Predicting within-city spatiotemporal variations in daily median outdoor ultrafine particle number concentrations and size in Montreal and Toronto, Canada.

Background: Epidemiological evidence suggests that long-term exposure to outdoor ultrafine particles (UFPs, <0.1 µm) may have important human health impacts. However, less is known about the acute health impacts of these pollutants as few models are available to estimate daily within-city spatiotemporal variations in outdoor UFPs. Methods: Several machine learning approaches (i.e., generalized additive models, random forest models, and extreme gradient boosting) were used to predict daily spatiotemporal variations in outdoor UFPs (number concentration and size) across Montreal and Toronto, Canada using a large database of mobile monitoring measurements. Separate models were developed for each city and all models were evaluated using a 10-fold cross-validation procedure. Results: In total, our models were based on measurements from 12,705 road segments in Montreal and 10,929 road segments in Toronto. Daily median outdoor UFP number concentrations varied substantially across both cities with 1st-99th percentiles ranging from 1389 to 181,672 in Montreal and 2472 to 118,544 in Toronto. Outdoor UFP size tended to be smaller in Montreal (mean [SD]: 34 nm [15]) than in Toronto (mean [SD]: 44 nm [25]). Extreme gradient boosting models performed best and explained the majority of spatiotemporal variations in outdoor UFP number concentrations (Montreal, R 2: 0.727; Toronto, R 2: 0.723) and UFP size (Montreal, R 2: 0.823; Toronto, R 2: 0.898) with slopes close to one and intercepts close to zero for relationships between measured and predicted values. Conclusion: These new models will be applied in future epidemiological studies examining the acute health impacts of outdoor UFPs in Canada's two largest cities.

Airborne Nanoparticle Concentrations Are Associated with Increased Mortality Risk in Canada's Two Largest Cities.

RATIONALE: Outdoor fine particulate air pollution (PM2.5) contributes to millions of deaths around the world each year, but much less is known about the long-term health impacts of other particulate air pollutants including ultrafine particles (a.k.a. nanoparticles) which are in the nanometer size range (<100 nm), widespread in urban environments, and not currently regulated. OBJECTIVES: Estimate the associations between long-term exposure to outdoor ultrafine particles and mortality. METHODS: Outdoor air pollution levels were linked to the residential addresses of a large, population-based cohort from 2001 - 2016. Associations between long-term exposure to outdoor ultrafine particles and nonaccidental and cause-specific mortality were estimated using Cox proportional hazards models. MEASUREMENTS: An increase in long-term exposure to outdoor ultrafine particles was associated with an increased risk of nonaccidental mortality (Hazard Ratio = 1. 073, 95% Confidence Interval = 1. 061, 1. 085) and cause-specific mortality, the strongest of which was respiratory mortality (Hazard Ratio = 1.174, 95% Confidence Interval = 1.130, 1.220). MAIN RESULTS: Long-term exposure to outdoor ultrafine particles was associated with increased risk of mortality. We estimated the mortality burden for outdoor ultrafine particles in Montreal and Toronto, Canada to be approximately 1100 additional nonaccidental deaths every year. Furthermore, we observed possible confounding by particle size which suggests that previous studies may have underestimated or missed important health risks associated with ultrafine particles. CONCLUSIONS: As outdoor ultrafine particles are not currently regulated, there is great potential for future regulatory interventions to improve population health by targeting these common outdoor air pollutants.

Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution.

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.

Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories.

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

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/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. CONCLUSION: Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.

Pregnancy outcome in women with mechanical prosthetic heart valvesat their first trimester of pregnancy treated with unfractionated heparin (UFH) or enoxaparin: A randomized clinical trial.

