Land use regression modelling estimating nitrogen oxides exposure in industrial south Durban, South Africa

The South Durban (SD) area of Durban, South Africa, has a history of air pollution issues due to the juxtaposition of low-income communities with industrial areas. This study used measurements of oxides of nitrogen (NOx) to develop a land use regression (LUR) model to explain the spatial variation of air pollution concentrations in this area. Ambient NOx was measured over two two-week sampling periods at 32 sites using Ogawa badges. Following the ESCAPE approach, an annual adjusted average was calculated for these results and regressed against pre-selected geographic predictor variables in a multivariate regression model. The LUR model was then applied to predict the NOx exposure of a sample of pregnant women living in South Durban. Measured NOx levels ranged from 22.3–50.9μg/m3 with a median of 36μg/m3. The model developed accounts for 73% of the variance in ambient NOx measurements using three input variables (length of minor roads within a 1000m radius, length of major roads within a 300m radius, and area of open space within a 1000m radius). Model cross validation yielded a R2 of 0.59. Subsequent participant exposure estimates indicated exposure to ambient NOx ranged from 19.9–53.2μg/m3, with a mean of 39μg/m3. This is the first study to develop a land use regression model that predicts ambient concentrations of NOx in a South African context. The findings of this study indicate that the participants in the South Durban are exposed to high levels of NOx that can be attributed mainly to traffic. [Display omitted] •Measured NOx levels were observed to be higher during the winter sampling campaign as compared to summer.•Measured and modelled mean NOx levels were highly correlated, thus illustrating the strength of the model to accurately predict exposure at un-monitored locations.•Fewer than the recommended 40 NOx sampling sites were sufficient for LUR model development for this study.•This study indicates that ambient NOx levels are strongly influenced by levels of local traffic.