A Region-Based Model for Estimating Urban Air Pollution

Air pollution has a direct impact to human health, and data-driven air quality models are useful for evaluating population exposure to air pollutants. In this paper, we propose a novel region-based Gaussian process model for estimating urban air pollution dispersion, and applied it to a large dataset of ultrafine particle (UFP) measurements collected from a network of sensors located on several trams in the city of Zurich. We show that compared to existing grid-based models, the regionbased model produces better predictions across aggregates of all time scales. The new model is appropriate for many useful user applications such as exposure assessment and anomaly detection.