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Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO₂ and PM 2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015⁻2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO₂ and PM 2.5 were 22.1 &amp;mu;g/m³ and 10.2 &amp;mu;g/m³, respectively. The NO₂ models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R²). The PM 2.5 annual models had lower explanatory power (R² = 0.36, 0.29, and 0.29). The best predictors for NO₂ were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM 2.5 , together with population density. This study demonstrates that land-use-regression modelling for NO₂ can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM 2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO₂ and PM 2.5 seasonal exposure estimates and maps for further health studies.

Land Use Regression Modelling of Outdoor NO2 and PM2.5 Concentrations in Three Low Income Areas in the Western Cape Province, South Africa