Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments. (August 2016)

• Record Type: Journal Article
• Authors: Gilani, Owais; Berrocal, Veronica J.; Batterman, Stuart A.
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 18(2016)
• Page Start: 24
• Page End: 37
• Publication Date: 2016-08
• Subjects: Spatial dependence -- Non-stationary covariance function -- Gaussian processes -- Mixture model -- Covariates-in-covariance function -- MCMC algorithm; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2016.03.003
• Languages: English
• ISSNs: 1877-5845

Abstract

Abstract: A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.