The last two decades have seen intense scientific and regulatory
interest in the health effects of particulate matter (PM).
Influential epidemiological studies that characterize chronic
exposure of individuals rely on monitoring data that are sparse
in space and time, so they often assign the same exposure to
participants in large geographic areas and across time. We
estimate monthly PM during 1988–2002 in a large spatial domain
for use in studying health effects in the Nurses’ Health Study.
We develop a conceptually simple spatio-temporal model that uses
a rich set of covariates. The model is used to estimate
concentrations of PM10 for the full time period and
PM2.5 for a subset of the period. For the
earlier part of the period, 1988–1998, few PM2.5
monitors were operating, so we develop a simple extension to the
model that represents PM2.5 conditionally on
PM10 model predictions. In the epidemiological
analysis, model predictions of PM10 are more strongly
associated with health effects than when using simpler
approaches to estimate exposure.
Our modeling approach supports the application in estimating both
fine-scale and large-scale spatial heterogeneity and capturing
space–time interaction through the use of monthly-varying
spatial surfaces. At the same time, the model is computationally
feasible, implementable with standard software, and readily
understandable to the scientific audience. Despite simplifying
assumptions, the model has good predictive performance and
uncertainty characterization.
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