Ammonia emissions have contributed to negative societal impacts on human health and terrestrial ecosystems. However, their estimates have large uncertainties, illustrated by low spatial resolutions, coarse categorizations and inaccurate temporal allocation.
This thesis describes a novel ammonia emission model with improved spatial details and temporal dynamics. The improvement in temporalization came from the integration of TIMELINES which provided predictions of the day of fertilization across Europe. The spatial details and emission categorizations was achieved by utilizing INTEGRATOR. The inputs in INTEGRATOR on crop and livestock distribution, and animal housing locations were updated using machine learning and national statistics analysis. Spatially explicit emission fractions of slurry application and animal housing were derived with ALFAM2 and a temperature-dependent scaling, respectively. The validation with satellite and in situ measurements illustrated that the updated model had improved the spatial details and temporal variation of ammonia emission, and its performance was more stable and robust.