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A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa
Journal
Environ Pollut
Volume
274
Pages / Article-Number
116513
Keywords
Air pollution; Dispersion modelling; Hybrid modelling; Land use regression; competing financial interests or personal relationships that could have appeared; to influence the work reported in this paper.
Abstract
The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO2, SO2, and PM10 concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO2 and SO2 were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM10 were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO2 models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO2 models were less robust with lower R(2) annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R(2) for PM10 models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.
