  The substantial threat of concurrent air pollutants to public health is
increasingly severe under climate change. To identify the common drivers and
extent of spatio-temporal similarity of PM2.5 and ozone, this paper proposed a
log Gaussian-Gumbel Bayesian hierarchical model allowing for sharing a
SPDE-AR(1) spatio-temporal interaction structure. The proposed model
outperforms in terms of estimation accuracy and prediction capacity for its
increased parsimony and reduced uncertainty, especially for the shared ozone
sub-model. Besides the consistently significant influence of temperature
(positive), extreme drought (positive), fire burnt area (positive), and wind
speed (negative) on both PM2.5 and ozone, surface pressure and GDP per capita
(precipitation) demonstrate only positive associations with PM2.5 (ozone),
while population density relates to neither. In addition, our results show the
distinct spatio-temporal interactions and different seasonal patterns of PM2.5
and ozone, with peaks of PM2.5 and ozone in cold and hot seasons, respectively.
Finally, with the aid of the excursion function, we see that the areas around
the intersection of San Luis Obispo and Santa Barbara counties are likely to
exceed the unhealthy ozone level for sensitive groups throughout the year. Our
findings provide new insights for regional and seasonal strategies in the
co-control of PM2.5 and ozone. Our methodology is expected to be utilized when
interest lies in multiple interrelated processes in the fields of environment
and epidemiology.
