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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]

Background: The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. Methods: A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year. Results: The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. Conclusions: This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available. [ABSTRACT FROM AUTHOR]

BACKGROUND: Little is known about the respiratory effects of short-term exposures to petroleum refinery emissions in young children. This study is an extension of an ecologic study that found an increased rate of hospitalizations for respiratory conditions among children living near petroleum refineries in Montreal (Canada). METHODS: We used a time-stratified case--crossover design to assess the risk of asthma episodes in relation to short-term variations in sulfur dioxide levels among children 2--4 years of age living within 0.5--7.5 km of the refinery stacks. Health data used to measure asthma episodes included emergency department (ED) visits and hospital admissions from 1996 to 2004. We estimated daily levels of SO2 at the residence of children using a) two fixed-site SO2 monitors located near the refineries and b) the AERMOD (American Meteorological Society/Environmental Protection Agency Regulatory Model) atmospheric dispersion model. We used conditional logistic regression to estimate odds ratios associated with an increase in the interquartile range of daily SO2 mean and peak exposures (31.2 ppb for AERMOD peaks). We adjusted for temperature, relative humidity, and regional/urban background air pollutant levels. RESULTS: The risks of asthma ED visits and hospitalizations were more pronounced for same-day (lag 0) SO2 peak levels than for mean levels on the same day, or for other lags: the adjusted odds ratios estimated for same-day SO2 peak levels from AERMOD were 1.10 [95% confidence interval (CI), 1.00--1.22] and 1.42 (95% CI, 1.10--1.82), over the interquartile range, for ED visits and hospital admissions, respectively. CONCLUSIONS: Short-term episodes of increased SO2 exposures from refinery stack emissions were associated with a higher number of asthma episodes in nearby children. [ABSTRACT FROM AUTHOR]

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. • Traffic noise surfaces were simulated by a propagation model. • Residuals between traffic noise and total environmental noise were estimated by RF. • Total environmental noise was predicted by the hybrid propagation-RF approach. • Closer-to-reality noise levels was simulated by the hybrid model. [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]

Background: Effects of air pollutants are related to oxidative stress which is also linked to the pathogenesis of dementia including Alzheimer's and related diseases., Objective: 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., Methods: 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., Results: 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., Conclusions: Results suggest that the onset of dementia may be related to residential exposure to PM 2.5 , NO 2 , and distance to major roads., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Land use and transportation scenarios can help evaluate the potential impacts of urban compact or transit-oriented development (TOD). Future scenarios have been based on hypothetical developments or strategic planning but both have rarely been compared. We developed scenarios for an entire metropolitan area (Montreal, Canada) based on current strategic planning documents and contrasted their potential impacts on car use and active transportation with those of hypothetical scenarios. We collected and analyzed available urban planning documents and obtained key stakeholders' appreciation of transportation projects on their likelihood of implementation. We allocated 2006-2031 population growth according to recent trends (Business As Usual, BAU) or alternative scenarios (current planning; all in TOD areas; all in central zone). A large-scale and representative Origin-Destination Household Travel Survey was used to measure travel behavior. To estimate distances travelled by mode, in 2031, we used a mode choice model and a simpler method based on the 2008 modal share across population strata. Compared to the BAU, the scenario that allocated all the new population in already dense areas and that also included numerous public transit projects (unlikely to be implemented in 2031), was associated with greatest impacts. Nonetheless such major changes had relatively minor impacts, inducing at most a 15% reduction in distances travel by car and a 28% increase in distances walked, compared to a BAU. Strategies that directly target the reduction of car use, not considered in the scenarios assessed, may be necessary to induce substantial changes in a metropolitan area.

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 (R 2 : 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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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 R 2 (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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objectives: 1) To describe grassroots projects aimed at the built environment and associated with active transportation on the Island of Montreal; and 2) to examine associations between the number of projects and indicators of neighbourhood material and social deprivation and the built environment., Method: We identified funding agencies and community groups conducting projects on built environments throughout the Island of Montreal. Through website consultation and a snowballing procedure, we inventoried projects that aimed at transforming built environments and that were carried out by community organizations between January 1, 2006, and November 1, 2010. We coded and validated information about project activities and created an interactive map using Geoclip software. Correlational analyses quantified associations between number of projects, neighbourhood characteristics and deprivation., Results: A total of 134 community organizations were identified, and 183 grassroots projects were inventoried. A large number of projects were aimed at increasing awareness of/improving active or public transportation (n=95), improving road safety (n=84) and enhancing neighbourhood beautification and greening (n=69). The correlation between the presence of projects and the extent of neighbourhood material deprivation was small (Kendall's t=0.26, p&lt;0.001), but in areas with greater social deprivation there were more projects (Kendall's t=0.38, p&lt;0.001). Larger numbers of projects were also associated with the presence of more extensive land-use mix (Kendall's t=0.23, p&lt;0.001) and a greater proportion of road intersections with injured pedestrians, cyclists and motor vehicle users (Kendall's t=0.43, p&lt;0.001)., Conclusion: There is significant community mobilization around built environments and active transportation. Investigations of the implementation processes and impacts are warranted.

Objective: Estimate the number of awakenings additional to spontaneous awakenings, induced by the nighttime aircraft movements at an international airport in Montreal, in the population residing nearby in 2009., Methods: Maximum sound levels (LAS,max) were derived from aircraft movements using the Integrated Noise Model 7.0b, on a 28 x 28 km grid centred on the airport and with a 0.1 x 0.1 km resolution. Outdoor LAS,max were converted to indoor LAS,max by reducing noise levels by 15 dB(A) or 21 dB(A). For all grid points, LAS,max were transformed into probabilities of additional awakening using a function developed by Basner et al. (2006). The probabilities of additional awakening were linked to estimated numbers of exposed residents for each grid location to assess the number of aircraft-noise-induced awakenings in Montreal., Results: Using a 15 dB(A) sound attenuation, 590 persons would, on average, have one or more additional awakenings per night for the year 2009. In the scenario using a 21 dB(A) sound attenuation, on average, no one would be subjected to one or more additional awakenings per night due to aircraft noise., Conclusion: Using the 2009 flight patterns, our data suggest that a small number of Montreal residents are exposed to noise levels that could induce one or more awakenings additional to spontaneous awakenings per night.