Learning from the COVID-19 lockdown in Berlin: Observations and modelling to support understanding policies to reduce NO2.
Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30–50 % in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 % (traffic and urban background locations) and a ca. 22 % increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.
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- Academic Articles
von Schneidemesser, E., Sibiya, B., Caseiro, A., Butler, T. M., Lawrence, M. G., Leitao, J., Lupascu, A., & Salvador, P. (2021). Learning from the COVID-19 lockdown in Berlin: Observations and modelling to support understanding policies to reduce NO2. Atmospheric environment: X, 12: 100122. doi:10.1016/j.aeaoa.2021.100122.
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- Air Quality Modelling for Policy Advice Climate Change and Air Pollution: Research Needs and Pathways to Policy Implementation