Health impacts of urban heat island mitigation in Europe
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What are the current and future health impacts of heat exposure in large European cities? How much could these impacts be reduced by measures aimed at mitigating the urban heat island (UHI) effect? Currently, almost 15 000 annual deaths are attributable to summertime heat exposure in densely populated metropolitan areas within the 23 European countries included in the assessment. The number of heat related deaths is likely to increase drastically (by 270-460% depending on the climate scenario) during the next 40 years as climate becomes warmer and the population ages. By 2050, mitigation of UHI in the cities by increasing vegetation and surface albedo could potentially result in thousands of averted premature deaths annually. |
This assessment aims to quantify health impacts of heat exposure in large European cities and the effectiveness of different urban heat island mitigation policies in reducing these effects. It is a part of the Intarese Common Case Study.
Contents
Scope
Purpose
What are the current and future annual health impacts of heat exposure in large European cities? What is the potential and relative effectiveness of different urban heat island mitigation measures in reducing these impacts?
Boundaries
- Year: 2010, 2020, 2030, 2050
- Geographical area: EU-27, excluding Bulgaria, Cyprus, Latvia, Romania
- North-continental region: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Lithuania, Luxemburg, Netherlands, Poland, Slovakia, Sweden, United Kingdom
- Mediterranean region: Greece, Italy, Malta, Portugal, Slovenia, Spain
- Health impacts: natural mortality
Scenarios
Future climate scenarios:
- IPCC A1B
- IPCC B1
Intended users
- Intarese/Heimtsa Common Case Study
- Anyone interested
Participants
- The National Institute for Health and Welfare (THL), Finland
- University of Stuttgart, Germany
- University of Maastricht, the Netherlands
Definition
Decision variables
Evaluated policies for mitigating urban heat island effect
1) Reference (Business-as-usual (BAU))
- No new UHI mitigation measures
2) Vegetation
- Urban forestry is increased by planting trees on open grassy areas and street curbsides
- Trees are assumed to be deciduous and mature
- 10.8% of the total urban area is redeveloped from open grass to trees (open area planting)
- 6.7% of the total urban area is redeveloped from street to trees (curbside planting)
3) Albedo
- Albedo of the city is increased by converting impervious roof and street surfaces (sidewalks and roadways) to light colored surfaces
- For light roof surface, albedo is assumed to increase form 0.15 to 0.5
- For light street level surface, albedo is assumed to increase from 0.15 to 0.2
- 13.6% of the total urban area is redeveloped from impervious roof surface to light roof surface
- 34.4% of the total urban area is redeveloped from impervious street surface to light street surface
4) Veg+alb
- Both urban forestry and albedo is increased
- 10.8% of the total urban area is redeveloped from open grass to trees (open area planting)
- 6.7% of the total urban area is redeveloped from street to trees (curbside planting)
- 13.6% of the total urban area is redeveloped from impervious roof surface to light roof surface
- 34.4% of the total urban area is redeveloped from impervious street surface to light street surface
- Trees are assumed to be deciduous and mature
- For light roof surface, albedo is assumed to increase form 0.15 to 0.5
- For light street level surface, albedo is assumed to increase from 0.15 to 0.2
The fraction of the total urban area in large European cities available to be redeveloped from one land use or surface cover type to another is based on an urban heat island mitigation study conducted in New York City [1]
Indicators
Other variables
- Ambient air temperature in Europe
- Ambient air dew point temperature in Europe
- In the reference scenarios, city-specific daily maximum apparent temperatures were calculated based on EMEP grid level data on hourly air temperatures and dew points modelled for IPCC climate scenarios A1B (corresponds with reference scenario 1) and B1 (corresponds with reference scenario 2) using the Max Planck Institute for Meteorology Regional Model (REMO). Reductions in the city-specific temperatures due to the different UHI mitigation policies were estimated by first subtracting the policy-specific city-wide average decrease in the near-surface air temperature (see chapter 5.5) from the daily maximum air temperature in each city. The daily maximum apparent temperature (AT) was then calculated based on this and the original dew point data.
