Urban Heat Island: Spatiotemporal Patterns and Implications for Human Thermal Comfort in Kampala
This project will develop tools to facilitate the real-time analysis of the urban heat island (UHI) effect and flooding in Kampala city, and then extend this application to other rapidly urbanising regions of Uganda. The project will use approaches that cut across energy, telecommunication and ICTs, and environmental sciences. A network of sensors and gateways will be deployed to aid in the real-time collection and analysis of environmental data which will be used to characterise the UHI and ooding. We will apply machine learning-based techniques on the collected data to generate accurate insights. The network nodes will use well-dimensioned renewable energy sources to protect the environment from greenhouse gas emissions associated with non-renewable energy sources. For comparison purposes, we will deploy sensors in Mbarara and Gulu to collect data and characterise their UHI. However, in this pilot phase for Gulu and Mbarara, the data will be logged on memory and collected physically. In future phases of the project, the smart city network will be extended to these and other urban areas of interest and will be combined with spatial modelling of future urban climatic conditions using Geographical Information Systems (GIS).
Over 70% of Africa’s urban growth is expected to happen in small and mid-sized cities. Often, municipal services cannot keep up with the settlement process, leading to unorganised informal developments that are deprived of environmental quality. Uganda is experiencing rapid population growth and spiralling rural-urban migration. In addition, Kampala is rapidly urbanising at a rate of 5.2% each year, and 70% of its inhabitants live in informal settlements. As a result, Kampala’s vegetation and wet land cover have largely been replaced by an irregular morphology of informal developments. Kampala’s green infrastructure an interconnected network of multi-functional green spaces that controls oods, disperses air pollutants and regulates thermal comfort, has reduced signicantly. This has increased the frequency of ooding, worsened air quality (increased dust and pollutants), and signicantly increased thermal discomfort due to the rising temperatures. It is also noticeable that most of the ooding happens in informal settlements. All these are characteristics of a worsening UHI, yet there is lack of data to characterise the spatiotemporal variation of the UHI in Kampala. The current urbanisation practices in Kampala have accelerated climate change, caused negative implications for public health, and limited work productivity due to thermal stress, leading to economic losses.
To inform and change the status-quo, we propose a solution that combines strong scientic knowledge through innovative data-based research, and a ransdisciplinary approach that relies on cutting-edge technology such as renewable energies, machine learning and the Internet of Things. Given the lack of enough data to characterise the UHI effects in Kampala, we will deploy a pilot low-power low-cost LoRaWAN-based wireless sensor network to create a ‘smart city’ platform for data collection. The network will cover locations that typify the variability in urban green infrastructure in Kampala city for collection of real-time open data on UHI and ooding. The collected data will be analysed to quantify the benefit of the current vegetation cover in Kampala and identify Kampala’s potential locations of revegetation. Machine learning techniques will be applied to derive denitive outcomes. We will analyse the relationship between the UHI and the urbanisation structure and analyse the current and future UHI and flooding phenomena in Kampala and Mbarara cities. To create a lasting and sustainable environment, we will train students in some primary and secondary schools in a citizen science initiative scheme aimed at knowledge transfer on the use of sensor communications and assessment of environmental quality.
The Multi-Year project funded by Makerere University under the Research Innovation Fund (RIF). The Principal Investigator on the project is Dr. Edwin Mugume and the project team is comprised of:
Dr Peter Kabano
Dr Maximum Byamukama
Mr Moses Kalema
Mr Grace Namugalu
Ms Olivia Nakayima
Mr Alexander Muhangi
