Accurate and timely air quality monitoring relies on data integration methods that bridge the spatial coverage of satellite imagery and the temporal granularity of low-cost sensors (LCSs). LCSs provide high-frequency measurements but are prone to noise, while satellite data offer extensive coverage but suffer from coarse temporal resolution and retrieval uncertainties. To address these limitations, we present a novel probabilistic data fusion framework rooted in generic Bayesian filtering. Our approach employs Kalman Filters (KFs) for dynamic state estimation and uncertainty quantification. We enrich the KF state estimation with covariates generated by Land Use Regression (LUR) to incorporate local spatial context.

We fuse nitrogen dioxide (NO2) data from low-cost sensor networks with satellite-derived aerosol optical depth (AOD) measurements from Sentinel-5P, using ground reference data for calibration and validation. Evaluated in the Dublin City area, the preliminary results demonstrate a significant reduction in bias and improved accuracy and precision of air quality estimates.

This framework addresses critical challenges in multi-source data integration, including a lack of a consistent model, resolution mismatches, noise propagation, and bias correction. By bridging global and local scales, it provides actionable insights for air quality management and environmental policy. Our case study in urban environments highlights our framework’s potential for scalable applications in public health and environmental monitoring elsewhere.