- Effect of urban land use change on ambient air temperature
- The impact of the alternative policies on heat exposure was modelled based on the percentage of a given surface cover type that could be converted to another in a city and the change in near-surface air temperatures caused by these changes per unit area. The city-wide cooling effect then depends on the number of units that is redeveloped and the type of redevelopment. Estimates for both available surface area that could be converted from one surface cover type to another in large European cities and the unit cooling effects of these changes (table 5.5.1) were based on a modelling study on urban heat island mitigation in New York City (NYSERDA 2006). In this study, they used a regional climate model MM5 in combination with observed meteorological, satellite, and GIS data to determine the potential impacts of mitigation strategies on the surface air temperature (2 m height) over space and time in the New York Metropolitan Region.
- Heat exposure in Europe
- Evaluation of heat exposure in large cities in Europe depends on the definition and location of densely populated metropolitan areas. In the context of this assessment, the geographical location of large cities was based on EMEP grid level data on total population. If the population density within a grid cell surpassed 200 per square kilometre (i.e. 500000 within a 50 km x 50 km grid cell), the grid cell was considered to include a large city.
- Heat exposure high enough to lead to increased mortality in reference and policy scenarios was evaluated by determining the number of days between April and September when the sub-region-specific apparent temperature (AT) threshold (North-continental 23.3°C (95% Cl 22.5-24), Mediterranean 29.4°C (25.7-32.4)) is exceeded. These days were then classified into different exposure classes based on the level of threshold exceedance on 1°C accuracy.
- Population of Europe
- The number of people living within the densely populated metropolitan areas was defined based on EMEP grid level data on the fraction of the total population living in an urban as opposed to rural environment in each grid cell
- Mortality in Europe
- Country-specific current daily mortality risk levels (all non-accidental causes) were derived from the most recent annual mortality data available for each country in the WHO mortality database and the corresponding population data. Used datasets were for most countries from the years 2005-2006. However, somewhat older datasets were used for Belgium (1997), Denmark (2001), Italy and Portugal (2003). The daily mortality risk was assumed stay on the same level throughout the year. This may lead to slight overestimation of the impacts, as population mortality is known to vary between seasons and to be usually higher in winter than summer.
- ERF of ambient temperature on mortality
- Assessment concentrated on mortality effects of short-term heat exposure. Impacts were evaluated based on the results of the PHEWE-project (Baccini et al. 2008, Michelozzi et al. 2007), which investigated acute health effects of weather in European cities. In PHEWE, epidemiological time-series studies on the association between daily heat exposure and mortality were conducted in 15 cities. The city-specific estimates were then pooled into two groups based of meteorological and geographical criteria, Mediterranean (7 cities) and North-continental (8 cities), to produce meta-analytical ERFs for these sub-regions.
- ERFs for natural mortality (all non-accidental causes of death) for three different age categories (15-64, 65-74, 75+) were used in the assessment. The ERFs are based on a linear threshold model and describe the percent change in daily mortality associated with a 1°C increase in daily maximum apparent temperature above a sub-region specific temperature threshold in the warm season (April-September).
- In PHEWE, heat exposure in cities was evaluated based on weather data from the nearest airport. Thus, the ERFs describe the association between background ambient temperature level and mortality risk in a city. Because of the urban heat island effect, heat exposure in a city is in reality likely to be higher than the background temperature indicates. Therefore, the impact of the urban heat island effect is embedded in and inseparable from these ERF estimates, and the health effects of heat exposure and the changes in these effects due to different UHI mitigation policies have to be evaluated against ambient background temperature data.
Analyses
Probabilistic modelling (Monte Carlo simulation, 1000 iterations) was conducted to quantitatively estimate uncertainty in the assessment results. Temperature thresholds and ERFs were defined as probability distributions. Triangular distributions were assumed for all probabilistic inputs. Minimum and maximum values for distributions were based on the lower and upper 95% confidence limits and mode value on the central estimate.
