  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. Specifically, in the
first stage of the model, we isolate underlying patterns in pollution
concentrations due to global factors such as underlying climate and topography,
which are modelled together with spatial structure. At this stage measurements
from monitoring sites located within rural areas are used which, as far as
possible, are chosen to reflect background concentrations. Having isolated
these global effects, in the second stage we assess the effects of human
activity on pollution in urban areas. The proposed model was applied to
concentrations of nitrogen dioxide measured throughout the EU for which
significant increases are found to be associated with human activity in urban
areas. The approach proposed here provides valuable information that could be
used in performing health impact assessments and to inform policy.
