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Towards Fine-Grained Sensor-Based Probabilistic Individual Air Pollution Exposure Prediction using Wind Information
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Datum
2023
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
The 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.