Estimates of the organic aerosol volatility in a boreal forest using two independent methods

Abstract. Volatility distribution of secondary organic aerosols, i.e. the particle mass fractions of semi-volatile, low-volatility and extremely low-volatility organic compounds was characterized in a boreal forest environment of Hyytiälä, Southern Finland. This was done by interpreting field measurements using a Volatility Tandem Differential Mobility Analyzer (VTDMA) with a kinetic evaporation model. The field measurements were performed during April and May of 2014. On average, 40 % of organics in particles was semi-volatile; 34 % low-volatility organics and 26 % extremely low-volatility organics. The model was, however, very sensitive towards the vaporization enthalpies assumed for the organics (ΔHVAP). The best agreement between the observed and modeled temperature-dependence of the evaporation was obtained when effective vaporization enthalpy values of 80 kJ/mol were assumed. The low effective enthalpy value might result from several potential reasons, including molecular decomposition or dissociation that might occur in the particle phase upon heating, mixture effects and compound-dependent uncertainties in the mass accommodation coefficient. In addition to the VTDMA-based analysis, semi-volatile and low-volatile organic mass fractions were independently determined by applying Positive Matrix Factorization (PMF) to High-Resolution Aerosol Mass Spectrometer (HR-AMS) data. The factor separation was based on the oxygenation levels of organics, specifically the relative abundance of mass ions at m/z 43 (f43) and m/z 44 (f44). The mass fractions of these two organic groups were compared against the VTDMA-based results. In general, the agreement between the VTDMA results and the PMF-derived mass fractions of organics was reasonable with a linear correlation coefficient of around 0.4 with ΔHVAP = 80 kJ/mol set for all organic groups. The prospect of determining of extremely low volatile organic aerosols (ELVOA) from AMS data using the PMF analysis should be assessed in future studies.