  The COVID-19 related lockdown measures offer a unique opportunity to
understand how changes in economic activity and traffic affect ambient air
quality and how much pollution reduction potential can the society offer
through digitalization and mobilitylimiting policies. 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. Our reduction
estimates are consistent with recent publications, yet in contrast to prior
works, our method takes local weather into account. What can we learn from
pollution emissions during lockdown? The lockdown period was too short to train
meaningful models from scratch. To tackle this problem, we use transfer
learning to newly fit only traffic-dependent variables. We show that the
resulting models are accurate, suitable for an analysis of the post-lockdown
period and capable of estimating the future air pollution reduction potential.
