Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations
Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This s...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000433 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540374707470336 |
|---|---|
| author | Jianfeng Gao Qingyan Meng Linlin Zhang Xinli Hu Die Hu Jiangkang Qian |
| author_facet | Jianfeng Gao Qingyan Meng Linlin Zhang Xinli Hu Die Hu Jiangkang Qian |
| author_sort | Jianfeng Gao |
| collection | DOAJ |
| description | Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19 pandemic. Based on z-scores and multiscale geographically weighted regression models, heat anomaly changes and transfer patterns of different land uses in cities with varying degrees of pandemic impact and drivers were estimated. During the entire year, we found that although the pandemic significantly reduced surface urban heat island intensity during 5 % to 35 % of days, it did not change significantly throughout 2020. During the first-level public health emergency response, the land surface temperatures of residential and commercial lands notably affected by the pandemic decreased by −0.195°C and −0.371°C, and the shifting of strong heat anomaly zones in industrial lands increased heat anomaly and no heat anomaly zones by 6.1 % and 1.4 %, respectively. Furthermore, thermal anomalies were highly correlated with changes in biophysical parameters during the pandemic. These findings provide insights and mitigation strategies for the fluctuations in the urban thermal environment caused by emergencies. |
| format | Article |
| id | doaj-art-230a3ab9eab5485e80a61fe3b2a6d524 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-230a3ab9eab5485e80a61fe3b2a6d5242025-02-05T04:31:17ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104396Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variationsJianfeng Gao0Qingyan Meng1Linlin Zhang2Xinli Hu3Die Hu4Jiangkang Qian5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Hainan Aerospace Information Research Institute, Sanya 572029, China; Corresponding author at: Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Beijing, China.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Hainan Aerospace Information Research Institute, Sanya 572029, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Hainan Aerospace Information Research Institute, Sanya 572029, ChinaSino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaInfluenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19 pandemic. Based on z-scores and multiscale geographically weighted regression models, heat anomaly changes and transfer patterns of different land uses in cities with varying degrees of pandemic impact and drivers were estimated. During the entire year, we found that although the pandemic significantly reduced surface urban heat island intensity during 5 % to 35 % of days, it did not change significantly throughout 2020. During the first-level public health emergency response, the land surface temperatures of residential and commercial lands notably affected by the pandemic decreased by −0.195°C and −0.371°C, and the shifting of strong heat anomaly zones in industrial lands increased heat anomaly and no heat anomaly zones by 6.1 % and 1.4 %, respectively. Furthermore, thermal anomalies were highly correlated with changes in biophysical parameters during the pandemic. These findings provide insights and mitigation strategies for the fluctuations in the urban thermal environment caused by emergencies.http://www.sciencedirect.com/science/article/pii/S1569843225000433COVID-19Surface urban heat islandUrban thermal environment anomaliesDriving factorsMultiscale geographically weighted regression |
| spellingShingle | Jianfeng Gao Qingyan Meng Linlin Zhang Xinli Hu Die Hu Jiangkang Qian Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations International Journal of Applied Earth Observations and Geoinformation COVID-19 Surface urban heat island Urban thermal environment anomalies Driving factors Multiscale geographically weighted regression |
| title | Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations |
| title_full | Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations |
| title_fullStr | Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations |
| title_full_unstemmed | Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations |
| title_short | Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations |
| title_sort | modeling the impact of pandemic on the urban thermal environment over megacities in china spatiotemporal analysis from the perspective of heat anomaly variations |
| topic | COVID-19 Surface urban heat island Urban thermal environment anomalies Driving factors Multiscale geographically weighted regression |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000433 |
| work_keys_str_mv | AT jianfenggao modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations AT qingyanmeng modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations AT linlinzhang modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations AT xinlihu modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations AT diehu modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations AT jiangkangqian modelingtheimpactofpandemicontheurbanthermalenvironmentovermegacitiesinchinaspatiotemporalanalysisfromtheperspectiveofheatanomalyvariations |