Bayesian geostatistical modelling of high-resolution [Formula: see text] exposure in Europe combining data from monitors, satellites and chemical transport models

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide ([Formula: see text]) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged [Formula: see text] concentrations at 1 [Formula: see text] spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of [Formula: see text] derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration’s (NASA’s) Aura satellite were converted to near ground [Formula: see text] concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° [Formula: see text] 0.625° spatial resolution and surface-to-column [Formula: see text] ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° [Formula: see text] 0.1° were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar [Formula: see text] values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface [Formula: see text] estimates, showing that, in 2016, 16.17 (95% C.I. 6.34–29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for [Formula: see text]. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to [Formula: see text] in 2016.