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With their continued increase in coverage and quality, data collected frompersonal air quality monitors has become an increasingly valuable tool tocomplement existing public health monitoring systems over urban areas. However,the potential of using such `citizen science data' for automatic early warningsystems is hampered by the lack of models able to capture the high resolution,nonlinear spatio-temporal features stemming from local emission sources such astraffic, residential heating and commercial activities. In this work, wepropose a machine learning approach to forecast high frequency spatial fieldswhich has two distinctive advantages from standard neural network methods intime: 1) sparsity of the neural network via a spike-and-slab prior, and 2) asmall parametric space. The introduction of stochastic neural networksgenerates additional uncertainty, and in this work we propose a fast approachfor ensure that the forecast is correctly assessed (calibration), bothmarginally and spatially. We focus on assessing exposure to urban air pollutionin San Francisco, and our results suggest an improvement of over 58% in themean squared error over standard time series approach with a calibratedforecast for up to 5 days.

Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks