Biomass burning organic aerosol in Europe and Asia: Molecular composition and impact on air quality

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Biomass burning emits large amounts of organic aerosol particles into the atmosphere. Important sources are the burning of wood for house heating in Europe and the field burning of agricultural waste during the harvesting season in Asia. These emissions have a strong impact on air quality and can have adverse effects on human health. Besides primary particles which are directly emitted by the fire, the formation of secondary organic aerosols from volatile organic gases plays an important role during transport and chemical aging of smoke plumes. Most of these chemical transformations, as well as the detailed chemical composition of primary and secondary emissions from biomass burning are not well understood at present.In this project, biomass burning emissions and their atmospheric processing will be studied both in a smog chamber and in the real atmosphere.Several European wood and Asian straw types will serve as fuels for the chamber experiments. Their emissions during burning will be chemically processed under different conditions, which are typical e.g. for day- and night atmospheric chemistry, polluted urban and less polluted rural areas, as well as dry and humid air masses. Both the primary as well as the secondary formed particles will then be analysed for their molecular composition. Given the chemical complexity of biomass burning organic aerosols, complementary methods from two well-established labs in Germany and Japan will be combined in this joint project, to allow for an unprecedentedly comprehensive characterisation. The identified compounds and chemical fingerprints from the chamber studies will be compared with real samples from air masses with high impact of wood or agricultural waste burning from Germany and Japan. The goal is to better quantify the impact of biomass burning emissions to air quality, making use of the detailed chemical information from chamber studies and real samples and multivariate statistical models for source apportionment.