Risk of asthmatic episodes in children exposed to sulfur dioxide stack emissions from a refinery point source in Montreal, Canada.

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

Modelling the variation of land surface temperature as determinant of risk of heat-related health events

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Risk of Asthmatic Episodes in Children Exposed to Sulfur Dioxide Stack Emissions from a Refinery Point Source in Montreal, Canada

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

Annoyance from road traffic, trains, airplanes and from total environmental noise levels

There is a lack of studies assessing the exposure-response relationship between transportation noise and annoyance in North America. Our aims were to investigate the prevalence of noise annoyance induced by road traffic, trains and airplanes in relation to distance to transportation noise sources, and to total environmental noise levels in Montreal, Canada; annoyance was assessed as noise-induced disturbance. A telephone-based survey among 4336 persons aged &gt;18 years was conducted. Exposure to total environmental noise (A-weighted outdoor noise levels-LAeq24h and day-evening-night equivalent noise levels-Lden) for each study participant was determined using a statistical noise model (land use regression-LUR) that is based on actual outdoor noise measurements. The proportion of the population annoyed by road traffic, airplane and train noise was 20.1%, 13.0% and 6.1%, respectively. As the distance to major roads, railways and the Montreal International Airport increased, the percentage of people disturbed and highly disturbed due to the corresponding traffic noise significantly decreased. When applying the statistical noise model we found a relationship between noise levels and disturbance from road traffic and total environmental noise, with Prevalence Proportion Ratios (PPR) for highly disturbed people of 1.10 (95% CI: 1.07-1.13) and 1.04 (1.02-1.06) per 1 dB(A) Lden, respectively. Our study provides the first comprehensive information on the relationship between transportation noise levels and disturbance in a Canadian city. LUR models are still in development and further studies on transportation noise induced annoyance are consequently needed, especially for sources other than road traffic

Sleep disturbance from road traffic, railways, airplanes and from total environmental noise levels in Montreal

The objective of our study was to measure the impact of transportation-related noise and total environmental noise on sleep disturbance for the residents of Montreal, Canada. A telephone-based survey on noise-related sleep disturbance among 4336 persons aged 18 years and over was conducted. LNight for each study participant was estimated using a land use regression (LUR) model. Distance of the respondent's residence to the nearest transportation noise source was also used as an indicator of noise exposure. The proportion of the population whose sleep was disturbed by outdoor environmental noise in the past 4 weeks was 12.4%. The proportion of those affected by road traffic, airplane and railway noise was 4.2%, 1.5% and 1.1%, respectively. We observed an increased prevalence in sleep disturbance for those exposed to both rail and road noise when compared for those exposed to road only. We did not observe an increased prevalence in sleep disturbance for those that were both exposed to road and planes when compared to those exposed to road or planes only. We developed regression models to assess the marginal proportion of sleep disturbance as a function of estimated LNight and distance to transportation noise sources. In our models, sleep disturbance increased with proximity to transportation noise sources (railway, airplane and road traffic) and with increasing LNight values. Our study provides a quantitative estimate of the association between total environmental noise levels estimated using an LUR model and sleep disturbance from transportation noise

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LAeq24h, Lnight, and Lden to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LAeq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R(2)) was 0.68, 0.59, and 0.69 for LAeq24h, Lnight, and Lden, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LAeq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise