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Nitrogen dioxide (NO2) is an important contributor to air pollution and can adversely affect human health(1–9) . A decrease in NO2 concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-19(10–20). Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO2 concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO2 decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO2 to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Satellite-based estimates of ground-level nitrogen dioxide (NO2) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO2 concentrations are biased low with respect to ground-based measurements. In this work we demonstrate that these biases are due to both the coarse resolution of previous satellite NO2 products and inaccuracies in vertical mixing assumptions used to convert satellite-observed tropospheric columns to surface concentrations. We develop an algorithm that now allows for different mixing assumptions to be used based on observed NO2 conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO2 column observations at an unprecedented spatial resolution for over a year. This new product achieves estimates of ground-level NO2 with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO2 concentrations from our updated algorithm have higher correlation and lower bias than those found using TROPOMI and the prior algorithm. TROPOMI-inferred estimates of the population exposed to NO2 conditions exceeding health standards are at least three times higher than for OMI-inferred estimates. These developments provide an exciting opportunity for air quality monitoring.

Although growing evidence links air pollution to stroke incidence, less is known about the effect of air pollution on atrial fibrillation (AF), an important risk factor for stroke. We assessed the associations between air pollution and incidence of AF and stroke. We also sought to characterize the shape of pollutant-disease relationships. The population-based cohort comprised 5,071,956 Ontario residents, age 35–85 y and without the diagnoses of both outcomes on 1 April 2001 and was followed up until 31 March 2015. AF and stroke cases were ascertained using health administrative databases with validated algorithms. Based on annual residential postal codes, we assigned 5-y running average concentrations of fine particulate matter ([Formula: see text]), nitrogen dioxide ([Formula: see text]), and ozone ([Formula: see text]) from satellite-derived data, a land-use regression model, and a fusion-based method, respectively, as well as redox-weighted averages of [Formula: see text] and [Formula: see text] ([Formula: see text]) for each year. Using Cox proportional hazards models, we estimated the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of AF and stroke with each of these pollutants, adjusting for individual- and neighborhood-level variables. We used newly developed nonlinear risk models to characterize the shape of pollutant–disease relationships. Between 2001 and 2015, we identified 313,157 incident cases of AF and 122,545 cases of stroke. Interquartile range increments of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were associated with increases in the incidence of AF [HRs (95% CIs): 1.03 (1.01, 1.04), 1.02 (1.01, 1.03), 1.01 (1.00, 1.02), and 1.01 (1.01, 1.02), respectively] and the incidence of stroke [HRs (95% CIs): 1.05 (1.03, 1.07), 1.04 (1.01, 1.06), 1.05 (1.03, 1.06), and 1.05 (1.04, 1.06), respectively]. Associations of similar magnitude were found in various sensitivity analyses. Furthermore, we found a near-linear association for stroke with [Formula: see text], whereas [Formula: see text], [Formula: see text]-, and [Formula: see text] relationships exhibited sublinear shapes. Air pollution was associated with stroke and AF onset, even at very low concentrations. https://doi.org/10.1289/EHP4883.

We estimated mortality and economic loss attributable to PM 2·5 air pollution exposure in 429 counties of Iran in 2018. Ambient PM 2.5 -related deaths were estimated using the Global Exposure Mortality Model (GEMM). According to the ground-monitored and satellite-based PM 2.5 data, the annual mean population-weighted PM 2·5 concentrations for Iran were 30.1 and 38.6 μg m −3 , respectively. We estimated that long-term exposure to ambient PM 2.5 contributed to 49,303 (95% confidence interval (CI) 40,914–57,379) deaths in adults ≥ 25 yr. from all-natural causes based on ground monitored data and 58,873 (95% CI 49,024–68,287) deaths using satellite-based models for PM 2.5 . The crude death rate and the age-standardized death rate per 100,000 population for age group ≥ 25 year due to ground-monitored PM 2.5 data versus satellite-based exposure estimates was 97 (95% CI 81–113) versus 116 (95% CI 97–135) and 125 (95% CI 104–145) versus 149 (95% CI 124–173), respectively. For ground-monitored and satellite-based PM 2.5 data, the economic loss attributable to ambient PM 2.5 -total mortality was approximately 10,713 (95% CI 8890–12,467) and 12,792.1 (95% CI 10,652.0–14,837.6) million USD, equivalent to nearly 3.7% (95% CI 3.06–4.29) and 4.3% (95% CI 3.6–4.5.0) of the total gross domestic product in Iran in 2018.

