  In air pollution studies, dispersion models provide estimates of
concentration at grid level covering the entire spatial domain, and are then
calibrated against measurements from monitoring stations. However, these
different data sources are misaligned in space and time. If misalignment is not
considered, it can bias the predictions. We aim at demonstrating how the
combination of multiple data sources, such as dispersion model outputs, ground
observations and covariates, leads to more accurate predictions of air
pollution at grid level. We consider nitrogen dioxide (NO2) concentration in
Greater London and surroundings for the years 2007-2011, and combine two
different dispersion models. Different sets of spatial and temporal effects are
included in order to obtain the best predictive capability. Our proposed model
is framed in between calibration and Bayesian melding techniques for data
fusion red. Unlike other examples, we jointly model the response (concentration
level at monitoring stations) and the dispersion model outputs on different
scales, accounting for the different sources of uncertainty. Our
spatio-temporal model allows us to reconstruct the latent fields of each model
component, and to predict daily pollution concentrations. We compare the
predictive capability of our proposed model with other established methods to
account for misalignment (e.g. bilinear interpolation), showing that in our
case study the joint model is a better alternative.
