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An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak
With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM(2.5), the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese govern...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119641/ https://www.ncbi.nlm.nih.gov/pubmed/37146469 http://dx.doi.org/10.1016/j.envint.2023.107941 |
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author | Zhang, Yongjun Wu, Wenpin Li, Yiliang Li, Yansheng |
author_facet | Zhang, Yongjun Wu, Wenpin Li, Yiliang Li, Yansheng |
author_sort | Zhang, Yongjun |
collection | PubMed |
description | With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM(2.5), the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM(2.5) concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM(2.5) concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM(2.5) concentrations. We find that PM(2.5) concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM(2.5) concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM(2.5) concentrations drop by more than 30 %. By 2021, PM(2.5) concentrations rebound slightly, rising by 10 % in most provinces. |
format | Online Article Text |
id | pubmed-10119641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101196412023-04-21 An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak Zhang, Yongjun Wu, Wenpin Li, Yiliang Li, Yansheng Environ Int Full Length Article With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM(2.5), the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM(2.5) concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM(2.5) concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM(2.5) concentrations. We find that PM(2.5) concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM(2.5) concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM(2.5) concentrations drop by more than 30 %. By 2021, PM(2.5) concentrations rebound slightly, rising by 10 % in most provinces. The Author(s). Published by Elsevier Ltd. 2023-05 2023-04-20 /pmc/articles/PMC10119641/ /pubmed/37146469 http://dx.doi.org/10.1016/j.envint.2023.107941 Text en © 2023 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Full Length Article Zhang, Yongjun Wu, Wenpin Li, Yiliang Li, Yansheng An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title | An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title_full | An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title_fullStr | An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title_full_unstemmed | An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title_short | An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak |
title_sort | investigation of pm2.5 concentration changes in mid-eastern china before and after covid-19 outbreak |
topic | Full Length Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119641/ https://www.ncbi.nlm.nih.gov/pubmed/37146469 http://dx.doi.org/10.1016/j.envint.2023.107941 |
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