Adam A. Szpiro, Ph.D. Senior Fellow,
Department of Biostatistics, University of Washington
 
Modeling Intra-urban Variation in Air Pollution Exposure to
Assess Effects on Cardiovascular Health

Abstract

In order to estimate the long-term effect of air pollution on
cardiovascular health in a cohort study, it is necessary to predict
intra-urban variation in individual exposure levels based on
relatively sparse measurements.  The U.S. EPA Air Quality System (AQS)
has only a few sites in any given city as the network is primarily
designed to assess regional air pollution levels.  To address
small-scale variation in air pollution near roads, the EPA-funded MESA
Air project is carrying out additional monitoring according to a
complex sampling design.

We consider the problem of estimating exposure to gaseous nitrogen
oxides (NOx) using a spatio-temporal Bayesian hierarchical regression
model.  Two features of our dataset present unique challenges.  First,
to accurately represent small-scale variation in traffic-related
pollution near roadway sources, we must pay close attention to the
choice of covariates and how these relate to local meteorology and
residual correlation.  Incorporating physics-based plume modeling
significantly improves the statistical properties of predictions.
Second, in order to take advantage of irregularly sampled data and to
maximize the prediction accuracy, we need a sufficiently rich
statistical model with space-time interactions in the correlation
structure.  Estimation of the model parameters depends on specialized
computational techniques.

In this talk, we describe our approach to addressing the modeling and
computational challenges outlined above, and we present some
illustrative results.