Aim in Climate Change and City Pollution
This is an incremental review for researchers and practitioners in environmental science and urban planning.
The chapter reviews machine-learning methods for modeling urban air pollution, highlighting their ability to increase accuracy and reduce development costs compared to traditional approaches.
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.