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This paper presents an engine able to forecast jointly the concentrations ofthe main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3)and particulate matter (PM2.5 and PM10, which are respectively the particleswhose diameters are below 2.5 um and 10 um respectively).  The forecasts are performed on a regular grid (the results presented in thepaper are produced with a 0.5{\deg} resolution grid over Europe and the UnitedStates) with a neural network whose architecture includes convolutional LSTMblocks. The engine is fed with the most recent air quality monitoring stationsmeasures available, weather forecasts as well as air quality physical andchemical model (AQPCM) outputs. The engine can be used to produce air qualityforecasts with long time horizons, and the experiments presented in this papershow that the 4 days forecasts beat very significantly simple benchmarks.  A valuable advantage of the engine is that it does not need much computingpower: 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 areavailable (generally every hour), which is not the case of AQPCMs traditionallyused for air quality forecasting.  The engine described in this paper relies on the same principles as aprediction engine deployed and used by Plume Labs in several products aiming atproviding air quality data to individuals and businesses.

PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network