Aim in Climate Change and City Pollution

12/30/2021
by   Pablo Torres, et al.
17

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.

READ FULL TEXT
research
08/31/2021

Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

Air pollution is a major driver of climate change. Anthropogenic emissio...
research
01/31/2012

Ontologies for the Integration of Air Quality Models and 3D City Models

The holistic approach to sustainable urban planning implies using differ...
research
02/11/2022

A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data

Analyzing air pollution data is challenging as there are various analysi...
research
04/21/2021

Gross polluters and vehicles' emissions reduction

Vehicles' emissions produce a significant share of cities' air pollution...
research
11/17/2022

Air pollution models in epidemiologic studies with geostatistics and machine learning

Development of air pollution models for large regions is a priority for ...
research
06/26/2021

Mining atmospheric data

This paper overviews two interdependent issues important for mining remo...
research
08/09/2022

Inferring the heritability of bacterial traits in the era of machine learning

Quantification of heritability is a fundamental aim in genetics, providi...

Please sign up or login with your details

Forgot password? Click here to reset