Indices
Result
Results
Annual deaths (mean and 95% confidence limits) attributable to heat exposure in densely populated metropolitan areas.
| Year | UHI mitigation scenario | Climate scenario A1B | Climate scenario B1 |
| 2010 | 14639 (8142-22847) | 14639 (8142-22847) | |
| 2050 | Reference (BAU) | 78357 (42046-123275) | 46341 (24366-77494) |
| Vegetation | 75248 (40184-118849) | 44436 (23406-74422) | |
| Albedo | 70462 (37534-111824) | 41426 (21876-69871) | |
| Veg+alb | 67882 (36154-108226) | 39692 (20897-67081) |
Uncertainties
The quantitative uncertainty assessment took into consideration only the uncertainty in the exposure-response functions and exposure thresholds related to these. However, there are also several other sources of uncertainty, the impact of which could not be quantified:
- The temperature estimates modelled for the future are uncertain due to both assumptions and simplifications in the model as well as assumptions made in the IPCC scenarios on world development.
- The temperature data data may lead to underestimation of heat exposure in coastline cities. This is especially the case for those cities, or parts of the cities, which are located in grid cells where greater part of the cell area consists of sea.
- In the UHI mitigation policy analysis, uncertainties relate to both estimates on the amount of surface area that could be redeveloped from one surface cover type to another, as well as the actual cooling effects of these changes. It is unknown how well the estimates from the mitigation study conducted in New York City translate to the average European urban setting.
- Heat exposure assessment is complicated because urban areas contain numerous microclimates. The actual heat exposure of the city inhabitants, therefore, depends on the microclimatic conditions in which people actually spend their time. It is unclear how changes in the urban surface energy budget and average urban heat island intensity relate to changes in actual heat exposure.
- It is assumed in the assessment, that the absolute air humidity stays the same regardless of the implementation of UHI mitigation measures. This may very well not be true. For example, increasing urban vegetation would likely increase air humidity through higher evapotranspiration. This would subsequently be reflected in the level of discomfort related to a given level of air temperature.
- The interpretation of the results is complicated because the health impact of heat island intensity is actually embedded in the exposure-response function. Thus, in reality the cooling effect of the mitigation measures would be reflected in the association between background ambient temperature and mortality risk in a city, not in the used exposure indicator. In addition, the urban infrastructure is also likely to go through other changes in the future, which is ignored in the assessment. For example, the use of air conditioning could increase in European cities because of the increasing summer temperatures, which would lead to less severe heat exposure even if ambient temperatures rise. These types of factors would also be reflected in the exposure-response function.
Conclusions
Currently, almost 15000 deaths are attributable to summertime heat exposure annually in the densely populated European metropolitan areas within the countries included in the assessment. Over 70% of these deaths occur in those aged 75 or older. The number of heat related deaths is likely to increase drastically (by 270% in climate scenario B1 and 460% in A1B) during the next 40 years as climate becomes warmer and the population ages.
By 2050, thousands of heat exposure related deaths could be averted annually by implementing the evaluated urban heat island mitigation policies. Increasing albedo was more effective policy scenario than icreasing vegetation due to the assumption that higher share of city area could be converted to high reflectance surface than vegetation. The benefits from the policies increase the warmer the climate becomes, and are higher in the IPCC climate scenario A1B, which represents stronger climate change and warming and seems, at the moment, to be the more realistic one of the evaluated climate scenarios.
When conducting an assessment on the scale of the whole Europe, it is necessary to make many simplifying assumptions. It is also evident, that there is a lack of simple modelling tools for this type and scale of assessment. The majority of the studies that are conducted on urban heat island mitigation are more local in their nature and make use of complex regional climate models. This kind of approach is not feasible in the time and budget frame of this particular assessment. Given these constraints, the validation of the used methodology is also difficult, as it would require comparisons with more sophisticated modelling approaches. Nevertheless, despite the various sources of uncertainty, the simple methodology used in this assessment can provide valuable insight into the magnitude of health impacts caused by heat exposure in Europe, as well as the potential and relative effectiveness of different policy options in mitigating urban heat island effect and heat exposure in cities.
