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|Title:||Geochemical characterization of an abandoned mine site: A combined positive matrix factorization and GIS approach compared with principal component analysis|
|Authors:||COMERO Sara; SERVIDA Diego; CAPITANI Luisa; GAWLIK Bernd|
|Citation:||JOURNAL OF GEOCHEMICAL EXPLORATION vol. 118 p. 30-37|
|Publisher:||ELSEVIER SCIENCE BV|
|Type:||Articles in periodicals and books|
|Abstract:||Statistical methods are increasingly used for geochemical characterization of contaminated sites. The geochemical characteristics of the abandoned Coren del Cucì mine dump (Upper Val Seriana, Italy) were modelled by principal component analysis (PCA) and positivematrix factorization (PMF) of 56 soil samples analyzed for 11 elements and pH. PCA and PMF were used to investigate how different approaches deal with the preset type of data. PCA was performed on two data subsets—samples inside and outside the dump—recognized by cluster analysis. PMF was performed on the whole data set. However, a GIS-based approach was combined with PMF for better factor resolution. Three main principal components (PCs) were identified inside the dump: (i) the local ore mineralization; (ii) the background/regional metal content of rocks; and (iii) the variability of Cd. Two main PCs were obtained outside the dump: (i) the background/regionalmetal content of rocks; and (ii) the local ore elements. Five factors were determined by PMF: (i) two background geo-morphological characteristics of the area outside the dump; (ii) a source ofmineralization situated inside the waste disposal area; and (iii) two different geochemical anomaly zones. PMFwas found to be useful for estimating the number and composition of sources or processes that govern data characterized by heterogeneous behavior. In contrast to the application of PCA, no data pre-treatments procedures are needed to apply PMF.|
|JRC Institute:||Institute for Environment and Sustainability|
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