The raster dataset of urban heat island modelling shows the fine-scale (100m pixel size) temperature differences (in degrees Celsius °C) across 100 European cities, depending on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation. In the framework of the Copernicus European Health contract for the Copernicus Climate Change Service (C3S), VITO provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints). From this data set, the average urban heat island intensity is mapped for the summer season (JJA), which is the standard way of working in the scientific literature (e.g. Dosio, 2016). The UHI is calculated by subtracting the rural (non-water) spatial P10 temperature value from the average temperature map. The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This vector dataset shows the Urban Heat Island (UHI) intensity (in degrees Celsius °C) for 100 European cities, based on their elevation above sea level, land use, soil sealing, vegetation index and anthropogenic heat flux. The Urban Heat Island intensity exacerbates high temperatures in cities and thus may pose additional risks to human thermal comfort and health. The UHI intensity is represented by spatial P90 (90th percentile) urban heat island intensity of a given city ("P90" field in the dataset). This indicator is calculated by subtracting the rural (non-water) spatial P10 (10th percentile) temperature value from the average, height-corrected (to exclude terrain effects), air temperature map. This indicator represents the specific exposure of single cities and due to the height correction will be comparable across Europe. The dataset has been created by VITO within the Copernicus Health contract for C3S and is based on UrbClim model (De Ridder et al. 2015). The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This metadata refer to the dataset presenting the annual change in the estimated West Nile Virus transmission risk between 1950 and 2020 by country. The risk varies between 0 (no risk) and 1 (very high risk). This indicator uses machine learning models incorporating WNV reported cases and climate variables (temperature, precipitation) to estimate WNV transmission probability. West Nile virus is a climate-sensitive multi-host and multi-vector pathogen. Human infection is associated with severe disease risk and death. In the past few decades, European countries have had a large increase in the intensity, frequency, and geographical expansion of West Nile virus outbreaks. The 2018 outbreak has been the largest yet, with 11 European countries reporting 1584 locally acquired infections. Increasing ambient temperatures are increasing the vectorial capacity of the Culex mosquito vector, and thus increasing the outbreak probability.
This vector dataset provides the climate suitability index values (0-100%) for tiger mosquito (Aedes albopictus) for 100 European cities for the years 2008-2009 (P90 - 90th percentile). Aedes Albopictus has become a common occurrence in Southern Europe and transmits diseases such as Zika, dengue and chikungunya. The climatic suitability for tiger mosquito depends on factors such as sufficient amounts of rainfall, high summer temperatures and mild winters. Climate change is anticipated to further facilitate the spread of tiger mosquitoes across Europe by changing temperature and precipitation patterns, thereby increasing the suitable habitat. In the framework of the Copernicus Climate Change Service (C3S) SIS European Health, VITO (https://vito.be/en) has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this dataset, this climate suitability dataset has been generated based on annual precipitation and the average temperature in January and during the summer period (months June, July and August) for the years 2008-2009, following the methodology by European Centre for Disease Prevention and Control (ECDC, 2009). This approach considers empirical suitability functions, which link a number of (aggregated) climate variables to the suitability of a habitat for a given vector species, e.g. for a species to be active a minimum threshold of temperature is required below which the species is not active. Similarly some species cannot overwinter if the winter is too cold (e.g. January temperature lower than a given value). The P90 indicator represents the specific exposure of single cities and is independent of the model domain or size of a city. The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This metadata refer to the dataset presenting the annual change in the number of months suitable for the transmission of the Plasmodium vivax parasite causing malaria. The suitable months are those with precipitation above 80 mm, average temperature between 14.5°C and 33°C, and relative humidity above 60%, in land types highly suitable for Anopheles mosquitoes.
This metadata refer to the dataset presenting the annual change in heatwave exposure of people over 65, expressed as the deviation in annual person-days of heatwave exposure relative to the 1986-2005 baseline. Heat exposure poses acute health risks, particularly to older people (ie, people older than 65 years), people with underlying, chronic respiratory, kidney, or heart disease, people living in urban areas, and people with little means to access cooling mechanisms. These heat-related health risks are of particular relevance to Europe, as the continent is experiencing ageing populations, urbanisation, and a high prevalence of chronic diseases.
This metadata refer to the dataset presenting the annual change in the basic reproduction number (R0) for zika transmission in the period 1951-2021. The basic reproduction number of zika from Aedes mosquitos is calculated using a model to capture the influence of temperature and rainfall on mosquito vectorial capacity and mosquito abundance, and overlaying it with human population density data to estimate the R0 (i.e., the expected number of secondary infections resulting from one infected person).
This raster dataset provides the modelling of the climate suitability index values (0-100%) for tiger mosquito (Aedes albopictus) for 100 European cities for the years 2008-2009, with a resolution of 100 m. Aedes Albopictus has become a common occurrence in Southern Europe and transmits diseases such as Zika, dengue and chikungunya. The climatic suitability for tiger mosquito depends on factors such as sufficient amounts of rainfall, high summer temperatures and mild winters. Climate change is anticipated to further facilitate the spread of tiger mosquitoes across Europe by changing temperature and precipitation patterns, thereby increasing the suitable habitat. In the framework of the Copernicus Climate Change Service (C3S) SIS European Health, VITO has provided to the Climate Data Store 100m resolution hourly temperature data for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). From this dataset, this climate suitability dataset has been generated based on annual precipitation and the average temperature in January and during the summer period (months June, July and August) for the years 2008-2009, following the methodology by European Centre for Disease Prevention and Control (ECDC, 2009). The 100 European cities for the urban simulations were selected based on user requirements within the health community.
This metadata refer to the dataset presenting the annual change in the basic reproduction number (R0) for dengue transmission in the period 1951-2021. The basic reproduction number of dengue from Aedes mosquitos is calculated using a model to capture the influence of temperature and rainfall on mosquito vectorial capacity and mosquito abundance, and overlaying it with human population density data to estimate the R0 (i.e., the expected number of secondary infections resulting from one infected person).
The grid is based on proposal at the 1st European Workshop on Reference Grids in 2003 and later INSPIRE geographical grid systems.