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Article: Who are more exposed to PM2.5 pollution: A mobile phone data approach

TitleWho are more exposed to PM2.5 pollution: A mobile phone data approach
Authors
KeywordsMobile phone location data
PM2.5 exposure
Economic status
Multi-temporal scales
Issue Date2020
PublisherElsevier: Creative Commons Licenses. The Journal's web site is located at http://www.elsevier.com/locate/envint
Citation
Environment International, 2020, v. 143, p. article no. 105821 How to Cite?
AbstractBackground: Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. Objectives: This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales. Methods: The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects. Results: We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons). Conclusions: There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.
Persistent Identifierhttp://hdl.handle.net/10722/290280
ISSN
2019 Impact Factor: 7.577
2015 SCImago Journal Rankings: 2.684

 

DC FieldValueLanguage
dc.contributor.authorGUO, H-
dc.contributor.authorLi, W-
dc.contributor.authorYao, F-
dc.contributor.authorWu, J-
dc.contributor.authorZhou, X-
dc.contributor.authorYue, Y-
dc.contributor.authorYeh, AGO-
dc.date.accessioned2020-10-22T08:24:30Z-
dc.date.available2020-10-22T08:24:30Z-
dc.date.issued2020-
dc.identifier.citationEnvironment International, 2020, v. 143, p. article no. 105821-
dc.identifier.issn0160-4120-
dc.identifier.urihttp://hdl.handle.net/10722/290280-
dc.description.abstractBackground: Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. Objectives: This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales. Methods: The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects. Results: We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons). Conclusions: There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.-
dc.languageeng-
dc.publisherElsevier: Creative Commons Licenses. The Journal's web site is located at http://www.elsevier.com/locate/envint-
dc.relation.ispartofEnvironment International-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMobile phone location data-
dc.subjectPM2.5 exposure-
dc.subjectEconomic status-
dc.subjectMulti-temporal scales-
dc.titleWho are more exposed to PM2.5 pollution: A mobile phone data approach-
dc.typeArticle-
dc.identifier.emailLi, W: wfli@hku.hk-
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hk-
dc.identifier.authorityLi, W=rp01507-
dc.identifier.authorityYeh, AGO=rp01033-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.envint.2020.105821-
dc.identifier.pmid32702593-
dc.identifier.scopuseid_2-s2.0-85088130801-
dc.identifier.hkuros315902-
dc.identifier.volume143-
dc.identifier.spagearticle no. 105821-
dc.identifier.epagearticle no. 105821-
dc.publisher.placeUnited Kingdom-

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