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- ItemOrganic 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.
- ItemChemical mass balance of 300 °c non-volatile particles at the tropospheric research site Melpitz, Germany(München : European Geopyhsical Union, 2014) Poulain, L.; Birmili, W.; Canonaco, F.; Crippa, M.; Wu, Z.J.; Nordmann, S.; Wiedensohler, A.; Held, A.; Spindler, G.; Prévôt, A.S.H.; Wiedensohler, A.; Herrmann, H.In the fine-particle mode (aerodynamic diameter < 1 μm) non-volatile material has been associated with black carbon (BC) and low-volatile organics and, to a lesser extent, with sea salt and mineral dust. This work analyzes non-volatile particles at the tropospheric research station Melpitz (Germany), combining experimental methods such as a mobility particle-size spectrometer (3–800 nm), a thermodenuder operating at 300 °C, a multi-angle absorption photometer (MAAP), and an aerosol mass spectrometer (AMS). The data were collected during two atmospheric field experiments in May–June 2008 as well as February–March 2009. As a basic result, we detected average non-volatile particle–volume fractions of 11 ± 3% (2008) and 17 ± 8% (2009). In both periods, BC was in close linear correlation with the non-volatile fraction, but not sufficient to quantitatively explain the non-volatile particle mass concentration. Based on the assumption that BC is not altered by the heating process, the non-volatile particle mass fraction could be explained by the sum of black carbon (47% in summer, 59% in winter) and a non-volatile organic contribution estimated as part of the low-volatility oxygenated organic aerosol (LV-OOA) (53% in summer, 41% in winter); the latter was identified from AMS data by factor analysis. Our results suggest that LV-OOA was more volatile in summer (May–June 2008) than in winter (February–March 2009) which was linked to a difference in oxidation levels (lower in summer). Although carbonaceous compounds dominated the sub-μm non-volatile particle mass fraction most of the time, a cross-sensitivity to partially volatile aerosol particles of maritime origin could be seen. These marine particles could be distinguished, however from the carbonaceous particles by a characteristic particle volume–size distribution. The paper discusses the uncertainty of the volatility measurements and outlines the possible merits of volatility analysis as part of continuous atmospheric aerosol measurements.
- ItemPrimary and secondary organic aerosol origin by combined gas-particle phase source apportionment(München : European Geopyhsical Union, 2013) Crippa, M.; Canonaco, F.; Slowik, J.G.; El Haddad, I.; DeCarlo, P.F.; Mohr, C.; Heringa, M.F.; Chirico, R.; Marchand, N.; Temime-Roussel, B.; Abidi, E.; Poulain, L.; Wiedensohler, A.; Baltensperger, U.; Prévôt, A.S.H.Secondary organic aerosol (SOA), a prominent fraction of particulate organic mass (OA), remains poorly constrained. Its formation involves several unknown precursors, formation and evolution pathways and multiple natural and anthropogenic sources. Here a combined gas-particle phase source apportionment is applied to wintertime and summertime data collected in the megacity of Paris in order to investigate SOA origin during both seasons. This was possible by combining the information provided by an aerosol mass spectrometer (AMS) and a proton transfer reaction mass spectrometer (PTR-MS). A better constrained apportionment of primary OA (POA) sources is also achieved using this methodology, making use of gas-phase tracers. These tracers made possible the discrimination between biogenic and continental/anthropogenic sources of SOA. We found that continental SOA was dominant during both seasons (24–50% of total OA), while contributions from photochemistry-driven SOA (9% of total OA) and marine emissions (13% of total OA) were also observed during summertime. A semi-volatile nighttime component was also identified (up to 18% of total OA during wintertime). This approach was successfully applied here and implemented in a new source apportionment toolkit.
- ItemOrganic 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.
