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