Type 2 diabetes has become an urban epidemic influenced by neighbourhood environments. However, conventional risk models focusing solely on individual factors fail to account for these neighbourhood influences and often require detailed patient data that may not be available. To address this gap, we developed an integrated approach combining machine learning and causal inference to map type 2 diabetes risk at the neighbourhood level. Using demographic, health, and socioeconomic data from 1,149 Census Tracts (CTs; the neighbourhood unit in this study) in a large metropolitan region, we trained seven machine learning models to identify neighbourhoods with high diabetes prevalence. Although neighbourhood-level diabetes data were available for this study area, our model’s high predictive accuracy on external validation data (area under the curve (AUC) = 0.95), particularly from a distinct geographical region, suggests potential utility for predicting diabetes risk in other Canadian regions or elsewhere where such data are unavailable, provided comparable covariates are available and the model is locally retrained and validated using spatially aware procedures. The top models achieved high recall ( ) and AUC up to 0.96 on test data, indicating accurate identification of high-risk neighbourhoods with few missed high-risk areas. Survey-derived neighbourhood health indicators, including obesity rate, physical inactivity, and median age were strong predictors of diabetes prevalence. We then applied a Causal Forest approach to estimate conditional average treatment effects (CATE, ) for selected potentially modifiable factors and summarized the results with the mean . Higher work stress ( ) and daily smoking ( ) were moderately associated with increased risk, whereas better mental health ( ) was protective, highlighting mental health as a priority for further evaluation, especially in neighbourhoods predicted to have high diabetes prevalence. These findings could help identify modifiable neighbourhood-level factors for local prevention efforts and inform equity-oriented planning in diverse urban populations. Prospective or quasi-experimental studies are needed to evaluate intervention effects. Our integrated machine-learning and causal framework lays the groundwork for precision public health, suggesting that modifiable neighbourhood factors may indicate diabetes risk when patient-level data are scarce. Furthermore, the pipeline is conceptually adaptable to other chronic diseases influenced by social and environmental determinants and may inform targeted prevention beyond type 2 diabetes, contingent on disease-specific feature sets and external validation.

New ‘big data’ streams such as street-level imagery are offering unprecedented possibilities for developing health-relevant data on the urban environment. Urban environmental features derived from street-level imagery have been used to assess pedestrian-friendly neighbourhood design and to predict active commuting, but few such studies have been conducted in Canada. Using 1.15 million Google Street View (GSV) images in seven Canadian cities, we applied image segmentation and object detection computer vision methods to extract data on persons, bicycles, buildings, sidewalks, open sky (without trees or buildings), and vegetation at postal codes. The associations between urban features and walk-to-work rates obtained from the Canadian Census were assessed. We also assessed how GSV-derived urban features perform in predicting walk-to-work rates relative to more widely used walkability measures. Results showed that features derived from street-level images are better able to predict the percent of people walking to work as their primary mode of transportation compared to data derived from traditional walkability metrics. Given the increasing coverage of street-level imagery around the world, there is considerable potential for machine learning and computer vision to help researchers study patterns of active transportation and other health-related behaviours and exposures.

Background: Few cohort studies have evaluated the risk of mortality associated with long-term exposure to fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM₂.₅)]. This is the first national-level cohort study to investigate these risks in Canada. Objective: We investigated the association between long-term exposure to ambient PM₂.₅ and cardiovascular mortality in nonimmigrant Canadian adults. Methods: We assigned estimates of exposure to ambient PM₂.₅ derived from satellite observations to a cohort of 2.1 million Canadian adults who in 1991 were among the 20% of the population mandated to provide detailed census data. We identified deaths occurring between 1991 and 2001 through record linkage. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for available individual-level and contextual covariates using both standard Cox proportional survival models and nested, spatial random-effects survival models. Results: Using standard Cox models, we calculated HRs of 1.15 (95% CI: 1.13, 1.16) from nonaccidental causes and 1.31 (95% CI: 1.27, 1.35) from ischemic heart disease for each 10-μg/m³ increase in concentrations of PM₂.₅. Using spatial random-effects models controlling for the same variables, we calculated HRs of 1.10 (95% CI: 1.05, 1.15) and 1.30 (95% CI: 1.18, 1.43), respectively.We found similar associations between nonaccidental mortality and PM₂.₅ based on satellite-derived estimates and ground-based measurements in a subanalysis of subjects in 11 cities. Conclusions: In this large national cohort of nonimmigrant Canadians, mortality was associated with long-term exposure to PM₂.₅. Associations were observed with exposures to PM₂.₅ at concentrations that were predominantly lower (mean, 8.7 μg/m³; interquartile range, 6.2 μg/m³) than those reported previously.

