  Air pollution constitutes the highest environmental risk factor in relation
to heath. In order to provide the evidence required for health impact analyses,
to inform policy and to develop potential mitigation strategies comprehensive
information is required on the state of air pollution. Information on air
pollution traditionally comes from ground monitoring (GM) networks but these
may not be able to provide sufficient coverage and may need to be supplemented
with information from other sources (e.g. chemical transport models; CTMs).
However, these may only be available on grids and may not capture micro-scale
features that may be important in assessing air quality in areas of high
population. We develop a model that allows calibration between multiple data
sources available at different levels of support by allowing the coefficients
of calibration equations to vary over space and time, enabling downscaling
where the data is sufficient to support it. The model is used to produce
high-resolution (1km $\times$ 1km) estimates of NO$_2$ and PM$_{2.5}$ across
Western Europe for 2010-2016. Concentrations of both pollutants are decreasing
during this period, however there remain large populations exposed to levels
exceeding the WHO Air Quality Guidelines and thus air pollution remains a
serious threat to health.
