Modelling spatial & temporal variability of air pollution in an area of unconventional natural gas operations

Despite the growing unconventional natural gas production industry in northeastern British Columbia, Canada, few studies have explored the air quality implications on human health in nearby communities. Researchers who have worked with pregnant women in this area have found higher levels of volatile organic compounds (VOCs) in the indoor air of their homes associated with higher density and closer proximity to gas wells. To inform ongoing exposure assessments, this study develops land use regression (LUR) models to predict ambient air pollution at the homes of pregnant women by using natural gas production activities as predictor variables. Using the existing monitoring network, the models were developed for three temporal scales for 12 air pollutants. The models predicting monthly, bi-annual, and annual mean concentrations explained 23%-94%, 54%-94%, and 73%-91% of the variability in air pollutant concentrations, respectively. These models can be used to investigate associations between prenatal exposure to air pollutants associated with natural gas production and adverse health outcomes in northeastern British Columbia.

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.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Daily Summer Temperatures and Hospitalization for Acute Cardiovascular Events: Impact of Outdoor PM 2.5 Oxidative Potential on Observed Associations Across Canada

BACKGROUND

Oxidative stress plays an important role in the health impacts of both outdoor fine particulate air pollution (PM 2.5 ) and thermal stress. However, it is not clear how the oxidative potential of PM 2.5 may influence the acute cardiovascular effects of temperature.

METHODS

We conducted a case-crossover study of hospitalization for cardiovascular events in 35 cities across Canada during the summer months (July-September) between 2016 and 2018. We collected three different metrics of PM 2.5 oxidative potential each month in each location. We estimated associations between lag-0 daily temperature (per 5ºC) and hospitalization for all cardiovascular (n = 44,876) and ischemic heart disease (n = 14,034) events across strata of monthly PM 2.5 oxidative potential using conditional logistical models adjusting for potential time-varying confounders.

RESULTS

Overall, associations between lag-0 temperature and acute cardiovascular events tended to be stronger when outdoor PM 2.5 oxidative potential was higher. For example, when glutathione-related oxidative potential (OP GSH ) was in the highest tertile, the odds ratio (OR) for all cardiovascular events was 1.040 (95% confidence intervals [CI] = 1.004, 1.074) compared with 0.980 (95% CI = 0.943, 1.018) when OP GSH was in the lowest tertile. We observed a greater difference for ischemic heart disease events, particularly for older subjects (age >70 years).

CONCLUSIONS

The acute cardiovascular health impacts of summer temperature variations may be greater when outdoor PM 2.5 oxidative potential is elevated. This may be particularly important for ischemic heart disease events.

Within-city spatial variations in long-term average outdoor oxidant gas concentrations and cardiovascular mortality: Effect modification by oxidative potential in the Canadian Census Health and Environment Cohort

Health effects of oxidant gases may be enhanced by components of particulate air pollution that contribute to oxidative stress. Our aim was to examine if within-city spatial variations in the oxidative potential of outdoor fine particulate air pollution (PM2.5) modify relationships between oxidant gases and cardiovascular mortality.

Methods

We conducted a retrospective cohort study of participants in the Canadian Census Health and Environment Cohort who lived in Toronto or Montreal, Canada, from 2002 to 2015. Cox proportional hazards models were used to estimate associations between outdoor concentrations of oxidant gases (Ox, a redox-weighted average of nitrogen dioxide and ozone) and cardiovascular deaths. Analyses were performed across strata of two measures of PM2.5 oxidative potential and reactive oxygen species concentrations (ROS) adjusting for relevant confounding factors.

Results

PM2.5 mass concentration showed little within-city variability, but PM2.5 oxidative potential and ROS were more variable. Spatial variations in outdoor Ox were associated with an increased risk of cardiovascular mortality [HR per 5 ppb = 1.028, 95% confidence interval (CI): 1.001, 1.055]. The effect of Ox on cardiovascular mortality was stronger above the median of each measure of PM2.5 oxidative potential and ROS (e.g., above the median of glutathione-based oxidative potential: HR = 1.045, 95% CI: 1.009, 1.081; below median: HR = 1.000, 95% CI: 0.960, 1.043).

Conclusion

Within-city spatial variations in PM2.5 oxidative potential may modify long-term cardiovascular health impacts of Ox. Regions with elevated Ox and PM2.5 oxidative potential may be priority areas for interventions to decrease the population health impacts of outdoor air pollution.

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.

Assessment of children's personal and land use regression model-estimated exposure to NO2 in Springfield, Massachusetts

Ambient nitrogen dioxide (NO₂) is derived from tailpipe vehicle emission and is linked with various of health outcomes. Personal exposure monitoring is crucial for accurate assessment of the associated disease risks. This study aimed to evaluate the utility of a wearable air pollutant sampler in determining the personal NO₂ exposure of school children for comparison with a model-based personal exposure assessment. We employed cost-effective, wearable passive samplers to directly measure personal exposure of 25 children (aged 12-13 years) in Springfield, MA to NO₂ over a five-day period in winter 2018. NO₂ levels were additionally measured at 40 outdoor sites in the same region using stationary passive samplers. A land use regression (LUR) model was developed based on the ambient NO₂ measures, with a good prediction performance (R² = 0.72) using road lengths, distance to highway, and institutional land area as predictor variables. Time-weighted averages (TWA), which incorporated the time-activity patterns of participants and LUR-derived estimates in children's primary microenvironments (homes, the school and commute paths), were calculated as an indirect measure of personal NO₂ exposure. Results indicated that the conventional residence-based exposure estimate approach, often used in epidemiological studies, differed from the direct personal exposure and could overestimate the personal exposure by up to 109 %. TWA improved personal NO₂ exposure estimates by accounting for the time activity patterns of individuals, a difference of 5.4 % ± 34.2 % was found for exposures compared to wristband measurements. Nevertheless, the personal wristband measurements exhibited a large variability due to the potential contributions from indoor and in-vehicle NO₂ sources. The findings suggest that exposure to NO₂ can be highly personalized based on individual activities and contact with pollutants in specific microenvironments, reaffirming the importance of measuring personal exposure.

