Mass spectrometry-based Aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol

Abstract

Abstract. Particulate matter (PM) largely consists of secondary organic aerosol (SOA) that is formed via oxidation of biogenic and anthropogenic volatile organic compounds (VOCs). Unambiguous identification of SOA molecules and their assignment to their precursor vapors are challenges that have so far only succeeded for a few SOA marker compounds, which are now well characterized and (partly) available as authentic standards. In this work, we resolve the complex composition of SOA by means of a top-down approach based on the newly created Aerosolomics database, which is fed by non-target analysis results of filter samples from oxidation flow reactor experiments. We investigated the oxidation products from the five biogenic VOCs α-pinene, β-pinene, limonene, 3-carene, and trans-caryophyllene and from the four anthropogenic VOCs toluene, o-xylene, 1,2,4-trimethylbenzene, and naphthalene. Using ultrahigh-performance liquid chromatography coupled to a high-resolution (Orbitrap) mass spectrometer, we determine the molecular formula of 596 chromatographically separated compounds based on exact mass and isotopic pattern. We utilize retention time and fragmentation mass spectra as a basis for unambiguous attribution of the oxidation products to their parent VOCs. Based on the molecular-resolved application of the database, we are able to assign roughly half of the total signal of oxygenated hydrocarbons in ambient suburban PM2.5 to one of the nine studied VOCs. The application of the database enabled us to interpret the appearance of diurnal compound clusters that are formed by different oxidation processes. Furthermore, by performing a hierarchical cluster analysis (HCA) on the same set of filter samples, we identified compound clusters that depend on sulfur dioxide mixing ratio and temperature. This study demonstrates how Aerosolomics tools (database and HCA) applied to PM filter samples can improve our understanding of SOA sources, their formation pathways, and temperature-driven partitioning of SOA compounds.