A recent type of receptor modelling technique the Positive Matrix Factorization (PMF) has been applied to a geochemical dataset obtained by XRF analysis on sediments from 11 alpine lakes located in Italy. Also, two usual pattern recognition techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA), were investigated. Four interpretable factors were identified through PMF analysis, in connection with the mineralogical/chemical features of lake sediments in the catchment areas: phosphate and sulphur source, carbonates, silicates and heavy metal-bearing minerals. Also, to properly modify individual uncertainty estimates, a new PMF factor was identified, explaining a possible Pb contamination source.

Characterisation of Alpine lake sediments using multivariate statistical techniques / S. Comero, G. Locoro, G. Free, S. Vaccaro, L. De Capitani, B.M. Gawlik. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - 107:1(2011), pp. 24-30. [10.1016/j.chemolab.2011.01.002]

Characterisation of Alpine lake sediments using multivariate statistical techniques

S. Comero
Primo
;
L. De Capitani
Penultimo
;
2011

Abstract

A recent type of receptor modelling technique the Positive Matrix Factorization (PMF) has been applied to a geochemical dataset obtained by XRF analysis on sediments from 11 alpine lakes located in Italy. Also, two usual pattern recognition techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA), were investigated. Four interpretable factors were identified through PMF analysis, in connection with the mineralogical/chemical features of lake sediments in the catchment areas: phosphate and sulphur source, carbonates, silicates and heavy metal-bearing minerals. Also, to properly modify individual uncertainty estimates, a new PMF factor was identified, explaining a possible Pb contamination source.
Italy; Lake sediments; Positive Matrix Factorization
Settore GEO/08 - Geochimica e Vulcanologia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/159071
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