Personal Mobility Choices and Disparities in Carbon Emissions

The promotion of sustainable mobility choices is a crucial element of transport decarbonization. It requires a fundamental understanding of the choices available to urban dwellers and of the equity and justice implications of green mobility solutions. In this study, we quantified personal mobility-related greenhouse gas (GHG) emissions in the Greater Toronto and Hamilton Area (GTHA) and their associations with various land use, built environment, and socioeconomic factors. Our study captured personal, household, and neighborhood-level characteristics that are related to high emissions and disparities in emissions across the study region. We observed that the top 30% of emitters generated 70% of all transportation GHG emissions. Household income, family size, and vehicle ownership were associated with increased mobility emissions, while increased population density was associated with lower emissions. The percentage of visible minorities in a neighborhood was associated with lower emissions, but this effect was small. We further contrasted the spatial distribution of traffic-related air pollution with mobility GHG emissions. The results suggest that individuals who emit less GHG live in areas with higher air pollution. A computer vision-based model was used to predict GHG emissions from aerial images of neighborhoods, demonstrating that areas with high land use mixture were linked to a lower generation of mobility-based GHG emissions.

Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform

This paper describes a mobile air pollution sampling system, the Urban Scanner, which aims at gathering dense spatiotemporal air quality data to support urban air quality and exposure science. Urban Scanner comprises custom vehicle-mounted sensors for air pollution, meteorology, and built environment data collection (low-cost sensors, wind anemometer, 360 deg camera, LIDAR, GPS) as well as a server to store, process, and map all gathered geo-referenced sensory information. Two levels of sensor calibration were implemented, both in a chamber and in the field, against reference instrumentation. Chamber tests and a set of mathematical tools were developed to correct for sensor noise (wavelet denoising), misalignment (linear and nonlinear), and hysteresis removal. Models based on chamber testing were further refined based on field co-location. While field co-location captures natural changes in air pollution and meteorology, chamber tests allow for simulating fast transitions in these variables, like the transitions experienced by a mobile sensor in an urban environment. The best suite of models achieved an R² higher than 0.9 between sensor output and reference station observations and an RMSE of 2.88 ppb for nitrogen dioxide and 4.03 ppb for ozone. A mobile sampling campaign was conducted in the city of Toronto, Canada, to further test Urban Scanner. We observe that the platform adequately captures spatial and temporal variability in urban air pollution, leading to the development of land-use regression models with high explanatory power.

Fifteen Years of Community Exposure to Heavy-Duty Emissions: Capturing Disparities over Space and Time

Disparities in exposure to traffic-related air pollution have been widely reported. However, little work has been done to simultaneously assess the impact of various vehicle types on populations of different socioeconomic/ethnic backgrounds. In this study, we employed an extreme gradient-boosting approach to spatially distribute light-duty vehicle (LDV) and heavy-duty truck emissions across the city of Toronto from 2006 to 2020. We examined associations between these emissions and different marginalization indices across this time span. Despite a large decrease in traffic emissions, disparities in exposure to traffic-related air pollution persisted over time. Populations with high residential instability, high ethnic concentration, and high material deprivation were found to reside in regions with significantly higher truck and LDV emissions. In fact, the gap in exposure to traffic emissions between the most residentially unstable populations and the least residentially unstable populations worsened over time, with trucks being the larger contributor to these disparities. Our data also indicate that the number of trucks and truck emissions increased substantially between 2019 and 2020 whilst LDVs decreased. Our results suggest that improvements in vehicle emission technologies are not sufficient to tackle disparities in exposure to traffic-related air pollution.

Air Pollution and Pediatric Respiratory Hospitalizations: Effect Modification by Particle Constituents and Oxidative Potential

Rationale: Outdoor particulate and gaseous air pollutants impair respiratory health in children, and these associations may be influenced by particle composition. Objectives: To examine whether associations between short-term variations in fine particulate air pollution, oxidant gases, and respiratory hospitalizations in children are modified by particle constituents (metals and sulfur) or oxidative potential. Methods: We conducted a case-crossover study of 10,500 children (0-17 years of age) across Canada. Daily fine particle mass concentrations and oxidant gases (nitrogen dioxide and ozone) were collected from ground monitors. Monthly estimates of fine particle constituents (metals and sulfur) and oxidative potential were also measured. Conditional logistic regression models were used to estimate associations between air pollutants and respiratory hospitalizations, above and below median values for particle constituents and oxidative potential. Measurements and Main Results: Lag-1 fine particulate matter mass concentrations were not associated with respiratory hospitalizations (odds ratio and 95% confidence interval per 10 μg/m³ increase in fine particulate matter: 1.004 [0.955-1.056]) in analyses ignoring particle constituents and oxidative potential. However, when models were examined above or below median metals, sulfur, and oxidative potential, positive associations were observed above the median. For example, the odds ratio and 95% confidence interval per 10 μg/m³ increase in fine particulate matter were 1.084 (1.007-1.167) when copper was above the median and 0.970 (0.929-1.014) when copper was below the median. Similar trends were observed for oxidant gases. Conclusions: Stronger associations were observed between outdoor fine particles, oxidant gases, and respiratory hospitalizations in children when metals, sulfur, and particle oxidative potential were elevated.

Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM 2.5 Transition Metals and Oxidative Potential

BACKGROUND

Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O 3 and NO 2 ) and fine particulate air pollution (PM 2.5 ). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM 2.5 is more capable of causing oxidative stress.

METHODS

We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM 2.5 components and oxidative potential. O x exposures (i.e., the redox-weighted average of O 3 and NO 2 ) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average O x and mortality outcomes across strata of transition metals and sulfur in PM 2.5 and three measures of PM 2.5 oxidative potential adjusting for possible confounding factors.

RESULTS

Associations between O x and mortality were consistently stronger in regions with elevated PM 2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in O x was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OP GSH ) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OP GSH levels below the median (interaction P value <0.001).

CONCLUSION

Spatial variations in PM 2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.

Does early life exposure to exogenous sources of reactive oxygen species (ROS) increase the risk of respiratory and allergic diseases in children? A longitudinal cohort study

BACKGROUND

Excess reactive oxygen species (ROS) can cause oxidative stress damaging cells and tissues, leading to adverse health effects in the respiratory tract. Yet, few human epidemiological studies have quantified the adverse effect of early life exposure to ROS on child health. Thus, this study aimed to examine the association of levels of ROS exposure at birth and the subsequent risk of developing common respiratory and allergic diseases in children.

