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Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019

The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quali...

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Autores principales: Tao, Tianhui, Shi, Yishao, Gilbert, Katabarwa Murenzi, Liu, Xinyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915768/
https://www.ncbi.nlm.nih.gov/pubmed/35277593
http://dx.doi.org/10.1038/s41598-022-08377-9
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author Tao, Tianhui
Shi, Yishao
Gilbert, Katabarwa Murenzi
Liu, Xinyi
author_facet Tao, Tianhui
Shi, Yishao
Gilbert, Katabarwa Murenzi
Liu, Xinyi
author_sort Tao, Tianhui
collection PubMed
description The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quality on 19 urban agglomerations during the 13th Five-Year Plan period in China were explored by mathematical statistics. The comparison results are all visualized and show that clean air days gradually increased and occurred mainly in summer, especially in South and Southwest China. PM(2.5), PM(10) and O(3) were still the main primary pollutants. PM(2.5) is mainly concentrated in December, January and February, and PM(10) is mainly concentrated in October–November and March–April. The O(3) pollution in the Pearl River Delta and Beibu Gulf urban agglomerations located in the south is mainly concentrated from August to November, which is different from others from May to September. Second, from 2015 to 2019, the increasing rate of O(3) concentration in any hour is higher than that of particulate matter (PM). Diurnal trends in O(3) concentration in all directions also showed a single peak, with the largest increments that appeared between 13:00 and 16:00, while the spatial distribution of this peak was significantly regional, earlier in the east but later in the west. Third, this analysis indicated that the annual average air quality index (AQI) showed a gradually decreasing trend outward, taking the Central Plain urban agglomeration as the center. The ambient air pollutants are gradually moving southward and mainly concentrated in the Central Plains urban agglomeration from 2015 to 2019. Furthermore, in each urban agglomeration, the cumulative emission of PM(2.5) is consisted of the four average emissions, which is approximately 2.5 times of that of PM(10,) and industries are the main sources of PM(2.5), PM(10) and VOCs (volatile organic compounds). VOCs and NO(X) increased in half of the urban agglomerations, which are the reasons for the increase in ozone pollution. The outcomes of this study will provide targeted insights on pollution prevention in urban agglomerations in the future.
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spelling pubmed-89157682022-03-11 Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019 Tao, Tianhui Shi, Yishao Gilbert, Katabarwa Murenzi Liu, Xinyi Sci Rep Article The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quality on 19 urban agglomerations during the 13th Five-Year Plan period in China were explored by mathematical statistics. The comparison results are all visualized and show that clean air days gradually increased and occurred mainly in summer, especially in South and Southwest China. PM(2.5), PM(10) and O(3) were still the main primary pollutants. PM(2.5) is mainly concentrated in December, January and February, and PM(10) is mainly concentrated in October–November and March–April. The O(3) pollution in the Pearl River Delta and Beibu Gulf urban agglomerations located in the south is mainly concentrated from August to November, which is different from others from May to September. Second, from 2015 to 2019, the increasing rate of O(3) concentration in any hour is higher than that of particulate matter (PM). Diurnal trends in O(3) concentration in all directions also showed a single peak, with the largest increments that appeared between 13:00 and 16:00, while the spatial distribution of this peak was significantly regional, earlier in the east but later in the west. Third, this analysis indicated that the annual average air quality index (AQI) showed a gradually decreasing trend outward, taking the Central Plain urban agglomeration as the center. The ambient air pollutants are gradually moving southward and mainly concentrated in the Central Plains urban agglomeration from 2015 to 2019. Furthermore, in each urban agglomeration, the cumulative emission of PM(2.5) is consisted of the four average emissions, which is approximately 2.5 times of that of PM(10,) and industries are the main sources of PM(2.5), PM(10) and VOCs (volatile organic compounds). VOCs and NO(X) increased in half of the urban agglomerations, which are the reasons for the increase in ozone pollution. The outcomes of this study will provide targeted insights on pollution prevention in urban agglomerations in the future. Nature Publishing Group UK 2022-03-11 /pmc/articles/PMC8915768/ /pubmed/35277593 http://dx.doi.org/10.1038/s41598-022-08377-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Tao, Tianhui
Shi, Yishao
Gilbert, Katabarwa Murenzi
Liu, Xinyi
Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title_full Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title_fullStr Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title_full_unstemmed Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title_short Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019
title_sort spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 chinese urban agglomerations during 2015–2019
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915768/
https://www.ncbi.nlm.nih.gov/pubmed/35277593
http://dx.doi.org/10.1038/s41598-022-08377-9
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AT gilbertkatabarwamurenzi spatiotemporalvariationsofairpollutantsbasedongroundobservationandemissionsourcesover19chineseurbanagglomerationsduring20152019
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