Geographically weighted Urban Heat Island modelling using the Netatmo sensors

Abstract

The continuously increasing population within cities imposes the future challenges related to planning and managing the sustainable environment, where people’s health and wellbeing is prioritized. Currently more than half of the world’s population live in cities which results in the rise of the human footprint, affecting the local climate. Simultaneously the planet’s climate is changing towards less predictable weather conditions with high extremes, e.g., heavy rainfalls, followed by long dry periods or severe heatwaves. Frequently extreme weather conditions are associated with flooding and hurricanes, while heatwaves represent an equally important danger for the health of city’s citizens.
UHI has been recognized as one of the leading environmental issues of the 21st century. The UHI is defined as the area within a city with the higher surface or air temperatures compared to its surroundings. The higher urban temperatures are resulting in health-related issues among the population, greater energy demands, and various economic losses. Thus, the current work is focusing on researching the air temperatures in the urban canopy layer, which are mainly affected by the heat radiated from the urban surfaces during the night. Moreover, there are differences in the heat exchange phases between the neighborhoods, which are caused by the heterogenicity of the morphological characteristics of the city. Such information can be of great use for the development of UHI mitigation strategies, but it is currently very sparse or not detailed enough. Therefore this master thesis investigates the possibility for generation of more detailed models, taking into account the intraurban variability. For the development of the statistical models explaining the UHI effect in the city of the Hague different spatial and sensor datasets have been used. The data about the temperatures in the city for 2017 have been collected with the means of the Netatmo weather stations. Additionally, different spatial representations and their effect on the statistical analysis have been investigated. In combination with the different spatial models, six distinct UHI contributing factors have been researched, namely the Buildings density, the Land cover index, the Vegetation index, the Sky View Factor, the Non-permeable surfaces in the city and the Vehicle traffic density. These variables have been calculated and utilized in the statistical analysis of their relationship with the air temperatures in the Hague. The results indicated a weak relationship between the air temperatures and the different spatial characteristics of the city where only the Sky View Factor and the Non-permeable surfaces proved to be statistically significant variables.