METHODS

1,284 Toronto Child Health Evaluation Questionnaire (T-CHEQ) participants were followed from birth (born between 1996 and 2000) until outcome, March 31, 2016 or loss-to-follow-up. Using ROS data from air monitoring campaigns and land use data in Toronto, ROS concentrations generated in the human respiratory tract in response to inhaled pollutants were estimated using a kinetic multi-layer model. These ROS values were assigned to participants' postal codes at birth. Cox proportional hazards regression models, adjusted for confounders, were then used to estimate hazard ratios (HR) with 95% confidence intervals (CI) per unit increase in interquartile range (IQR).

RESULTS

After adjusting for confounders, iron (Fe) and copper (Cu) were not significantly associated with the risk of asthma, allergic rhinitis, nor eczema. However, ROS, a measure of the combined impacts of Fe and Cu in PM_2.5, was associated with an increased risk of asthma (HR = 1.11, 95% CI: 1.02-1.21, p < 0.02) per IQR. There were no statistically significant associations of ROS with allergic rhinitis (HR = 0.96, 95% CI: 0.88-1.04, p = 0.35) and eczema (HR = 1.03, 95% CI: 0.98-1.09, p = 0.24).

CONCLUSION

These findings showed that ROS exposure in early life significantly increased the childhood risk of asthma, but not allergic rhinitis and eczema.

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements

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 R² = 0.66 and RMSE = 9391.8#/cm³ (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.

Predicting Spatial Variations in Multiple Measures of PM2.5 Oxidative Potential and Magnetite Nanoparticles in Toronto and Montreal, Canada

There is growing interest to move beyond fine particle mass concentrations (PM_2.5) when evaluating the population health impacts of outdoor air pollution. However, few exposure models are currently available to support such analyses. In this study, we conducted large-scale monitoring campaigns across Montreal and Toronto, Canada during summer 2018 and winter 2019 and developed models to predict spatial variations in (1) the ability of PM_2.5 to generate reactive oxygen species in the lung fluid (ROS), (2) PM_2.5 oxidative potential based on the depletion of ascorbate (OP^AA) and glutathione (OP^GSH) in a cell-free assay, and (3) anhysteretic magnetic remanence (X_ARM) as an indicator of magnetite nanoparticles. We also examined how exposure to PM oxidative capacity metrics (ROS/OP) varied by socioeconomic status within each city. In Montreal, areas with higher material deprivation, indicating lower area-level average household income and employment, were exposed to PM_2.5 characterized by higher ROS and OP. This relationship was not observed in Toronto. The developed models will be used in epidemiologic studies to assess the health effects of exposure to PM_2.5 and iron-rich magnetic nanoparticles in Toronto and Montreal.

Relationships between long-term residential exposure to total environmental noise and stroke incidence

BACKGROUND

Noise has been related to several cardiovascular diseases (CVDs) such as coronary heart disease and to their risk factors such as hypertension, but associations with stroke remain under-researched, even if CVD likely share similar pathophysiologic mechanisms.

AIM

The objective of the study was to examine the association between long-term residential exposure to total environmental noise and stroke incidence in Montreal, Canada.

MATERIALS AND METHODS

We created an open cohort of adults aged ≥45years, free of stroke before entering the cohort for the years 2000 to 2014 with health administrative data. Residential total environmental noise levels were estimated with land use regression (LUR) models. Incident stroke was based on hospital admissions. Cox hazard models with age as the time axis and time-varying exposures were used to estimate associations, which were adjusted for material deprivation, year, nitrogen dioxide, stratified for sex, and indirectly adjusted for smoking.

RESULTS

There were 9,072,492 person-years of follow-up with 47% men; 26,741 developed stroke (21,402 ischemic; 4947 hemorrhagic; 392 had both). LUR total noise level acoustic equivalent for 24 hours (LAeq24h) ranged 44 to 79 dBA. The adjusted hazard ratio (HR) for stroke (all types), for a 10-dBA increase in LAeq24h, was 1.06 [95% confidence interval (CI): 1.03-1.09]. The LAeq24h was associated with ischemic (HR per 10 dBA: 1.08; 95% CI: 1.04-1.12) but not hemorrhagic stroke (HR per 10 dBA: 0.97; 95% CI: 0.90-1.04).

CONCLUSION

The results suggest that total environmental noise is associated with incident stroke, which is consistent with studies on transportation noise and other CVD.

Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities: Toronto and Beijing

Driving behavior and speed enforcement are both important to road safety and affect vehicle exhaust emissions. Relationships between driving characteristics and safety or emissions have been assessed in multiple studies. However, there is scant information on whether safe driving also reduces emissions and how this relationship changes across urban areas. This study makes use of two similar GPS datasets collected in the metropolitan areas of Toronto and Beijing to conduct a comparative analysis of driving characteristics, speed limit violations, and emissions. Emissions for all trips were computed using the same emission rate database derived from a Portable Emissions Monitoring System (PEMS). We observe that the average speeds in the two cities are close to 25 km/h. In Toronto, the fraction of time spent at speeds over 80 km/h on expressways is 40% higher than in Beijing. We also note a higher level of accelerations in Toronto. The trips in Beijing have approximately 14%, 57%, 14%, and 21% lower emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), and particle number (PN), respectively. Drivers in Toronto violate speed limits in 93% of their trips for 21% of trip travel time while the numbers for Beijing are 43% and 4%. These differences are not necessarily due to driving behavior, but rather to driving characteristics, which encompass the effects of behavior, road network design, traffic congestion, trip patterns, and speed enforcement. A scenario was evaluated by reconstructing drive-cycles to assess the effects of speed limit enforcement for trips where violations were detected. In Toronto, if obeying the speed limit, the mean trip travel time was estimated to increase by 1.8 min. In contrast, trip emissions of CO₂, CO, NO_x, and PN were found to decrease, on average, by 5.2%, 19.1%, 5.2%, and 2.9%, respectively. Speed limit enforcement can result in lower emissions, by reducing aggressive accelerations.

Real-world emissions and fuel consumption of gasoline and hybrid light duty vehicles under local and regulatory drive cycles

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.

Association of Sulfur, Transition Metals, and the Oxidative Potential of Outdoor PM2.5 with Acute Cardiovascular Events: A Case-Crossover Study of Canadian Adults

BACKGROUND

We do not currently understand how spatiotemporal variations in the composition of fine particulate air pollution [fine particulate matter with aerodynamic diameter ≤2.5μm (PM2.5)] affects population health risks. However, recent evidence suggests that joint concentrations of transition metals and sulfate may influence the oxidative potential (OP) of PM2.5 and associated health impacts.

