块状分布特征。三江平原农田土壤中Pb、Ni和Zn的平均值低于黑龙江省土壤背景值,其他5种重金属含量均高于黑龙江省土壤背景值,即表现为累积的特征,但8种重金属含量的平均值与最大值均未超过(GB 15618-1995)国家二级标准限值,土壤质量良好。经Pearson相关系数法、地统计分析和主成分分析得出,Pb、Cu和Cr主要来自于自然源,其余各元素主要由人为源构成,且人为源中占主导地位的是化肥和农药的大量施用及燃煤尘。

关键词: 三江平原, 土壤重金属, 空间变异, 地统计分析, 主成分分析

Abstract: In order to understand the content of heavy metals in Sanjiang plain farmland soil,and find out its source.In the study,content of Cd,Cr,Pb,Cu,Zn,Ni,Hg and As in forest soil in Sanjiang plain were analyzed after field investigation.And the spatial variability and source of these heavy metal elements in the region were evaluated by using geo-statistics combined with classic analysis methods.The results showed that:The coefficient variation of Hg was 100%,it varied in high level in terms of spatial distribution,while the other heavy metals varied in middle level in terms of spatial distribution in the soil with variation coefficients ranging from 34.68% to 51.93%.The distribution pattern of heavy metal content in the region presented as patchy,banded,big patchy and fragmentary patchy modes.The average content of Pb,Ni and Zn was lower than background value of soil in Heilongjiang,while other heavy metals content was higher than background value of soil in Heilongjiang,so they showed a trend of accumulation.However,the average and maximum contents were below the soil threshold levels of the second grade criteria of "the Chinese environmental quality standard"(GB 15618-1995),the quality of soil was good.According to Pearson correlation coefficient method,geo-statistics analysis and principal component analysis,Pb,Cu and Cr were mostly from natural sources while other heavy metal elements are derived from anthropogenic sources,and anthropogenic sources is dominated by the excessive use of fertilizer and pesticide and coal combustion.

Key words: Sanjiang plain, Soil heavy metal, Spatial variability, Geo-statistics analysis, Principal component analysis

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• X53