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Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data

The PM(2.5) 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 PM(2.5) exposure risk at high spatiotemporal resolutions. However, due to the no‐s...

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Autores principales: Cao, Zheng, Guo, Guanhua, Wu, Zhifeng, Li, Shaoying, Sun, Hui, Guan, Wenchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576961/
https://www.ncbi.nlm.nih.gov/pubmed/34786531
http://dx.doi.org/10.1029/2021GH000468
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author Cao, Zheng
Guo, Guanhua
Wu, Zhifeng
Li, Shaoying
Sun, Hui
Guan, Wenchuan
author_facet Cao, Zheng
Guo, Guanhua
Wu, Zhifeng
Li, Shaoying
Sun, Hui
Guan, Wenchuan
author_sort Cao, Zheng
collection PubMed
description The PM(2.5) 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 PM(2.5) exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM(2.5) exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM(2.5) exposure risk. Hourly exceedance PM(2.5) exposure risk indicators based on population modeling (HEPE(pmd)) and social media data (HEPE(sm)) were developed. Daily accumulative HEPE(sm) and HEPE(psd) ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPE(sm) and HEPE(psd) were observed at 13:00, 18:00, and 22:00. The peak value of HEPE(sm) increased with time, which exhibited a reverse trend to HEPE(psd). The spatial center of HEPE(sm) moved from the northwest of the study area to the center. The spatial center of HEPE(psd) moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPE(sm) was nearly 1.5 times larger than that of HEPE(psd). The expansion areas of HEPE(psd) aggregated in the old downtown, in which the contribution of HEPE(psd) was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM(2.5) exposure risk. Consequently, exposure risk results provided foundations to develop PM(2.5) pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement.
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spelling pubmed-85769612021-11-15 Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data Cao, Zheng Guo, Guanhua Wu, Zhifeng Li, Shaoying Sun, Hui Guan, Wenchuan Geohealth Research Article The PM(2.5) 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 PM(2.5) exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM(2.5) exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM(2.5) exposure risk. Hourly exceedance PM(2.5) exposure risk indicators based on population modeling (HEPE(pmd)) and social media data (HEPE(sm)) were developed. Daily accumulative HEPE(sm) and HEPE(psd) ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPE(sm) and HEPE(psd) were observed at 13:00, 18:00, and 22:00. The peak value of HEPE(sm) increased with time, which exhibited a reverse trend to HEPE(psd). The spatial center of HEPE(sm) moved from the northwest of the study area to the center. The spatial center of HEPE(psd) moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPE(sm) was nearly 1.5 times larger than that of HEPE(psd). The expansion areas of HEPE(psd) aggregated in the old downtown, in which the contribution of HEPE(psd) was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM(2.5) exposure risk. Consequently, exposure risk results provided foundations to develop PM(2.5) pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement. John Wiley and Sons Inc. 2021-11-01 /pmc/articles/PMC8576961/ /pubmed/34786531 http://dx.doi.org/10.1029/2021GH000468 Text en © 2021 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cao, Zheng
Guo, Guanhua
Wu, Zhifeng
Li, Shaoying
Sun, Hui
Guan, Wenchuan
Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_full Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_fullStr Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_full_unstemmed Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_short Mapping Total Exceedance PM(2.5) Exposure Risk by Coupling Social Media Data and Population Modeling Data
title_sort mapping total exceedance pm(2.5) exposure risk by coupling social media data and population modeling data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576961/
https://www.ncbi.nlm.nih.gov/pubmed/34786531
http://dx.doi.org/10.1029/2021GH000468
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