OBJECTIVES

The purpose of the study was to evaluate how combinations of transition metals/OP and sulfur content in outdoor PM2.5 influence associations with acute cardiovascular events.

METHODS

We conducted a national case-crossover study of outdoor PM2.5 and acute cardiovascular events in Canada between 2016 and 2017 (93,344 adult cases). Monthly mean transition metal and sulfur (S) concentrations in PM2.5 were determined prospectively along with estimates of OP using acellular assays for glutathione (OPGSH), ascorbate (OPAA), and dithiothreitol depletion (OPDTT). Conditional logistic regression models were used to estimate odds ratios (OR) [95% confidence intervals (CI)] for PM2.5 across strata of transition metals/OP and sulfur.

RESULTS

Among men, the magnitudes of observed associations were strongest when both transition metal and sulfur content were elevated. For example, an OR of 1.078 (95% CI: 1.049, 1.108) (per 10μg/m3) was observed for cardiovascular events in men when both copper and S were above the median, whereas a weaker association was observed when both elements were below median values (OR=1.019, 95% CI: 1.007, 1.031). A similar pattern was observed for OP metrics. PM2.5 was not associated with acute cardiovascular events in women.

DISCUSSION

The combined transition metal and sulfur content of outdoor PM2.5 influences the strength of association with acute cardiovascular events in men. Regions with elevated concentrations of both sulfur and transition metals in PM2.5 should be examined as priority areas for regulatory interventions. https://doi.org/10.1289/EHP9449.

Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area

High-resolution air quality maps are critical towards assessing and understanding exposures to elevated air pollution in dense urban areas. However, these surfaces are rarely available in low- and middle-income countries that suffer from some of the highest air pollution levels worldwide. In this study, we make use of land use regressions (LURs) to generate annual and seasonal, high-resolution nitrogen dioxide (NO₂), nitrogen oxides (NO_x), and ozone (O₃) exposure surfaces for the Greater Beirut Area (GBA) in Lebanon. NO₂, NO_x and O₃ concentrations were monitored using passive samplers that were deployed at 55 pre-defined monitoring locations. The average annual concentrations of NO₂, NO_x, and O₃ across the GBA were 36.0, 89.7, and 26.9 ppb, respectively. Overall, the performance of the generated models was appropriate, with low biases, high model robustness, and acceptable R² values that ranged between 0.66 and 0.73 for NO₂, 0.56 and 0.60 for NO_x, and 0.54 and 0.65 for O₃. Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO₂ and NO_x levels in the GBA, whereas they acted as sinks for O₃ concentrations. No seasonally significant differences were found for the NO₂ and NO_x pollution surfaces; as their seasonal and annual models were largely similar (Pearson's r > 0.85 for both pollutants). On the other hand, seasonal O₃ pollution surfaces were significantly different. The model results showed that around 99% of the population of the GBA were exposed to NO₂ levels that exceeded the World Health Organization defined annual standard.

Near-road air quality modelling that incorporates input variability and model uncertainty

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.

Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt

This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m³, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared-in terms of mean square error (MSE) and mean absolute error (MAE)-with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).

Within-City Variation in Reactive Oxygen Species from Fine Particle Air Pollution and COVID-19

Rationale: Evidence linking outdoor air pollution with coronavirus disease (COVID-19) incidence and mortality is largely based on ecological comparisons between regions that may differ in factors such as access to testing and control measures that may not be independent of air pollution concentrations. Moreover, studies have yet to focus on key mechanisms of air pollution toxicity such as oxidative stress. Objectives: To conduct a within-city analysis of spatial variations in COVID-19 incidence and the estimated generation of reactive oxygen species (ROS) in lung lining fluid attributable to fine particulate matter (particulate matter with an aerodynamic diameter ⩽2.5 μm [PM2.5]). Methods: Sporadic and outbreak-related COVID-19 case counts, testing data, population data, and sociodemographic data for 140 neighborhoods were obtained from the City of Toronto. ROS estimates were based on a mathematical model of ROS generation in lung lining fluid in response to iron and copper in PM2.5. Spatial variations in long-term average ROS were predicted using a land-use regression model derived from measurements of iron and copper in PM2.5. Data were analyzed using negative binomial regression models adjusting for covariates identified using a directed acyclic graph and accounting for spatial autocorrelation. Measurements and Main Results: A significant positive association was observed between neighborhood-level ROS and COVID-19 incidence (incidence rate ratio = 1.07; 95% confidence interval, 1.01-1.15 per interquartile range ROS). Effect modification by neighborhood-level measures of racialized group membership and socioeconomic status was also identified. Conclusions: Examination of neighborhood characteristics associated with COVID-19 incidence can identify inequalities and generate hypotheses for future studies.

Long-term exposure to iron and copper in fine particulate air pollution and their combined impact on reactive oxygen species concentration in lung fluid: a population-based cohort study of cardiovascular disease incidence and mortality in Toronto, Canada

BACKGROUND

Exposure to fine particulate (PM2.5) air pollution is associated with increased cardiovascular disease (CVD), but less is known about its specific components, such as metals originating from non-tailpipe emissions. We investigated the associations of long-term exposure to metal components [iron (Fe) and copper (Cu)] in PM2.5 with CVD incidence.

METHODS

We conducted a population-based cohort study in Toronto, Canada. Exposures to Fe and Cu in PM2.5 and their combined impact on the concentration of reactive oxygen species (ROS) in lung fluid were estimated using land use regression models. Incidence of acute myocardial infarction (AMI), congestive heart failure (CHF) and CVD death was ascertained using health administrative datasets. We used mixed-effects Cox regression models to examine the associations between the exposures and health outcomes. A series of sensitivity analyses were conducted, including indirect adjustment for individual-level cardiovascular risk factors (e.g. smoking), and adjustment for PM2.5 and nitrogen dioxide (NO2).

RESULTS

In single-pollutant models, we found positive associations between the three exposures and all three outcomes, with the strongest associations detected for the estimated ROS. The associations of AMI and CHF were sensitive to indirect adjustment, but remained robust for CVD death in all sensitivity analyses. In multi-pollutant models, the associations of the three exposures generally remained unaltered. Interestingly, adjustment for ROS did not substantially change the associations between PM2.5 and CVD, but attenuated the associations of NO2.

CONCLUSIONS

Long-term exposure to Fe and Cu in PM2.5 and their combined impact on ROS were consistently associated with increased CVD death.

