  This paper presents an engine able to forecast jointly the concentrations 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 diameters are below 2.5 um and 10 um respectively).
  The forecasts are performed on a regular grid (the results presented in the
paper are produced with a 0.5{\deg} resolution grid over Europe and the United
States) with a neural network whose architecture includes convolutional LSTM
blocks. The engine is fed with the most recent air quality monitoring stations
measures available, weather forecasts as well as air quality physical and
chemical model (AQPCM) outputs. The engine can be used to produce air quality
forecasts with long time horizons, and the experiments presented in this paper
show that the 4 days forecasts beat very significantly simple benchmarks.
  A valuable advantage of the engine is that it does not need much computing
power: the forecasts can be built in a few minutes on a standard GPU. Thus,
they can be updated very frequently, as soon as new air quality measures are
available (generally every hour), which is not the case of AQPCMs traditionally
used for air quality forecasting.
  The engine described in this paper relies on the same principles as a
prediction engine deployed and used by Plume Labs in several products aiming at
providing air quality data to individuals and businesses.
