Aim in Climate Change and City Pollution

TL;DR

This paper reviews machine-learning methods for modeling urban air pollution, highlighting their improved accuracy and new applications like flow-dynamics and remote sensing, which enhance understanding and management of city pollution.

Contribution

It provides a comprehensive overview of machine-learning techniques applied to air pollution modeling, emphasizing their advantages over traditional methods and introducing innovative approaches.

Findings

Machine-learning improves air pollution model accuracy.

New applications include flow-dynamics and remote sensing.

Machine-learning reduces development costs of pollution models.

Abstract

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.