A Bayesian spatio-temporal model for estimating daily nitrogen dioxide (NO2) levels is described. The model uses two datasets with different temporal resolutions. The dataset from the Study of Traffic, Air quality and Respiratory health in children (STAR) contains NO2 measurements at a relatively large number of sites (most of which are in the state of Connecticut) but for one month in each season over a year. The dataset from the U.S. Environmental Protection Agency (EPA) contains measurements on an hourly level but only at a limited number of sites (four sites in Connecticut). The modal first establishes relationship between STAR observations and EPA observations on the monthly level (the duration-level of the STAR study). The relationship is then assumed to hold at the daily level and thus daily NO 2 levels at the STAR study sites can be estimated from the average daily NO2 levels at the EPA sites. The model can also provide predictions of daily NO2 levels at random sites. The model performed well (R 2 > 0.7) and two important implications follow. First, daily pollution information as estimated by the model makes it possible to study the relationship between pollution and pollution-related symptoms such as childhood asthma severity in a more meaningful way. Second, the model offers significant cost reduction on future studies of pollution levels: the readily available pollutant information at EPA monitoring sites and the observations at some sites can be used to make predictions over a long period and at random sites. The model is implemented under the Bayes framework with a Gibbs sampler.
|Advisor:||Holford, Theodore R.|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 72/10, Dissertation Abstracts International|
|Subjects:||Biostatistics, Statistics, Environmental science|
|Keywords:||Air quality, Longitudinal models, Nitrogen dioxide, Respiratory health, Space-time|
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