Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia. (September 2017)

• Record Type: Journal Article
• Authors: Rahman, Md Mahmudur; Yeganeh, Bijan; Clifford, Sam; Knibbs, Luke D.; Morawska, Lidia
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 168
• Page End: 179
• Publication Date: 2017-09
• Subjects: Air pollution -- Land use regression (LUR) model -- Nitrogen dioxide (NO2) -- Oxides of nitrogen (NOx) -- Urban area; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.029
• Languages: English
• ISSNs: 1364-8152

Abstract

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 R 2 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. Highlights: This study developed a novel land use regression model for predicting daily average NO2 and NOx concentration. Model development was based on a forward regression method which incorporated monitoring data and predictor variables. Distance to major road, open area, residential area, and industrial were major predictor variables in the final model. Model showed reliable predictive ability and its performance was comparable to much more data demanding models.