Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data
Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/4/1068 |
| _version_ | 1827755734861873152 |
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| author | Jing Kang Bailing Zhang Junyi Zhang Anrong Dang |
| author_facet | Jing Kang Bailing Zhang Junyi Zhang Anrong Dang |
| author_sort | Jing Kang |
| collection | DOAJ |
| description | Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO<sub>2</sub>. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO<sub>2</sub> levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO<sub>2</sub> anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution. |
| first_indexed | 2024-03-11T08:13:11Z |
| format | Article |
| id | doaj.art-c0a43c5487494b58bd7c0f5146924342 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T08:13:11Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c0a43c5487494b58bd7c0f51469243422023-11-16T23:03:15ZengMDPI AGRemote Sensing2072-42922023-02-01154106810.3390/rs15041068Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station DataJing Kang0Bailing Zhang1Junyi Zhang2Anrong Dang3Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8529, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8529, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8529, JapanSchool of Architecture, Tsinghua University, Beijing 100084, ChinaCities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO<sub>2</sub>. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO<sub>2</sub> levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO<sub>2</sub> anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.https://www.mdpi.com/2072-4292/15/4/1068Sentinel 5Ptime-series modellingobservation versus predictiongeo-detectorremote sensingair pollution |
| spellingShingle | Jing Kang Bailing Zhang Junyi Zhang Anrong Dang Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data Remote Sensing Sentinel 5P time-series modelling observation versus prediction geo-detector remote sensing air pollution |
| title | Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data |
| title_full | Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data |
| title_fullStr | Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data |
| title_full_unstemmed | Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data |
| title_short | Quantifying the Effects of Different Containment Policies on Urban NO<sub>2</sub> Decline: Evidence from Remote Sensing and Ground-Station Data |
| title_sort | quantifying the effects of different containment policies on urban no sub 2 sub decline evidence from remote sensing and ground station data |
| topic | Sentinel 5P time-series modelling observation versus prediction geo-detector remote sensing air pollution |
| url | https://www.mdpi.com/2072-4292/15/4/1068 |
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