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

14 Jun 2020Antoine AlléonGrégoire JauvionBoris QuennehenDavid Lissmyr

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... (read more)

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