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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D24, 9004, doi:10.1029/2002JD002914, 2003

Spatiotemporal modeling of PM_2.5 data with missing values

Richard L. Smith

Department of Statistics, University of North Carolina, Chapel Hill, North Carolina, USA

Stanislav Kolenikov

Department of Statistics, University of North Carolina, Chapel Hill, North Carolina, USA

Lawrence H. Cox

National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland, USA

Abstract

We propose a method of analyzing spatiotemporal data by decomposition into deterministic nonparametric functions of time and space, linear functions of other covariates, and a random component that is spatially, though not temporally, correlated. The resulting model is used for spatial interpolation and especially for estimation of a spatially dependent temporal average. The results are applied to part of the PM_2.5 network established by the U.S. Environmental Protection Agency, covering three southeastern U.S. states. A novel feature of the analysis is a variant of the expectation-maximization algorithm to account for missing data. The results show, among other things, that a substantial part of the region is in violation of the proposed long-term average standard for PM_2.5.

Received 5 September 2002; accepted 25 March 2003; published 25 October 2003.

Index Terms: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0345 Atmospheric Composition and Structure: Pollution—urban and regional (0305); 3210 Mathematical Geophysics: Modeling.

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Citation: Smith, R. L., S. Kolenikov, and L. H. Cox (2003), Spatiotemporal modeling of PM_2.5 data with missing values, J. Geophys. Res., 108(D24), 9004, doi:10.1029/2002JD002914.

Copyright 2003 by the American Geophysical Union.