Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
Environ Sci Technol. 2023 Nov 21;57(46):18271-18281. doi: 10.1021/acs.est.2c09596. Epub 2023 Aug 11.
Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO), 0% to +4% for ozone (O), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM), and there was no response for PM. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.
在 COVID-19 封锁期间，活动的变化为我们提供了一个机会，让我们了解潜在的排放控制和空气质量管理政策可能对减少空气污染产生的影响。我们使用回归不连续性设计进行因果分析，结果表明，英国首次全国封锁导致道路交通量前所未有地减少了 65%，但伦敦的空气污染却出现了不成比例且不均匀的反应。在不同的地点，由于封锁而导致的空气污染变化，二氧化氮（NO）的变化范围为-50%至 0%，臭氧（O）的变化范围为 0%至+4%，空气动力学直径小于 10μm 的颗粒物（PM）的变化范围为-5%至+0%，而 PM 没有变化。我们使用可解释的机器学习来解释预测模型的输出，结果表明，一个地区的 NO 污染减少程度与空间特征（包括公路货运交通以及与主要机场和市中心的距离）相关，并且现有的空气污染暴露不平等现象加剧了：污染减少幅度在居民更富裕和公共交通服务更便利的地方更大。
