Practical large-scale spatio-temporal modeling of particulate matter concentrations

Christopher J. Paciorek, Jeff D. Yanosky, Robin C. Puett, Francine Laden, Helen H. Suh

Research output: Contribution to journalArticle

58 Scopus citations

Abstract

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 PM 10 for the full time period and PM 2.5 for a subset of the period. For the earlier part of the period, 1988-1998, few PM 2.5 monitors were operating, so we develop a simple extension to the model that represents PM 2.5 conditionally on PM 10 model predictions. In the epidemiological analysis, model predictions of PM 10 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.

Original languageEnglish (US)
Pages (from-to)370-397
Number of pages28
JournalAnnals of Applied Statistics
Volume3
Issue number1
DOIs
StatePublished - Mar 1 2009

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All Science Journal Classification (ASJC) codes

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

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