In this paper, the effect of the lockdown measures on nitrogen dioxide (NO₂) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO₂, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO₂ reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO₂ data during the lockdown and the corresponding changes in tropospheric vertical NO₂ column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

中文翻译：

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量化Covid-19封锁措施对整个欧洲地区二氧化氮水平的影响

本文中，锁定措施对二氧化氮（NO ₂）在欧洲通过基于广义加性模型（GAM）的统计模型方法进行了分析。GAM旨在一方面找到各种气象参数和时间指标（星期几，季节等）之间的关系，另一方面又找到污染物的水平之间的关系。该模型首先在2015-2019年期间接受了近2000个监测站​​的测量数据训练，然后在2020年应用于相同的站，在没有锁定的情况下提供了预期浓度的预测。建模水平与2020年以来的实际测量值之间的差异用于计算针对混杂因素（如气象和时间趋势）而调整的锁定措施的影响。该研究的重点是2020年4月，这是NO ₂减少量最大的月份，并逐步恢复到7月底。确定了两国之间的显着差异，其中西班牙，法国，意大利，英国和葡萄牙的NO ₂减少量最大，而东方国家（波兰和匈牙利）的NO ₂减少量最小。发现该模型对城市和郊区站点的性能最佳。TROPOMI仪器在Sentinel-5P上观测到的，在锁定期间发现的城市表面NO ₂数据的相对变化与对流层垂直NO ₂柱密度的相应变化之间的比较表明，尽管观测方法存在显着差异，但一致性很好。