The relationship between short-term exposure to air pollution and mortality
or morbidity has been the subject of much recent research, in which the
standard method of analysis uses Poisson linear or additive models.
Ambient concentrations of many pollutants are associated with emissions due
to human activity, such as road transport and other combustion sources. In this
paper we consider air pollution as a multi--level phenomenon within a Bayesian
hierarchical model. We examine different scales of variation in pollution
concentrations ranging from large scale transboundary effects to more localised
effects which are directly related to human activity.
In this paper, we propose a novel approach to modeling nonstationary spatial
fields. The proposed method works by expanding the geographic plane over which
these processes evolve into higher dimensional spaces, transforming and
clarifying complex patterns in the physical plane. By combining aspects of
multi-dimensional scaling, group lasso, and latent variables models, a
dimensionally sparse projection is found in which the originally nonstationary
field exhibits stationarity.