GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Abstract

This study presents a procedure to differentiate the local and remote sources of particulate-bound polycyclic aromatic hydrocarbons (PAHs). Data were collected during an extended PM(2.5) sampling campaign (2009-2010) carried out for 1 year in Venice-Mestre, Italy, at three stations with different emissive scenarios: urban, industrial, and semirural background. Diagnostic ratios and factor analysis were initially applied to point out the most probable sources. In a second step, the areal distribution of the identified sources was studied by applying the discriminant analysis on factor scores. Third, samples collected in days with similar atmospheric circulation patterns were grouped using a cluster analysis on wind data. Local contributions to PM(2.5) and PAHs were then assessed by interpreting cluster results with chemical data. Results evidenced that significantly lower levels of PM(2.5) and PAHs were found when faster winds changed air masses, whereas in presence of scarce ventilation, locally emitted pollutants were trapped and concentrations increased. This way, an estimation of pollutant loads due to local sources can be derived from data collected in days with similar wind patterns. Long-range contributions were detected by a cluster analysis on the air mass back-trajectories. Results revealed that PM(2.5) concentrations were relatively high when air masses had passed over the Po Valley. However, external sources do not significantly contribute to the PAHs load. The proposed procedure can be applied to other environments with minor modifications, and the obtained information can be useful to design local and national air pollution control strategies.