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@ARTICLE{Fountoukis:186555,
author = {Fountoukis, C. and Megaritis, A. G. and Skyllakou, K. and
Charalampidis, P. E. and Pilinis, C. and Denier van der Gon,
H. A. C. and Crippa, M. and Canonaco, F. and Mohr, C. and
Prévôt, A. S. H. and Allan, J. D. and Poulain, L. and
Petäjä, T. and Tiitta, P. and Carbone, S. and
Kiendler-Scharr, A. and Nemitz, E. and O'Dowd, C. and
Swietlicki, E. and Pandis, S. N.},
title = {{O}rganic aerosol concentration and composition over
{E}urope: insights from comparison of regional model
predictions with aerosol mass spectrometer factor analysis},
journal = {Atmospheric chemistry and physics},
volume = {14},
number = {17},
issn = {1680-7324},
address = {Katlenburg-Lindau},
publisher = {EGU},
reportid = {FZJ-2015-00628},
pages = {9061 - 9076},
year = {2014},
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.},
cin = {IEK-8},
ddc = {550},
cid = {I:(DE-Juel1)IEK-8-20101013},
pnm = {233 - Trace gas and aerosol processes in the troposphere
(POF2-233)},
pid = {G:(DE-HGF)POF2-233},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000341992000014},
doi = {10.5194/acp-14-9061-2014},
url = {https://juser.fz-juelich.de/record/186555},
}