Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach
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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Elsevier BV
2020
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| Online Access: | https://hdl.handle.net/1721.1/126882 |
| _version_ | 1811089800660254720 |
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| author | Xu, Yanyan Jiang, Shan Li, Ruiqi Zhang, Jiang Zhao, Jinhua Abbar, Sofiane Gonzalez, Marta C. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Xu, Yanyan Jiang, Shan Li, Ruiqi Zhang, Jiang Zhao, Jinhua Abbar, Sofiane Gonzalez, Marta C. |
| author_sort | Xu, Yanyan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:24:53Z |
| format | Article |
| id | mit-1721.1/126882 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:24:53Z |
| publishDate | 2020 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1268822022-10-01T21:13:07Z Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach Xu, Yanyan Jiang, Shan Li, Ruiqi Zhang, Jiang Zhao, Jinhua Abbar, Sofiane Gonzalez, Marta C. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Department of Urban Studies and Planning 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. 2020-09-01T20:07:33Z 2020-09-01T20:07:33Z 2019-05 2018-12 2020-08-31T12:00:26Z Article http://purl.org/eprint/type/JournalArticle 0198-9715 https://hdl.handle.net/1721.1/126882 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 en http://dx.doi.org/10.1016/j.compenvurbsys.2018.12.006 Computers, Environment and Urban Systems Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Other repository |
| spellingShingle | Xu, Yanyan Jiang, Shan Li, Ruiqi Zhang, Jiang Zhao, Jinhua Abbar, Sofiane Gonzalez, Marta C. Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title | Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title_full | Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title_fullStr | Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title_full_unstemmed | Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title_short | Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach |
| title_sort | unraveling environmental justice in ambient pm2 5 exposure in beijing a big data approach |
| url | https://hdl.handle.net/1721.1/126882 |
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