Search

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.)

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.)