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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]

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]