Innovative methods for monitoring pollutant concen-trations in surface and subsurface soil are crucial tasks in environmental research. Actually, the main purpose is to develop monitoring strategies able to provide detailed in-formation about temporal and spatial evolution of contami-nants in subsoil. Here, we present a monitoring strategy for a municipal solid waste disposal, integrating a field sur-vey to measure chemical-physical parameters of soil, and a multivariate statistical procedure for data analysis. On a georeferenced sampling grid, we collected superficial soil and determined ten parameters. Particularly, we measured: in situ soil magnetic susceptibility, total concentrations of 7 heavy metals (Co, Cu, Fe, Mn, Ni, Pb, Zn), soil electric conductivity, and pH. Data analysis is based on a multi-variate procedure aimed to characterize the underlying cor-relation structure. Principal component analysis and clus-tering algorithm are applied in successive runs, for indi-viduating a set of new independent variables and a classi-fication of sampling points.

Correlation structure among chemical-physical variables measured in municipal solid waste disposal

RAGOSTA, Maria;
2008

Abstract

Innovative methods for monitoring pollutant concen-trations in surface and subsurface soil are crucial tasks in environmental research. Actually, the main purpose is to develop monitoring strategies able to provide detailed in-formation about temporal and spatial evolution of contami-nants in subsoil. Here, we present a monitoring strategy for a municipal solid waste disposal, integrating a field sur-vey to measure chemical-physical parameters of soil, and a multivariate statistical procedure for data analysis. On a georeferenced sampling grid, we collected superficial soil and determined ten parameters. Particularly, we measured: in situ soil magnetic susceptibility, total concentrations of 7 heavy metals (Co, Cu, Fe, Mn, Ni, Pb, Zn), soil electric conductivity, and pH. Data analysis is based on a multi-variate procedure aimed to characterize the underlying cor-relation structure. Principal component analysis and clus-tering algorithm are applied in successive runs, for indi-viduating a set of new independent variables and a classi-fication of sampling points.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/4502
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