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OBJECTIVES: We aimed to assess the relationships between deprivation at Quebec public schools, their proximity to polluting industries, and their exposure to industrial air emission sources including ambient fine particulate matter ([PM.sub.2.5]), sulphur dioxide (S[O.sub.2]) and nitrogen dioxide (N[O.sub.2]). METHODS: We obtained four indicators of school deprivation using data from the 2006 Canadian census called the low-income threshold indicator, the neighbourhood SES indicator, and the social and material deprivation indicators of Pampalon. Using proximity spatial tools, we constructed three buffers of 2.5, 5 and 7.5 km around each school and summed up total emissions of [PM.sub.2.5], S[O.sub.2] and N[O.sub.2] for each school. Industrial air emissions were estimated using data from the 2006 Canadian National Pollutant Release Inventory. The Pearson correlations and LOESS regressions and natural log-transformed industrial air emissions were evaluated for Quebec public schools within the three buffers. RESULTS: Of the 2189 public schools in Quebec, 608 (27.8%), 1108 (50.6%) and 1384 (63.2%) schools were located near at least one industry emitting one or more pollutants of interest in buffers of 2.5 km, 5 km and 7.5 km of schools respectively. Weak positive Pearson correlations (r) were found between logtransformed tons of industrial emissions of [PM.sub.2.5], S[O.sub.2] and N[O.sub.2] and both the social deprivation (r = {0.23; 0.33}) and low-income threshold (r = {0.17; 0.29}) indicators in a buffer of 2.5 km. However, we found negative associations between emissions and the neighbourhood SES (r = {0.06; 0.16}) and material deprivation (r = {-0.04; 0.08}) indicators. CONCLUSION: Our study suggests that schools in Quebec with higher rates of socio-economic deprivation among their students may be more likely to be exposed to higher emissions of industrial air pollutants. KEY WORDS: Air pollution; child; industry; school; social class OBJECTIFS : L'objectif de cette etude etait d'explorer la relation entre le niveau de defavorisation des ecoles publiques quebecoises, leur proximite aux sources industrielles et leur exposition aux emissions industrielles de particules fines ([PM.sub.2.5]), de dioxyde de soufre (S[O.sub.2]) et d'oxydes d'azote (NOx). METHODES : L'indice de faible revenu (SFR), l'indice de milieu socioeconomique (IMSE) et les indices de defavorisation sociale et materielle de Pampalon, bases sur le recensement de 2006, ont ete utilises. Des cercles de rayons de 2,5 km, 5 km et 7,5 km ont ete construits autour des ecoles. En utilisant l'Inventaire national de rejets de polluants, les emissions industrielles de [PM.sub.2.5], S[O.sub.2] et N[O.sub.2] de l'annee 2006 ont ete sommees dans chaque rayon. Les relations entre les emissions industrielles log-transformees et la defavorisation ont ete evaluees pour les ecoles en utilisant des correlations de Pearson et des regressions LOESS. RESULTATS : Des 2189 ecoles incluses dans cette etude, 608 (27,8%), 1108 (50,6%) et 1384 (63,2%) etaient localisees a proximite d'au moins une industrie emettrice d'un ou plusieurs polluants d'interet dans un rayon de 2,5 km, 5 km et 7,5 km autour des ecoles, respectivement. Des correlations de Pearson (r) positives ont ete notees entre les tonnes d'emissions industrielles log-transformees de [PM.sub.2.5], S[O.sub.2] et N[O.sub.2] et l'indice de defavorisation sociale de Pampalon (r = {0,23; 0,33}) et le SFR (r = {0,17; 0,29}) dans un rayon de 2.5 km. Cependant des correlations contre-intuitives ont ete observees avec l'IMSE (r = {0,06; 0,16}) et l'indice de defavorisation materielle de Pampalon (r = {-0,04; 0,08}). CONCLUSION : Cette etude suggere que les ecoles quebecoises plus defavorisees pourraient etre davantage exposees aux emissions industrielles de polluants de l'air que les autres. MOTS-CLES : ecole; enfant; industrie; statut socio-economique; pollution de l'air, Primary ambient air pollutants, namely particulate matter ([PM.sub.2.5]), sulphur dioxide (S[O.sub.2]) and nitrogen dioxide (N[O.sub.2]), are largely emitted by anthropogenic sources such as vehicular traffic and industry. (1-3) Exposure to [...]

