High-resolution spatio-temporal estimation of street-level air pollution using mobile monitoring and machine learning

High spatio-temporal resolution street-level air pollution (SLAP) estimation is essential for urban air quality management, yet traditional methods face significant challenges in capturing the detailed spatial and temporal variability of pollution. Methods relying on fixed monitoring networks provide limited spatial coverage, while those utilizing mobile monitoring campaigns, despite their flexibility, often suffer from data sparsity and temporal incompleteness. To address these limitations, we propose a Two-Step Machine Learning Gap-Filling Framework employing a Multi-task Graph-based XGBoost (MTGXGB) model to enhance SLAP resolution. This framework expands high-resolution pollution estimation from a purely spatial perspective to a spatio-temporal view and effectively addresses data gaps. Our approach achieves spatial resolutions of 30-200 m and hourly temporal resolutions, capturing both short- and long-term variations in PM2.5 concentrations. Applying this framework to London's urban environment, we identify critical pollution hotspots and uncover correlations between SLAP, traffic speed, and urban environmental features. Additionally, the derived uncertainty maps provide actionable insights for optimizing mobile monitoring strategies. This study advances machine learning methodologies for spatio-temporal SLAP estimation and highlights the potential of high-resolution spatio-temporal SLAP data to inform policy-making, such as Low Emission Zones (LEZs), thereby demonstrating its practicality and scalability for urban air quality management.