Studies of atmospheric dispersion of pollutants on a local scale are increasingly performed with Computational Fluid Dynamics (CFD) simulations. However, CFD computations can be numerically expensive, and are often only performed on a limited number of situations. Machine learning approaches offer the possibility to build surrogate models, i.e. fast approximations to the CFD solver, allowing to quickly simulate new scenarios....