Laboratory investigation of photochemical oxidation of organic aerosol from wood fires 2: analysis of aerosol mass spectrometer data
- 1Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- *now at: Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, British Columbia, Canada
Abstract. Experiments were conducted to investigate the effects of photo-oxidation on organic aerosol (OA) in dilute wood smoke by exposing emissions from soft- and hard-wood fires to UV light in a smog chamber. This paper focuses on changes in OA composition measured using a unit-mass-resolution quadrupole Aerosol Mass Spectrometer (AMS). The results highlight how photochemical processing can lead to considerable evolution of the mass, volatility and level of oxygenation of biomass-burning OA. Photochemical oxidation produced substantial new OA, more than doubling the OA mass after a few hours of aging under typical summertime conditions. Aging also decreased the volatility of the OA and made it progressively more oxygenated. The results also illustrate strengths of, and challenges with, using AMS data for source apportionment analysis. For example, the mass spectra of fresh and aged BBOA are distinct from fresh motor-vehicle emissions. The mass spectra of the secondary OA produced from aging wood smoke are very similar to those of the oxygenated OA (OOA) that dominates ambient AMS datasets, further reinforcing the connection between OOA and OA formed from photo-chemistry. In addition, aged wood smoke spectra are similar to those from OA created by photo-oxidizing dilute diesel exhaust. This demonstrates that the OOA observed in the atmosphere can be produced by photochemical aging of dilute emissions from different types of combustion systems operating on fuels with modern or fossil carbon. Since OOA is frequently the dominant component of ambient OA, the similarity of spectra of aged emissions from different sources represents an important challenge for AMS-based source apportionment studies.