Factor analysis of combined organic and inorganic aerosol mass spectra from high resolution aerosol mass spectrometer measurements

Positive matrix factorization (PMF) was applied to the merged high resolution mass spectra of organic and inorganic aerosols from aerosol mass spectrometer (AMS) measurements to investigate the sources and evolution processes of submicron aerosols in New York City in summer 2009. This new approach is able to study the distribution of organic and inorganic species in different types of aerosols, the acidity of organic aerosol (OA) factors, and the fragment ion patterns related to photochemical processing. In this study, PMF analysis of the unified AMS spectral matrix resolved 8 factors. The hydrocarbon-like OA (HOA) and cooking OA (COA) factors contain negligible amounts of inorganic species. The two factors that are primarily ammonium sulfate (SO4-OA) and ammonium nitrate (NO3-OA), respectively, are overall neutralized. Among all OA factors the organic fraction of SO4-OA shows the highest degree of oxidation (O/C=0.69). Two semi-volatile oxygenated OA (OOA) factors, i.e., a less oxidized (LO-OOA) and a more oxidized (MO-OOA), were also identified. MO-OOA represents local photochemical products with a diurnal profile exhibiting a pronounced noon peak, consistent with those of formaldehyde (HCHO) and O-x(=O-3 + NO2). The NO+/NO2+ ion ratio in MO-OOA is much higher than that in NO3-OA and in pure ammonium nitrate, indicating the formation of organic nitrates. The nitrogen-enriched OA (NOA) factor contains similar to 25% of acidic inorganic salts, suggesting the formation of secondary OA via acid-base reactions of amines. The size distributions of OA factors derived from the size-resolved mass spectra show distinct diurnal evolving behaviors but overall a progressing evolution from smaller to larger particle mode as the oxidation degree of OA increases. Our results demonstrate that PMF analysis of the unified aerosol mass spectral matrix which contains both inorganic and organic aerosol signals may enable the deconvolution of more OA factors and gain more insights into the sources, processes, and chemical characteristics of OA in the atmosphere.