Atmospheric composition has strong influence on human health, ecosystems and also Earth's climate. Among the atmospheric constituents, particulate matter has been recognized as both a strong climate forcer and a significant risk factor for human health, although the health relevance of the specific aerosol characteristics, such as its chemical composition, is still debated.
Clouds and aerosols also contribute the largest uncertainty to the radiative budget estimates for climate projections. Thus, reliable estimates of emissions and distributions of pollutants are necessary for assessing the future climate and air-quality related health effects. Chemistry-transport models (CTMs) are valuable tools for understanding the processes influencing the atmospheric composition. This thesis consists of a collection of developments and applications of the chemistry-transport model SILAM.
SILAM's ability to reproduce the observed aerosol composition was evaluated and compared with three other commonly used CTM-s in Europe. Compared to the measurements, all models systematically underestimated dry PM10 and PM2.5 by 10-60%, depending on the model and the season of the year. For majority of the PM chemical components the relative underestimation was smaller than that, exceptions being the carbonaceous particles and mineral dust - species that suffer from relatively small amount of available oservational data. The study stressed the necessity for high-quality emissions from wild-land fires and wind-suspended dust, as well as the need for an explicit consideration of aerosol water content in model-measurement comparison.
The average water content at laboratory conditions was estimated between 5 and 20% for PM2.5 and between 10 and 25% for PM10. SILAM predictions were used to assess the annual mortality attributable to short-term exposures to vegetation-fire originated PM2.5 in different regions in Europe. PM2.5 emitted from vegetation fires was found to be a relevant risk factor for public health in Europe, more than 1000 premature deaths per year were attributed to vegetation-fire released PM2.5.
CTM predictions critically depend on emission data quality. An error was found in the EMEP anthropogenic emission inventory regarding the SOx and PM missions of metallurgy plants on the Kola Peninsula and SILAM was applied to
estimate the accuracy of the proposed correction. Allergenic pollen is arguably the type of aerosol with most widely recognised effect to health.
SILAM's ability to predict allergenic pollen was extended to include Ambrosia Artemisiifolia - an invasive weed spreading in Southern Europe, with extremely allergenic pollen capable of inducing rhinoconjuctivitis and asthma in the sensitive individuals even in very low concentrations. The model compares well with the pollen observations and predicts occasional exceedances of allergy relevant thresholds even in areas far from the plants' habitat.
The variations of allergenicity in grass pollen were studied and mapped to the source areas by adjoint computations with SILAM. Due to the high year-to-year variability of the observed pollen potency between the studied years and the sparse observational network, no clear geographic pattern of pollen allergenicity was detected
