Spatial variations in PM2.5 oxidative potential in Toronto and Montreal, Canada

Abstract

BACKGROUND AND AIM: Particulate matter air pollution is a recognized threat to human health. Recently, measures of particulate air pollution have been developed that incorporate information on the effects that particles may have inside the human respiratory tract. Among these is particle oxidative potential (OP), which is a measure of the ability of PM to cause oxidative reactions. OP can be quantified using assays that measure the ability of PM to deplete antioxidants in a synthetic respiratory tract lining fluid model. An alternative approach considers the ability of particles to generate reactive oxygen species (ROS) in the respiratory tract, as estimated using a mathematical model and concentrations of transition metals. The aim of our study was to develop land-use regression models to characterize the spatial distribution of ROS-generating capacity of PM2.5 and two measures of OP. METHODS: We conducted large-scale spatial monitoring campaigns across Montreal and Toronto, Canada and developed land use regression models to predict the spatial distribution of ROS-generating capacity of PM2.5 and the ability of PM2.5 extracts to deplete the antioxidants ascorbate (OPAA) and glutathione (OPGSH). RESULTS:In Montreal, the best models explained 54% of variation in ROS, 45% in OPAA and 31% in OPGSH. In Toronto, models explained 63% of variation in ROS, 77% in OPAA, and 44% in OPGSH. Variables that were identified as predictors across multiple models included distance to PM2.5 and NOX emitting facilities, total traffic counts, and distance to highways. CONCLUSIONS:These results contribute to existing knowledge of within-city spatial variations in particle oxidative potential using an unprecedented number of sensors. Exposure surfaces generated by these models can be applied in future studies of the health impact of PM2.5. Future work could clarify the sources of the most harmful components of PM2.5 to human health to aid in targeted reductions of emissions. KEYWORDS: particulate matter, oxidative potential, land use regression