Spatial and temporal variability of airborne ultrafine particles in the Greater Montreal area: Results of monitoring campaigns in two seasons

It has been hypothesized that ultrafine particles (UFP) in air pollution may cause lung cancer. In preparation for an epidemiologic case-control study to assess this hypothesis in Montreal, Canada, we conducted a UFP measurement campaign in order to create an exposure surface with which we could assign UFP exposure to subjects corresponding to their residential addresses. The purpose of this paper is to describe the temporal and spatial variability that underlies the creation of an exposure surface in the Montreal area, and to consider the implications for epidemiological exposure assessment. We identified 249 fixed sampling sites, selected to provide a dense spatial representation of the areas of residence of Montreal residents. We conducted a winter campaign and a summer campaign, and each of the sites was visited three times during each seasonal campaign. Each visit entailed a 20-minute measurement period for UFPs with a separate measurement each second. This provided data for temporal comparisons at each site between seasons, between visits and between seconds. The median of UFP measurements was 16,593 particles/cm³ in winter and 8919 particles/cm³ in summer. Across the 249 sampling sites the Spearman correlation coefficient between the UFP measurements of winter and summer was 0.35. Within each visit, correlation was below 0.50 between pairs of UFP measurements taken more than 60 s apart, and there was hardly any correlation among measurements taken more than 300 s apart. When sites were grouped by proximity to certain types of pollution sources, and the seven resulting groups compared, there were modest, albeit statistically significant, differences in UFP levels. There was moderate positive spatial autocorrelation in UFPs over the study area. High temporal variability of UFPs from short-term measurements campaigns will likely compromise the predictive validity of the exposure surface, and will eventually attenuate the epidemiologic risk estimates.

Health and Climate Incentives for the Deployment of Cleaner On-Road Vehicle Technologies

Reducing greenhouse gas (GHG) emissions of private passenger vehicles, transit buses, and commercial vehicles with newer technology can improve air quality, and, subsequently, population exposure and public health. For the Greater Toronto and Hamilton Area, we estimated the burden of each vehicle fleet on population health in the units of years of life lost and premature deaths. We then assessed the separate health benefits of electrifying private vehicles, transit buses, and replacing the oldest commercial vehicles with newer trucks. A complete deployment of electric passenger vehicles would lead to health benefits similar to replacing all trucks older than 8 years (i.e., about 300 premature deaths prevented) in the first year of implementation; however, GHG emissions would be mainly reduced with passenger fleet electrification. Transit bus electrification has similar health benefits as electrifying half of the passenger fleet (i.e., about 150 premature deaths prevented); however, the GHG emission reductions reached under the bus electrification scenario are lower by 90%. By accelerating policies to electrify cars and buses and renew older trucks, governments can save hundreds of lives per year and mitigate the impacts of climate change.

Integrating random forests and propagation models for high-resolution noise mapping

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.

A Population-Based Cohort Study of Respiratory Disease and Long-Term Exposure to Iron and Copper in Fine Particulate Air Pollution and Their Combined Impact on Reactive Oxygen Species Generation in Human Lungs

Metal components in fine particulate matter (PM_2.5) from nontailpipe emissions may play an important role in underlying the adverse respiratory effects of PM_2.5. We investigated the associations between long-term exposure to iron (Fe) and copper (Cu) in PM_2.5 and their combined impact on reactive oxygen species (ROS) generation in human lungs, and the incidence of asthma, chronic obstructive pulmonary disease (COPD), COPD mortality, pneumonia mortality, and respiratory mortality. We conducted a population-based cohort study of ∼0.8 million adults in Toronto, Canada. Land-use regression models were used to estimate the concentrations of Fe, Cu, and ROS. Outcomes were ascertained using validated health administrative databases. We found positive associations between long-term exposure to Fe, Cu, and ROS and the risks of all five respiratory outcomes. The associations were more robust for COPD, pneumonia mortality, and respiratory mortality than for asthma incidence and COPD mortality. Stronger associations were observed for ROS than for either Fe or Cu. In two-pollutant models, adjustment for nitrogen dioxide somewhat attenuated the associations while adjustment for PM_2.5 had little influence. Long-term exposure to Fe and Cu in PM_2.5 and estimated ROS concentration in lung fluid was associated with increased incidence of respiratory diseases, suggesting the adverse respiratory effects of nontailpipe emissions.

Effects of ambient temperature and cold starts on excess NOx emissions in a gasoline direct injection vehicle

Studies have demonstrated that vehicles with gasoline direct injection (GDI) engines produce significantly higher emissions during a cold start than under hot-stabilized periods. A cold start is typically defined by the temperature of the engine or the catalytic converter; its extended effect on emissions, after the vehicle reaches the warm-up stage, has seldom been investigated. In this study, the influence of the post cold start period on nitrogen oxides (NO_x) emissions was evaluated using real-world measurements. Vehicle on-board diagnostic data, fuel consumption, and emissions of multiple pollutants were collected on a 2020 GDI sports utility vehicle equipped with a Portable Emission Measurement System (PEMS). A total of 31 trips, with two drives per day, were conducted along arterial roads and highways in Toronto, Canada. The results indicate that during the first trip of the day after an overnight soak, the average NO_x emission rate was 0.27 g/litre and 0.037 g/km, 384% and 299% higher than the emission rate on the second trip of the day. The amount of trip total NO_x emissions is positively associated with the length of the catalytic converter warm-up period with correlation coefficient 0.67. We also observe that the catalyst warm-up time is negatively correlated with ambient temperature, and a negative relationship between ambient temperature and NO_x emissions throughout the trip is depicted with correlation coefficient -0.44. The measured data reveal an extended effect of the cold start on NO_x emissions even after the temperatures of the engine coolant and catalyst reach a stable level.

Manuscript title: Long─term residential exposure to environmental/transportation noise and the incidence of myocardial infarction

BACKGROUND

Cardiovascular effects of environmental noise are a growing concern. However, the evidence remains largely limited to the association between road traffic noise and hypertension and coronary heart diseases.

OBJECTIVES

To investigate the association between long-term residential exposure to environmental/transportation noise and the incidence of myocardial infarction (MI) in the adult population living in Montreal.

