AI Driven Air Quality Analysis for Health: An Experimental Review

Title

AI Driven Air Quality Analysis for Health: An Experimental Review

Creator

Samyuktaa, S.; Chandra, J.; Immanuel, Ashok

Description

Air pollution, both indoor and outdoor, was linked to 6.7 million premature deaths in 2020, including over 237,000 children under the age of 5, according to WHO. Indoor Air Pollution (IAP) is a crisis of public health that affects billions of people by exposing them to IAP pollutants like particulate matter (PM2.5), volatile organic compounds(VOCs), polycyclic aromatic hydrocarbons (PAHs), and carbon monoxide (CO). The most common cause of IAP varies from incense burning and biomass fuel to ventilation, leading to a horrific human health effect by causing respiratory disease, cardiovascular disease, sick building syndrome, and mental impairment. This review brings together evidence from various studies on the effects of indoor air quality on the environment, health, and productivity. Apart from pollutant exposure, determinants of well-being, i.e., thermal, acoustic, and visual comfort, are the subject of this article. Developments in artificial intelligence (AI), the Internet of Things (IoT), and computational modeling have revolutionized Indoor Air Quality monitoring to detect pollutants and exposures in real-time. All these technologies have the potential to intervene effectively but are intimidating through the prism of high cost, sensor calibration, and the need for large-scale epidemiological studies. To restrict indoor air pollution risks, inter-disciplinary studies need to be adopted to combine effective ventilation technologies and advanced pollutant control systems. Large-scale applications of clean fuel like solar, biogas, electricity, liquefied petroleum gas (LPG), and efficient biomass stoves need to be employed to restrict home air pollution. The present review calls for an emergent public campaign and policy intervention to enhance indoor air quality, health, and well-being. 2025 IEEE.

Source

Proceedings of the 2025 International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Intelligence (AI); Indoor Air Quality; Machine Learning (ML); Respiratory Health Risk Assessment

Coverage

Samyuktaa S., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Chandra J., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Immanuel A., Christ (Deemed to be University), Department of Computer Science, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159563-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICETCI67340.2025.11257887

https://www.scopus.com/pages/publications/105030129437?origin=resultslist