# Aim in Climate Change and City Pollution

arXiv: https://arxiv.org/abs/2112.15115 · HTML: https://mlsanity.com/paper/2112.15115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Convolutional-network models to predict wall-bounded turbulence from wall quantities](https://mlsanity.com/paper/2006.12483/) (2020)
- [Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks](https://mlsanity.com/paper/1912.12969/) (2019)

## Abstract (arXiv)

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.