Innovative methods for monitoring pollutant concentrations in surface and subsurface soil are crucial tasks in environmental research. Actually, the main purpose is to develop monitoring strategies able to provide detailed information about temporal and spatial evolution of contaminants in subsoil. Here, we present a monitoring strategy for a municipal solid waste disposal, integrating a field survey 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 multivariate procedure aimed to characterize the underlying correlation structure. Principal component analysis and clustering, algorithm are applied in successive runs. for individuating a set of new independent variables and a classification of sampling points.

CORRELATION STRUCTURE AMONG CHEMICAL-PHYSICAL VARIABLES MEASURED IN MUNICIPAL SOLID WASTE DISPOSAL

Caggiano R;D'Emilio M;Sabia S
2008

Abstract

Innovative methods for monitoring pollutant concentrations in surface and subsurface soil are crucial tasks in environmental research. Actually, the main purpose is to develop monitoring strategies able to provide detailed information about temporal and spatial evolution of contaminants in subsoil. Here, we present a monitoring strategy for a municipal solid waste disposal, integrating a field survey 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 multivariate procedure aimed to characterize the underlying correlation structure. Principal component analysis and clustering, algorithm are applied in successive runs. for individuating a set of new independent variables and a classification of sampling points.
2008
Istituto di Metodologie per l'Analisi Ambientale - IMAA
soil pollution
magnetic susceptibility
heavy metals
electric conductivity
multivariate analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/22872
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