METHODS

An open cohort of adults aged 45 years old and over, living on the island of Montreal and free of MI before entering the cohort was created for the years 2000-2014 with the Quebec Integrated Chronic Disease Surveillance System; a systematic surveillance system from the Canadian province of Quebec starting in 1996. Residential noise exposure was calculated in three ways: 1) total ambient noise levels estimated by Land use regression (LUR) models; 2) road traffic noise estimated by a noise propagation model CadnaA and 3) distances to transportation sources (roads, airport, railways). Incident MI was based on diagnostic codes in hospital admission records. Cox models with time-varying exposures (age as the time axis) were used to estimate the associations with various adjustments (material deprivation indicator, calendar year, nitrogen dioxide, stratification for sex). Indirect adjustment based on ancillary data for smoking was performed.

RESULTS

1,065,414 individuals were followed (total of 9,000,443 person-years) and 40,718 (3.8%) developed MI. We found positive associations between total environmental noise, estimated by LUR models and the incidence of MI. Total noise LUR levels ranged from ~44 to ~79 dBA and varied slightly with the metric used. The adjusted hazard ratios (HRs) (also adjusted for smoking) were 1.12 (95% Confidence Intervals [CI]: 1.08-1.15), 1.11 (95%CI: 1.07-1.14) and 1.10 (95%CI: 1.06-1.14) per 10 dBA noise levels increase respectively in Level Accoustic equivalent 24 h (LAeq24 h), Level day-evening-night (Lden) and night level (Lnight). We found a borderline negative association between road noise levels estimated with CadnaA and MI (HR: 0.99 per 10 dBA; 95%CI: 0.98-1.00). Distances to major roads and highways were not associated with MI while the proximity to railways was positively associated with MI (HR for ≤100 vs > 1000 m: 1.07; 95%CI: 1.01-1.14). A negative association was found with the proximity to the airport noise exposure forecast (NEF25); HR (<1 vs >1000 m) = 0.88 (95%CI: 0.81-0.96).

CONCLUSIONS

These associations suggest that exposure to total environmental noise at current urban levels may be related to the incidence of MI. Additional studies with more accurate road noise estimates are needed to explain the counterintuitive associations with road noise and specific transportation sources.

Generation of spikes in ultrafine particle emissions from a gasoline direct injection vehicle during on-road emission tests

This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.

Ambient ultrafine particle concentrations and incidence of childhood cancers

BACKGROUND

Ambient air pollution has been associated with childhood cancer. However, little is known about the possible impact of ambient ultrafine particles (<0.1 μm) (UFPs) on childhood cancer incidence.

OBJECTIVE

This study aimed to evaluate the association between prenatal and childhood exposure to UFPs and development of childhood cancer.

METHODS

We conducted a population-based cohort study of within-city spatiotemporal variations in ambient UFPs across the City of Toronto, Canada using 653,702 singleton live births occurring between April 1, 1998 and March 31, 2017. Incident cases of 13 subtypes of paediatric cancers among children up to age 14 were ascertained using a cancer registry. Associations between ambient air pollutant concentrations and childhood cancer incidence were estimated using random-effects Cox proportional hazards models. We investigated both single- and multi-pollutant models accounting for co-exposures to PM_2.5 and NO₂.

RESULTS

A total of 1,066 childhood cancers were identified. We found that first trimester exposure to UFPs (Hazard Ratio (HR) per 10,000/cm³ increase = 1.13, 95% CI: 1.03-1.22) was associated with overall cancer incidence diagnosed before 6 years of age after adjusting for PM_2.5, NO₂, and for personal and neighborhood-level covariates. Association between UFPs and overall cancer incidence exhibited a linear shape. No statistically significant associations were found for specific cancer subtypes.

CONCLUSION

Ambient UFPs may represent a previously unrecognized risk factor in the aetiology of cancers in children. Our findings reinforce the importance of conducting further research on the effects of UFPs given their high prevalence of exposure in urban areas.

Health and climate benefits of Electric Vehicle Deployment in the Greater Toronto and Hamilton Area

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

A gradient boost approach for predicting near-road ultrafine particle concentrations using detailed traffic characterization

This study investigates the influence of meteorology, land use, built environment, and traffic characteristics on near-road ultrafine particle (UFP) concentrations. To achieve this objective, minute-level UFP concentrations were measured at various locations along a major arterial road in the Greater Toronto Area (GTA) between February and May 2019. Each location was visited five times, at least once in the morning, mid-day, and afternoon. Each visit lasted for 30 min, resulting in 2.5 h of minute-level data collected at each location. Local traffic information, including vehicle class and turning movements, were processed using computer vision techniques. The number of fast-food restaurants, cafes, trees, traffic signals, and building footprint, were found to have positive impacts on the mean UFP, while distance to the closest major road was negatively associated with UFP. We employed the Extreme Gradient Boosting (XGBoost) method to develop prediction models for UFP concentrations. The Shapley additive explanation (SHAP) measures were used to capture the influence of each feature on model output. The model results demonstrated that minute-level counts of local traffic from different directions had significant impacts on near-road UFP concentrations, model performance was robust under random cross-validation as coefficients of determination (R²) ranged from 0.63 to 0.69, but it revealed weaknesses when data at specific locations were eliminated from the training dataset. This result indicates that proper cross-validation techniques should be developed to better evaluate machine learning models for air quality predictions.

Association between Kawasaki Disease and Prenatal Exposure to Ambient and Industrial Air Pollution: A Population-Based Cohort Study

BACKGROUND

Environmental factors may contribute to the development of Kawasaki disease in children, but prenatal environmental exposures are understudied.

OBJECTIVE

We used a population-based cohort to investigate whether prenatal exposure to outdoor air pollution is associated with the incidence of Kawasaki disease in childhood.

METHODS

We performed a longitudinal cohort study of all children born in Quebec, Canada, between 2006 and 2012. Children were followed for Kawasaki disease from birth until 31 March 2018. We assigned prenatal air pollutant exposure according to the residential postal code at birth. The main exposure was annual average concentration of ambient fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2) from satellite-based estimates and land-use regression models. As secondary exposures, we considered industrial PM2.5, NO2, and sulfur dioxide (SO2) emissions estimated from dispersion models. We estimated hazard ratios (HRs) using Cox proportional hazards models, adjusted for maternal age, parity, sex, multiple birth, maternal smoking during pregnancy, socioeconomic status, birth year, and rural residence. We considered single and multipollutant models. We performed several sensitivity analyses, including assessing modifying effects of maternal comorbidities (e.g., diabetes, preeclampsia).

