Air pollution has been identified as the world's largest single environmental health risks by
the World Health Organization. Real time air-quality
information is necessary, to pretect humans against from the damage casused by air pollution.
In this Thesis we will address this problem by creating a new framework capable of predicting
and interpolating the PM2.5 concentration.
We will use a Biderectional LSTM for the prediction part and an Artificial Neural Network with Self Training for the interpolation part.
We will create 1km x 1km maps of the city of Chicago and we will compare our results with different baselines and existing frameworks
