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Previous research has associated snowfall with risk of myocardial infarction (MI). Most studies have been conducted in regions with harsh winters; it remains unclear whether snowfall is associated with risk of MI in regions with milder or more varied climates. A case-crossover design was used to investigate the association between snowfall and death from MI in British Columbia, Canada. Deaths from MI among British Columbia residents between October 15 and March 31 from 2009 to 2017 were identified. The day of each death from MI was treated as the case day, and each case day was matched to control days drawn from the same day of the week during the same month. Daily snowfall amount was assigned to case and control days at the residential address, using weather stations within 15 km of the residence and 100 m in elevation. In total, 3,300 MI case days were matched to 10,441 control days. Compared with days that had no snowfall, odds of death from MI increased 34% (95% confidence interval: 0%, 80%) on days with heavy snowfall (≥5 cm). In stratified analysis of deaths from MI as a function of both maximum temperature and snowfall, risk was significantly increased on snowfall days when the temperature was warmer.

Background Indoor and outdoor fine particulate matter (PM2.5) are both leading risk factors for death and disease, but making indoor measurements is often infeasible for large study populations. Methods We developed models to predict indoor PM2.5 concentrations for pregnant women who were part of a randomized controlled trial of portable air cleaners in Ulaanbaatar, Mongolia. We used multiple linear regression (MLR) and random forest regression (RFR) to model indoor PM2.5 concentrations with 447 independent 7-day PM2.5 measurements and 87 potential predictor variables obtained from outdoor monitoring data, questionnaires, home assessments, and geographic data sets. We also developed blended models that combined the MLR and RFR approaches. All models were evaluated in a 10-fold cross-validation. Results The predictors in the MLR model were season, outdoor PM2.5 concentration, the number of air cleaners deployed, and the density of gers (traditional felt-lined yurts) surrounding the apartments. MLR and RFR had similar performance in cross-validation (R2 = 50.2%, R2 = 48.9% respectively). The blended MLR model that included RFR predictions had the best performance (cross validation R2 = 81.5%). Intervention status alone explained only 6.0% of the variation in indoor PM2.5 concentrations. Conclusions We predicted a moderate amount of variation in indoor PM2.5 concentrations using easily obtained predictor variables and the models explained substantially more variation than intervention status alone. While RFR shows promise for modelling indoor concentrations, our results highlight the importance of out-of-sample validation when evaluating model performance. We also demonstrate the improved performance of blended MLR/RFR models in predicting indoor air pollution.

Background Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and joint effects of noise and greenness, on non-Alzheimer’s dementia, Parkinson’s and Alzheimer’s disease and multiple sclerosis within a population-based cohort. Methods We assembled administrative health database cohorts of 45–84 year old residents (N ~ 678,000) of Metro Vancouver, Canada. Cox proportional hazards models were built to assess associations between exposures and non-Alzheimer’s dementia and Parkinson’s disease. Given reduced case numbers, associations with Alzheimer’s disease and multiple sclerosis were evaluated in nested case-control analyses by conditional logistic regression. Results Road proximity was associated with all outcomes (e.g. non-Alzheimer’s dementia hazard ratio: 1.14, [95% confidence interval: 1.07–1.20], for living Conclusions Road proximity was associated with incidence of non-Alzheimer’s dementia, Parkinson’s disease, Alzheimer’s disease and multiple sclerosis. This association may be partially mediated by air pollution, whereas noise exposure did not affect associations. There was some evidence of protective effects of greenness.

Additional file 1: Table S1. Correlations between road proximity, air pollution, noise and greenness in cohorts of Non-Alzheimer’s dementia, Parkinson’s disease, Alzheimer’s disease and Multiple sclerosis. Table S2. Distributions of exposures from Canadian Urban Environmental Health Research Consortium (CANUE). Table S3. Hazard ratios between exposures and Non-Alzheimer’s dementia stratified by sex (Males, Females), ethnicity (Chinese, South Asian, Visible Minority) and age (> = 65 years, = 65 years

Background: Indoor and outdoor exposures to fine particulate matter (PM2.5) are both leading risk factors for death and disease, but collecting indoor measurements is often infeasible for large stu...

Background: Limited evidence suggests the potential for associations between air pollution with neurodegenerative disorders such as Multiple Sclerosis (MS), Parkinson’s Disease (PD) and demen...

