Air Quality Monitoring in Two South African Townships: Modelling Spatial and Temporal Trends in O 3 and CO Hotspots

Aluwani Innocent Muneri, Benett Siyabonga Madonsela and Thabang Maphanga ()
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Aluwani Innocent Muneri: Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town 8000, South Africa
Benett Siyabonga Madonsela: Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town 8000, South Africa
Thabang Maphanga: Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town 8000, South Africa

Challenges, 2025, vol. 16, issue 4, 1-24

Abstract: Air quality is a key priority in environmental policy agendas worldwide, yet rapid urban growth in developing countries disproportionately affects urban air quality. In sub-Saharan Africa, the spatial and temporal dynamics of key pollutants remain underexplored. This knowledge gap limits the ability to understand how pollution hotspots emerge, how they shift over time, and how they interact with the broader planetary processes such as climate change. This study analysed the spatial distribution of ozone (O 3 ) and carbon monoxide (CO) hotspots in Diepkloof and Klieprivier townships, Johannesburg, South Africa, using data from 2019 to 2023 obtained from air quality monitoring stations. Spatial patterns were mapped using Inverse Distance Weighting (IDW) interpolation in a Geographic Information System (GIS), and meteorological influences were assessed through multiple linear regression. Results showed distinct spatial trends: Diepkloof experienced a decrease in O 3 from 23 ppb to 16 ppb, whereas Klieprivier remained stable but exhibited marked seasonal variation, peaking at 30 ppb in spring. Wind speed, wind direction, and humidity were significant predictors ( p < 0.05) of both CO and O 3 . In Klieprivier, meteorological factors explained 54.2% of O 3 variability, with temperature being the strongest predictor. These findings provide valuable insight into pollutant behaviour in urban townships and highlight the importance of integrating spatial analysis with meteorological modelling for targeted air quality management.

Keywords: air pollution; ozone; carbon monoxide; spatial analysis; urban air quality (search for similar items in EconPapers)
JEL-codes: A00 C00 Z00 (search for similar items in EconPapers)
Date: 2025
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