TRAPSim: An agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul. (January 2023)

• Record Type: Journal Article
• Authors: Shin, Hyesop; Bithell, Mike
• Is Part Of: Computers, environment and urban systems. Volume 99(2023)
• Publication Date: 2023-01
• Subjects: Traffic simulation -- Exposure -- Health -- Agent-based model -- NetLogo; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2022.101894
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Non-exhaust emissions (NEEs) from brake and tyre wear cause detrimental health effects, yet their relationship with mobility has not been examined rigorously. We constructed an agent-based traffic simulator to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 for groups of drivers and subway commuters in Seoul CBD. Having calibrated the parameters, the results regarding the air quality revealed that roughly 25–30% of the roadside PM10 was significantly higher than the background PM10 . Additionally, compared to intra-urban cars, pedestrians who commuted for longer periods of time and were exposed to more ambient particles suffered significant health losses; however, drivers only became aware of the health risk when PM10 levels were consistently high for a few days. Compared to the business-as-usual scenario of vehicle entry, a 90% vehicle restriction was able to reduce PM10 by 18–24% and cut the percentage of resident drivers who were at risk. However, it was not effective for subway commuters. Using an agent-based traffic simulator in a health context can provide insights into how exposure and health effects can vary depending on the time of exposure and the form of transportation. Highlights: An agent-based model simulated the vehicles' NEEs and the adverse health effects. Our model found that non-exhaust emissions contributed 25–30% of the roadside PM10 . Banning 90% of vehicles to the study area led up to a 24% decrease in ambient PM10 . 90% vehicle ban halved the at-risk drivers but negligible in subway commuters. The estimates of health effects depend strongly on the parameterisation.