| Summary: | The study of aerosol composition is fundamental to highlight the quality of the air and its environmental impact, distinguishing between the natural and the anthropogenic components. In particular the investigation of several important environmental markers, in the aerosol, allow the investigation of their potential sources, their chemical evolutions and their transport mechanisms. The aim of this PhD Thesis was to study the large applicability of a wide range of different environmental markers in very different environments and validate their use as a research tool. On one hand, two studies focused on environmental markers in one remote area, Antarctica, that, thanks to its geographical position, is a perfect open-air laboratory, optimal for studying those emission sources exclusively natural. On the other hand, one study focused on environmental markers in one urban site, Sernaglia della Battaglia, province of Treviso, Italy. This medium-sized village is proposed as an ideal place for the study of environmental markers which can discriminate between natural and anthropic markers. In Antarctica, the first study focused on ionic species in two different sampling sites: one coastal site, near the scientific base “Mario Zucchelli” and one inland site, near the research base “Concordia”. The results of this study highlighted that aerosol collected close to the coast was mainly a primary aerosol characterized by sea salt input species such as Na+,Mg2+, and SO42−, while inland samples showed a secondary aerosol with high concentrations of anionic species: non-sea salt sulfate, methanesulfonic acid and nitrate. The second study in Antarctica focused only one the coastal site nearby the scientific base “Mario Zucchelli”, adding to the ionic species further environmental markers, such as sugars, phenolic compounds and amino acids. The most concentrated environmental markers were glucose among sugars, vanillin among the phenolic compounds and glycine among the amino acids. The third study completely focused on samples ...
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