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Chemometric interpretation of heavy metal patterns in soils worldwide

Authors
Journal
Chemosphere
0045-6535
Publisher
Elsevier
Publication Date
Volume
80
Issue
11
Identifiers
DOI: 10.1016/j.chemosphere.2010.06.010
Keywords
  • Heavy Metals
  • Soil
  • Soil Metal Index
  • Box–Cox
  • Principal Component Analysis

Abstract

Abstract Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs < 100%, and the set of polluted soils with SMIs > 100%. Additionally, univariate descriptive statistics and the Spearman’s non-parametric rank correlation coefficients were calculated for these three sets. A Box–Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the “masking” effect of the polluted soil patterns over the non-polluted ones.

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