Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality

1 code implementation • 19 Nov 2020 • Johanna Einsiedler, Yun Cheng, Franz Papst, Olga Saukh

In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations, training a long-term prediction model and comparing its predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China: evaluate up to -15. 8% / +34. 4% change in NO2 / PM10 in Zurich; -35. 3 % / -3. 5 % and -42. 4 % / -34. 7 % in NO2 / PM2. 5 in Beijing and Wuhan respectively.

Transfer Learning