Global combustion sources of organic aerosols: model comparison with 84 AMS factor-analysis data sets
- 1Max Planck Institute for Chemistry, Mainz, Germany
- 2Department of Chemical Engineering, University of Patras, Patras, Greece
- 3Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- 4Energy, Environment and Water Research Center, Cyprus Institute, Nicosia, Cyprus
Abstract. Emissions of organic compounds from biomass, biofuel, and fossil fuel combustion strongly influence the global atmospheric aerosol load. Some of the organics are directly released as primary organic aerosol (POA). Most are emitted in the gas phase and undergo chemical transformations (i.e., oxidation by hydroxyl radical) and form secondary organic aerosol (SOA). In this work we use the global chemistry climate model ECHAM/MESSy Atmospheric Chemistry (EMAC) with a computationally efficient module for the description of organic aerosol (OA) composition and evolution in the atmosphere (ORACLE). The tropospheric burden of open biomass and anthropogenic (fossil and biofuel) combustion particles is estimated to be 0.59 and 0.63 Tg, respectively, accounting for about 30 and 32 % of the total tropospheric OA load. About 30 % of the open biomass burning and 10 % of the anthropogenic combustion aerosols originate from direct particle emissions, whereas the rest is formed in the atmosphere. A comprehensive data set of aerosol mass spectrometer (AMS) measurements along with factor-analysis results from 84 field campaigns across the Northern Hemisphere are used to evaluate the model results. Both the AMS observations and the model results suggest that over urban areas both POA (25–40 %) and SOA (60–75 %) contribute substantially to the overall OA mass, whereas further downwind and in rural areas the POA concentrations decrease substantially and SOA dominates (80–85 %). EMAC does a reasonable job in reproducing POA and SOA levels during most of the year. However, it tends to underpredict POA and SOA concentrations during winter indicating that the model misses wintertime sources of OA (e.g., residential biofuel use) and SOA formation pathways (e.g., multiphase oxidation).