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Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, and ensuring transportation equity. We conducted a large-scale mobile measurement campaign and employed a gradient boost model to generate exposure surfaces using land use, built environment, and meteorological conditions. The Ontario Marginalization Index was used to quantify various indicators of social disadvantage for Toronto's neighborhoods. Our findings reveal that people in socioeconomically disadvantaged areas experience elevated UFP exposures. We highlight significant disparities in accessing sustainable transportation, particularly in areas with higher ethnic concentrations. When factoring in daily mobility, UFP exposure disparities in disadvantaged populations are further exacerbated. Furthermore, individuals who do not generate emissions themselves are consistently exposed to higher UFPs, with active transportation users experiencing the highest UFP exposures both at home and at activity locations. Finally, we proposed a novel index, the Community Prioritization Index (CPI), incorporating three indicators, including air quality, social disadvantage, and sustainable transportation. This index identifies neighborhoods experiencing a triple burden, often situated near major infrastructure hubs with high diesel truck activity and lacking greenspace, marking them as high-priority areas for policy action and targeted interventions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marianne Hatzopoulou reports financial support was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) Engage Grant. Scott Weichenthal reports financial support was provided by Health Effects Institute (HEI). Marianne Hatzopoulou reports financial support was provided by Health Effects Institute (HEI). Marianne Hatzopoulou reports financial support was provided by United States Environmental Protection Agency (EPA). Scott Weichenthal reports financial support was provided by United States Environmental Protection Agency (EPA)., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: 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., Objective: 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., Methods: 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., Results: In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm 3 , 33.7 nm, and 1225 ng/m 3 , respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm 3 , 29.7 nm, and 1060 ng/m 3 , respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R 2 ) 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 R 2 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 R 2 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., Conclusion: Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Scott Weichenthal reports financial support was provided by Health Effects Institute., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R 2 = 0.66 and RMSE = 9391.8#/cm 3 (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.

In this study, driving trajectory data from private vehicles were collected in Toronto, Canada to construct representative local drive cycles. In addition, real-driving emission testing for four conventional gasoline vehicles (ICEV) and one hybrid electric vehicle (HEV) was conducted in the same region using a Portable Emissions Measurement System. Instantaneous fuel consumption and emissions of Carbon Monoxide (CO), Nitrogen Oxides (NO x ), and Particle Number (PN) were measured. The results for all vehicles indicate that the acceleration state tends to generate the highest emissions and fuel consumption with the largest variation due to higher power demand. When accelerating, the HEV was observed to generate four times more CO emissions than some ICEVs. Instantaneous fuel consumption and emissions were analyzed as a function of operating modes to estimate the fuel efficiency (FE) and emission factors (EF) associated with six representative local drive cycles and four regulatory drive cycles. With most regulatory drive cycles, vehicles can reach the labeled FE and EPA emission limits, except under the New York City Cycle with frequent stop-and-go conditions. In contrast, except for highway cycles, the FE of Toronto-specific drive cycles can hardly meet the labeled values. CO EFs of the HEV can be higher than ICEVs, while it is lower than the emission limit by 42% on average. ICEVs may exceed the CO limit by 131% under local highway cycles, while they can violate NO x and PN limits under local arterial cycles. The result of this study emphasizes the importance of local drive cycles and real driving emission tests., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)