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Author: Carbone, S. × Subject: organic matter ×

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    Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach
    (München : European Geopyhsical Union, 2014) Crippa, M.; Canonaco, F.; Lanz, V.A.; Äijälä, M.; Allan, J.D.; Carbone, S.; Capes, G.; Ceburnis, D.; Dall'Osto, M.; Day, D.A.; DeCarlo, P.F.; Ehn, M.; Eriksson, A.; Freney, E.; Hildebrandt Ruiz, L.; Hillamo, R.; Jimenez, J.L.; Junninen, H.; Kiendler-Scharr, A.; Kortelainen, A.-M.; Kulmala, M.; Laaksonen, A.; Mensah, A.A.; Mohr, C.; Nemitz, E.; O'Dowd, C.; Ovadnevaite, J.; Pandis, S.N.; Petäjä, T.; Poulain, L.; Saarikoski, S.; Sellegri, K.; Swietlicki, E.; Tiitta, P.; Worsnop, D.R.; Baltensperger, U.; Prévôt, A.S.H.
    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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    Organic aerosol concentration and composition over Europe: Insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis
    (München : European Geopyhsical Union, 2014) Fountoukis, C.; Megaritis, A.G.; Skyllakou, K.; Charalampidis, P.E.; Pilinis, C.; van der Gon, H.A.C. Denier; Crippa, M.; Canonaco, F.; Mohr, C.; Prévôt, A.S.H.; Allan, J.D.; Poulain, L.; Petäjä, T.; Tiitta, P.; Carbone, S.; Kiendler-Scharr, A.; Nemitz, E.; O'Dowd, C.; Swietlicki, E.; Pandis, S.N.
    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.