This manuscript based thesis examines and advances methods for constraining sources of atmospheric trace constituents with a Lagrangian particle dispersion model. The method of Bayesian inversion is demonstrated, and a new method is introduced to a class of similar problems where established methods are not applicable. First, A new regression based methodology was developed and applied to observations of atmospheric methanesulfonic acid mass concentrations at Alert, Nunavut. The methodology was used to compare the importance of phytoplankton blooms vs. the ice-free ocean as sources of the dimethylsulfide precursor, and to compare the importance of bromine monoxide vs. hydroxyl as agents oxidizing dimethylsul de to methanesulfonic acid. These issues are relevant to the application of methanesulfonic acid concentrations in ice cores to determine historic sea ice properties. The analysis indicated that source regions to Alert during the spring are primarily ice-free ocean with a significant contribution from ice edge blooms, and during the summer to be dominated by the ice-free ocean. The model also indicated that oxidation of DMS by BrO was the dominant source of MSA in the spring, while DMS oxidation by OH was the dominant source in the summer. Secondly, Bayesian inversion was applied to observations of atmospheric elemental carbon mass concentrations at Tsinghua University in Beijing, China. The analysis provided evidence that current bottom-up elemental carbon emissions estimates in northern China are likely underpredicted. Global chemical transport models show ubiquitous underestimates of the atmospheric burden of elemental carbon, especially near large sources of emissions. Northern China is among the regions with the most intensive elemental carbon emissions in the world, and an underestimate of emissions in this region may be partially responsible for the global chemical transport model underestimates.

This manuscript based thesis examines and advances methods for constraining sources of atmospheric trace constituents with a Lagrangian particle dispersion model. The method of Bayesian inversion is demonstrated, and a new method is introduced to a class of similar problems where established methods are not applicable. First, A new regression based methodology was developed and applied to observations of atmospheric methanesulfonic acid mass concentrations at Alert, Nunavut. The methodology was used to compare the importance of phytoplankton blooms vs. the ice-free ocean as sources of the dimethylsulfide precursor, and to compare the importance of bromine monoxide vs. hydroxyl as agents oxidizing dimethylsul de to methanesulfonic acid. These issues are relevant to the application of methanesulfonic acid concentrations in ice cores to determine historic sea ice properties. The analysis indicated that source regions to Alert during the spring are primarily ice-free ocean with a significant contribution from ice edge blooms, and during the summer to be dominated by the ice-free ocean. The model also indicated that oxidation of DMS by BrO was the dominant source of MSA in the spring, while DMS oxidation by OH was the dominant source in the summer. Secondly, Bayesian inversion was applied to observations of atmospheric elemental carbon mass concentrations at Tsinghua University in Beijing, China. The analysis provided evidence that current bottom-up elemental carbon emissions estimates in northern China are likely underpredicted. Global chemical transport models show ubiquitous underestimates of the atmospheric burden of elemental carbon, especially near large sources of emissions. Northern China is among the regions with the most intensive elemental carbon emissions in the world, and an underestimate of emissions in this region may be partially responsible for the global chemical transport model underestimates.

Dust aerosol emission is a critical topic in agriculture, occurring either by aeolian process from bare or sparsely vegetated cropland or as fugitive emission during tilling, harvest and many other farming activities. Aerosols, which are in the case of agriculture either mineral dust, organic particles or a mixture, are known for impacting human health, cloud formation and ultimately, the earth’s climate and ecosystem. Coupled atmosphere and aerosol transport models are commonly used to study aerosol dispersion in the atmosphere, but so far, agricultural sources are under-represented. Hence, estimations of these emissions’ actual impact are still somewhat uncertain regarding their seasonality, spatial distribution and the fraction of the global aerosol load. To fill this gap, this study aims at identifying suitable approaches for modelling aeolian emissions from sparsely vegetated cropland and fugitive emissions from tilling. Fugitive emissions are challenging since they mainly depend on human activity that is not predictable, but observed events can be used as case studies. For this, a Lagrangian particle dispersion model was chosen, which can trace the trajectory of individual particles in the emitted dust plume. So the particle model “Itpas” was developed to tackle fugitive emissions and to be capable of simulating the complex turbulent mixing of dust particles inside the atmospheric boundary layer. This model was used to simulate a case study based on measured tilling emissions, showing the particle dispersion for a stable and unstable stratified boundary layer. It was shown that within a stably stratified boundary layer, the dust plume is restricted to the near-source region. In contrast, emissions in unstable boundary layers go into long-range transport. This illustrates the spatial range a single tillage operation can have an impact. Aeolian dust emissions are controlled by the wind. For cropland, the emission variability is caused mainly by the frequently changing vegetation cover. Emissions can only occur in the time between tillage and newly grown crops or during drought periods. A parametrisation based on high-resolution satellite observations of the vegetation cover was created to include this process into a model. With this, a new dust emission scheme for cropland emission was developed for the model system COSMO-MUSCAT. In a case study of a dust outbreak from cropland in Poland in 2019, the model’s ability was tested extensively on multiple spatial resolutions. Validation against satellite-measured AOD, ground-measured PM10 and the vertical profile of the PollyNET lidar in Warsaw showed an overall good agreement of the model simulation with the observations. In the framework of this thesis, one dedicated model approach was developed for both the fugitive emissions and the aeolian emissions and validated upon case studies. These approaches could help better understand agricultural dust emissions, their spatial distribution, seasonality and, ultimately, global impact.