  This paper presents an engine able to predict jointly the real-time
concentration of the main pollutants harming people's health: nitrogen dioxyde
(NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are
respectively the particles whose size are below 2.5 um and 10 um).
  The engine covers a large part of the world and is fed with real-time
official stations measures, atmospheric models' forecasts, land cover data,
road networks and traffic estimates to produce predictions with a very high
resolution in the range of a few dozens of meters. This resolution makes the
engine adapted to very innovative applications like street-level air quality
mapping or air quality adjusted routing.
  Plume Labs has deployed a similar prediction engine to build several products
aiming at providing air quality data to individuals and businesses. For the
sake of clarity and reproducibility, the engine presented here has been built
specifically for this paper and differs quite significantly from the one used
in Plume Labs' products. A major difference is in the data sources feeding the
engine: in particular, this prediction engine does not include mobile sensors
measurements.
