Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring
Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship hav...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/145571 |
| _version_ | 1826208742015762432 |
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| author | Tian, Ye deSouza, Priyanka Mora, Simone Yao, Xiaobai Duarte, Fabio Norford, Leslie K Lin, Hui Ratti, Carlo |
| author2 | Massachusetts Institute of Technology. Department of Architecture |
| author_facet | Massachusetts Institute of Technology. Department of Architecture Tian, Ye deSouza, Priyanka Mora, Simone Yao, Xiaobai Duarte, Fabio Norford, Leslie K Lin, Hui Ratti, Carlo |
| author_sort | Tian, Ye |
| collection | MIT |
| description | Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels. |
| first_indexed | 2024-09-23T14:11:20Z |
| format | Article |
| id | mit-1721.1/145571 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:11:20Z |
| publishDate | 2022 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1455712023-02-02T04:48:18Z Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring Tian, Ye deSouza, Priyanka Mora, Simone Yao, Xiaobai Duarte, Fabio Norford, Leslie K Lin, Hui Ratti, Carlo Massachusetts Institute of Technology. Department of Architecture Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form-air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels. 2022-09-26T17:08:38Z 2022-09-26T17:08:38Z 2022 2022-09-26T16:58:53Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145571 Tian, Ye, deSouza, Priyanka, Mora, Simone, Yao, Xiaobai, Duarte, Fabio et al. 2022. "Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring." Environmental Science and Technology, 56 (11). en 10.1021/ACS.EST.1C04854 Environmental Science and Technology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Chemical Society (ACS) MIT web domain |
| spellingShingle | Tian, Ye deSouza, Priyanka Mora, Simone Yao, Xiaobai Duarte, Fabio Norford, Leslie K Lin, Hui Ratti, Carlo Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title | Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title_full | Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title_fullStr | Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title_full_unstemmed | Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title_short | Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring |
| title_sort | evaluating the meteorological effects on the urban form air quality relationship using mobile monitoring |
| url | https://hdl.handle.net/1721.1/145571 |
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