URBAN HEAT ISLAND: CAUSES, IMPACTS, AND MITIGATION STRATEGIES

Abstract

The Urban Heat Island (UHI) is a significant environmental issue in fast urbanizing cities that contributes to elevation of temperature, health hazards and economic cost setbacks. The thesis explores the spatial distribution, socioeconomic vulnerability, and effects of UHI in London, especially using the heatwave in 2022, as an example. The article combines satellite-based land surface temperature (LST), climate models (UrbClim) and demographic evidence to assess heat hotspots and determine the risks. The study employs geospatial analysis of the Landsat 8 and 9 thermal imagery (30 m resolution) with the UrbClim air temperature data (250 m resolution) to research spatial temperature changes. The statistics that were obtained by the analysis presented the possibility of significant variation in the thermal range, with a mean of the nighttime temperature of 29.43C and a temperature deviation at 2.75C across the boroughs. The vegetation cover and impervious surfaces emerged to be paramount in the local temperature variations, as evidenced in the comparison of Kilburn and the Regent Parks, in which a 7°C nighttime difference was identified. An intersectional approach was used to examine heat exposure and demographic vulnerability (age groups) and socioeconomic deprivation that was based on a triple jeopardy framework. Findings indicate that the high-risk areas are clustered between the boroughs that have high population density, overcrowding levels, and income deprivation. The analysis of factors also justifies the existence of multiple underlying dimensions with strong contributions made by environmental and economic variables to the risk of urban heat. Health and economic implications of UHI are also discussed in the study. The statistics reveal that the national death rates per borough (those related to heat) sum up to 23, which is around 50 per cent caused by UHI. Economic evaluation indicates the average total expenditure of £259.7 million, both in healthcare costs and productivity losses. The regression analysis was found statistically useless, as it is affected by the multicollinearity; hence, the results were based on the descriptive and exploratory approaches.