Mapping heatwave health risk at the community level for public health action

Abstract

BackgroundClimate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations.ResultsThe study area encompasses Rennes, a medium-sized French city. A Landsat ETM + image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R2 = 0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north–south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r = 0.98, p < 10-12).ConclusionsBuilding on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.

Adaptation to Climate Change through Spatial Planning in Compact Urban Areas: A Case Study in the City of Thessaloniki

“…Vulnerability to the UHI effect is determined by a combination of factors, such as higher temperatures and climatological trends, demographic factors, quality of life and other socio-economic components, as well as by urban design characteristics [2,3,11,[38][39][40][41][42][43][44][45][46][47]. Therefore, data from the study area were classified according to three sets: climatic conditions/environmental data, including temperature, humidity, wind, rising sea levels, air pollutants; demographic data, including population change, demographic and socio-economic structure; and urban form data, including layout plan, building characteristics, land uses, densities, open spaces/Green Infrastructure.…”

“…Even though the percentage of people living alone is low, it had a notable increase in the period of 2001-2011. Households with more than one person are considered to be less affected by heat stress, as they can seek and receive care when needed [41]. Regarding employment data, 15% of the employed population accounts for upper professions, while unskilled laborers account for just 1.6%.…”

“…The composition of population by sex was not examined as a variable, since the relevant studies report conflicting evidence [40,45,47]. Social isolation is another important factor determining vulnerability [41,44,45]. Even though the percentage of people living alone is low, it had a notable increase in the period of 2001-2011.…”

“…The municipality featured a small increase in the population of small children (0-4 years old) in the period 2001-2011. Both of these groups are considered to be particularly exposed and sensitive to the impacts of climate change and find it harder to adapt [2,12,41,[43][44][45][46][47][51][52][53]. The composition of population by sex was not examined as a variable, since the relevant studies report conflicting evidence [40,45,47].…”

“…All of these features could have resulted in the observed intensity of UHI and climate change. Given that the felt effects of climate change depend on gender, age, education and social class [2,11,12,[38][39][40][41][44][45][46][47], the Municipality of Kalamaria has a high degree of so-called "generic vulnerability" (Table 1). This is due to the fact that it features an increased percentage of elderly (over the age of 65), higher than the average in the Prefecture of Thessaloniki.…”