- ItemACTRIS ACSM intercomparison - Part 1: Reproducibility of concentration and fragment results from 13 individual Quadrupole Aerosol Chemical Speciation Monitors (Q-ACSM) and consistency with co-located instruments(München : European Geopyhsical Union, 2015) Crenn, V.; Sciare, J.; Croteau, P.L.; Verlhac, S.; Fröhlich, R.; Belis, C.A.; Aas, W.; Äijälä, M.; Alastuey, A.; Artiñano, B.; Baisnée, D.; Bonnaire, N.; Bressi, M.; Canagaratna, M.; Canonaco, F.; Carbone, C.; Cavalli, F.; Coz, E.; Cubison, M.J.; Esser-Gietl, J.K.; Green, D.C.; Gros, V.; Heikkinen, L.; Herrmann, H.; Lunder, C.; Minguillón, M.C.; Močnik, G.; O'Dowd, C.D.; Ovadnevaite, J.; Petit, J.-E.; Petralia, E.; Poulain, L.; Priestman, M.; Riffault, V.; Ripoll, A.; Sarda-Estève, R.; Slowik, J.G.; Setyan, A.; Wiedensohler, A.; Baltensperger, U.; Prévôt, A.S.H.; Jayne, J.T.; Favez, O.As part of the European ACTRIS project, the first large Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM) intercomparison study was conducted in the region of Paris for 3 weeks during the late-fall – early-winter period (November–December 2013). The first week was dedicated to the tuning and calibration of each instrument, whereas the second and third were dedicated to side-by-side comparison in ambient conditions with co-located instruments providing independent information on submicron aerosol optical, physical, and chemical properties. Near real-time measurements of the major chemical species (organic matter, sulfate, nitrate, ammonium, and chloride) in the non-refractory submicron aerosols (NR-PM1) were obtained here from 13 Q-ACSM. The results show that these instruments can produce highly comparable and robust measurements of the NR-PM1 total mass and its major components. Taking the median of the 13 Q-ACSM as a reference for this study, strong correlations (r2 > 0.9) were observed systematically for each individual Q-ACSM across all chemical families except for chloride for which three Q-ACSMs showing weak correlations partly due to the very low concentrations during the study. Reproducibility expanded uncertainties of Q-ACSM concentration measurements were determined using appropriate methodologies defined by the International Standard Organization (ISO 17025, 1999) and were found to be 9, 15, 19, 28, and 36 % for NR-PM1, nitrate, organic matter, sulfate, and ammonium, respectively. However, discrepancies were observed in the relative concentrations of the constituent mass fragments for each chemical component. In particular, significant differences were observed for the organic fragment at mass-to-charge ratio 44, which is a key parameter describing the oxidation state of organic aerosol. Following this first major intercomparison exercise of a large number of Q-ACSMs, detailed intercomparison results are presented, along with a discussion of some recommendations about best calibration practices, standardized data processing, and data treatment.
- ItemACTRIS ACSM intercomparison - Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers(München : European Geopyhsical Union, 2015) Fröhlich, R.; Crenn, V.; Setyan, A.; Belis, C.A.; Canonaco, F.; Favez, O.; Riffault, V.; Slowik, J.G.; Aas, W.; Aijälä, M.; Alastuey, A.; Artiñano, B.; Bonnaire, N.; Bozzetti, C.; Bressi, M.; Carbone, C.; Coz, E.; Croteau, P.L.; Cubison, M.J.; Esser-Gietl, J.K.; Green, D.C.; Gros, V.; Heikkinen, L.; Herrmann, H.; Jayne, J.T.; Lunder, C.R.; Minguillón, M.C.; Močnik, G.; O'Dowd, C.D.; Ovadnevaite, J.; Petralia, E.; Poulain, L.; Priestman, M.; Ripoll, A.; Sarda-Estève, R.; Wiedensohler, A.; Baltensperger, U.; Sciare, J.; Prévôt, A.S.H.Chemically resolved atmospheric aerosol data sets from the largest intercomparison of the Aerodyne aerosol chemical speciation monitors (ACSMs) performed to date were collected at the French atmospheric supersite SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToF-ACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about 3 weeks in November and December~2013. Part 1 of this study reports on the accuracy and precision of the instruments for all the measured species. In this work we report on the intercomparison of organic components and the results from factor analysis source apportionment by positive matrix factorisation (PMF) utilising the multilinear engine 2 (ME-2). Except for the organic contribution of mass-to-charge ratio m/z 44 to the total organics (f44), which varied by factors between 0.6 and 1.3 compared to the mean, the peaks in the organic mass spectra were similar among instruments. The m/z 44 differences in the spectra resulted in a variable f44 in the source profiles extracted by ME-2, but had only a minor influence on the extracted mass contributions of the sources. The presented source apportionment yielded four factors for all 15 instruments: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA), biomass burning-related organic aerosol (BBOA) and secondary oxygenated organic aerosol (OOA). ME-2 boundary conditions (profile constraints) were optimised individually by means of correlation to external data in order to achieve equivalent / comparable solutions for all ACSM instruments and the results are discussed together with the investigation of the influence of alternative anchors (reference profiles). A comparison of the ME-2 source apportionment output of all 15 instruments resulted in relative standard deviations (SD) from the mean between 13.7 and 22.7 % of the source's average mass contribution depending on the factors (HOA: 14.3 ± 2.2 %, COA: 15.0 ± 3.4 %, OOA: 41.5 ± 5.7 %, BBOA: 29.3 ± 5.0 %). Factors which tend to be subject to minor factor mixing (in this case COA) have higher relative uncertainties than factors which are recognised more readily like the OOA. Averaged over all factors and instruments the relative first SD from the mean of a source extracted with ME-2 was 17.2 %.