Characterisation of Alpine lake sediments using multivariate statistical techniques
A new type of receptor modelling techniques, the Positive Matrix Factorization (PMF) has been applied to a geochemical dataset obtained by XRF analysis on sediments from 11 alpine lakes situated in Italy. A comparison with the customary multivariate techniques, Principal Component Analysis (PCA) and Cluster Analysis (CA) was carried out. Rejection of below-detection-limit data and data transformation were applied for analysis with the last two methods. Four interpretable factors were extracted in PMF analysis, which are identified as mineralogical features of lake sediments like sulphides and phosphates, carbonates, aluminosilicate and heavy metals bearing ores.
COMERO Sara;
LOCORO Giovanni;
FREE Gary;
VACCARO Stefano;
CARDOSO Ana;
CAPITANI Luisa;
GAWLIK Bernd;
2011-05-19
ELSEVIER SCIENCE BV
JRC56916
0169-7439,
https://publications.jrc.ec.europa.eu/repository/handle/JRC56916,
10.1016/j.chemolab.2011.01.002,
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