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| Titel |
Organic aerosol concentration and composition over Europe: insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis |
| VerfasserIn |
C. Fountoukis, A. G. Megaritis, K. Skyllakou, P. E. Charalampidis, C. Pilinis, H. A. C. Denier van der Gon, M. Crippa, F. Canonaco, C. Mohr, A. S. H. Prévôt, J. D. Allan, L. Poulain, T. Petäjä, P. Tiitta, S. Carbone, A. Kiendler-Scharr, E. Nemitz, C. O'Dowd, E. Swietlicki, S. N. Pandis |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 17 ; Nr. 14, no. 17 (2014-09-03), S.9061-9076 |
| Datensatznummer |
250119000
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| Publikation (Nr.) |
 copernicus.org/acp-14-9061-2014.pdf |
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| Zusammenfassung |
| 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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