A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources

Ben, YuJie1,2; Ma, FuJun3; Wang, Hao4,5; Hassan, Muhammad Azher4; Yevheniia, Romanenko4; Fan, WenHong4,5; Li, Yubiao6; Dong, ZhaoMin4,5

2019-10

10.1016/j.envpol.2019.07.034

ENVIRONMENTAL POLLUTION   影响因子和分区

0269-7491

1873-6424

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 mu m (PM2.5 ) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM2.5 concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM2.5 exposure. Using Shanghai as a case study, the annual average indoor PM2.5 concentration was estimated to be 29.3 +/- 27.1 mu g/m(3) (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM2.5 exposure was estimated to be 32.1 +/- 13.9 mu g/m(3) (n = 365), with indoor PM2.5 contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM2.5 makes a significant contribution to indoor PM2.5, outdoor PM2.5 was responsible for most of the exposure in Shanghai. A heatmap of PM2.5 exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation. (C) 2019 Elsevier Ltd. All rights reserved.

Exposure assessment Indoor PM2.5 Ambient PM2.5 In-home monitors Shanghai

SCI ; EI

英语

其他

Beihang University[ZG216S1876] ; Beihang University[KG12058101] ; Beihang University[ZG226S18S3]

Environmental Sciences & Ecology

Environmental Sciences

WOS:000483406700043

ELSEVIER SCI LTD

20192907197760

Air quality ; Big data ; Smart cards ; Subways

Passenger Railroad Transportation:433.2 ; Air Pollution Control:451.2 ; Biomedical Engineering:461.1 ; Digital Computers and Systems:722.4 ; Data Processing and Image Processing:723.2

ENVIRONMENT/ECOLOGY

Web of Science

1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen 518055, Peoples R China
3.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China
4.Beihang Univ, Sch Space & Environm, Beijing, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
6.Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Hubei, Peoples R China

Ben, YuJie,Ma, FuJun,Wang, Hao,et al. A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources[J]. ENVIRONMENTAL POLLUTION,2019,253:403-411.

Ben, YuJie.,Ma, FuJun.,Wang, Hao.,Hassan, Muhammad Azher.,Yevheniia, Romanenko.,...&Dong, ZhaoMin.(2019).A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources.ENVIRONMENTAL POLLUTION,253,403-411.

Ben, YuJie,et al."A spatio-temporally weighted hybrid model to improve estimates of personal PM2.5 exposure: Incorporating big data from multiple data sources".ENVIRONMENTAL POLLUTION 253(2019):403-411.