Open Access
September 2013 Estimating daily nitrogen dioxide level: Exploring traffic effects
Lixun Zhang, Yongtao Guan, Brian P. Leaderer, Theodore R. Holford
Ann. Appl. Stat. 7(3): 1763-1777 (September 2013). DOI: 10.1214/13-AOAS642


Data used to assess acute health effects from air pollution typically have good temporal but poor spatial resolution or the opposite. A modified longitudinal model was developed that sought to improve resolution in both domains by bringing together data from three sources to estimate daily levels of nitrogen dioxide ($\mathrm{NO} _{2}$) at a geographic location. Monthly $\mathrm{NO} _{2}$ measurements at 316 sites were made available by the Study of Traffic, Air quality and Respiratory health (STAR). Four US Environmental Protection Agency monitoring stations have hourly measurements of $\mathrm{NO} _{2}$. Finally, the Connecticut Department of Transportation provides data on traffic density on major roadways, a primary contributor to $\mathrm{NO} _{2}$ pollution. Inclusion of a traffic variable improved performance of the model, and it provides a method for estimating exposure at points that do not have direct measurements of the outcome. This approach can be used to estimate daily variation in levels of $\mathrm{NO} _{2}$ over a region.


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Lixun Zhang. Yongtao Guan. Brian P. Leaderer. Theodore R. Holford. "Estimating daily nitrogen dioxide level: Exploring traffic effects." Ann. Appl. Stat. 7 (3) 1763 - 1777, September 2013.


Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 06237196
MathSciNet: MR3127967
Digital Object Identifier: 10.1214/13-AOAS642

Keywords: Air pollution , Bayesian model , EPA , longitudinal model , nitrogen dioxide

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 3 • September 2013
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