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|Title:||Application of Positive Matrix Factorization – PMF on Soils from a Contamination-suspected Rural Area in Northern Italy|
|Authors:||VACCARO STEFANO; SOBIECKA ELZBIETA; CONTINI SERAFINO; LOCORO GIOVANNI; GAWLIK BERND|
|Citation:||CHEMOSPHERE vol. 69 p. 1055-1063|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Type:||Articles in periodicals and books|
|Abstract:||Multivariate factor analytical techniques are a well established statistical tool aimed at the approximation in terms of a linear combination of a number of basic components (called factors) of such experimental data where a number of different numerical parameters have been measured on each single sample of a numerically significant series of samples. Measured chemical compositions of soil samples are an example of a multivariate data set. Recent developments of multivariate factor analytical techniques have led to the approach of Positive Matrix Factorization (PMF), a weighted least squares fit of the data matrix in which the weights are determined depending on the error estimates of each individual data value. This approach relies on more physically significant assumptions than previous methods like Principal Components Analysis (PCA), which have been applied so far in the analysis of sets of soil samples. In this paper we apply PMF to characterise the pollutant source in set of geographically referenced soil samples from a rural area of 200m radius around a point characterised by high heavy metals concentration. Each sample have been analysed by WD-XRF for major and minor elements, CHN elemental analyzer and CV-AAS for Hg.|
|JRC Institute:||Institute for Environment and Sustainability|
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