Huagui Guo1, Weifeng Li2, Fei Yao3, Jiansheng Wu4, Xingang Zhou5, Yang Yue6, Anthony G O Yeh7. 1. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China. Electronic address: huaguiguo@hku.hk. 2. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China. Electronic address: wfli@hku.hk. 3. School of GeoSciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom. Electronic address: Fei.Yao@ed.ac.uk. 4. Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, PR China; Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China. Electronic address: wujs@pkusz.edu.cn. 5. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, PR China. Electronic address: zxg@tongji.edu.cn. 6. Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518052, PR China. Electronic address: yueyang@szu.edu.cn. 7. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China. Electronic address: hdxugoy@hkucc.hku.hk.
Abstract
BACKGROUND: 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.
BACKGROUND: 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.
Authors: Siyu Ma; Lin Yang; Mei-Po Kwan; Zejun Zuo; Haoyue Qian; Minghao Li Journal: Int J Environ Res Public Health Date: 2021-04-26 Impact factor: 3.390