We apply the entropy-based Bayesian optimizing approach of Le and Zidek to the spatial
redesign of the extensive air pollution monitoring network operated by Metro Vancouver, in …

A geostatistical framework for joint spatiotemporal modeling of atmospheric pollution is
presented. The spatiotemporal distribution of concentration levels is modeled as a joint …

Air quality problem related to fine particulate matter (PM2. 5) in Ohio is associated with both
local emission sources and pollutants transported here from great distances. Favorable …

A growing number of physical objects with embedded sensors with typically high volume
and frequently updated data sets has accentuated the need to develop methodologies to …

Recent successful developments in Bayesian modeling and computation for large and
complex data sets have led to a step change in the analysis of space–time air pollution data …

In this article we develop a spatial predictive distribution for the ambient space–time
response field of daily ambient PM10 in Vancouver, Canada. Observed responses have a …

Statistical analyses of time-series or spatial data have been widely used to investigate the
behavior of ambient air pollutants. Because air pollution data are generally collected in a …

China experiences severe particulate matter (PM) pollution problems closely linked to its
rapid economic growth. Advancing the understanding and characterization of …

In this article we develop and compare several space–time models for hourly ambient PM10
collected in the Vancouver area. The models consist of a deterministic trend plus stochastic …