Organic molecular markers and source contributions in a polluted municipality of north-east Italy: Extended PCA-PMF statistical approach

Exceeding the maximum levels for environmental pollutants creates public and scientific interest for the environmental and human health impact it may have. In Northern Italy, the Po Valley, and in particular the Veneto region, is still a hotspot for air quality improvement. Several monitoring campaigns were carried out in this area to acquire information about sources of pollutants which are considered critical. For the first time, a deep study of the aerosol organic fraction was performed in the town Sernaglia della Battaglia, nearby Treviso. During three seasons of 2017, PM1 and PM2.5 samples were collected simultaneously. Organic molecular markers have been analyzed by in-situ derivatization thermal desorption gas chromatography time-of-flight mass spectrometry (IDTD-GC-TOFMS). Alkanes, polycyclic aromatic hydrocarbons, oxi-polycyclic aromatic hydrocarbons, anhydrous sugars, resins acids, triterpenoids, and acids were considered. The organic chemical composition has been analyzed based on seasonal variation and source contributions. Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) have been combined to deeply investigate the main sources of particulate organic matter. On the one hand, PCA evaluates the correlations between the organic markers and their seasonal distribution. On the other hand, the source contributions to aerosol composition are estimated by PMF. Four main emission sources were found by PMF: solid fuel combustion (coal, wood), combustion of petroleum distillates (gas and fuel oil) and exhaust gases of vehicles, industrial combustion processes, home heating, and forest fires are evaluated as the most important sources for the air quality and pollution in this municipality of Northern Italy. •Extensive analysis of the Particulate Matter in a hot spot for air quality in Europe, the Po Valley.•Chemical and environmental characterization of Particulate Matter.•Complementary use of two statistical techniques: PCA and PMF.