Zhang Changbo, Wu Longhua, Luo Yongming, Zhang Haibo, Christie Peter
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China.
Environ Pollut. 2008 Feb;151(3):470-6. doi: 10.1016/j.envpol.2007.04.017. Epub 2007 Jul 2.
Principal components analysis (PCA) and correlation analysis were used to estimate the contribution of four components related to pollutant sources on the total variation in concentrations of Cu, Zn, Pb, Cd, As, Se, Hg, Fe and Mn in surface soil samples from a valley in east China with numerous copper and zinc smelters. Results indicate that when carrying out source identification of inorganic pollutants their tendency to migrate in soils may result in differences between the pollutant composition of the source and the receptor soil, potentially leading to errors in the characterization of pollutants using multivariate statistics. The stability and potential migration or movement of pollutants in soils must therefore be taken into account. Soil physicochemical properties may offer additional useful information. Two different mechanisms have been hypothesized for correlations between soil heavy metal concentrations and soil organic matter content and these may be helpful in interpreting the statistical analysis.
运用主成分分析（PCA）和相关性分析，估算了与污染源相关的四个组分对来自中国东部某山谷表层土壤样品中铜、锌、铅、镉、砷、硒、汞、铁和锰浓度总变化的贡献。该山谷有众多铜锌冶炼厂。结果表明，在进行无机污染物源解析时，其在土壤中的迁移趋势可能导致源污染物组成与受体土壤之间存在差异，从而可能导致使用多元统计方法表征污染物时出现误差。因此，必须考虑污染物在土壤中的稳定性以及潜在的迁移或移动情况。土壤理化性质可能会提供额外的有用信息。针对土壤重金属浓度与土壤有机质含量之间的相关性，已提出两种不同的机制，这可能有助于解释统计分析结果。
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