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Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM 2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM 2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM 2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM 2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM 2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM 2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM 2.5 concentrations at twelve out of the eighteen locations. [Display omitted] • A traffic-emission-dispersion model is developed within the R-LINE framework. • Trucks emit half of traffic PM 2.5 and contribute most to emission uncertainty. • Modelled PM 2.5 levels match measurements at locations least affected by background. • Traffic variability is the main driver of uncertainty. [ABSTRACT FROM AUTHOR]

• A two-step modal emission model for HEVs was developed using real-driving emissions. • The HEV can achieve over 50% CO 2 and CO emission reduction in on-road tests. • The model was applied to large real-driving datasets from Toronto and Beijing. • Emission reductions were lower for Toronto, which has more aggressive driving. • The study highlights the conditions where HEV can yield the most emission benefits. This study tested exhaust emissions and fuel consumption for a hybrid electric vehicle (HEV) in real-world conditions using a portable emissions measurement system (PEMS). A gradient boosting model was developed to predict the electric motor's operation and emissions using only vehicle kinematic data. The model was applied to estimate the potential emission reductions that would be achieved with HEVs compared to conventional vehicles, using two large real-driving activity datasets collected in Greater Toronto and Metropolitan Beijing. The emission reductions estimated for Toronto were 21.6%, 31.3%, and 53.0% for CO 2 , CO, and NO x , and 41.0%, 28.9%, and 68.5% for Beijing. We observed higher emission reductions for CO 2 and NO x under low power demand vehicle operations, which occur more frequently in Beijing, while more aggressive driving was noted in Toronto, leading to smaller estimated benefits of HEVs. Compared to previous studies, our explainable gradient boosting model improved prediction accuracy and robustness substantially by achieving an average Pearson correlation of 0.741 from cross-validation. This study goes beyond an analysis of HEV emissions from engine and motor operations by applying real-world driving data from two large metropolitan areas to the model. By doing so, a novel investigation of the traffic situations, roads, and driving behaviours that yield the highest emission benefits for HEVs was conducted. [ABSTRACT FROM AUTHOR]

[Display omitted] • A year-long campaign measured ultrafine particle and black carbon concentrations. • Land use regression models developed using land use parameters. • Deep learning models developed using digital satellite view images. • Combining land use regression and deep learning models resulted in best performance. • Residential instability was associated with elevated pollution concentrations. Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto. We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1–2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses. Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC. [ABSTRACT FROM AUTHOR]