Model-based dense air pollution maps from sparse sensing in multi-source scenarios

Abstract A method for producing dense air pollution maps, based on any given air-pollution dispersion model, is presented. The scheme consists of two phases. At the first stage, sources' locations and emission rates, i.e., source term estimation, as a function of the model's parameter space are sought (“backward computation”). Then, the source term is used to generate the dense maps utilizing the same dispersion model (“forward computation”). The algorithm is model-invariant to the dispersion model, and thus is suitable for a wide range of applications according to the required accuracy and available resources. A simulation of an industrial area demonstrated that this method produced more accurate maps than current state-of-the-art techniques. The resulting dense air pollution map is thus a valuable tool for air pollution mitigation, regulation and research.