Search

Excess mortality can be caused by extreme hot weather events, which are increasing in severity and frequency in Canada due to climate change. Individual and social vulnerability factors influence the mortality risk associated with a given heat exposure. We constructed heat vulnerability indices using census data from 2006 to 2011 in Canada, developed a novel design to compare spatiotemporal changes of heat vulnerability, and identified locations that may be increasingly vulnerable to heat. The results suggest that 1) urban areas in Canada are particularly vulnerable to heat, 2) suburban areas and satellite cities around major metropolitan areas show the greatest increases in vulnerability, and 3) heat vulnerability changes are driven primarily by changes in the density of older ages and infants. Our approach is applicable to heat vulnerability analyses in other countries. [ABSTRACT FROM AUTHOR]

Extreme heat events have caused excess mortality in Canadian cities. In order to map the population groups most vulnerable to extreme heat in Vancouver, we overlaid multiple layers of socio-economic, environmental, and infrastructural data. By superimposing multiple disparate data layers, we were able to detect and visualize 105 socio-economically deprived dissemination areas with high vulnerability to extreme heat events. The three dissemination areas found to be most vulnerable to heat varied from the rest of the sample in terms of environmental and infrastructural variables. These three vulnerable dissemination areas also have relatively low vegetation cover as well as relatively hot surface temperatures. As such, they are socio-economically vulnerable, far from cooling and health infrastructure, and have an environment that elevates heat exposure. Our results are a preliminary step toward the development of tools that can help health authorities, city officials, and policymakers better understand who is at risk during extreme heat events, where they reside, what factors drive the risk, and ultimately what can be done to mitigate it. L'utilisation de sources de données diverses et variées pour cartographier la vulnérabilité à la chaleur : une étude de cas à Vancouver Des épisodes de chaleur accablante sont une cause de surmortalité dans les villes canadiennes. Une carte des groupes de populations les plus vulnérables à la chaleur accablante a été dressée à Vancouver en combinant plusieurs couches de données socio-économiques, environnementales et liées aux infrastructures. La superposition de couches de données diverses et variées a permis de détecter et de visualiser les 105 aires de diffusion défavorisées sur le plan socio-économique qui présentent une grande vulnérabilité en situation de chaleur accablante. Les trois aires de diffusion les plus vulnérables se distinguent du reste de l'échantillon en matière d'environnement et d'infrastructure. En outre, ces dernières ont, toute proportion gardée, un faible couvert végétal et des températures de surface chaudes. En plus de la vulnérabilité socio-économique, elles se caractérisent par un éloignement aux infrastructures de refroidissement et de santé, et par un environnement qui accroît l'exposition à la chaleur. Les résultats obtenus constituent un premier pas vers la conception d'outils pour éclairer les autorités sanitaires, les responsables municipaux et les décideurs afin d'identifier qui est exposé au risque pendant les périodes de chaleur accablante, où ils (elles) résident, quels facteurs accentuent le risque, et, finalement, les mesures à prendre pour l'atténuer. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution ofTa, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximumTain British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimateTa. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observedTaand spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimatingTafrom MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximumTa, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C,R2 = 0.74) than the linear regression model (MAE = 2.41°C,R2 = 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximumTaat a resolution of 1 km in British Columbia in the summer of 2003–2012 was derived, and the spatial distribution of summerTain this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain. [ABSTRACT FROM AUTHOR]