DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown
The COVID-19 lockdown in early 2020 and subsequent lifting in late 2022 had a significant impact on air pollution levels in the Yangtze River Delta (YRD). Previous studies have not provided a clear understanding of the detailed spatiotemporal characteristics of PM2.5 concentrations in various functi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2378186 |
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| _version_ | 1846126455678828544 |
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| author | Sombor Borjigen Fengji Zhang Yong Zha Min Shao SuYang Wang Qing Mu |
| author_facet | Sombor Borjigen Fengji Zhang Yong Zha Min Shao SuYang Wang Qing Mu |
| author_sort | Sombor Borjigen |
| collection | DOAJ |
| description | The COVID-19 lockdown in early 2020 and subsequent lifting in late 2022 had a significant impact on air pollution levels in the Yangtze River Delta (YRD). Previous studies have not provided a clear understanding of the detailed spatiotemporal characteristics of PM2.5 concentrations in various functional areas of cities during different periods before and after the outbreak of the epidemic. However, by employing a deep neural network (DNN) model and integrating satellite data, meteorological reanalysis, and PM2.5 observations, established an estimation of high-resolution PM2.5 distribution during the period from 2019 to 2022. The DNN model performed well (R2 = 0.78). During the lockdown, PM2.5 concentrations in 14 YRD cities were over 50% lower than in previous years. Interestingly, even after the lockdown was lifted, PM2.5 levels remained relatively low due to reduced human activities caused by widespread infections. Found that PM2.5 reductions varied across different intra-city functional regions during both the lockdown and lift lockdown periods. Overall, the changes in PM2.5 levels during the 2022 lift lockdown were smaller than during the 2020 lockdown. These findings emphasize the need for tailored government policies to address COVID-19's impact on air pollution, considering diverse functional areas within the region. |
| format | Article |
| id | doaj-art-d19c1635999949be853320505fdd0bf9 |
| institution | Kabale University |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-d19c1635999949be853320505fdd0bf92024-12-12T18:11:18ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2378186DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdownSombor Borjigen0Fengji Zhang1Yong Zha2Min Shao3SuYang Wang4Qing Mu5School of Geography, Nanjing Normal University, Nanjing, ChinaSchool of Geography, Nanjing Normal University, Nanjing, ChinaSchool of Geography, Nanjing Normal University, Nanjing, ChinaSchool of Environment, Nanjing Normal University, Nanjing, ChinaShanghai Rural Commercial Bank, Shanghai, ChinaDepartment of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaThe COVID-19 lockdown in early 2020 and subsequent lifting in late 2022 had a significant impact on air pollution levels in the Yangtze River Delta (YRD). Previous studies have not provided a clear understanding of the detailed spatiotemporal characteristics of PM2.5 concentrations in various functional areas of cities during different periods before and after the outbreak of the epidemic. However, by employing a deep neural network (DNN) model and integrating satellite data, meteorological reanalysis, and PM2.5 observations, established an estimation of high-resolution PM2.5 distribution during the period from 2019 to 2022. The DNN model performed well (R2 = 0.78). During the lockdown, PM2.5 concentrations in 14 YRD cities were over 50% lower than in previous years. Interestingly, even after the lockdown was lifted, PM2.5 levels remained relatively low due to reduced human activities caused by widespread infections. Found that PM2.5 reductions varied across different intra-city functional regions during both the lockdown and lift lockdown periods. Overall, the changes in PM2.5 levels during the 2022 lift lockdown were smaller than during the 2020 lockdown. These findings emphasize the need for tailored government policies to address COVID-19's impact on air pollution, considering diverse functional areas within the region.https://www.tandfonline.com/doi/10.1080/19475705.2024.2378186Covid-19Yangtze River Deltapm2.5DNNlockdown |
| spellingShingle | Sombor Borjigen Fengji Zhang Yong Zha Min Shao SuYang Wang Qing Mu DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown Geomatics, Natural Hazards & Risk Covid-19 Yangtze River Delta pm2.5 DNN lockdown |
| title | DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown |
| title_full | DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown |
| title_fullStr | DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown |
| title_full_unstemmed | DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown |
| title_short | DNN model reveals sharp decline in PM2.5 concentration in the Yangtze River Delta during COVID-19 lockdown and lift lockdown |
| title_sort | dnn model reveals sharp decline in pm2 5 concentration in the yangtze river delta during covid 19 lockdown and lift lockdown |
| topic | Covid-19 Yangtze River Delta pm2.5 DNN lockdown |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2378186 |
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