RESULTS

The cohort comprised 505,336 children, including 539 with Kawasaki disease. HRs for each interquartile range increase in ambient air pollution were 1.16 (95% CI: 0.96, 1.39) for PM2.5 and 1.12 (95% CI: 0.96, 1.31) for NO2. For industrial air pollution, HRs were 1.07 (95% CI: 1.01, 1.13) for SO2, 1.09 (95% CI: 0.99, 1.20) for NO2, and 1.01 (95% CI: 0.97, 1.05) for PM2.5. In multipollutant models, associations for ambient PM2.5 and NO2 (i.e., from all sources) were robust to adjustment for industrial pollution, and vice versa.

DISCUSSION

In this population-based cohort study, both prenatal exposure to ambient and industrial air pollution were associated with the incidence of Kawasaki disease in childhood. Further studies are needed to consolidate the observed associations. https://doi.org/10.1289/EHP6920.

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R² of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm³ and 1300 ng/m³ for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R² of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/cm³ and 1600 ng/m³ for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.

Environmental and health impacts of transportation and land use scenarios in 2061

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

Long-term exposure to a mixture of industrial SO2, NO2, and PM2.5 and anti-citrullinated protein antibody positivity

BACKGROUND

Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and fine particles matter (PM_2.5) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA).

METHODS

Serum ACPA was determined for 7600 randomly selected CARTaGENE general population subjects in Quebec, Canada. Industrial SO₂, NO₂, and PM_2.5 concentrations, estimated by the California Puff (CALPUFF) atmospheric dispersion model, were assigned based on residential postal codes at the time of sera collection. Single-exposure logistic regressions were performed for ACPA positivity defined by 20 U/ml, 40 U/ml, and 60 U/ml thresholds, adjusting for age, sex, French Canadian origin, smoking, and family income. Associations between regional overall PM_2.5 exposure and ACPA positivity were also investigated. The associations between the combined three industrial exposures and the ACPA positivity were assessed by weighted quantile sum (WQS) regressions.

RESULTS

Significant associations between individual industrial exposures and ACPA positivity defined by the 20 U/ml threshold were seen with single-exposure logistic regression models, for industrial emissions of PM_2.5 (odds ratio, OR = 1.19, 95% confidence intervals, CI: 1.04-1.36) and SO₂ (OR = 1.03, 95% CI: 1.00-1.06), without clear associations for NO₂ (OR = 1.01, 95% CI: 0.86-1.17). Similar findings were seen for the 40 U/ml threshold, although at 60 U/ml, the results were very imprecise. The WQS model demonstrated a positive relationship between combined industrial exposures and ACPA positivity (OR = 1.36, 95% CI: 1.10-1.69 at 20 U/ml) and suggested that industrial PM_2.5 may have a closer association with ACPA positivity than the other exposures. Again, similar findings were seen with the 40 U/ml threshold, though 60 U/ml results were imprecise. No clear association between ACPA and regional overall PM_2.5 exposure was seen.

CONCLUSIONS

We noted positive associations between ACPA and industrial emissions of PM_2.5 and SO₂. Industrial PM_2.5 exposure may play a particularly important role in this regard.

Drivers of seasonal and annual air pollution exposure in a complex urban environment with multiple source contributions

Outdoor air pollution is a global health concern, but detailed exposure information is still limited for many parts of the world. In this study, high-resolution exposure surfaces were generated for annual and seasonal fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and carbon monoxide (CO) for the Greater Beirut Area (GBA), Lebanon, an urban zone with a complex topography and multiple source contributions. Land use regression models (LUR) were calibrated and validated with monthly data collected from 58 locations between March 2017 and March 2018. The annual mean (±1 SD) concentrations of PM_2.5, PM₁₀, and CO across the monitoring locations were 68.1 (±15.7) μg/m³, 83.5 (±19.5) μg/m³, and 2.48 (±1.12) ppm, respectively. The coefficients of determination for LUR models ranged from 56 to 67% for PM_2.5, 44 to 63% for the PM₁₀ models, and 50 to 60% for the CO. LUR model structures varied significantly by season for both PM_2.5 and PM₁₀ but not for CO. Traffic emissions were consistently the main source of CO emissions throughout the year. The relative importance of industrial emissions and power generation sources towards predicted PM levels increased during the hot season while the contribution of the international airport diminished. Moreover, the complex topography of the study area along with the seasonal changes in the predominant wind directions affected the spatial predicted concentrations of all three pollutants. Overall, the predicted exposure surfaces were able to conserve the inter-pollution correlations determined from the field monitoring campaign, with the exception of the cold season. Our pollution surfaces suggest that the entire population of Beirut is regularly exposed to concentrations exceeding the World Health Organization (WHO) air quality standards for both PM_2.5 and PM₁₀.

Influence of travel behaviour and daily mobility on exposure to traffic-related air pollution

This study evaluates the daily exposure of urban residents across various commuting modes and destinations by intersecting data from a travel survey with exposure surfaces for ultrafine particles and black carbon, in Toronto, Canada. We demonstrate that exposure misclassification is bound to arise when we approximate daily exposure with the concentration at the home location. We also identify potential inequities in the distribution of exposure to traffic-related air pollution whereby those who are mostly responsible for the generation of traffic-related air pollution (drivers and passengers) are exposed the least while active commuters and transit riders, are exposed the most.

Quantifying the air quality and health benefits of greening freight movements

Commercial vehicle movements have a large effect on traffic-related air pollution in metropolitan areas. In the Greater Toronto and Hamilton Area (GTHA), commercial vehicles include large and medium diesel trucks as well as light-duty gasoline-fuelled trucks. In this study, the emissions of various air pollutants associated with diesel commercial vehicles were estimated and their impacts on urban air quality, population exposure, and public health were quantified. Using data on diesel trucks in the GTHA and a chemical transport model at a spatial resolution of 1 km², the contribution of commercial diesel movements to air quality was estimated. This contribution amounts to about 6-22% of the mean population exposure to nitrogen dioxide (NO₂) and black carbon (BC), depending on the municipality, but is systematically lower than 3% for fine particulate matter (PM_2.5) and ozone (O₃). Using a comparative risk assessment approach, we estimated that the emissions of all diesel commercial vehicles within the GTHA are responsible for an annual total of at least 9810 Years of Life Lost (YLL), corresponding to $3.2 billion of annual social costs. We also assessed the impact of decreasing freeway-sourced diesel emissions along Highway 401, one of the busiest highways in North America. This is comparable with a removal of 250 to 1000 diesel trucks per day along that corridor, which could be replaced by alternative technologies. The mean NO₂ and BC exposures of the population living within 500 m of the highway would decrease by 9% and 11%, respectively, with reductions as high as 22%. Such a measure would save 1310 YLL annually, equivalent to $428 million in social benefits.

