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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]