SOURCE IDENTIFICATION OF ENVIRONMENTAL POLLUTANTS USING CHEMICAL ANALYSIS AND POSITIVE MATRIX FACTORIZATION

Multivariate modeling techniques are successfully used in different areas of environmental research because of their ability to process large data sets. The main objective of their application lies in the determination of data structures and hidden information which account for the data set variability. This thesis work seeks to explore the application of the positive matrix factorization (PMF) technique to different geochemical data sets on three spatial scales: local, pan-regional and pan-European. In particular, we focus on PMF identification of pollutants/contamination sources (e.g., anthropogenic and natural pollution) and chemical/physical processes (e.g., mineralization, weathering and corrosion) characterizing the data sets under examination. PMF analysis was carried out on four data sets with different spatial scale: \uf0a7 at local scale, geochemical characteristics of soil samples at the abandoned Coren del Cuc\uec mine dump were examined. A GIS-based approach was also combined with PMF results for a better source resolution. Five factors were determined: (i) two geo-morphological backgrounds characteristic of the area outside the dump; (ii) a source of mineralization situated inside the waste disposal area; and (iii) two different geochemical anomaly zones; \uf0a7 at a national level, eleven alpine lakes site in the Northern Italy were considered. X-ray fluorescence analyses on lake sediments were evaluated by PMF. Four interpretable mineralogical/chemical features were identified: (i) phosphate and sulphur source; (ii) carbonates; (iii) silicates; and (iv) heavy metal-bearing minerals. Also, to properly modify input information, a new PMF factor was determined, explaining a possible Pb contamination source; \uf0a7 in the pan-regional context, sediments of the Danube River basin, which cover an area of 817.000 km2, flowing through nine European countries, were analysed. The objective was to draw out information about the natural vs. anthropogenic origin of heavy metals and to determine the role of tributaries. Three factors were identified: (i) a carbonate component characterized by Ca and Mg; (ii) an alumino-silicate component dominated by Si and Al content and the presence of some metals attributed to natural processes; (iii) an anthropogenic source identified by Hg, S, P and some heavy metals load. Considering only the tributaries input, an additional source probably attributed to the use of fertilizers in agriculture was determined; \uf0a7 finally, a pan-European data set comprising sewage sludge from European waste water treatment plants was obtained. The final objective was to link the silver content to the increasingly use of silver nanoparticles in a variety of house-hold and personal care products. Here, method validation procedure was applied to the measured elements in order to compute correct uncertainties to be used in PMF application. The four resulting factors could be described by: (i) copper dissolution from water pipe lines; (ii) engineered silver nanoparticles load; (iii) anthropogenic influence suggested by the presence of different metals; and (iv) iron variation due to the use of this element for phosphorus removal in sewage sludge. These studies provide first evidence that PMF could be successfully applied to geochemical data sets at different spatial scale