Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative Potential in Toronto, Canada.

Few studies have characterized within-city spatial variations in the oxidative potential of fine particulate air pollution (PM2.5). In this study, we evaluated multiple measures of PM2.5 oxidative potential across Toronto, Canada (2016-2017), including glutathione/ascorbate-related oxidative potential (OPGSH and OPAA) and dithiothreitol depletion (OPDTT). Integrated 2-week samples were collected from 67 sites in summer and 42 sites in winter. Multivariable linear models were developed to predict OP based on various land use/traffic factors, and PM2.5 metals and black carbon were also examined. All three measures of PM2.5 oxidative potential varied substantially across Toronto. OPAA and OPDTT were primarily associated with traffic-related components of PM2.5 (i.e., Fe, Cu, and black carbon) whereas OPGSH was not a strong marker for traffic during either season. During summer, multivariable models performed best for OPAA ( RCV2 = 0.48) followed by OPDTT ( RCV2 = 0.32) and OPGSH ( RCV2 = 0.22). During winter, model performance was best for OPDTT ( RCV2 = 0.55) followed by OPGSH ( RCV2 = 0.50) and OPAA ( RCV2 = 0.23). Model parameters varied between seasons, and between-season differences in PM2.5 mass concentrations were weakly/moderately correlated with seasonal differences in OP. Our findings highlight substantial within-city variations in PM2.5 oxidative potential. More detailed information is needed on local sources of air pollution to improve model performance.

Accommodating exogenous variable and decision rule heterogeneity in discrete choice models: Application to bicyclist route choice.

The proposed research contributes to our understanding of incorporating heterogeneity in discrete choice models with respect to exogenous variables and decision rules. Specifically, the proposed latent segmentation based mixed models segment population to different classes with their own decision rules while also incorporating unobserved heterogeneity within the segment level models. In our analysis, we choose to consider both random utility and random regret theories. Further, instead of assuming the number of segments (as 2), we conduct an exhaustive exploration with multiple segments across the two decision rules. The model estimation is conducted using a stated preference data from 695 commuter cyclists compiled through a web-based survey. The probabilistic allocation of respondents to different segments indicates that female commuter cyclists are more utility oriented; however, the majority of the commuter cyclist's choice pattern is consistent with regret minimization mechanism. Overall, cyclists' route choice decisions are influenced by roadway attributes, cycling infrastructure availability, pollution exposure, and travel time. The analysis approach also allows us to investigate time based trade-offs across cyclists belonging to different classes. Interestingly, we observe that the trade-off values in regret and utility based segments for roadway attributes are similar in magnitude; but the values differ greatly for cycling infrastructure and pollution exposure attributes, particularly for maximum exposure levels.

Spatial variations in the estimated production of reactive oxygen species in the epithelial lung lining fluid by iron and copper in fine particulate air pollution.

BACKGROUND: Certain metals may play an important role in the adverse health effects of fine particulate air pollution (PM2.5), but few models are available to predict spatial variations in these pollutants. METHODS: We conducted large-scale air monitoring campaigns during summer 2016 and winter 2017 in Toronto, Canada, to characterize spatial variations in iron (Fe) and copper (Cu) concentrations in PM2.5. Information on Fe and Cu concentrations at each site was paired with a kinetic multilayer model of surface and bulk chemistry in the lung epithelial lining fluid to estimate the possible impact of these metals on the production of reactive oxygen species (ROS) in exposed populations. Land use data around each monitoring site were used to develop predictive models for Fe, Cu, and their estimated combined impact on ROS generation. RESULTS: Spatial variations in Fe, Cu, and ROS greatly exceeded that of PM2.5 mass concentrations. In addition, Fe, Cu, and estimated ROS concentrations were 15, 18, and 9 times higher during summer compared with winter with little difference observed for PM2.5. In leave-one-out cross-validation procedures, final multivariable models explained the majority of spatial variations in annual mean Fe (R 2 = 0.68), Cu (R 2 =0.79), and ROS (R 2 = 0.65). CONCLUSIONS: The combined use of PM2.5 metals data with a kinetic multilayer model of surface and bulk chemistry in the human lung epithelial lining fluid may offer a novel means of estimating PM2.5 health impacts beyond simple mass concentrations.

Comparison of crashes during public holidays and regular weekends.

Traffic collisions and fatalities during the holiday festive periods are apparently on the rise in Alberta, Canada, despite the enhanced enforcement and publicity campaigns conducted during these periods. Using data from 2004 to 2008, this research identifies the factors that delineate between crashes that occur during public holidays and those occurring during normal weekends. We find that fatal and injury crashes are over-represented during holidays. Amongst the three risky behaviors targeted in the holiday blitzes (driver intoxication, unsafe speeding and restraint use), non-use of restraint is more prevalent whereas driver intoxication and unsafe speeding are less prevalent during holidays. The mixed results obtained suggest that it may be time to consider a more balanced approach to the enhanced enforcement and publicity campaigns.