Published in

American Chemical Society, Environmental Science and Technology, 21(43), p. 8166-8172, 2009

DOI: 10.1021/es901420b

Links

Tools

Export citation

Search in Google Scholar

Aerosol Mass Spectrometric Features of Biogenic SOA: Observations from a Plant Chamber and in Rural Atmospheric Environments

This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
  • Must obtain written permission from Editor
  • Must not violate ACS ethical Guidelines
Orange circle
Postprint: archiving restricted
  • Must obtain written permission from Editor
  • Must not violate ACS ethical Guidelines
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Secondary organic aerosol (SOA) is known to form from a variety of anthropogenic and biogenic precursors. Current estimates of global SOA production vary over 2 orders of magnitude. Since no direct measurement technique for SOA exists, quantifying SOA remains a challenge for atmospheric studies. The identification of biogenic SOA (BSOA) based on mass spectral signatures offers the possibility to derive source information of organic aerosol (OA) with high time resolution. Here we present data from simulation experiments. The BSOA from tree emissions was characterized with an Aerodyne quadrupole aerosol mass spectrometer (Q-AMS). Collection efficiencies were close to 1, and effective densities of the BSOA were found to be 1.3 +/- 0.1 g/cm(3). The mass spectra of SOA from different trees were found to be highly similar. The average BSOA mass spectrum from tree emissions is compared to a BSOA component spectrum extracted from field data. It is shown that overall the spectra agree well and that the mass spectral features of BSOA are distinctively different from those of OA components related to fresh fossil fuel and biomass combustions. The simulation chamber mass spectrum may potentially be useful for the identification and interpretation of biogenic SOA components in ambient data sets.