Calibration of spatiotemporal forecasts from citizen science urban air pollution data with sparse recurrent neural networks

With their continued increase in coverage and quality, data collected from personal air quality monitors has become an increasingly valuable tool to complement existing public health monitoring systems over urban areas. However, the potential of using such "citizen science data" for automatic early warning systems is hampered by the lack of models able to capture the high-resolution, nonlinear spatiotemporal features stemming from local emission sources such as traffic, residential heating and commercial activi-ties. In this work we propose a machine-learning approach to forecast high-frequency spatial fields which has two distinctive advantages from standard neural network methods in time: (1) sparsity of the neural network via a spike-and-slab prior and (2) a small parametric space. The introduction of stochastic neural networks generates additional uncertainty, and in this work we propose a fast approach for ensure that the forecast is correctly assessed (calibration), both marginally and spatially. We focus on assessing exposure to urban air pollution in San Francisco, and our results suggest an improvement of over 58% in the mean squared error over standard time-series approach with a calibrated forecast for up to five days.