Assessing COVID-19 lockdowns’ impacts on global urban PM2.5 air quality with observations and modeling

Yu, C.M., M. Chin, Q. Tan, H. Bian, P.R. Colarco, and H. Yu (2025), Assessing COVID-19 lockdowns’ impacts on global urban PM2.5 air quality with observations and modeling, Atmos. Chem. Phys., doi:10.5194/acp-25-14411-2025.
Abstract

The regional lockdowns, implemented around the world over 2020–2022 to contain the rapid spread of the novel coronavirus disease 2019 (COVID-19), inadvertently created a natural laboratory for investigating the effect of reducing anthropogenic emissions on urban air quality at unprecedentedly large temporal and spatial scales. In this study, we analyze multi-year surface PM2.5 observations in 21 cities around the globe to examine an anomaly of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) concentrations during major COVID-19 lockdowns with respect to that measured in the pre-pandemic years. We then use a set of Goddard Earth Observing System (GEOS) global aerosol transport modeling experiments to disentangle the effect of the lockdown emission reductions from other non-lockdown effects. Our analysis shows that no systematic reductions in PM2.5 are found in response to the lockdowns globally. In some locations, we find the coincidences of an increasing stringency index and decreasing surface PM2.5 , which often lead to the record low of PM2.5 over extensive periods. These observations clearly suggest the positive impacts of COVID-19lockdown-induced anthropogenic emission reductions on air quality. In other stations, however, the lockdowns’ impacts could be masked by differing meteorology and the occurrence of dust and wildfire events. We also found that current satellite remote sensing of aerosol optical depth cannot be used to reliably discern the change of surface PM2.5 due to the COVID-19 lockdowns. The results of this study provide a preview of potential mixed effects on urban air quality when implementing air pollution control regulations such as transitioning from gasoline- and diesel-powered vehicles to electric vehicles.

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Research Program
Modeling Analysis and Prediction Program (MAP)
Funding Sources
MAP