Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach
Name
CEUS_Xu_2019.pdf
Description
Accepted version
Size
10.38 MB
Format
Adobe PDF
Checksum (MD5)
331d9b56bf60311b7004c8e15aec6036
Author(s)
Xu, Yanyan
Jiang, Shan
Li, Ruiqi
Zhang, Jiang
Zhao, Jinhua
Abbar, Sofiane
Gonzalez, Marta C.
Date Issued
May 2019
Journal
Computers, Environment and Urban Systems
Publisher
Elsevier BV
Citation
Xu, Yanyan et al. "Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach." Computers, Environment and Urban Systems 75 (May 2019): 12-21 © 2019 Elsevier
Version
Author's final manuscript
Abstract
Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents’ stay and commuters’ travel exposure to outdoor PM 2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents’ stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM 2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM 2.5 concentration and travel time spent on the road. By combining the estimated PM 2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM 2.5 per hour than those with high wealth level when staying at home, but exposed to less PM 2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM 2.5 , especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.compenvurbsys.2018.12.006