The evaporation and atmospheric oxidation of low-volatility organic vapours from mined oil sands material is shown to be responsible for a large amount of secondary organic aerosol mass—which affects air quality and climate change—observed during airborne measurements in Canada. Environmental impact of oil sands mining Oil production from oil sands has raised numerous environmental concerns, but the contribution of oil sand exploration to secondary organic aerosol formation, an important component of atmospheric particulate matter that affects air quality and climate, remains poorly understood. John Liggio et al . use data from airborne measurements over the Canadian oil sands, together with laboratory experiments and a box-model study, to determine the magnitude of secondary organic aerosol production from oil sand emissions. They find that the evaporation and atmospheric oxidation of low-volatility organic vapours from mined oil sands material is responsible for most of the observed secondary organic aerosol mass. The findings suggest that oil sands are one of the largest sources of anthropogenic secondary organic aerosols in North America. Worldwide heavy oil and bitumen deposits amount to 9 trillion barrels of oil distributed in over 280 basins around the world 1 , with Canada home to oil sands deposits of 1.7 trillion barrels 2 . The global development of this resource and the increase in oil production from oil sands has caused environmental concerns over the presence of toxic compounds in nearby ecosystems 3 , 4 and acid deposition 5 , 6 . The contribution of oil sands exploration to secondary organic aerosol formation, an important component of atmospheric particulate matter that affects air quality and climate 7 , remains poorly understood. Here we use data from airborne measurements over the Canadian oil sands, laboratory experiments and a box-model study to provide a quantitative assessment of the magnitude of secondary organic aerosol production from oil sands emissions. We find that the evaporation and atmospheric oxidation of low-volatility organic vapours from the mined oil sands material is directly responsible for the majority of the observed secondary organic aerosol mass. The resultant production rates of 45–84 tonnes per day make the oil sands one of the largest sources of anthropogenic secondary organic aerosols in North America. Heavy oil and bitumen account for over ten per cent of global oil production today 8 , and this figure continues to grow 9 . Our findings suggest that the production of the more viscous crude oils could be a large source of secondary organic aerosols in many production and refining regions worldwide, and that such production should be considered when assessing the environmental impacts of current and planned bitumen and heavy oil extraction projects globally.

Emerging links between household cleaning products and childhood overweight may involve the gut microbiome. We determined mediating effects of infant gut microbiota on associations between home use of cleaning products and future overweight. From the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort, we tested associations between maternal report of cleaning product use and overweight at age 3, and whether associations were mediated by microbial profiles of fecal samples in 3- to 4-month-old infants. Among 757 infants, the abundance of specific gut microbiota was associated with household cleaning with disinfectants and eco-friendly products in a dose-dependent manner. With more frequent use of disinfectants, Lachnospiraceae increasingly became more abundant (highest v. lowest quintile of use: adjusted odds ratio [AOR] 1.93, 95% confidence interval [CI] 1.08 to 3.45) while genus Haemophilus declined in abundance (highest v. lowest quintile of use: AOR 0.36, 95% CI 0.20 to 0.65). Enterobacteriaceae were successively depleted with greater use of eco-friendly products (AOR 0.45, 95% CI 0.27 to 0.74). Lachnospiraceae abundance significantly mediated associations of the top 30th centile of household disinfectant use with higher body mass index (BMI) z score (p = 0.02) and with increased odds of overweight or obesity (p = 0.04) at age 3. Use of eco-friendly products was associated with decreased odds of overweight or obesity independently of Enterobacteriaceae abundance (AOR 0.44, 95% CI 0.22 to 0.86), with no significant mediation (p = 0.2). Exposure to household disinfectants was associated with higher BMI at age 3, mediated by gut microbial composition at age 3–4 months. Although child overweight was less common in households that cleaned with eco-friendly products, the lack of mediation by infant gut microbiota suggests another pathway for this association.

The province of Alberta, Canada, is home to three oil sands regions which, combined, contain the third largest deposit of oil in the world. Of these, the Athabasca oil sands region is the largest. As part of Environment and Climate Change Canada's program in support of the Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring program, concentrations of trace elements in PM2. 5 (particulate matter smaller than 2.5 µm in diameter) were measured through two campaigns that involved different methodologies: a long-term filter campaign and a short-term intensive campaign. In the long-term campaign, 24 h filter samples were collected once every 6 days over a 2-year period (December 2010–November 2012) at three air monitoring stations in the regional municipality of Wood Buffalo. For the intensive campaign (August 2013), hourly measurements were made with an online instrument at one air monitoring station; daily filter samples were also collected. The hourly and 24 h filter data were analyzed individually using positive matrix factorization. Seven emission sources of PM2. 5 trace elements were thereby identified: two types of upgrader emissions, soil, haul road dust, biomass burning, and two sources of mixed origin. The upgrader emissions, soil, and haul road dust sources were identified through both the methodologies and both methodologies identified a mixed source, but these exhibited more differences than similarities. The second upgrader emissions and biomass burning sources were only resolved by the hourly and filter methodologies, respectively. The similarity of the receptor modeling results from the two methodologies provided reassurance as to the identity of the sources. Overall, much of the PM2. 5-related trace elements were found to be anthropogenic, or at least to be aerosolized through anthropogenic activities. These emissions may in part explain the previously reported higher levels of trace elements in snow, water, and biota samples collected near the oil sands operations.