Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective

Abstract

The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO 2 ), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO 2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO 2 , coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO 2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO 2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO 2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO 2 emissions, and fossil fuel consumption have larger NO 2 TVCD reductions regarding workplace closures and public transport closures.

Quantifying the Effects of Different Containment Policies on Urban NO₂ Decline: Evidence From Remote Sensing Integrated With Ground-Station Data

“…Beyond the effects of policies on NO2 anomalies widely discussed in many studies [7][8]18], the main novelty and contributions lied in that this study succeed to tell the difference of human activity containment and quantify their contributions to NO2 anomalies. The top three measures showing dominant effects were C2 "Workplace closure", C5 "Restricted public transport usage", and C1 "School closure", accounting for 54.8%, 52.3%, and 46.4%, respectively.…”

“…More specifically, NO2 concentrations derived from TROPOMI in Beijing decreased by 45% in March 2020 [9], while ground-based NO2 concentrations in Beijing decreased by 41.8% compared with the previous 3-year average level, and by 33.7% compared to 2019 [22]; one recent study from global perspective, showed that the COVID-19 outbreak explained reductions in NO2, from OMI and TROPOMI, by only 6% averagely in China when considering meteorology effects [7]. Another globally study based on OMI showed that NO2 level between January and February was seen a drastic reduction while it further observed to rise in the March and April 2020 [18]. As spatial heterogeneity of NO2 anomalies have been confirmed, the differences between observations recall that research findings may vary due to different spatial scales regarding the Modifiable Areal Unit Problem (MAUP) [23].…”

“…Xing et al [5] used machine learning with multisource data to prove that reduced economic activity, inferred from NO2 reductions (restrictions-induced), drove the slowdown in the number of COVID-19 infections in most regions [5]. Highlighting that pollution has no borders, Li et al [18] clustered global continent grid cells, which were based on historical NO2 pollution levels from OMI satellites for which seasonal trends had been removed, by subtracting 10-year daily mean values to investigate the impact of policy stringencies on different clusters (regions). There are significant differences between regions due to differences in measures taken, the duration of lockdowns, and the intensity of measures implemented by the government, which allows much potential for in-depth explorations.…”

Correlation Between Daily PM10, Nitrogen Dioxide, and Ozone Measurements with the Stringency Index in 15 Different Districts of a Big Metropolis

“…Li et al 38 showed that the COVID-19 containment and closure policies did not significantly reduce NO 2 tropospheric NO 2 vertical column density (TVCD) in the mild or moderate clusters, but they did so in the poor cluster. They utilized the SI for monitoring pandemic restrictions like in our study and demonstrated that specifically, a rise of 1 SD (23.58) in the SI is associated with a drop of 3.2% NO 2 TVCD in the poor cluster (coefficient = −0.033, P < .05).…”

Public Attitude towards Nuclear and Renewable Energy as a Factor of Their Development in a Circular Economy Frame: Two Case Studies

“…The volume of CO 2 emissions in Italy shows a stable downward trend: in 2010, the volume of emissions was 397.1 million tons of CO 2 , in 2020-317.1 million tons of CO 2 , i.e., a decrease of 20.1%. It should be noted that in 2020, the global emissions indicator decreased due to the restrictions imposed due to the COVID-19 pandemic [48].…”