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. [ABSTRACT FROM AUTHOR]

Background: This study's objective was to determine whether socioeconomically deprived populations are exposed to greater levels of environmental noise. Methods: Indicators of socioeconomic status were correlated with LAeq24h noise levels estimated with a land-use regression model at a small geographic scale. Results: We found that noise exposure was associated with all socioeconomic indicators, with the strongest correlations found for median household income, proportion of people who spend over 30% of their income on housing, proportion of people below the low income boundary and with a social deprivation index combining several socio-economic variables. Conclusion: Our results were inconsistent with a number of studies performed elsewhere, indicating that locally conducted studies are imperative to assessing whether this double burden of noise exposure and low socioeconomic status exists in other contexts. The primary implication of our study is that noise exposure represents an environmental injustice in Montreal, which is an issue that merits both investigation and concern. [ABSTRACT FROM AUTHOR]

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. Image 1 • The RF model has higher accuracy than the LUR model at the local and global scales. • People living in Montreal and Longueuil exposed to relatively high noise levels. • The noise estimates were assigned to postal code areas for future health studies. We demonstrated that the random forests models performed better than land use regression models for estimating spatial variation in noise levels. [ABSTRACT FROM AUTHOR]

Abstract Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LA eq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44–0.64), deviance (51−72%) and Akaike information criterion (AIC) (469.2–434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data. Highlights • A Generalized Additive Model (GAM) was developed to generate noise exposure surfaces. • Noise data were collected based on short-term measurements in Toronto, Canada. • Various model specifications were tested in terms of the resulting predictions. • Predictions were validated against data from a panel and from a long-term campaign. • Models that involved manual adjustments resulted in more realistic surfaces. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Effects of air pollutants are related to oxidative stress which is also linked to the pathogenesis of dementia including Alzheimer's and related diseases. We assessed associations between exposure to air pollutants and the onset of dementia; the association with the distance between residence and major roads was also assessed for the island of Montreal. We created an open cohort of adults aged 65 years and older starting in 2000 and ending in 2012 in the province of Québec, Canada using linked medico-administrative databases. New cases of dementia were defined based on a validated algorithm. Annual residential levels of nitrogen dioxide (NO 2) and fine particles (PM 2.5) at residential levels were estimated for each year of follow up using estimates based on satellite images and ground air monitoring data. Hazard ratios (HRs) were assessed with Extended (time dependent exposure) Cox models with age as the time axis and stratified for sex, for the annual exposure level at each residential address. Models were adjusted for the calendar year, area-wide social and material deprivation indexes and for NO 2 or PM 2.5 ; they were also indirectly adjusted for smoking. 1,807,133 persons (13,242,270 person-years) were followed and 199,826 developed dementia. From models (adjusted for calendar year, social and material deprivation indexes), HRs for an interquartile range (IQR) increase in time-varying exposure to NO 2 (IQR 13.26 ppb), PM 2.5 (IQR 3.90 μg/m³), and distance to major roads (IQR 150 m, in Montreal only), were 1.005 (CI 95% 0.994–1.017), 1.016 (CI 95% 1.003–1.028) and 0.969 (CI 95% 0.958–0.980), respectively. Results suggest that the onset of dementia may be related to residential exposure to PM 2.5 , NO 2 , and distance to major roads. • We studied dementia onset and residential exposure to air pollutants. • We used a cohort based on administrative health data in Quebec, Canada, 2000–2012. • Residential exposure to PM2.5, NO2 was based on satellite images and monitoring data. • PM2.5, NO2, distances to major roads (Montreal) were associated with dementia onset. [ABSTRACT FROM AUTHOR]

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. • We modelled 2061 densification, telework & decrease car share (~15%) impacts. • Walking & Public Transit use were >25% higher for this scenario in Y2061. • NO2 was slightly lower in the Y2061 scenario but always higher in the urban core. • Greater decrease in DALY was noted in suburb areas (Globally Y2061 scenario > -30%). • Drastic car share reduction & densification are necessary to greatly reduce impacts. [ABSTRACT FROM AUTHOR]