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Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown
To control the spread of COVID-19, the Chinese government announced a “lockdown” policy, and the citizens’ activities were restricted. This study selected three standard air quality indexes, AQI, PM2.5, and PM10, of 2017–2021 in 40 major cities in six regions in China to analyze their changes, spati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640879/ https://www.ncbi.nlm.nih.gov/pubmed/36342604 http://dx.doi.org/10.1007/s11356-022-23927-4 |
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author | Yan, Xinlin Sun, Tao |
author_facet | Yan, Xinlin Sun, Tao |
author_sort | Yan, Xinlin |
collection | PubMed |
description | To control the spread of COVID-19, the Chinese government announced a “lockdown” policy, and the citizens’ activities were restricted. This study selected three standard air quality indexes, AQI, PM2.5, and PM10, of 2017–2021 in 40 major cities in six regions in China to analyze their changes, spatial–temporal distributions, and socioeconomic influencing factors. Compared with 2019, the values of AQI, PM2.5, and PM10 decreased, and the days with AQI levels “AQI ≤ 100” increased during the “lockdown” in 2020. Due to different degrees of industrialization, the concentration of air pollutants shows significant regional characteristics. The AQI values before and after the “lockdown” in 2020 show significant spatial autocorrelation, and the cities’ AQI values in the north present high autocorrelation, and the cities in the south are in low autocorrelation. From the data at the national level, carbon emission intensity (CEI), per capita energy consumption (PEC), per capita GDP (PCG), industrialization rate (IR), and proportion of construction value added (PCVA) have the greatest impact on AQI. This study gives regulators confidence that if the government implements regionalized air quality improvement policies according to the characteristics of each region in China and reasonably plans socioeconomic activities, it is expected to improve China’s air quality sustainably. |
format | Online Article Text |
id | pubmed-9640879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-96408792022-11-14 Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown Yan, Xinlin Sun, Tao Environ Sci Pollut Res Int Research Article To control the spread of COVID-19, the Chinese government announced a “lockdown” policy, and the citizens’ activities were restricted. This study selected three standard air quality indexes, AQI, PM2.5, and PM10, of 2017–2021 in 40 major cities in six regions in China to analyze their changes, spatial–temporal distributions, and socioeconomic influencing factors. Compared with 2019, the values of AQI, PM2.5, and PM10 decreased, and the days with AQI levels “AQI ≤ 100” increased during the “lockdown” in 2020. Due to different degrees of industrialization, the concentration of air pollutants shows significant regional characteristics. The AQI values before and after the “lockdown” in 2020 show significant spatial autocorrelation, and the cities’ AQI values in the north present high autocorrelation, and the cities in the south are in low autocorrelation. From the data at the national level, carbon emission intensity (CEI), per capita energy consumption (PEC), per capita GDP (PCG), industrialization rate (IR), and proportion of construction value added (PCVA) have the greatest impact on AQI. This study gives regulators confidence that if the government implements regionalized air quality improvement policies according to the characteristics of each region in China and reasonably plans socioeconomic activities, it is expected to improve China’s air quality sustainably. Springer Berlin Heidelberg 2022-11-07 2023 /pmc/articles/PMC9640879/ /pubmed/36342604 http://dx.doi.org/10.1007/s11356-022-23927-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Yan, Xinlin Sun, Tao Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title | Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title_full | Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title_fullStr | Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title_full_unstemmed | Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title_short | Spatial–temporal variations and influencing factors of air quality in China’s major cities during COVID-19 lockdown |
title_sort | spatial–temporal variations and influencing factors of air quality in china’s major cities during covid-19 lockdown |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640879/ https://www.ncbi.nlm.nih.gov/pubmed/36342604 http://dx.doi.org/10.1007/s11356-022-23927-4 |
work_keys_str_mv | AT yanxinlin spatialtemporalvariationsandinfluencingfactorsofairqualityinchinasmajorcitiesduringcovid19lockdown AT suntao spatialtemporalvariationsandinfluencingfactorsofairqualityinchinasmajorcitiesduringcovid19lockdown |