Kang, J.; Zhang, B.; Zhang, J.; Dang, A. Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data. Remote Sens.2023, 15, 1068.
Kang, J.; Zhang, B.; Zhang, J.; Dang, A. Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data. Remote Sens. 2023, 15, 1068.
Kang, J.; Zhang, B.; Zhang, J.; Dang, A. Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data. Remote Sens.2023, 15, 1068.
Kang, J.; Zhang, B.; Zhang, J.; Dang, A. Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data. Remote Sens. 2023, 15, 1068.
Abstract
Cities exposed their vulnerabilities during COVID-19 pandemic. Unprecedented policies restricted human activities but left an unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to address the key questions of urban development behind the restrictions with the goal of supporting sustainable transition. Data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, allowing us better to track the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The anomalies between the observations and predictions as the deviation were identified due to the policy interventions and quantified different effects using spatial stratified heterogeneity statistics. The top three restrictions showing dominant effects were workplace closures, restricted public transport usage, and school closures, accounting for 54.8%, 52.3%, and 46.4% of NO2 anomalies, respectively; and they are directly linked to the mismatch of employment and housing (deter-mining the commuting patterns), educational inequality and the long-term unsolved road con-gestion. Promoting the transformation of urban spatial structure will effectively alleviate air pollution.
Keywords
Sentinel 5P; Time-series modelling; Observation versus Prediction; Geo-detector; Remote sensing; Air pollution; Urban transition
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
