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Design of a 3D convolutional neural network model to forecast PM2.5 and O3 concentrations in Colombia

Here a 3D convolutional recurrent neural network performance to forecast PM2.5 and O3 at the different stages of the lockdown in Colombia are presented. The network makes a 24 hours forecast and is evaluated using 4 statistical parameters that are then averaged for every lockdown period to quantify the change in the model precision due to lockdown policies. For Colombia, PM2.5 predictions are better for partial and full lockdown, due to the high values produced by biomass burning (pre-lockdown) that are sub-estimated by the network. This does not happen for O3 where the model is fairly similar for the three periods, but in the full lockdown, the model has a slightly worst performance, probably because O3 in full-lockdown have an abnormal growth that leads to a sub estimation of its magnitude, even though the behavior is well represented by the model.On the other hand, the model is more precise for PM2.5 in the full and partial lockdowns possibly due to the biomass burning in pre-lockdown that produces abnormal increases of the pollutant. In terms of the ozone, the pre and partial lockdown have better performance since in the full lockdown the O3 rises to higher values than usual, which has not happened before, so the model was not able to learn this behavior in a precise manner. The topography and the pressure levels seem to be the principal source of uncertainty for the model (apart from the biomass burning).