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

TL;DR

This study develops interpretable, transferable models to analyze the impact of COVID-19 lockdown measures on local air quality, accounting for weather and traffic, and estimates pollution reductions in Switzerland and China.

Contribution

The paper introduces a transfer learning approach for modeling air pollution, incorporating local weather effects, to assess lockdown impacts and future reduction potential.

Findings

Models achieve state-of-the-art performance in Switzerland and China.

Pollution reductions of up to -15.8% in Zurich and -42.4% in Wuhan.

Models can predict post-lockdown air quality and potential improvements.

Abstract

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.