Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia

Abstract

Land use regression models are an established method for estimating spatial variability in gaseous pollutant levels across urban areas. Existing LUR models have been developed to predict annual average concentrations of airborne pollutants. None of those models have been developed to predict daily average concentrations, which are useful in health studies focused on the acute impacts of air pollution. In this study, we developed LUR models to predict daily NO2 and NOx concentrations during 2009–2012 in the Brisbane Metropolitan Area (BMA), Australia's third-largest city. The final models explained 64% and 70% of spatial variability in NO2 and NOx, respectively, with leave-one-out-cross-validation R2 of 3–49% and 2–51%. Distance to major road and industrial area were the common predictor variables for both NO2 and NOx, suggesting an important role for road traffic and industrial emissions. The novel modeling approach adopted here can be applied in other urban locations in epidemiological studies.