Study of multicomponent chromatograms using a chemometric approach: characterization of the organic fraction of atmospheric aerosol

In this Ph.D. project it has been developed a study of multicomponent chromatograms of complex mixtures, using a chemometric approach. The activity has been concentrated in the study of analytical-separative methods (in particular Gas Cromatography-Mass Spectrometry, GC-MS) for complex samples of environmental interest, especially for PM (particulate matter) samples. A fundamental part of this Ph.D. project has been dedicated to the development of mathematical and statistical algorithms for the data treatment of the GC-MS signal obtained from the analysis, in order to extract relevant information from the complex chromatogram, such as important indexes involved in the environmental studies. In particular, the project involved the identification and the characterization of homologous series of organic compounds (n-alkanes and carboxylic acids) that could be usually found in environmental samples, because they contain fundamental information to distinguish, for example, different types of emission sources, anthropic or biogenic. It has been developed a chemometric approach, which uses the AutoCoVariance Function (ACVF) computed on the digitized chromatogram, in order to quantificate the number of terms of the homologous series (nmax) and their distribution, with particular attention to the relative abundance and, consequently, the prevalance of the odd to even terms of the series (CPI). This is one of the most important parameters (environmental biomarkers) to perform a study of source apportionment. The method has been validated using simulated chromatograms and its applicability has been tested, with successful results, on real samples of known origin (e.g. gasoil or plant samples) and, finally, to particulate matter samples, obtained thanks to a collaboration with the research group of Environmental Sciences Department of the University of Milano Bicocca