A physics-based Gaussian kernel for air pollutant dispersion over large Eulerian grids

The Gaussian atmospheric dispersion theory is widely used to assess the dispersion of an emitting point sources. For the first time, we generalize this concept to possibly millions of emitting grid cells over large domains, with a high spatial resolution (100 m to some kilometers). The model requires a gridded emission dataset and a minimal number of meteorological variables to reconstruct stability conditions. A combination of a fortran routine embedded in a python architecture makes the module usable in a simple computing infrastructure. This code, called AIRIK (as AtmospheIc dispeRsIon Kernel), is freely available and allows for rapid dispersion analysis of multiple emission sources or to inform metamodels with more realistic dispersion conditions. This approach opens new developments to elaborate more physical convolutional filters in neural network architectures for atmospheric dispersion.