Background/aim Intra-urban air pollution has been associated with adverse health effects, such as cardiovascular or respiratory disorders. Land Use Regression (LUR) modelling is one method to describe small-scale spatial variation in air pollution levels based on several measurements and geographical predictors.

Methods The main goal of the study is to characterise and model the spatial distribution of air pollutants in three neighbourhoods in the Western Cape, South Africa. Weekly measurements of NO2 and PM2.5 were performed in these areas (Khayelitsha, Marconi-Beam and Masiphumulele) during 2015–2016. They were temporally adjusted to obtain seasonal means using routinely monitored pollution data in Cape Town region. We developed six LUR models (four seasonal and two annual averages) using supervised forward stepwise regression for NO2 and PM2.5. Predictor variables, like road, land use and emission data were either obtained or collected on site. The models were validated using leave-one-out-cross-validation (LOOCV) and were tested for spatial autocorrelation.

Results Measured air pollution levels were generally low. The annual mean NO2 levels were 21.5 µg/m3 and 10.0 µg/m3 for PM2.5. The NO2 annual model explained 45% of the variance (R2) in the study areas and was found to have a satisfactory internal validity (LOOCV R2=70%). The PM2.5 annual model presented lower explanatory power (R2=25%, LOOCV R2=13%). The best predictors for NO2 modelling were traffic-related variables (major roads and bus routes) and proximity to some land-use features. Smaller local sources such as open grills and waste burning sites were good predictors for PM2.5 spatial variability, together with population density. NO2 and PM2.5 mean exposure will be predicted for home and school locations of about 400 pupils at primary schools involved in an epidemiological health study.

Conclusion This research shows that land use regression modelling can be successfully applied to informal urban settings in South Africa using similar predictor variables to those performed in European and North American studies. We could also provide NO2 and PM2.5 seasonal exposure estimates and maps for the selected study areas.