Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model
Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely reso...
| Main Authors: | , , , , , , , , , , |
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Elsevier BV
2018
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| Online Access: | http://hdl.handle.net/1721.1/118452 https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 |
| _version_ | 1826188968373256192 |
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| author | Sobolevsky, Stanislav Kang, Chaogui Corti, Andrea Szell, Michael Streets, David Lu, Zifeng Barrett, Steven R.H. Nyhan, Marguerite Robinson, Prudence Britter, Rex E Ratti, Carlo |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Sobolevsky, Stanislav Kang, Chaogui Corti, Andrea Szell, Michael Streets, David Lu, Zifeng Barrett, Steven R.H. Nyhan, Marguerite Robinson, Prudence Britter, Rex E Ratti, Carlo |
| author_sort | Sobolevsky, Stanislav |
| collection | MIT |
| description | Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that high-resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain. Keywords: Air quality; Transportation; Emissions; Microscopic emissions model; Microscopic vehicle movement |
| first_indexed | 2024-09-23T08:07:28Z |
| format | Article |
| id | mit-1721.1/118452 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:07:28Z |
| publishDate | 2018 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1184522022-09-30T07:43:52Z Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model Sobolevsky, Stanislav Kang, Chaogui Corti, Andrea Szell, Michael Streets, David Lu, Zifeng Barrett, Steven R.H. Nyhan, Marguerite Robinson, Prudence Britter, Rex E Ratti, Carlo Massachusetts Institute of Technology. Department of Urban Studies and Planning Massachusetts Institute of Technology. SENSEable City Laboratory Nyhan, Marguerite Robinson, Prudence Britter, Rex E Ratti, Carlo Air pollution related to traffic emissions pose an especially significant problem in cities; this is due to its adverse impact on human health and well-being. Previous studies which have aimed to quantify emissions from the transportation sector have been limited by either simulated or coarsely resolved traffic volume data. Emissions inventories form the basis of urban pollution models, therefore in this study, Global Positioning System (GPS) trajectory data from a taxi fleet of over 15,000 vehicles were analyzed with the aim of predicting air pollution emissions for Singapore. This novel approach enabled the quantification of instantaneous drive cycle parameters in high spatio-temporal resolution, which provided the basis for a microscopic emissions model. Carbon dioxide (CO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and particulate matter (PM) emissions were thus estimated. Highly localized areas of elevated emissions levels were identified, with a spatio-temporal precision not possible with previously used methods for estimating emissions. Relatively higher emissions areas were mainly concentrated in a few districts that were the Singapore Downtown Core area, to the north of the central urban region and to the east of it. Daily emissions quantified for the total motor vehicle population of Singapore were found to be comparable to another emissions dataset. Results demonstrated that high-resolution spatio-temporal vehicle traces detected using GPS in large taxi fleets could be used to infer highly localized areas of elevated acceleration and air pollution emissions in cities, and may become a complement to traditional emission estimates, especially in emerging cities and countries where reliable fine-grained urban air quality data is not easily available. This is the first study of its kind to investigate measured microscopic vehicle movement in tandem with microscopic emissions modeling for a substantial study domain. Keywords: Air quality; Transportation; Emissions; Microscopic emissions model; Microscopic vehicle movement 2018-10-11T20:10:33Z 2018-10-11T20:10:33Z 2016-06 2016-06 2018-09-25T18:19:42Z Article http://purl.org/eprint/type/JournalArticle 1352-2310 http://hdl.handle.net/1721.1/118452 Nyhan, Marguerite et al. “Predicting Vehicular Emissions in High Spatial Resolution Using Pervasively Measured Transportation Data and Microscopic Emissions Model.” Atmospheric Environment 140 (September 2016): 352–363 © 2016 Elsevier Ltd https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 http://dx.doi.org/10.1016/J.ATMOSENV.2016.06.018 Atmospheric Environment Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Other repository |
| spellingShingle | Sobolevsky, Stanislav Kang, Chaogui Corti, Andrea Szell, Michael Streets, David Lu, Zifeng Barrett, Steven R.H. Nyhan, Marguerite Robinson, Prudence Britter, Rex E Ratti, Carlo Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title | Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title_full | Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title_fullStr | Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title_full_unstemmed | Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title_short | Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| title_sort | predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model |
| url | http://hdl.handle.net/1721.1/118452 https://orcid.org/0000-0002-4292-8232 https://orcid.org/0000-0003-2026-5631 |
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