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

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dc.citedby2
dc.contributor.authorShalaby A.M.en_US
dc.contributor.authorOthman N.S.en_US
dc.contributor.authorShalaby M.en_US
dc.contributor.authorid57219433216en_US
dc.contributor.authorid56426823300en_US
dc.contributor.authorid57189881220en_US
dc.date.accessioned2025-03-03T07:43:36Z
dc.date.available2025-03-03T07:43:36Z
dc.date.issued2024
dc.description.abstractTraditional 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)en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2024.03.023
dc.identifier.epage196
dc.identifier.scopus2-s2.0-85189665055
dc.identifier.spage189
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189665055&doi=10.1016%2fj.aej.2024.03.023&partnerID=40&md5=776d5122e0677cada267cf5c74a07d22
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36643
dc.identifier.volume95
dc.pagecount7
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectAir quality
dc.subjectCarbon dioxide
dc.subjectCarbon monoxide
dc.subjectChirp modulation
dc.subjectGas detectors
dc.subjectIndoor air pollution
dc.subjectMolecular spectroscopy
dc.subjectNitrogen oxides
dc.subjectParticle swarm optimization (PSO)
dc.subjectAtmospheric pollution
dc.subjectAtmospheric pollution monitoring
dc.subjectBeer-Lambert
dc.subjectChirped spectral modulation
dc.subjectEnvironmental pollution monitoring
dc.subjectEnvironmental pollutions
dc.subjectFTIR
dc.subjectFTIR spectrometer
dc.subjectGas detection
dc.subjectHarmful gas
dc.subjectHarmful gas detection
dc.subjectHuman health
dc.subjectIndoor air quality
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectPollution monitoring
dc.subjectPublic safety
dc.subjectSpectral modulation
dc.subjectSwarm optimization
dc.subjectSulfur dioxide
dc.titleAdvanced chirped spectral modulation technique and particle swarm optimization algorithms for effective indoor air pollution detection and monitoringen_US
dc.typeArticleen_US
dspace.entity.typePublication
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