The highly resolved air quality distributions in the planned city boulevard will be simulated and analyzed using a Large Eddy Simulation (LES) model parallelized LES (PALM) which presents the state-of-the-art in urban atmospheric modelling (Maronga et al. 2019). These data will also be used in the reward function for DRL.
One of the major tasks in LES-based air quality modelling is the creation of modelling domain and the selection of appropriate boundary conditions. In the model runs, two-way nesting capability of PALM will be used. A higher resolution child domain will cover the neighbourhood of interest and within this domain the aerosol processes will only be treated. The parent domain covers a larger area. In the parent domain, cyclic boundary conditions will be used in lateral and non-cyclic in the stream-wise directions. Within the child domain, detailed city boulevard layouts will be obtained from the City of Helsinki, whereas for the parent domain 3D surface model will be used. Climate scenarios and present day knowledge on background concentrations will be used as model boundary conditions. Other boundary conditions include information about vehicle fleet and emission factors.
The spatial and temporal variability of aerosol particles (size distribution, mass) and gaseous compounds (NO, NO2 and O3) will be simulated for selected representative days in summer and winter periods in the planned city boulevard. Model runs will be made for different urban planning alternatives with varying traffic scenarios with the aim of creating reward functions for the DRL algorithm.
The exact number of the modelled alternatives depends on plausible options the city of Helsinki is considering, but will be in the range of 20-40. From these runs, the spatial and temporal variability of the concentrations fields and possible hotspots will be evaluated and controlling factors determined. Due to the highly variable pollutant fields and great amount of data the model is providing, together with the stakeholders researchers will need to carefully decide on a ranking system for which areas (i.e. pavements, tram stops, different building floors) the reward functions will be created.