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Volume 14, issue 17
Atmos. Chem. Phys., 14, 9061–9076, 2014
https://doi.org/10.5194/acp-14-9061-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The Pan European Gas-Aerosols Climate Interaction Study...

Atmos. Chem. Phys., 14, 9061–9076, 2014
https://doi.org/10.5194/acp-14-9061-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Sep 2014

Research article | 03 Sep 2014

Organic aerosol concentration and composition over Europe: insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis

C. Fountoukis1, A. G. Megaritis2, K. Skyllakou2, P. E. Charalampidis3, C. Pilinis3, H. A. C. Denier van der Gon4, M. Crippa5,*, F. Canonaco5, C. Mohr5,**, A. S. H. Prévôt5, J. D. Allan7,6, L. Poulain8, T. Petäjä9, P. Tiitta11,10, S. Carbone12, A. Kiendler-Scharr13, E. Nemitz14, C. O'Dowd15, E. Swietlicki16, and S. N. Pandis2,17 C. Fountoukis et al.
  • 1Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH), Patras, Greece
  • 2Department of Chemical Engineering, University of Patras, Patras, Greece
  • 3Department of Environment, University of the Aegean, Mytilene, Greece
  • 4Netherlands Organization for Applied Scientific Research TNO, Utrecht, the Netherlands
  • 5Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, PSI Villigen, Switzerland
  • 6National Centre for Atmospheric Science, University of Manchester, Manchester, UK
  • 7School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UK
  • 8Leibniz Institute for Tropospheric Research, Leipzig, Germany
  • 9Department of Physics, University of Helsinki, Finland
  • 10Department of Environmental Science, Univ. of Eastern Finland, Kuopio, Finland
  • 11Department of Applied Physics, Univ. of Eastern Finland, Kuopio, Finland
  • 12Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland
  • 13Institut für Energie- und Klimaforschung: Troposphäre (IEK 8), Forschungszentrum Jülich GmbH, Jülich, Germany
  • 14Centre for Ecology and Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, UK
  • 15School of Physics & Centre for Climate & Air Pollution Studies, National University of Ireland, Galway, Ireland
  • 16Division of Nuclear Physics, University of Lund, 221 00 Lund, Sweden
  • 17Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, USA
  • *now at: EC Joint Research Centre (JRC), Inst. Environment & Sustainability, Via Fermi, Ispra, Italy
  • **now at: Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany

Abstract. A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement between predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to −0.07 (mean error = 0.9 μg m−3, mean bias = −0.2 μg m−3). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to −0.8 μg m−3). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* ≤ 0.1 μg m−3 and semivolatile OOA against the OA with C* > 0.1 μg m−3.

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