A Bayesian multisource fusion model for spatiotemporal PM₂.₅ in an urban setting
Author(s)
Type
Journal Article
Abstract
Airborne particulate matter (PM2.5) is a major public health concern in urban environments, where population density and emission sources exacerbate exposure risks. We present a novel Bayesian spatiotemporal fusion model to estimate monthly PM2.5 concentrations over Greater London (2014–2019) at 1 km resolution. The model integrates multiple PM2.5 data sources, including outputs from two atmospheric air quality dispersion models, and predictive variables, such as vegetation and satellite aerosol optical depth, while explicitly modeling a latent spatiotemporal field. Spatial misalignment of the data is addressed through a hierarchical fusion and spatial interpolation approach to predict across the entire area. Building on stochastic partial differential equations (SPDE) within the integrated nested Laplace approximations (INLA) framework, our method introduces spatially- and temporally-varying coefficients to flexibly calibrate datasets and capture fine-scale variability. Model performance and complexity
are balanced using predictive metrics such as the predictive model choice criterion and thorough cross-validation. The best performing model shows excellent fit and robust predictive performance, enabling reliable high-resolution spatiotemporal mapping of PM2.5 concentrations with the associated uncertainty. Furthermore, the model outputs, including full posterior predictive distributions, can be used to map exceedance probabilities of regulatory thresholds, supporting air quality management and targeted
interventions in vulnerable urban areas, as well as providing refined exposure estimates of PM2.5 for epidemiological applications.
are balanced using predictive metrics such as the predictive model choice criterion and thorough cross-validation. The best performing model shows excellent fit and robust predictive performance, enabling reliable high-resolution spatiotemporal mapping of PM2.5 concentrations with the associated uncertainty. Furthermore, the model outputs, including full posterior predictive distributions, can be used to map exceedance probabilities of regulatory thresholds, supporting air quality management and targeted
interventions in vulnerable urban areas, as well as providing refined exposure estimates of PM2.5 for epidemiological applications.
Date Issued
2026-01-01
Date Acceptance
2025-12-02
Citation
Environmetrics, 2026, 37 (1)
ISSN
1180-4009
Publisher
Wiley
Journal / Book Title
Environmetrics
Volume
37
Issue
1
Copyright Statement
© 2025 The Author(s). Environmetrics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
e70065
Date Publish Online
2025-12-19