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Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China

With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the...

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Autores principales: Yang, Huilin, Yao, Rui, Sun, Peng, Ge, Chenhao, Ma, Zice, Bian, Yaojin, Liu, Ruilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915024/
https://www.ncbi.nlm.nih.gov/pubmed/36767683
http://dx.doi.org/10.3390/ijerph20032316
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author Yang, Huilin
Yao, Rui
Sun, Peng
Ge, Chenhao
Ma, Zice
Bian, Yaojin
Liu, Ruilin
author_facet Yang, Huilin
Yao, Rui
Sun, Peng
Ge, Chenhao
Ma, Zice
Bian, Yaojin
Liu, Ruilin
author_sort Yang, Huilin
collection PubMed
description With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM(2.5) concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM(2.5) were analyzed in UAs. The effects of natural and social factors on PM(2.5) concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM(2.5) was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM(2.5) in October. (2) The highest PM(2.5) concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM(2.5). Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM(2.5) concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM(2.5) concentration. However, PM(2.5) in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.
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spelling pubmed-99150242023-02-11 Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China Yang, Huilin Yao, Rui Sun, Peng Ge, Chenhao Ma, Zice Bian, Yaojin Liu, Ruilin Int J Environ Res Public Health Article With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM(2.5) concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM(2.5) were analyzed in UAs. The effects of natural and social factors on PM(2.5) concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM(2.5) was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM(2.5) in October. (2) The highest PM(2.5) concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM(2.5). Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM(2.5) concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM(2.5) concentration. However, PM(2.5) in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation. MDPI 2023-01-28 /pmc/articles/PMC9915024/ /pubmed/36767683 http://dx.doi.org/10.3390/ijerph20032316 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Huilin
Yao, Rui
Sun, Peng
Ge, Chenhao
Ma, Zice
Bian, Yaojin
Liu, Ruilin
Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title_full Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title_fullStr Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title_full_unstemmed Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title_short Spatiotemporal Evolution and Driving Forces of PM(2.5) in Urban Agglomerations in China
title_sort spatiotemporal evolution and driving forces of pm(2.5) in urban agglomerations in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915024/
https://www.ncbi.nlm.nih.gov/pubmed/36767683
http://dx.doi.org/10.3390/ijerph20032316
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