Air pollution is a major risk factor for death and disease, particularly in low- and middle-income countries (LMIC) where concentrations are relatively high and large populations are exposed. High-quality exposure assessment is integral to both air pollution epidemiologic studies and impact assessments. Land use regression (LUR) modelling is a powerful exposure assessment technique that uses the relationships between air pollution concentrations at discrete monitoring locations and the surrounding characteristics of those locations to model small-scale spatial concentration gradients within cities. Regardless of whether they are calibrated based on local measurements or transferred from another location, LUR models require spatially resolved data on predictor variables that may be unavailable or of insufficient quality in many settings. We describe methods for deriving LUR model predictors, including land cover, road locations, and ger (Mongolian yurt) locations, from satellite data and high-resolution imagery in Ulaanbaatar, Mongolia. These methods may allow LUR models to be developed for more locations in LMIC, potentially improving the quality of air pollution exposure assessments in those settings. Resume Survol des methodes pour degager des variables explicatives de la pollution atmospherique dans un modele de regression de l'utilisation du sol a partir de donnees de teledetection a Oulan-Bator, Mongolie La pollution atmospherique est un facteur de risque important de mortalite et de morbidite, en particulier dans les pays a revenu faible et intermediaire (PFR-PRI) marques par des concentrations relativement elevees auxquelles de nombreuses populations sont exposees. Une evaluation de haute qualite de l'exposition est indispensable pour les etudes epidemiologiques de la pollution atmospherique et les etudes d'impact. La modelisation de l'utilisation du sol par regression constitue un procede d'evaluation de l'exposition performant fonde sur les liens entre les concentrations de pollution atmospherique a des stations de surveillance et les attributs particuliers de ces emplacements afin de modeliser les gradients locaux de concentration dans les villes. Peu importe que le calibrage des modeles de regression de l'utilisation du sol soit effectue avec des mesures locales ou soit transpose d'un autre site, ils requierent des donnees spatiales fiables des variables explicatives qui peuvent neanmoins etre manquantes ou de faible qualite dans plusieurs contextes. Nous presentons un survol des methodes permettant de degager des indicateurs d'un modele de regression de l'utilisation du sol, y compris la couverture du sol, l'emplacement des voies, et les localisations des ger (yourte mongole), a partir de donnees satellitaires et d'images de haute resolution a Oulan-Bator, Mongolie. Ces methodes permettent d'elaborer des modeles de regression de l'utilisation du sol pour d'autres localites dans les PFR-PRI afin d'ameliorer la qualite des evaluations de l'exposition a la pollution atmospherique dans ces contextes.

Background/Aim: Extreme hot weather events are increasing in intensity, frequency, and duration as a consequence of climate change. As a result, hot weather mortality is a growing concern in many u...

Background: Epidemiological studies typically use subjects' residential address to estimate individuals' air pollution exposure. However, in reality this exposure is rarely static as people move from home to work/study locations and commute during the day. Integrating mobility and time-activity data may reduce errors and biases, thereby improving estimates of health risks. Objectives: To incorporate land use regression with movement and building infiltration data to estimate time-weighted air pollution exposures stratified by age, sex, and employment status for population subgroups in Hong Kong. Methods: A large population-representative survey (N = 89,385) was used to characterize travel behavior, and derive time-activity pattern for each subject. Infiltration factors calculated from indoor/outdoor monitoring campaigns were used to estimate micro-environmental concentrations. We evaluated dynamic and static (residential location-only) exposures in a staged modeling approach to quantify effects of each component. Results: Higher levels of exposures were found for working adults and students due to increased mobility. Compared to subjects aged 65 or older, exposures to PM2.5, BC, and NO2 were 13%, 39% and 14% higher, respectively for subjects aged below 18, and 3%, 18% and 11% higher, respectively for working adults. Exposures of females were approximately 4% lower than those of males. Dynamic exposures were around 20% lower than ambient exposures at residential addresses. Conclusions: The incorporation of infiltration and mobility increased heterogeneity in population exposure and allowed identification of highly exposed groups. The use of ambient concentrations may lead to exposure misclassification which introduces bias, resulting in lower effect estimates than ‘true’ exposures. Keywords: Air pollution, Dynamic exposure, Land use regression, Exposure assessment, Travel behavior, Time-activity

Portable HEPA filter air cleaners can reduce indoor fine particulate matter (PMWe randomized 540 participants to an intervention group receiving 1 or 2 HEPA filter air cleaners or a control group receiving no air cleaners. We followed 259 intervention and 253 control participants to the end of pregnancy. We measured one-week indoor residential PMThe overall geometric mean (GM) one-week outdoor PMPortable HEPA filter air cleaners can lower indoor PM

Fine particulate matter (PM 2.5 ) generated by forest fires has been associated with a wide range of adverse health outcomes, including exacerbation of respiratory diseases and increased risk of mortality. Due to the unpredictable nature of forest fires, it is challenging for public health authorities to reliably evaluate the magnitude and duration of potential exposures before they occur. Smoke forecasting tools are a promising development from the public health perspective, but their widespread adoption is limited by their inherent uncertainties. Observed measurements from air quality monitoring networks and remote sensing platforms are more reliable, but they are inherently retrospective. It would be ideal to reduce the uncertainty in smoke forecasts by integrating any available observations. This study takes spatially resolved PM 2.5 estimates from an empirical model that integrates air quality measurements with satellite data, and averages them with PM 2.5 predictions from two smoke forecasting systems. Two different indicators of population respiratory health are then used to evaluate whether the blending improved the utility of the smoke forecasts. Among a total of six models, including two single forecasts and four blended forecasts, the blended estimates always performed better than the forecast values alone. Integrating measured observations into smoke forecasts could improve public health preparedness for smoke events, which are becoming more frequent and intense as the climate changes.