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024 7 _ |a 10.5194/acp-14-6159-2014
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024 7 _ |a 1680-7316
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024 7 _ |a 1680-7324
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024 7 _ |a 2128/8270
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037 _ _ |a FZJ-2015-00631
082 _ _ |a 550
100 1 _ |a Crippa, M.
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245 _ _ |a Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach
260 _ _ |a Katlenburg-Lindau
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336 7 _ |a Journal Article
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520 _ _ |a Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM1) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May–June and September–October) and 2009 (February–March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.
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700 1 _ |a Canonaco, F.
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700 1 _ |a Lanz, V. A.
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700 1 _ |a Äijälä, M.
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700 1 _ |a Allan, J. D.
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700 1 _ |a Carbone, S.
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700 1 _ |a Capes, G.
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700 1 _ |a Ceburnis, D.
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700 1 _ |a Dall'Osto, M.
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700 1 _ |a Day, D. A.
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700 1 _ |a DeCarlo, P. F.
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700 1 _ |a Ehn, M.
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700 1 _ |a Eriksson, A.
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700 1 _ |a Freney, E.
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700 1 _ |a Hildebrandt Ruiz, L.
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700 1 _ |a Hillamo, R.
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700 1 _ |a Jimenez, J. L.
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700 1 _ |a Junninen, H.
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700 1 _ |a Kiendler-Scharr, A.
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700 1 _ |a Kortelainen, A.-M.
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700 1 _ |a Kulmala, M.
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700 1 _ |a Laaksonen, A.
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700 1 _ |a Mensah, A. A.
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700 1 _ |a Mohr, C.
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700 1 _ |a Nemitz, E.
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700 1 _ |a O'Dowd, C.
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700 1 _ |a Ovadnevaite, J.
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700 1 _ |a Pandis, S. N.
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700 1 _ |a Petäjä, T.
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700 1 _ |a Poulain, L.
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700 1 _ |a Saarikoski, S.
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700 1 _ |a Sellegri, K.
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700 1 _ |a Swietlicki, E.
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700 1 _ |a Tiitta, P.
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700 1 _ |a Worsnop, D. R.
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700 1 _ |a Baltensperger, U.
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700 1 _ |a Prévôt, A. S. H.
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773 _ _ |a 10.5194/acp-14-6159-2014
|g Vol. 14, no. 12, p. 6159 - 6176
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