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Inferring air quality from a limited number of observations is an essentialtask for monitoring and controlling air pollution. Existing inference methodstypically use low spatial resolution data collected by fixed monitoringstations and infer the concentration of air pollutants using additional typesof data, e.g., meteorological and traffic information. In this work, we focuson street-level air quality inference by utilizing data collected by mobilestations. We formulate air quality inference in this setting as a graph-basedmatrix completion problem and propose a novel variational model based on graphconvolutional autoencoders. Our model captures effectively the spatio-temporalcorrelation of the measurements and does not depend on the availability ofadditional information apart from the street-network topology. Experiments on areal air quality dataset, collected with mobile stations, shows that theproposed model outperforms state-of-the-art approaches.

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference