Spatial Variations of Non-Tailpipe Particulate Matter and Associated Oxidative Potential in Toronto, Canada

NICOLE TRIEU, Cheol H. Jeong, Yee Ka Wong, Christi Jose, Xing Wang, Greg J. Evans, University of Toronto

     Abstract Number: 195
     Working Group: Urban Aerosols

Abstract
While tailpipe emissions have decreased in the past decades, non-tailpipe (NTP) particulate matter (PM) emissions, such as those from the wear of materials found in roads, brakes, and tires, and road dust resuspensions, are increasing. NTP emissions contain redox-active metals that can generate reactive oxygen species, potentially causing oxidative stress and adverse health effects. However, the toxicity and spatial variability of NTP PM in urban environments remain poorly understood, highlighting the need for further research. One promising metric for assessing the toxicity of PM is the oxidative potential (OP), which reflects the ability of PM to oxidize biologically relevant molecules and generate reactive oxygen species. To better understand the spatial variation of NTP and their contributions to PM's OP, particularly with particle size and chemical composition, we conducted an extensive sampling campaign in Toronto, Canada, during the fall, winter and summer seasons in 2023-2024. Weekly PM2.5 and PM10 samples were collected using over 80 portable PM sampling devices across 40 sites. The metal composition of PM2.5 and PM10 was determined, and the OP was determined using the acellular ascorbic acid, glutathione and dithiothreitol assay. Our study aims to develop land-use regression models to characterize the spatial distribution of non-tailpipe emissions using metal markers and measures of OP to better assess exposure to non-tailpipe PM. This work also explores the relationship between OP and the chemical composition of PM in Toronto, focusing on locations with varying contributions of NTP PM. Preliminary findings suggest that NTP markers (e.g., Fe, Cu, Ba) in PM exhibited strong spatial but weak temporal variation. In addition, annual land use regression models were developed for these metals, with proximity to highways, major roads, and traffic signals as dominant predictors. The small buffer distances selected in the models also highlight the highly localized nature of NTP emissions.