Staff View: Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring

Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring

Traditional methods for detecting harmful gases in air are often limited in their widespread deployment, accuracy, and real-time monitoring capabilities due to their complexity and cost. To address this challenge, optimization algorithms such as the Particle Swarm Optimization (PSO) algorithm have s...

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Main Authors:	Shalaby A.M., Othman N.S., Shalaby M.
Other Authors:	57219433216
Format:	Article
Published:	Elsevier B.V. 2025
Subjects:	Air quality Carbon dioxide Carbon monoxide Chirp modulation Gas detectors Indoor air pollution Molecular spectroscopy Nitrogen oxides Particle swarm optimization (PSO) Atmospheric pollution Atmospheric pollution monitoring Beer-Lambert Chirped spectral modulation Environmental pollution monitoring Environmental pollutions FTIR FTIR spectrometer Gas detection Harmful gas Harmful gas detection Human health Indoor air quality Particle swarm Particle swarm optimization Pollution monitoring Public safety Spectral modulation Swarm optimization Sulfur dioxide
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id	my.uniten.dspace-36643
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spelling	my.uniten.dspace-366432025-03-03T15:43:36Z Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring Shalaby A.M. Othman N.S. Shalaby M. 57219433216 56426823300 57189881220 Air quality Carbon dioxide Carbon monoxide Chirp modulation Gas detectors Indoor air pollution Molecular spectroscopy Nitrogen oxides Particle swarm optimization (PSO) Atmospheric pollution Atmospheric pollution monitoring Beer-Lambert Chirped spectral modulation Environmental pollution monitoring Environmental pollutions FTIR FTIR spectrometer Gas detection Harmful gas Harmful gas detection Human health Indoor air quality Particle swarm Particle swarm optimization Pollution monitoring Public safety Spectral modulation Swarm optimization Sulfur dioxide Traditional methods for detecting harmful gases in air are often limited in their widespread deployment, accuracy, and real-time monitoring capabilities due to their complexity and cost. To address this challenge, optimization algorithms such as the Particle Swarm Optimization (PSO) algorithm have shown promise. The PSO algorithm, is applied to calculate the concentrations of harmful gases in air, maximizing detection accuracy. Detecting indoor gas pollution is a crucial concern due to the abundance of odors and vapors, particularly those emanating from activities such as cooking. The presence of these substances in the air poses a challenge in identifying traces of other harmful gases. This research endeavors to pioneer a novel approach characterized by heightened sensitivity, even in the presence of unidentified elements in the air. In this work, PSO algorithm is used in conjunction with Chirped Spectral Modulation (CSM) technique to increase system sensitivity to detect small traces of harmful gases inside buildings and protect the environment through early detection of pollution. The use of PSO and CSM altogether allowed for detecting carbon dioxide CO2, carbon monoxide CO, and nitrogen dioxide NO2 down to 10?6 % in volume, and sulfur dioxide SO2 down to 5?10?4 % in volume, while keeping the error below 0.1% ? 2024 The Author(s) Final 2025-03-03T07:43:36Z 2025-03-03T07:43:36Z 2024 Article 10.1016/j.aej.2024.03.023 2-s2.0-85189665055 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189665055&doi=10.1016%2fj.aej.2024.03.023&partnerID=40&md5=776d5122e0677cada267cf5c74a07d22 https://irepository.uniten.edu.my/handle/123456789/36643 95 189 196 All Open Access; Gold Open Access Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Air quality Carbon dioxide Carbon monoxide Chirp modulation Gas detectors Indoor air pollution Molecular spectroscopy Nitrogen oxides Particle swarm optimization (PSO) Atmospheric pollution Atmospheric pollution monitoring Beer-Lambert Chirped spectral modulation Environmental pollution monitoring Environmental pollutions FTIR FTIR spectrometer Gas detection Harmful gas Harmful gas detection Human health Indoor air quality Particle swarm Particle swarm optimization Pollution monitoring Public safety Spectral modulation Swarm optimization Sulfur dioxide
spellingShingle	Air quality Carbon dioxide Carbon monoxide Chirp modulation Gas detectors Indoor air pollution Molecular spectroscopy Nitrogen oxides Particle swarm optimization (PSO) Atmospheric pollution Atmospheric pollution monitoring Beer-Lambert Chirped spectral modulation Environmental pollution monitoring Environmental pollutions FTIR FTIR spectrometer Gas detection Harmful gas Harmful gas detection Human health Indoor air quality Particle swarm Particle swarm optimization Pollution monitoring Public safety Spectral modulation Swarm optimization Sulfur dioxide Shalaby A.M. Othman N.S. Shalaby M. Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
description	Traditional methods for detecting harmful gases in air are often limited in their widespread deployment, accuracy, and real-time monitoring capabilities due to their complexity and cost. To address this challenge, optimization algorithms such as the Particle Swarm Optimization (PSO) algorithm have shown promise. The PSO algorithm, is applied to calculate the concentrations of harmful gases in air, maximizing detection accuracy. Detecting indoor gas pollution is a crucial concern due to the abundance of odors and vapors, particularly those emanating from activities such as cooking. The presence of these substances in the air poses a challenge in identifying traces of other harmful gases. This research endeavors to pioneer a novel approach characterized by heightened sensitivity, even in the presence of unidentified elements in the air. In this work, PSO algorithm is used in conjunction with Chirped Spectral Modulation (CSM) technique to increase system sensitivity to detect small traces of harmful gases inside buildings and protect the environment through early detection of pollution. The use of PSO and CSM altogether allowed for detecting carbon dioxide CO2, carbon monoxide CO, and nitrogen dioxide NO2 down to 10?6 % in volume, and sulfur dioxide SO2 down to 5?10?4 % in volume, while keeping the error below 0.1% ? 2024 The Author(s)
author2	57219433216
author_facet	57219433216 Shalaby A.M. Othman N.S. Shalaby M.
format	Article
author	Shalaby A.M. Othman N.S. Shalaby M.
author_sort	Shalaby A.M.
title	Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
title_short	Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
title_full	Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
title_fullStr	Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
title_full_unstemmed	Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
title_sort	advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring
publisher	Elsevier B.V.
publishDate	2025
_version_	1825816278995042304
score	13.244413

Advanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoring

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