The Application of Multivariate Statistical Methods for the Evaluation of Soil Profiles (8 pp)

Abstract

Background, Aim and Scope Contamination of soils does not only occur on their surface over large areas, but also in depth. Therefore a characterization of soil state after pollution demands a three-dimensional soil sampling, by what a large number of samples has to be analyzed. Analytical results could be evaluated by multivariate statistical methods, which have already been used for the evaluation of data sets containing results from soil sampling of two dimensions like areas or single profiles. In this case study, multivariate statistical methods were applied to investigate structure and interactions between features in a data set containing results of three-dimensional soil sampling. The investigated soil profiles were contaminated by emissions of a former cement and phosphate fertilizer plant. The aim of this study was to determine the remaining extent of contamination and to analyze whether pollutants are mobilized and vertically transported within the profiles. Materials and Methods: Three soil profiles were sampled in the surroundings of the plant. Grain size, organic and carbonatic bonded carbon, pH value, and the total contents of Ca, Cd, Co, Cu, F, Fe, K, Mn, Mg, Na, Ni, P, Pb, and Zn were determined. The resulting data set was evaluated by cluster analysis, linear discriminant analysis, and principal components analysis. The sequential extraction procedure according to Zeien and Brummer was applied to analyze the binding properties of Ca, Cd, Cu, Na, Pb, and Zn from selected samples. Results: Cd was identified as contaminant of the top soils. The pH values of the bottom soils were determined to be in alkaline range, which is unnaturally high. Variables were clustered according to enrichment of variables in top soils. The samples were classified regarding their pollution state and their substrate by cluster analysis, which was confirmed by linear discriminant analysis. Geogenic and anthropogenic sources of variables as well as relationships between variables like the binding of heavy metals at organic matter were detected by using principal components analysis. The binding of heavy metals at organic matter in the top soils was confirmed by the results of the applied sequential extraction. A vertically altered distribution of Na binding was determined. Discussion: According to the current soil conditions, the uptake of heavy metals had probably occurred by the over ground part of plants during the deposition. The distribution of Na should likely result from the vertical transport of Na, which would also explain the high pH values of the bottom soils by ion exchange. Altogether, the main amount of deposited Ca, F, Na, P, and heavy metals is likely nearly insoluble bound in the top soils. Conclusions: Ten years after the end of production, the pollution of top soils in the surroundings of the former plant is still high. However, regarding the ecotoxicological relevance the now explored interactions between several soil features and elements strongly indicate that there is no short-term to medium-term risk of a mobilization of the deposited elements with the exception of Na. Recommendations and Perspectives: The results of this case study prove that multivariate statistical methods are powerful tools to explore interactions of variables and relationships in a data set derived from three dimensional soil sampling. The methods applied in this work can be highly recommended for evaluations of large data sets resulting from two- or three-dimensional samplings. Multivariate statistical methods enable the characterization of soils and their pollution state in a simple and economic way.