Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data

Abstract The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the...

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Autores principales:	Zheng Cao, Guanhua Guo, Zhifeng Wu, Shaoying Li, Hui Sun, Wenchuan Guan
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	exceedance PM2.5 exposure risk total people groups social media data population modeling data risk assessment Environmental protection TD169-171.8
Acceso en línea:	https://doaj.org/article/4a8a8cf817034b978e3fdebbd3d0e0e2
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spelling	oai:doaj.org-article:4a8a8cf817034b978e3fdebbd3d0e0e22021-11-23T17:17:50ZMapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data2471-140310.1029/2021GH000468https://doaj.org/article/4a8a8cf817034b978e3fdebbd3d0e0e22021-11-01T00:00:00Zhttps://doi.org/10.1029/2021GH000468https://doaj.org/toc/2471-1403Abstract The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement.Zheng CaoGuanhua GuoZhifeng WuShaoying LiHui SunWenchuan GuanAmerican Geophysical Union (AGU)articleexceedance PM2.5 exposure risktotal people groupssocial media datapopulation modeling datarisk assessmentEnvironmental protectionTD169-171.8ENGeoHealth, Vol 5, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	exceedance PM2.5 exposure risk total people groups social media data population modeling data risk assessment Environmental protection TD169-171.8
spellingShingle	exceedance PM2.5 exposure risk total people groups social media data population modeling data risk assessment Environmental protection TD169-171.8 Zheng Cao Guanhua Guo Zhifeng Wu Shaoying Li Hui Sun Wenchuan Guan Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
description	Abstract The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement.
format	article
author	Zheng Cao Guanhua Guo Zhifeng Wu Shaoying Li Hui Sun Wenchuan Guan
author_facet	Zheng Cao Guanhua Guo Zhifeng Wu Shaoying Li Hui Sun Wenchuan Guan
author_sort	Zheng Cao
title	Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_short	Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_full	Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_fullStr	Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_full_unstemmed	Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_sort	mapping total exceedance pm2.5 exposure risk by coupling social media data and population modeling data
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/4a8a8cf817034b978e3fdebbd3d0e0e2
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Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data

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