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Ozone and particulate matter PM2.5 are co-pollutants that have long beenassociated with increased public health risks. Information on concentrationlevels for both pollutants come from two sources: monitoring sites and outputfrom complex numerical models that produce concentration surfaces over largespatial regions. In this paper, we offer a fully-model based approach forfusing these two sources of information for the pair of co-pollutants which iscomputationally feasible over large spatial regions and long periods of time.Due to the association between concentration levels of the two environmentalcontaminants, it is expected that information regarding one will help toimprove prediction of the other. Misalignment is an obvious issue since themonitoring networks for the two contaminants only partly intersect and becausethe collection rate for PM2.5 is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2010), we introduce a bivariatedownscaler that provides a flexible class of bivariate space-time assimilationmodels. We discuss computational issues for model fitting and analyze a datasetfor ozone and PM2.5 for the ozone season during year 2002. We show a modestimprovement in predictive performance, not surprising in a setting where we cananticipate only a small gain.

A bivariate space-time downscaler under space and time misalignment