  The sustainability of urban environments is an increasingly relevant problem.
Air pollution plays a key role in the degradation of the environment as well as
the health of the citizens exposed to it. In this chapter we provide a review
of the methods available to model air pollution, focusing on the application of
machine-learning methods. In fact, machine-learning methods have proved to
importantly increase the accuracy of traditional air-pollution approaches while
limiting the development cost of the models. Machine-learning tools have opened
new approaches to study air pollution, such as flow-dynamics modelling or
remote-sensing methodologies.
