Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO₂) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO₂ at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO₂ over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

• Record URL:
  • https://doi.org/10.1016/j.scitotenv.2024.170075
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0048969724002092
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00489697
• Supplemental Notes:
  • © 2024 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ganji, Arman
  • Saeedi, Milad
  • Lloyd, Marshall
  • Xu, Junshi
  • Weichenthal, Scott
  • Hatzopoulou, Marianne
• Publication Date: 2024-3-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 170075
• Serial:
  • Science of the Total Environment
  • Volume: 915
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0048-9697
  • Serial URL: http://www.sciencedirect.com/science/journal/00489697

Subject/Index Terms

• TRT Terms: Air pollution; Emissions testing; Highway traffic; Monitoring; Nitrogen dioxide; Predictive models
• Geographic Terms: Toronto (Canada)
• Subject Areas: Environment; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01911537
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 11 2024 4:32PM