COVID-19 lockdowns cause global air pollution declines

Abstract

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

Keywords: COVID-19 confinement; air quality; emissions; nitrogen dioxide; particulate matter.

Fig. 1.

Global distribution of 2020 ground-level air pollution anomalies. Ground station measures of NO₂ (A), O₃ (B), and PM_2.5 (C) anomalies are mapped. Anomalies are defined as deviations in 2020 January−May averages from 3-y baseline levels for the same dates and are not corrected for weather variability. Insets show data density distributions for baseline and 2020 periods with median values as vertical lines.

Fig. 2.

Lockdown ground-level air pollution anomalies relative to weather benchmarks for NO₂ (A), O₃ (B), and PM_2.5 (C). The daily population-weighted average (n = 34 countries) ambient pollutant concentrations observed 1 mo before and up to 15 May after lockdowns are plotted in red. Benchmark levels which represent expected concentrations considering time of year and prevailing weather are plotted in black with 95% CIs.

Fig. 3.

Country-specific ground-level lockdown air pollution anomalies. The difference between observed (obs.) lockdown ambient pollutant concentrations and those predicted by the weather benchmark model (trained on 2017–2019 data) are plotted for 34 countries (points) with 95% CIs (error bars). Larger points represent regression models with greater R² values. Both absolute (A) and relative (B) changes are presented.

Fig. 4.

Lockdown mobility changes relative to ground-level air pollution anomalies. Country-specific ambient air pollution anomalies (observed relative to weather benchmark levels) are regressed on two measures of lockdown-induced citizen mobility declines derived from Google (A) and Apple (B). Significant (solid) and nonsignificant (dashed) linear regression lines are plotted for each pollutant. Each point represents a country’s aggregated value (n = 34).