  Air pollution kills around 7 million people annually, and approximately 2.4
billion people are exposed to hazardous air pollution. Accurate, fine-grained
air quality (AQ) monitoring is essential to control and reduce pollution.
However, AQ station deployment is sparse, and thus air quality inference for
unmonitored locations is crucial. Conventional interpolation methods fail to
learn the complex AQ phenomena. This work demonstrates that Deep Gaussian
Process models (DGPs) are a promising model for the task of AQ inference. We
implement Doubly Stochastic Variational Inference, a DGP algorithm, and show
that it performs comparably to the state-of-the-art models.
