Open AccessSpatiotemporal modeling of PM 2.5 data with missing values
Author(s) -
Smith Richard L.,
Kolenikov Stanislav,
Cox Lawrence H.
Publication year - 2003
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2002jd002914
Subject(s) - missing data , covariate , interpolation (computer graphics) , term (time) , nonparametric statistics , component (thermodynamics) , computer science , mathematics , statistics , remote sensing , geography , frame (networking) , physics , telecommunications , quantum mechanics , thermodynamics
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 .