论文标题
可解释且可转让的模型，以了解锁定措施对当地空气质量的影响
Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality
论文作者
论文摘要
COVID-19相关的锁定措施提供了一个独特的机会，可以了解经济活动和交通的变化如何影响环境空气质量以及社会通过数字化和行动限制政策提供的污染潜力。在这项工作中，我们通过使用地面空气污染监测站的测量，训练一个长期预测模型，并将其预测与锁定月份的测量值进行比较。我们表明，我们的模型表明，我们的模型在瑞士和中国的空气污染测量站的数据上实现最新数据，估算了整个锁定期的污染减少。北京和武汉分别为-35.3％ / -3.5％和-42.4％ / -34.7％。我们的减少估计与最近的出版物一致，但与先前的作品相反，我们的方法考虑了当地的天气。在锁定期间，我们可以从污染排放中学到什么？锁定期太短，无法从头开始训练有意义的模型。为了解决这个问题，我们使用转移学习仅适合流量依赖性变量。我们表明，所得模型是准确的，适合分析锁骨后时期，并能够估算未来的空气污染潜力。
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.
