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Development of Land Use Regression models for predicting exposure to NO2 and NOx in Metropolitan Perth, Western Australia


Dirgawati, M and Barnes, R and Wheeler, AJ and Arnold, A-L and McCaul, KA and Stuart, AL and Blake, D and Hinwood, A and Yeap, BB and Heyworth, JS, Development of Land Use Regression models for predicting exposure to NO2 and NOx in Metropolitan Perth, Western Australia, Environmental Modelling and Software, 74 pp. 258-267. ISSN 1364-8152 (2015) [Refereed Article]

Copyright Statement

2015 Elsevier

DOI: doi:10.1016/j.envsoft.2015.07.008


This study developed LUR models for predicting exposure to NO2 and NOx among of 12,203 elderly men in Perth. NOx and NO2 concentrations were determined for 2-week periods in summer, autumn, and winter, from January to September 2012, at 43 sites. The LUR models were developed to predict annual average concentrations of nitric oxides based upon land use, population/household density, and traffic variables within different buffer sizes, following the procedures of the European Study of Cohort for Air Pollution Effects program. The sample mean and standard deviation of the annual average concentrations of NO2 and NOx were 10.1 5.3 mg/m3 and 18.7 11.7 mg/m3 respectively, lower than those of ESCAPE study areas. The LUR models explained 69% of the variance in NO2 and 75% variance of NOx. Both the NO2 and NOx models had similar predictors, including traffic intensity on the nearest roads, household density within-1000 m industrial activities within-5000 m, and road length within-50 m.

Item Details

Item Type:Refereed Article
Keywords:air quality, land use regression, spatial, intra urban
Research Division:Environmental Sciences
Research Group:Pollution and contamination
Research Field:Pollution and contamination not elsewhere classified
Objective Division:Environmental Management
Objective Group:Air quality, atmosphere and weather
Objective Field:Air quality
UTAS Author:Wheeler, AJ (Dr Amanda Wheeler)
ID Code:107656
Year Published:2015
Web of Science® Times Cited:25
Deposited By:Menzies Institute for Medical Research
Deposited On:2016-03-21
Last Modified:2017-11-01

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