Oligomer formation in the troposphere: from experimental knowledge to 3-D modeling
- 1LISA/IPSL, Laboratoire Interuniversitaire des Systèmes Atmosphériques, UMR CNRS 7583, Université Paris Est Créteil (UPEC) et Université Paris Diderot (UPD), 94010 Créteil, France
- 2INERIS, Institut National de l'Environnement Industriel et des Risques, Parc technologique ALATA, 60550 Verneuil en Halatte, France
- 3CEREA, Joint Laboratory Ecole des Ponts ParisTech/EDF R&D, Université Paris-Est (UPE), 77455 Marne la Vallée, France
- anow at: Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, UK
Abstract. The organic fraction of atmospheric aerosols has proven to be a critical element of air quality and climate issues. However, its composition and the aging processes it undergoes remain insufficiently understood. This work builds on laboratory knowledge to simulate the formation of oligomers from biogenic secondary organic aerosol (BSOA) in the troposphere at the continental scale. We compare the results of two different modeling approaches, a first-order kinetic process and a pH-dependent parameterization, both implemented in the CHIMERE air quality model (AQM) (www.lmd.polytechnique.fr/chimere), to simulate the spatial and temporal distribution of oligomerized secondary organic aerosol (SOA) over western Europe. We also included a comparison of organic carbon (OC) concentrations at two EMEP (European Monitoring and Evaluation Programme) stations. Our results show that there is a strong dependence of the results on the selected modeling approach: while the irreversible kinetic process leads to the oligomerization of about 50 % of the total BSOA mass, the pH-dependent approach shows a broader range of impacts, with a strong dependency on environmental parameters (pH and nature of aerosol) and the possibility for the process to be reversible. In parallel, we investigated the sensitivity of each modeling approach to the representation of SOA precursor solubility (Henry's law constant values). Finally, the pros and cons of each approach for the representation of SOA aging are discussed and recommendations are provided to improve current representations of oligomer formation in AQMs.