Ibarra-Espinosa, S, Ynoue, RY, Ropkins, K orcid.org/0000-0002-0294-6997 et al. (2 more authors) (2020) High spatial and temporal resolution vehicular emissions in south-east Brazil with traffic data from real-time GPS and travel demand models. Atmospheric Environment, 222. 117136. ISSN 1352-2310
Abstract
Vehicular emissions are one of the most important source of pollution in urban centers, impacting air quality with a deleterious effect on human health and ecosystems. Air quality managers rely on emissions inventories to characterize pollution and sources. In this study we predicted vehicular emissions, using three sources of traffic data: 1) travel demand model outputs consisting of traffic simulations of light-duty vehicles, trucks, 2) and urban buses, and 3) a massive data set of real-time GPS coordinates of light-duty vehicles and trucks. The study area comprises the metropolitan areas of São Paulo, Santos, Vale de Paraíba, Sorocaba, and Campinas, which have a population of more than 30 million inhabitant. Once we generated hourly traffic flows, we used the Vehicular Emissions INventory Model (VEIN) to predict fuel consumption and emissions. Emissions using travel demand model for the metropolitan area of São Paulo are CO 177406 t/y, NOX 73554 t/y, NMHC 33999 t/y and PM2:5 2281 t/y. The emissions using GPS data were higher than using travel demand outputs, because GPS average speeds were lower, producing higher emission factors.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. All rights reserved. This is an author produced version of a paper published in Atmospheric Environment. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | VEIN; Vehicular emissions; Travel demand; Brazil; GPS |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Jan 2020 09:59 |
Last Modified: | 15 Nov 2020 01:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.atmosenv.2019.117136 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155761 |
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