Search Results
The second ACTRIS inter-comparison (2016) for Aerosol Chemical Speciation Monitors (ACSM): Calibration protocols and instrument performance evaluations
2019, Freney, Evelyn, Zhang, Yunjiang, Croteau, Philip, Amodeo, Tanguy, Williams, Leah, Truong, François, Petit, Jean-Eudes, Sciare, Jean, Sarda-Esteve, Roland, Bonnaire, Nicolas, Arumae, Tarvo, Aurela, Minna, Bougiatioti, Aikaterini, Mihalopoulos, Nikolaos, Coz, Esther, Artinano, Begoña, Crenn, Vincent, Elste, Thomas, Heikkinen, Liine, Poulain, Laurent, Wiedensohler, Alfred, Herrmann, Hartmut, Priestman, Max, Alastuey, Andres, Stavroulas, Iasonas, Tobler, Anna, Vasilescu, Jeni, Zanca, Nicola, Canagaratna, Manjula, Carbone, Claudio, Flentje, Harald, Green, David, Maasikmets, Marek, Marmureanu, Luminita, Cruz Minguillon, Maria, Prevot, Andre S.H., Gros, Valerie, Jayne, John, Favez, Olivier
This work describes results obtained from the 2016 Aerosol Chemical Speciation Monitor (ACSM) intercomparison exercise performed at the Aerosol Chemical Monitor Calibration Center (ACMCC, France). Fifteen quadrupole ACSMs (Q_ACSM) from the European Research Infrastructure for the observation of Aerosols, Clouds and Trace gases (ACTRIS) network were calibrated using a new procedure that acquires calibration data under the same operating conditions as those used during sampling and hence gets information representative of instrument performance. The new calibration procedure notably resulted in a decrease in the spread of the measured sulfate mass concentrations, improving the reproducibility of inorganic species measurements between ACSMs as well as the consistency with co-located independent instruments. Tested calibration procedures also allowed for the investigation of artifacts in individual instruments, such as the overestimation of m/z 44 from organic aerosol. This effect was quantified by the m/z (mass-to-charge) 44 to nitrate ratio measured during ammonium nitrate calibrations, with values ranging from 0.03 to 0.26, showing that it can be significant for some instruments. The fragmentation table correction previously proposed to account for this artifact was applied to the measurements acquired during this study. For some instruments (those with high artifacts), this fragmentation table adjustment led to an “overcorrection” of the f44 (m/z 44/Org) signal. This correction based on measurements made with pure NH4NO3, assumes that the magnitude of the artifact is independent of chemical composition. Using data acquired at different NH4NO3 mixing ratios (from solutions of NH4NO3 and (NH4)2SO4) we observe that the magnitude of the artifact varies as a function of composition. Here we applied an updated correction, dependent on the ambient NO3 mass fraction, which resulted in an improved agreement in organic signal among instruments. This work illustrates the benefits of integrating new calibration procedures and artifact corrections, but also highlights the benefits of these intercomparison exercises to continue to improve our knowledge of how these instruments operate, and assist us in interpreting atmospheric chemistry. © 2019, © 2019 Author(s). Published with license by Taylor & Francis Group, LLC.
EURODELTA III exercise: An evaluation of air quality models’ capacity to reproduce the carbonaceous aerosol
2019, Mircea, Mihaela, Bessagnet, Bertrand, D'Isidoro, Massimo, Pirovano, Guido, Aksoyoglu, Sebnem, Ciarelli, Giancarlo, Tsyro, Svetlana, Manders, Astrid, Bieser, Johannes, Stern, Rainer, Vivanco, Marta García, Cuvelier, Cornelius, Aas, Wenche, Prévôt, André S.H., Aulinger, Armin, Briganti, Gino, Calori, Giuseppe, Cappelletti, Andrea, Colette, Augustin, Couvidat, Florian, Fagerli, Hilde, Finardi, Sandro, Kranenburg, Richard, Rouïl, Laurence, Silibello, Camillo, Spindler, Gerald, Poulain, Laurent, Herrmann, Hartmut, Jimenez, Jose L., Day, Douglas A., Tiitta, Petri, Carbone, Samara
The carbonaceous aerosol accounts for an important part of total aerosol mass, affects human health and climate through its effects on physical and chemical properties of the aerosol, yet the understanding of its atmospheric sources and sinks is still incomplete. This study shows the state-of-the-art in modelling carbonaceous aerosol over Europe by comparing simulations performed with seven chemical transport models (CTMs) currently in air quality assessments in Europe: CAMx, CHIMERE, CMAQ, EMEP/MSC-W, LOTOS-EUROS, MINNI and RCGC. The simulations were carried out in the framework of the EURODELTA III modelling exercise and were evaluated against field measurements from intensive campaigns of European Monitoring and Evaluation Programme (EMEP) and the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI). Model simulations were performed over the same domain, using as much as possible the same input data and covering four seasons: summer (1–30 June 2006), winter (8 January – 4 February 2007), autumn (17 September- 15 October 2008) and spring (25 February - 26 March 2009). The analyses of models’ performances in prediction of elemental carbon (EC) for the four seasons and organic aerosol components (OA) for the last two seasons show that all models generally underestimate the measured concentrations. The maximum underestimation of EC is about 60% and up to about 80% for total organic matter (TOM). The underestimation of TOM outside of highly polluted area is a consequence of an underestimation of secondary organic aerosol (SOA), in particular of its main contributor: biogenic secondary aerosol (BSOA). This result is independent on the SOA modelling approach used and season. The concentrations and daily cycles of total primary organic matter (TPOM) are generally better reproduced by the models since they used the same anthropogenic emissions. However, the combination of emissions and model formulation leads to overestimate TPOM concentrations in 2009 for most of the models. All models capture relatively well the SOA daily cycles at rural stations mainly due to the spatial resolution used in the simulations. For the investigated carbonaceous aerosol compounds, the differences between the concentrations simulated by different models are lower than the differences between the concentrations simulated with a model for different seasons. © 2019 The Authors