Spatio-temporal fusion of reanalysis and in situ data for censored threshold exceedances of PM2.5

Abstract

Data fusion models are widely used in air quality monitoring to integrate in situ and large-scale gridded products, offering spatially complete and temporally detailed estimates. However, traditional Gaussian-based models often underestimate extreme pollution values, leading to biased risk assessments. To address this, we present a Bayesian hierarchical data fusion framework rooted in extreme value theory, using the Dirac-delta generalised Pareto distribution to jointly account for threshold and non-threshold exceedances while preserving the timing of exceedance and non-exceedance episodes. Our model is used to describe and predict censored threshold exceedances of PM2.5 pollution in the Greater London region by using CAMS atmospheric composition reanalysis, and in situ observation stations from the automatic urban and rural network (AURN) run by the UK government. Key features of our approach include combining data with varying spatio-temporal resolutions and fully accounting for parameter uncertainties. Results show that our model outperforms Gaussian-based alternatives and standalone reanalysis data in predicting threshold exceedances at the majority of observation sites and can even result in improved spatial patterns of PM2.5 pollution than those discernible from the background data. Moreover, our approach captures greater variability and spatial patterns, such as higher PM2.5 concentrations near coastal areas, which are not evident in the reanalysis data alone.