A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

Abstract In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, va...

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Autor principal:	Bocheng Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d4aa17cc8de24642bb224f641d9e0f00
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id	oai:doaj.org-article:d4aa17cc8de24642bb224f641d9e0f00
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spelling	oai:doaj.org-article:d4aa17cc8de24642bb224f641d9e0f002021-12-02T11:45:01ZA novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China10.1038/s41598-021-86304-02045-2322https://doaj.org/article/d4aa17cc8de24642bb224f641d9e0f002021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86304-0https://doaj.org/toc/2045-2322Abstract In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.Bocheng WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bocheng Wang A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
description	Abstract In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.
format	article
author	Bocheng Wang
author_facet	Bocheng Wang
author_sort	Bocheng Wang
title	A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
title_short	A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
title_full	A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
title_fullStr	A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
title_full_unstemmed	A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China
title_sort	novel causality-centrality-based method for the analysis of the impacts of air pollutants on pm2.5 concentrations in china
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d4aa17cc8de24642bb224f641d9e0f00
work_keys_str_mv	AT bochengwang anovelcausalitycentralitybasedmethodfortheanalysisoftheimpactsofairpollutantsonpm25concentrationsinchina AT bochengwang novelcausalitycentralitybasedmethodfortheanalysisoftheimpactsofairpollutantsonpm25concentrationsinchina
_version_	1718395312015409152

A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

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