Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
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| Affiliation: | 1. Department of Civil & Environmental Engineering, MIT, Cambridge, MA 02139, USA;2. Department of City and Regional Planning, University of California, Berkeley, CA 94720, USA;3. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;4. Department of Urban and Environmental Policy and Planning, Tufts University, Medford, MA 02155, USA;5. Department of Civil & Environmental Engineering, Tufts University, Medford, MA 02155, USA;6. School of Systems Science, Beijing Normal University, Beijing 100875, China;7. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;8. Department of Urban Studies & Planning, MIT, Cambridge, MA 02139, USA;9. Qatar Computing Research Institute, HBKU, Doha 5825, Qatar;1. Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14642, United States;2. Institute of Chemical Engineering Sciences, Foundation for Research and Technology – Hellas, GR-26504 Patras, Greece;3. Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY 14642, United States;4. Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699, United States;1. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China;2. Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, PR China;3. School of Urban Design, Wuhan University, Wuhan 430072, PR China;4. School of GeoSciences, The University of Edinburgh, Edinburgh EH9 3FF, United Kingdom;5. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, PR China;6. Key Laboratory for Urban Habitat Environmental Science and Technology, Shenzhen Graduate School, Peking University, Shenzhen 518055, PR China;7. Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China |
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| 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 PM2.5 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 PM2.5 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 PM2.5 concentration and travel time spent on the road. By combining the estimated PM2.5 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 PM2.5 per hour than those with high wealth level when staying at home, but exposed to less PM2.5 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 PM2.5, 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. |
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