Introduction: Pregnancy increases the risks of thromboembolism for the mother and fetus in patients with mechanical heart valves. The results of some studies have indicated that low molecular weight heparin (LMWH), in comparison with unfractionated heparin (UFH), leads to a lower incidence rate of thrombocytopenia and a decrease in bleeding. Methods: The present randomized clinical trial involved 31 pregnant women with mechanical heart valves at their first trimester (0-14 weeks) of pregnancy. To perform the study, the patients were divided into two groups, i.e. group A (LMWH group-16 patients) and group B (UFH group-15 patients). The birth weight, mode of delivery, and gestational age at birth as well as the maternal and fetal complications were compared between the two groups. Results: The mean age of mothers in the UFH and LMWH groups was 32.67±9.11 and 31.50±5.81years, respectively (P value > 0.05). Although the rate of maternal and fetal complications was higher in the UFH group as compared with the LMWH group, the observed difference was not significant (P value > 0.05). Conclusion: LMWH can be regarded as a safer therapy for both the mother and fetus due to its lower number of refill prescriptions and fewer changes in the blood level.

Fluid-Structure Interaction Simulation of Blood Flow and Cerebral Aneurysm: Effect of Partly Blocked Vessel.

In this study, using fluid-structure interaction (FSI), 3-dimensional blood flow in an aneurysm in the circle of Willis - which is located in the middle cerebral artery (MCA) - has been simulated. The purpose of this study is to evaluate the effect of a partly blocked vessel on an aneurysm. To achieve this purpose, two cases have been investigated using the FSI method: in the first case, an ideal geometry of aneurysm in the MCA has been simulated; in the second case, modeling is performed for an ideal geometry of the aneurysm in the MCA with a partly blocked vessel. All boundary conditions, properties and modeling methods were considered the same for both cases. The only difference between the two cases was that part of the MCA parent artery was blocked in the second case. In order to consider the hyperelastic property of the wall and the non-Newtonian properties of the blood, the Mooney-Rivlin model and the Carreau model have been used, respectively. In the second case, the Von Mises stress in the peak systole is 26% higher than in the first case. With regard to the high amount of Von Mises stress, the risk of rupture of the aneurysm is higher in this case. In the second case, the maximum wall shear stress (WSS) is 12% higher than in the first case. And maximum displacement in the second case is also higher than in the first. So, the risk of growth of the aneurysm is higher in cases with a partly blocked vessel.

Fluid-structure interaction simulation of a cerebral aneurysm: Effects of endovascular coiling treatment and aneurysm wall thickening.

In the present study, we investigate the effect of the hemodynamic factors of the blood flow on the cerebral aneurysms. To this end, a hypothetical geometry of the aneurysm in the circle of Willis, located in the bifurcation point of the anterior cerebral artery (ACA) and anterior communicating artery (ACoA) is modeled in a three-dimensional manner. Three cases are chosen in the current study: an untreated thin wall (first case), untreated thick wall (second case), and a treated aneurysm (third case). The effect of increasing the aneurysm wall thickness on the deformation and stress distribution of the walls are studied. The obtained results showed that in the second case, a reduction in the deformations of the walls was observed. It was also shown that the Von Mises stress has a 10% reduction in the untreated thick wall aneurysm compared to the untreated thin wall aneurysm. Thus, increasing the thickness of the aneurysm wall can be proposed as temporary remedial action. In the third case, an aneurysm that has been treated by endovascular coiling is investigated. The deformation and Von Mises stress in this case was decreased more than 43% and 87% compared to the first case, respectively. The wall shear stress distribution due to the fluid flow in the first and second cases showed small amounts of shear stress on the aneurysm sac. In these two cases, the oscillatory shear index was measured to have an approximate value of 0.47 in the aneurysm region, though, this value was measured to be about 0.1 for the third case. The hybrid effect of the wall shear stress and the oscillatory shear index on the relative residence time (RRT) was also studied. When this parameter reaches its maximum, the aneurysm rupture may occur. It was shown that by treating the aneurysm (the third case), RRT parameter can be decreased ~200 times relative to the first and second cases, which suggests an appropriate treatment of the aneurysm by choosing the coiling method.