[1]LIU Qing,LV Zhen-jiang.Statistical Analysis of Nutrients Variables and Heavy Metals in Surface Soils of Cixi County[J].Research of Soil and Water Conservation,2009,16(06):106-111.
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Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
16
Number of periods:
2009 06
Page number:
106-111
Column:
Public date:
2009-12-20
- Title:
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Statistical Analysis of Nutrients Variables and Heavy Metals in Surface Soils of Cixi County
- Author(s):
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LIU Qing1, LV Zhen-jiang2
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1. Key Laboratory of Eco-environmental Science of Yellow River Delta of Shandong Province, Binzhou Univercity, Binzhou, Shandong 256603, China;
2. College of Forestry Science, Northwest A&F University, Yangling Shaanxi 712100, China
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- Keywords:
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soil geochemistry variables; data transformation; multivariate analyses
- CLC:
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S153.61
- DOI:
-
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- Abstract:
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Geochemical variables are affected by multiple factors and processes in soils, and the different responses to these factors causing complicated multivariate relationships between them. In this study, the relationships between heavy metals and nutrients variables in top soils of Cixi county were investigated using multivariate analyses, the methods are histograms, normal quantile-quantile (Q-Q) plots, data transformation and Cluster analysis. The results showed that there was strong variation in the values of these variables. Multi-model features in the histograms and multi-kink features in the normal quantile-quantile (Q-Q) plots were observed for these variables implying the existence of multiple populations. Obvious outliers were identified using the normal Q-Q plots. Most of the variables did not pass a Kolmogorov-Smirnov test for either normal or lognormal distribution, and the Box-Cox transformation was effective in transposing data to a form suited to further parametric statistical analyses. Cluster analysis classified the variables into three groups and variables that are affected by different factors such as agricultural activities and industrial pollutions. Land use was found to be the most important influencing factor in studied area.