The distribution and speciation of heavy metals in five agricultural soils of Piedmont Region (north-western Italy) were investigated. Ten metals, namely Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Ti and Zn were considered. Analytical determinations were performed by atomic emission or atomic absorption spectroscopy after microwave sample dissolution in acid solution. Total metal concentrations fit in the typical concentration ranges for unpolluted soils, with the exception of cadmium and lead content in some horizons. The effect of sampling depth on concentrations was discussed. Speciation studies were carried out by applying Tessier's procedure, which allows to subdivide the total metal content into five fractions, representing portions bound to different components of the soil. Moreover, the element labilities in two soils were evaluated by extraction with EDTA. Correlations among the variables and/or similarities among the sampling points were identified by principal component analysis and hierarchical cluster analysis.
Heavy metals in agricultural soils from Piedmont, Italy. Distribution, speciation and chemometric data treatment
ACETO, Maurizio;
2002-01-01
Abstract
The distribution and speciation of heavy metals in five agricultural soils of Piedmont Region (north-western Italy) were investigated. Ten metals, namely Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Ti and Zn were considered. Analytical determinations were performed by atomic emission or atomic absorption spectroscopy after microwave sample dissolution in acid solution. Total metal concentrations fit in the typical concentration ranges for unpolluted soils, with the exception of cadmium and lead content in some horizons. The effect of sampling depth on concentrations was discussed. Speciation studies were carried out by applying Tessier's procedure, which allows to subdivide the total metal content into five fractions, representing portions bound to different components of the soil. Moreover, the element labilities in two soils were evaluated by extraction with EDTA. Correlations among the variables and/or similarities among the sampling points were identified by principal component analysis and hierarchical cluster analysis.File | Dimensione | Formato | |
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