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Author: Nemitz, E. × Subject: aerosol ×

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Now showing 1 - 4 of 4
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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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    Lessons learnt from the first EMEP intensive measurement periods
    (München : European Geopyhsical Union, 2012) Aas, W.; Tsyro, S.; Bieber, E.; Bergström, R.; Ceburnis, D.; Ellermann, T.; Fagerli, H.; Frölich, M.; Gehrig, R.; Makkonen, U.; Nemitz, E.; Otjes, R.; Perez, N.; Perrino, C.; Prévôt, A.S.H.; Putaud, J.-P.; Simpson, D.; Spindler, G.; Vana, M.; Yttri, K.E.
    The first EMEP intensive measurement periods were held in June 2006 and January 2007. The measurements aimed to characterize the aerosol chemical compositions, including the gas/aerosol partitioning of inorganic compounds. The measurement program during these periods included daily or hourly measurements of the secondary inorganic components, with additional measurements of elemental- and organic carbon (EC and OC) and mineral dust in PM1, PM2.5 and PM10. These measurements have provided extended knowledge regarding the composition of particulate matter and the temporal and spatial variability of PM, as well as an extended database for the assessment of chemical transport models. This paper summarise the first experiences of making use of measurements from the first EMEP intensive measurement periods along with EMEP model results from the updated model version to characterise aerosol composition. We investigated how the PM chemical composition varies between the summer and the winter month and geographically. The observation and model data are in general agreement regarding the main features of PM10 and PM2.5 composition and the relative contribution of different components, though the EMEP model tends to give slightly lower estimates of PM10 and PM2.5 compared to measurements. The intensive measurement data has identified areas where improvements are needed. Hourly concurrent measurements of gaseous and particulate components for the first time facilitated testing of modelled diurnal variability of the gas/aerosol partitioning of nitrogen species. In general, the modelled diurnal cycles of nitrate and ammonium aerosols are in fair agreement with the measurements, but the diurnal variability of ammonia is not well captured. The largest differences between model and observations of aerosol mass are seen in Italy during winter, which to a large extent may be explained by an underestimation of residential wood burning sources. It should be noted that both primary and secondary OC has been included in the calculations for the first time, showing promising results. Mineral dust is important, especially in southern Europe, and the model seems to capture the dust episodes well. The lack of measurements of mineral dust hampers the possibility for model evaluation for this highly uncertain PM component. There are also lessons learnt regarding improved measurements for future intensive periods. There is a need for increased comparability between the measurements at different sites. For the nitrogen compounds it is clear that more measurements using artefact free methods based on continuous measurement methods and/or denuders are needed. For EC/OC, a reference methodology (both in field and laboratory) was lacking during these periods giving problems with comparability, though measurement protocols have recently been established and these should be followed by the Parties to the EMEP Protocol. For measurements with no defined protocols, it might be a good solution to use centralised laboratories to ensure comparability across the network. To cope with the introduction of these new measurements, new reporting guidelines have been developed to ensure that all proper information about the methodologies and data quality is given.
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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.
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    General overview: European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI) – integrating aerosol research from nano to global scales
    (München : European Geopyhsical Union, 2011) Kulmala, M.; Asmi, A.; Lappalainen, H.K.; Carslaw, K.S.; Pöschl, U.; Baltensperger, U.; Hov, Ø.; Brenquier, J.-L.; Pandis, S.N.; Facchini, M.C.; Hansson, H.-C.; Wiedensohler, A.; O'Dowd, C.D.; Boers, R.; Boucher, O.; de Leeuw, G.; Denier van der Gon, H.A.C.; Feichter, J.; Krejci, R.; Laj, P.; Lihavainen, H.; Lohmann, U.; McFiggans, G.; Mentel, T.; Pilinis, C.; Riipinen, I.; Schulz, M.; Stohl, A.; Swietlicki, E.; Vignati, E.; Alves, C.; Amann, M.; Ammann, M.; Arabas, S.; Artaxo, P.; Baars, H.; Beddows, D.C.S.; Bergström, R.; Beukes, J.P.; Bilde, M.; Burkhart, J.F.; Canonaco, F.; Clegg, S.L.; Coe, H.; Crumeyrolle, S.; D'Anna, B.; Decesari, S.; Gilardoni, S.; Fischer, M.; Fjaeraa, A.M.; Fountoukis, C.; George, C.; Gomes, L.; Halloran, P.; Hamburger, T.; Harrison, R.M.; Herrmann, H.; Hoffmann, T.; Hoose, C.; Hu, M.; Hyvärinen, A.; Hõrrak, U.; Iinuma, Y.; Iversen, T.; Josipovic, M.; Kanakidou, M.; Kiendler-Scharr, A.; Kirkevåg, A.; Kiss, G.; Klimont, Z.; Kolmonen, P.; Komppula, M.; Kristjánsson, J.-E.; Laakso, L.; Laaksonen, A.; Labonnote, L.; Lanz, V.A.; Lehtinen, K.E.J.; Rizzo, L.V.; Makkonen, R.; Manninen, H.E.; McMeeking, G.; Merikanto, J.; Minikin, A.; Mirme, S.; Morgan, W.T.; Nemitz, E.; O'Donnell, D.; Panwar, T.S.; Pawlowska, H.; Petzold, A.; Pienaar, J.J.; Pio, C.; Plass-Duelmer, C.; Prévôt, A.S.H.; Pryor, S.; Reddington, C.L.; Roberts, G.; Rosenfeld, D.; Schwarz, J.; Seland, Ø.; Sellegri, K.; Shen, X.J.; Shiraiwa, M.; Siebert, H.; Sierau, B.; Simpson, D.; Sun, J.Y.; Topping, D.; Tunved, P.; Vaattovaara, P.; Vakkari, V.; Veefkind, J.P.; Visschedijk, A.; Vuollekoski, H.; Vuolo, R.; Wehner, B.; Wildt, J.; Woodward, S.; Worsnop, D.R.; van Zadelhoff, G.-J.; Zardini, A.A.; Zhang, K.; van Zyl, P.G.; Kerminen, V.-M.
    In this paper we describe and summarize the main achievements of the European Aerosol Cloud Climate and Air Quality Interactions project (EUCAARI). EUCAARI started on 1 January 2007 and ended on 31 December 2010 leaving a rich legacy including: (a) a comprehensive database with a year of observations of the physical, chemical and optical properties of aerosol particles over Europe, (b) comprehensive aerosol measurements in four developing countries, (c) a database of airborne measurements of aerosols and clouds over Europe during May 2008, (d) comprehensive modeling tools to study aerosol processes fron nano to global scale and their effects on climate and air quality. In addition a new Pan-European aerosol emissions inventory was developed and evaluated, a new cluster spectrometer was built and tested in the field and several new aerosol parameterizations and computations modules for chemical transport and global climate models were developed and evaluated. These achievements and related studies have substantially improved our understanding and reduced the uncertainties of aerosol radiative forcing and air quality-climate interactions. The EUCAARI results can be utilized in European and global environmental policy to assess the aerosol impacts and the corresponding abatement strategies.