Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality
- Conference Paper
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 can the society offer through digitalization and mobility-limiting policies. In this work, we estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring networks, training long-term prediction models and comparing their predictions to measured values over the lockdown month. We show that our models achieve state-of-the-art performance and evaluate up to -29.4 %, -28.1 %, and -52.8 %, change in NO2 in Eastern Switzerland, Beijing and Wuhan respectively. Our reduction estimates take 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. We therefore use transfer learning to update only mobility-dependent variables. We show that the obtained models are suitable for the analysis of the post-lockdown periods and capable of estimating the future air pollution reduction potential. Show more
Book title2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Pages / Article No.
SubjectCOVID-19; Lockdown; Air pollution; Transfer learning
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