MaLePoM (Machine Learning for Pollution Monitoring)
The project aims to build a Machine Learning model to estimate emissions using suitable proxy data due to anthropogenic activities. Initially, we will model the concentrations of NOx in Europe. Therefore, proxy data should frame these activities exploiting databases such as Land cover maps, Dynamic traffic data, lighthing data and others.
Subsequently, different approach will be tested in order to capture both spatial and temporal variability at high resolution and eventually allow accurate emissions estimates at global scales.
Follow the developments on GitHub
Mentors
- Joe Mc Norton
- Nicolas Bousserez
- Gianpaolo Balsamo
- Mark Parrington
- Anna Agusti-Panareda
Participants
Nicolo Brunello
Vidur Mithal
Paolo Fornoni
Luca Rampini