A strong discussion is ongoing in Germany how to lower particle concentrations of e.g. PM10 and NO2 in urban areas. As often only few measurements exist inside city centers, little to nothing is known about the horizontal and vertical distributions of air pollutants. To close this knowledge gap, I applied the WRF-Chem model and zoomed in the urban area of Stuttgart, a hot spot of air pollution in Germany in wintertime.
The model system was enhanced in many ways, e.g., with respect to the representation of land cover, urban canopy, soil properties, and emissions.
I will show that this model approach is likely the best means to understand and to predict air pollution, as the distribution of their constituents depends strongly and simultaneously on the vertical mixing by turbulence, the mesoscale circulation in the complex urban and orographic environment.