Modelling of SOA markers: simulation through detailed mechanisms and validation by comparison with measurements. A new approach to understand SOA formation.
Résumé
Secondary organic aerosol (SOA) is formed via the oxidation of both anthropogenic and biogenic gas-phase organic compounds and is a large and often dominant fraction of total OA (Kroll and Seinfeld, 2008). To assess the sources of secondary organic carbon, SOA “tracer” method related to specific precursors has been developed (Kleindienst et al., 2007). Due to the importance of SOA contribution on the total PM mass, it is necessary to develop atmospheric chemistry models that properly describe the formation of the SOA markers in the atmosphere in order to improve the understanding of SOA formation and to enhance air quality forecasts. In this context, the modelling approach developed has to be compared with data obtained through field measurements. The aim of this work is to implement SOA tracer mechanisms inside the air quality model CHIMERE and to compare the model with field measurements results. This comparison gives an insight on the ability of the model to form SOA from specific precursors and on several processes (e.g. emissions, gas/particle partitioning). Measurements of SOA markers were performed at SIRTA station, representing the suburban background air quality conditions of the Paris region (about 25 km SW from Paris city center). PM10 samples were collected every third day all over the year 2015. SOA markers have been quantified using native standard compounds by LC/M-MS and/or GC/MS after derivatization with MSTFA+1%TMCS. SOA marker concentrations (Figure 1) were compared with the results of the model. The mechanisms describing the formation of the markers were introduced into the chemistry-transport model CHIMERE (Menut et al. 2013). Mechanisms for the formation of SOA markers were taken from the Master Chemical Mechanism (MCM, NCAS, Universities of Leeds and York) otherwise, data were sought in the scientific literature. The gas phase mechanism simulation was performed using MELCHIOR2, the partitioning between particulate phase and gaseous phase was calculated using the thermodynamic model SOAP (Couvidat and Sartelet, 2015). Biogenic emissions were computed with MEGAN 2.1 algorithm (Guenther et al., 2012). Simulated markers included both biogenic, (e.g; pinonic acid, pinic acid and MBTCA from α-pinene oxidation), and anthropogenic (e.g. DHOPA and nitrophenols from toluene oxidation) precursors.