State estimation of urban air pollution with statistical, physical, and
  super-learning graph models
Matthieu Dolbeault Olga Mula Agustín Somacal
We consider the problem of real-time reconstruction of urban air pollution
maps. The task is challenging due to the heterogeneous sources of available
data, the scarcity of direct measurements, the presence of noise, and the large
surfaces that need to be considered. In this work, we introduce different
reconstruction methods based on posing the problem on city graphs. Our
strategies can be classified as fully data-driven, physics-driven, or hybrid,
and we combine them with super-learning models. The performance of the methods
is tested in the case of the inner city of Paris, France.