“Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution
Air pollution is now recognized as the world’s single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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American Chemical Society (ACS)
2017
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| Online Access: | http://hdl.handle.net/1721.1/108122 https://orcid.org/0000-0002-4642-9545 https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 |
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| author | Misstear, Bruce McNabola, Aonghus Laden, Francine Britter, Rex E Barrett, Steven R. H. Nyhan, Marguerite Grauwin, Sebastian Ratti, Carlo |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Misstear, Bruce McNabola, Aonghus Laden, Francine Britter, Rex E Barrett, Steven R. H. Nyhan, Marguerite Grauwin, Sebastian Ratti, Carlo |
| author_sort | Misstear, Bruce |
| collection | MIT |
| description | Air pollution is now recognized as the world’s single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed “Active Population Exposure” was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to “Home Population Exposure”, which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the “Active Population Exposure” scenario. Population-weighted exposure computed in each district of NYC for the “Active” scenario were found to be statistically significantly (p < 0.05) different to the “Home” scenario for most districts. In investigating the temporal variability of the “Active” population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health. |
| first_indexed | 2024-09-23T13:16:15Z |
| format | Article |
| id | mit-1721.1/108122 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:16:15Z |
| publishDate | 2017 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1081222022-09-28T13:05:36Z “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution Misstear, Bruce McNabola, Aonghus Laden, Francine Britter, Rex E Barrett, Steven R. H. Nyhan, Marguerite Grauwin, Sebastian Ratti, Carlo Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Urban Studies and Planning Britter, Rex E Barrett, Steven R. H. Nyhan, Marguerite Grauwin, Sebastian Ratti, Carlo Air pollution is now recognized as the world’s single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed “Active Population Exposure” was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to “Home Population Exposure”, which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the “Active Population Exposure” scenario. Population-weighted exposure computed in each district of NYC for the “Active” scenario were found to be statistically significantly (p < 0.05) different to the “Home” scenario for most districts. In investigating the temporal variability of the “Active” population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health. 2017-04-13T18:03:27Z 2017-04-13T18:03:27Z 2016-08 2016-08 Article http://purl.org/eprint/type/JournalArticle 0013-936X 1520-5851 http://hdl.handle.net/1721.1/108122 Nyhan, Marguerite et al. “‘Exposure Track’—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution.” Environmental Science & Technology 50.17 (2016): 9671–9681. © 2016 American Chemical Society https://orcid.org/0000-0002-4642-9545 https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 en_US http://dx.doi.org/10.1021/acs.est.6b02385 Environmental Science & Technology Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Misstear, Bruce McNabola, Aonghus Laden, Francine Britter, Rex E Barrett, Steven R. H. Nyhan, Marguerite Grauwin, Sebastian Ratti, Carlo “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title | “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title_full | “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title_fullStr | “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title_full_unstemmed | “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title_short | “Exposure Track”—The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution |
| title_sort | exposure track the impact of mobile device based mobility patterns on quantifying population exposure to air pollution |
| url | http://hdl.handle.net/1721.1/108122 https://orcid.org/0000-0002-4642-9545 https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 |
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