Estimating daily nitrogen dioxide level: Exploring traffic effects
Lixun Zhang, Yongtao Guan, Brian P. Leaderer, Theodore R. Holford
The Annals of Applied Statistics · 2013 · Volume 7 · Issue 3
Abstract
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.
Keywords
Air pollution, Bayesian model, EPA, longitudinal model, nitrogen dioxide
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