How urbanisation alters the intensity of the urban heat island in a tropical African city

A review of urban heat island mapping approaches with a special emphasis on the Indian region

With an estimated population of about 6 billion living in urban areas by 2050, controlling the rate of urbanization is one of the major challenges governments face in the twenty-first century. Increasing urbanization is problematic since it causes heat accumulation within urban areas due to the enhanced anthropogenic activities. With developing countries like India aiming for their economic growth, combating urban heat islands (UHIs) is a critical step in improving the quality of life. Efficient mitigation of UHI effects requires accurate mapping and monitoring, which can be enhanced through the use of machine learning and deep learning algorithms. This paper provides a critical review of UHI mapping approaches employed globally, with a particular emphasis on the Indian region along with a focus on AI-based methods. Key issues, challenges, and future research directions are also discussed.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Tropical surface urban heat islands in east Africa

The horn of Africa is susceptible to droughts because of the persistent heat waves and insufficient precipitation. The growth of urban population and built-up urban environments exacerbate the overheating problems due to the urban heat island effects. Understanding the impacts of anthropogenic activities in such dry environments is important to control or mitigate extreme heat leading to droughts. This is required to preserve soil moisture, pothole waters, lakes and rivers that are required for pasture and drinking water. Nonetheless, the intensity and duration of the urban heat island effects have not been investigated in this region resulting in the underestimation of the intensity and severity of the extreme heat events. This study therefore performs the quantitative analyses of the intensity, duration and causality of the tropical surface urban heat islands (TSUHIs) for the first time using earth observation information at a regional to local scale. It also identifies the factors that control TSUHIs, considering background climate, population, vegetation and the impervious urban fractions. Results showed that the TSUHI in the capital cities of tropical east Africa varies from 1 [Formula: see text]C in Dodoma to 4 [Formula: see text]C in Kampala and reaches up to 8 [Formula: see text]C in Khartoum. The mean temperature contribution to regional climate from 2000 to 2020 is 0.64 [Formula: see text]C during the day and 0.34 [Formula: see text]C during the night, a mean total of around 0.5 [Formula: see text]C, a 0.25 [Formula: see text]C increase per decade. This is a quarter of the increase in global surface temperature, which is [Formula: see text] 1.09 [Formula: see text]C from 2011 to 2020 compared to the 1850-1900 level. Most of these capital cities in this region exhibited high TSUHIs from late summer to winter and are dependent on mainly population, vegetation, evapotranspiration and soil moisture in different proportions. This urban induced additional temperature has been intensifying droughts in tropical east Africa. Therefore, urban planners are advised to consider the impacts of TSUHIs to reduce the severity of droughts in the tropical east Africa region.

Urban heat in Johannesburg and Ekurhuleni, South Africa: A meter-scale assessment and vulnerability analysis

Heat stress is an important threat for human health and urban areas are affected at higher rates compared to rural environments. Additionally, climate change will increase the vulnerability towards urban heat stress in the future. Current high-resolution urban heat stress assessments are limited in time and space due to the high computational costs. In this paper, the UrbClim numerical model is used to simulate urban heat accurately at a fast rate and high spatial resolution for the cities of Johannesburg and Ekurhuleni, South Africa. Using detailed terrain information, (future) urban heat stress assessments are provided at 30 m resolution for both city agglomerations, while meter-scale simulations are executed for a selection of neighborhoods. These model simulations are evaluated using an extensive monitoring campaign in which the local community was heavily engaged. Distinct spatial differences in the urban heat island effect are observed, with greatest heat stress in areas with high building densities and low vegetation numbers. These areas are often characterized by lower socio-economic living conditions. The meter-scale analysis further shows the importance of shade provided by vegetation to lower heat stress in both present and future climate. These assessments offer assistance in the design of climate-resilient urban planning strategies.

Suspect screening to support source identification and risk assessment of organic micropollutants in the aquatic environment of a Sub-Saharan African urban center

Organic micropollutants (OMPs) are contaminants of global concern and have garnered increasing attention in Africa, particularly in urban and urbanizing areas of Sub-Saharan Africa (SSA). In this work, we coupled suspect screening enabled by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with multivariate analysis to characterize OMPs in wastewater, surface water, and groundwater samples collected from Kampala, the capital and largest city of Uganda. Suspect screening prioritized and confirmed 157 OMPs in Kampala samples for target quantification. Many OMPs detected in Kampala samples occurred within concentration ranges similar to those documented in previous studies reporting OMP occurrence in SSA, but some have never or rarely been quantified in environmental water samples from SSA. Hierarchical cluster analysis established the source-related co-occurrence profiles of OMPs. Partial least squares regression and multiple linear regression analyses further pinpointed the concentration of nitrate and the content of a fluorescent organic matter component with excitation/emission maxima around 280/330 nm as predictors for the sample-specific cumulative concentrations of OMPs, suggesting the likely contribution of diffuse runoff and wastewater discharges to OMP occurrence in the aquatic environment of Kampala. Parallel calculations of exposure-activity ratios and multi-substance potentially affected fractions provided insights into the potential for biological effects associated with OMPs and highlighted the importance of expanded analytical coverage for screening-level risk assessments. Overall, our study demonstrates a versatile database-driven screening and data analysis methodology for the multipronged characterization of OMP contamination in a representative SSA urban center.