A new method to discriminate secondary organic aerosols from different sources using high-resolution aerosol mass spectra
Abstract. Organic aerosol (OA) represents a significant and often major fraction of the non-refractory PM1 (particulate matter with an aerodynamic diameter da < 1 μm) mass. Secondary organic aerosol (SOA) is an important contributor to the OA and can be formed from biogenic and anthropogenic precursors. Here we present results from the characterization of SOA produced from the emissions of three different anthropogenic sources. SOA from a log wood burner, a Euro 2 diesel car and a two-stroke Euro 2 scooter were characterized with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and compared to SOA from α-pinene.
The emissions were sampled from the chimney/tailpipe by a heated inlet system and filtered before injection into a smog chamber. The gas phase emissions were irradiated by xenon arc lamps to initiate photo-chemistry which led to nucleation and subsequent particle growth by SOA production.
Duplicate experiments were performed for each SOA type, with the averaged organic mass spectra showing Pearson's r values >0.94 for the correlations between the four different SOA types after five hours of aging. High-resolution mass spectra (HR-MS) showed that the dominant peaks in the MS, m/z 43 and 44, are dominated by the oxygenated ions C2H3O+ and CO2+, respectively, similarly to the relatively fresh semi-volatile oxygenated OA (SV-OOA) observed in the ambient aerosol. The atomic O:C ratios were found to be in the range of 0.25–0.55 with no major increase during the first five hours of aging. On average, the diesel SOA showed the lowest O:C ratio followed by SOA from wood burning, α-pinene and the scooter emissions. Grouping the fragment ions revealed that the SOA source with the highest O:C ratio had the largest fraction of small ions.
The HR data of the four sources could be clustered and separated using principal component analysis (PCA). The model showed a significant separation of the four SOA types and clustering of the duplicate experiments on the first two principal components (PCs), which explained 79% of the total variance. Projection of ambient SV-OOA spectra resolved by positive matrix factorization (PMF) showed that this approach could be useful to identify large contributions of the tested SOA sources to SV-OOA. The first results from this study indicate that the SV-OOA in Barcelona is strongly influenced by diesel emissions in winter while in summer at SIRTA at the southwestern edge of Paris SV-OOA is more similar to alpha-pinene SOA. However, contributions to the ambient SV-OOA from SOA sources that are not covered by the model can cause major interference and therefore future expansions of the PCA model with additional SOA sources is recommended.