Spatiotemporal Variations in Ambient Ultrafine Particles and the Incidence of Childhood Asthma

Rationale: Little is known regarding the impact of ambient ultrafine particles (UFPs; <0.1 μm) on childhood asthma development. Objectives: To examine the association between prenatal and early postnatal life exposure to UFPs and development of childhood asthma. Methods: A total of 160,641 singleton live births occurring in the City of Toronto, Canada between April 1, 2006, and March 31, 2012, were identified from a birth registry. Associations between exposure to ambient air pollutants and childhood asthma incidence (up to age 6) were estimated using random effects Cox proportional hazards models, adjusting for personal- and neighborhood-level covariates. We investigated both single-pollutant and multipollutant models accounting for coexposures to particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5) and NO₂. Measurements and Main Results: We identified 27,062 children with incident asthma diagnosis during the follow-up. In adjusted models, second-trimester exposure to UFPs (hazard ratio per interquartile range increase, 1.09; 95% confidence interval, 1.06-1.12) was associated with asthma incidence. In models additionally adjusted for PM_2.5 and nitrogen dioxide, UFPs exposure during the second trimester of pregnancy remained positively associated with childhood asthma incidence (hazard ratio per interquartile range increase, 1.05; 95% confidence interval, 1.01-1.09). Conclusions: This is the first study to evaluate the association between perinatal exposure to UFPs and the incidence of childhood asthma. Exposure to UFPs during a critical period of lung development was linked to the onset of asthma in children, independent of PM_2.5 and NO₂.

Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R²: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation. Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .

Extending the spatial scale of land use regression models for ambient ultrafine particles using satellite images and deep convolutional neural networks

We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite images and deep convolutional neural networks as a means of extending the spatial coverage of these models. Our findings demonstrate that this method can be used to expand the spatial scale of LUR models, thus providing exposure estimates for larger populations. The cost of this approach is a small loss in precision as the training data are themselves modelled values.

Spatial variations in ambient ultrafine particle concentrations and risk of congenital heart defects

BACKGROUND

Cardiovascular malformations account for nearly one-third of all congenital anomalies, making these the most common type of birth defects. Little is known regarding the influence of ambient ultrafine particles (<0.1 μm) (UFPs) on their occurrence.

OBJECTIVE

This population-based study examined the association between prenatal exposure to UFPs and congenital heart defects (CHDs).

METHODS

A total of 158,743 singleton live births occurring in the City of Toronto, Canada between April 1st 2006 and March 31st 2012 were identified from a birth registry. Associations between exposure to ambient UFPs between the 2nd and 8th week post conception when the foetal heart begins to form and CHDs identified at birth were estimated using random-effects logistic regression models, adjusting for personal- and neighbourhood-level covariates. We also investigated multi-pollutant models accounting for co-exposures to PM_2.5, NO₂ and O₃.

RESULTS

A total of 1468 CHDs were identified. In fully adjusted models, UFP exposures during weeks 2 to 8 of pregnancy were not associated with overall CHDs (Odds Ratio (OR) per interquartile (IQR) increase = 1.02, 95% CI: 0.96-1.08). When investigating subtypes of CHDs, UFP exposures were associated with ventricular septal defects (Odds Ratio (OR) per interquartile (IQR) increase = 1.13, 95% CI: 1.03-1.33), but not with atrial septal defect (Odds Ratio (OR) per interquartile (IQR) increase = 0.89, 95% CI: 0.74-1.06).

CONCLUSION

This is the first study to evaluate the association between prenatal exposure to UFPs and the risk of CHDs. UFP exposures during a critical period of embryogenesis were associated with an increased risk of ventricular septal defect.

Marginal Greenhouse Gas Emissions of Ontario's Electricity System and the Implications of Electric Vehicle Charging

To estimate greenhouse gas (GHG) emission reductions of electric vehicles (EVs) deployment, it is important to account for emissions from electricity generation. Since such emissions change according to temporal patterns of electricity generation and EV charging, this study operationalizes the concept of marginal emission factors (MEFs) and uses person-level travel activity data to simulate charging scenarios. Our study is set in the Greater Toronto and Hamilton Area in Ontario, Canada. After generating hourly MEFs using a multiple linear regression model, we estimated GHG emissions for EV charging at two EV penetration rates, 5% and 30%, and five charging scenarios: home, work and shopping, night, downtown vs suburb, and an optimal low emission charging scenario, matching charging time with the lowest available MEF. We observed that vehicle electrification substantially reduces GHG emissions, even when using MEFs that are up to seven times higher than average electricity emission factors. With Ontario's 2017 electricity generation mix, EVs achieve over 80% lower fuel cycle emissions compared with equivalent sets of gasoline vehicles. At 5% penetration, night charging nearly matches low emission charging, but night charging emissions increase with 30% EV penetration, suggesting a need for policy that can smooth out charging demand after midnight.

Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities

Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R²), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R²; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R² ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.

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 (PM_2.5). In this study, we evaluated multiple measures of PM_2.5 oxidative potential across Toronto, Canada (2016-2017), including glutathione/ascorbate-related oxidative potential (OP^GSH and OP^AA) and dithiothreitol depletion (OP^DTT). 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 PM_2.5 metals and black carbon were also examined. All three measures of PM_2.5 oxidative potential varied substantially across Toronto. OP^AA and OP^DTT were primarily associated with traffic-related components of PM_2.5 (i.e., Fe, Cu, and black carbon) whereas OP^GSH was not a strong marker for traffic during either season. During summer, multivariable models performed best for OP^AA ( R_CV² = 0.48) followed by OP^DTT ( R_CV² = 0.32) and OP^GSH ( R_CV² = 0.22). During winter, model performance was best for OP^DTT ( R_CV² = 0.55) followed by OP^GSH ( R_CV² = 0.50) and OP^AA ( R_CV² = 0.23). Model parameters varied between seasons, and between-season differences in PM_2.5 mass concentrations were weakly/moderately correlated with seasonal differences in OP. Our findings highlight substantial within-city variations in PM_2.5 oxidative potential. More detailed information is needed on local sources of air pollution to improve model performance.