Tremper, PaulRiedel, TillWohlgemuth, VolkerKranzlmüller, DieterHöb, Maximilian2023-12-152023-12-152023978-3-88579-736-4https://dl.gi.de/handle/20.500.12116/43341The estimation of pollutant exposure is highly dependent on the spatial and temporal resolution of the underlying model. This work presents a street-level Gaussian Process Regression model for urban air quality that uses a novel covariance kernel based on physical considerations to process wind information. This model can be driven by information from observations from low-cost sensor networks. We present the model, including the construction of the wind angle kernel, and discuss the inconclusive evaluation to date, the current challenges, and the way forward.enGaussian Process Regression; kriging; air quality; interpolation; wind direction; predictionText/Conference Paper10.18420/env2023-0201617-5468