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Interaction of interregional O(3) pollution using complex network analysis

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O(3) in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring statio...

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Autores principales: Zhang, Qiang, Zhu, Yunan, Xu, Dianxiang, Yuan, Jiaqiong, Wang, Zhihe, Li, Yong, Liu, Xueyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432306/
https://www.ncbi.nlm.nih.gov/pubmed/34589299
http://dx.doi.org/10.7717/peerj.12095
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author Zhang, Qiang
Zhu, Yunan
Xu, Dianxiang
Yuan, Jiaqiong
Wang, Zhihe
Li, Yong
Liu, Xueyan
author_facet Zhang, Qiang
Zhu, Yunan
Xu, Dianxiang
Yuan, Jiaqiong
Wang, Zhihe
Li, Yong
Liu, Xueyan
author_sort Zhang, Qiang
collection PubMed
description In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O(3) in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O(3) pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O(3) network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O(3) area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O(3) between each area revealed that the impact of O(3) on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O(3) governance.
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spelling pubmed-84323062021-09-28 Interaction of interregional O(3) pollution using complex network analysis Zhang, Qiang Zhu, Yunan Xu, Dianxiang Yuan, Jiaqiong Wang, Zhihe Li, Yong Liu, Xueyan PeerJ Atmospheric Chemistry In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O(3) in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O(3) pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O(3) network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O(3) area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O(3) between each area revealed that the impact of O(3) on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O(3) governance. PeerJ Inc. 2021-09-07 /pmc/articles/PMC8432306/ /pubmed/34589299 http://dx.doi.org/10.7717/peerj.12095 Text en © 2021 Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Atmospheric Chemistry
Zhang, Qiang
Zhu, Yunan
Xu, Dianxiang
Yuan, Jiaqiong
Wang, Zhihe
Li, Yong
Liu, Xueyan
Interaction of interregional O(3) pollution using complex network analysis
title Interaction of interregional O(3) pollution using complex network analysis
title_full Interaction of interregional O(3) pollution using complex network analysis
title_fullStr Interaction of interregional O(3) pollution using complex network analysis
title_full_unstemmed Interaction of interregional O(3) pollution using complex network analysis
title_short Interaction of interregional O(3) pollution using complex network analysis
title_sort interaction of interregional o(3) pollution using complex network analysis
topic Atmospheric Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432306/
https://www.ncbi.nlm.nih.gov/pubmed/34589299
http://dx.doi.org/10.7717/peerj.12095
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