Estimating daily nitrogen dioxide level: Exploring traffic effects

Lixun Zhang, Yongtao Guan, Brian P. Leaderer, Theodore R. Holford

Research output: Contribution to journalArticle

4 Scopus citations

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 (NO2) at a geographic location. Monthly NO2 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 NO2. Finally, the Connecticut Department of Transportation provides data on traffic density on major roadways, a primary contributor to NO2 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 NO2 over a region.

Original languageEnglish (US)
Pages (from-to)1763-1777
Number of pages15
JournalAnnals of Applied Statistics
Volume7
Issue number3
DOIs
StatePublished - Sep 1 2013

Keywords

  • Air pollution
  • Bayesian model
  • EPA
  • Longitudinal model
  • Nitrogen dioxide

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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