Identification of indoor air quality events ...
Document type :
Article dans une revue scientifique: Article original
Permalink :
Title :
Identification of indoor air quality events using a K-means clustering analysis of gas sensors data
Author(s) :
Caron, Alexandre [Auteur]
Physicochimie des Processus de Combustion et de l’Atmosphère - UMR 8522 [PC2A]
Redon, Nathalie [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Coddeville, Patrice [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Hanoune, Benjamin [Auteur]
Physicochimie des Processus de Combustion et de l’Atmosphère - UMR 8522 [PC2A]
Redon, Nathalie [Auteur]
Physicochimie des Processus de Combustion et de l’Atmosphère - UMR 8522 [PC2A]
Redon, Nathalie [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Coddeville, Patrice [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Hanoune, Benjamin [Auteur]
Physicochimie des Processus de Combustion et de l’Atmosphère - UMR 8522 [PC2A]
Redon, Nathalie [Auteur]
Journal title :
Sensors and Actuators B: Chemical
Abbreviated title :
Sensors and Actuators B: Chemical
Volume number :
297
Pages :
126709
Publisher :
Elsevier BV
Publication date :
2019-10
ISSN :
0925-4005
English keyword(s) :
(Indoor) air pollution
Electronic gas sensors
Unsupervised classification
K-means clustering
Electronic gas sensors
Unsupervised classification
K-means clustering
HAL domain(s) :
Chimie/Chimie théorique et/ou physique
Physique [physics]/Physique [physics]/Chimie-Physique [physics.chem-ph]
Physique [physics]/Physique [physics]/Chimie-Physique [physics.chem-ph]
English abstract : [en]
Commercial miniature gas sensors, because they are smaller and cheaper than conventional instruments, can be deployed in large numbers to investigate indoor air quality, for research and operational purposes. To compensate ...
Show more >Commercial miniature gas sensors, because they are smaller and cheaper than conventional instruments, can be deployed in large numbers to investigate indoor air quality, for research and operational purposes. To compensate for their limited metrological performances, it is necessary to develop relevant data treatment procedures. We applied an unsupervised classification approach based on the bisecting K-means algorithm to data acquired by online gas analyzers and by miniature sensors during a measurement campaign in a low energy school building. This procedure, applied to the analyzers measurements, was able to distinguish the ventilation status and the specific air quality events taking place in the classroom. The same procedure applied to the data from the sensors, even though they were not calibrated beforehand, was also able to identify the same events. The good agreement between the two sets of results validates the methodology and opens up new perspectives for a massive deployment of sensors inside buildings.Show less >
Show more >Commercial miniature gas sensors, because they are smaller and cheaper than conventional instruments, can be deployed in large numbers to investigate indoor air quality, for research and operational purposes. To compensate for their limited metrological performances, it is necessary to develop relevant data treatment procedures. We applied an unsupervised classification approach based on the bisecting K-means algorithm to data acquired by online gas analyzers and by miniature sensors during a measurement campaign in a low energy school building. This procedure, applied to the analyzers measurements, was able to distinguish the ventilation status and the specific air quality events taking place in the classroom. The same procedure applied to the data from the sensors, even though they were not calibrated beforehand, was also able to identify the same events. The good agreement between the two sets of results validates the methodology and opens up new perspectives for a massive deployment of sensors inside buildings.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
CNRS
CNRS
Research team(s) :
PhysicoChimie de l'Atmosphère (PCA)
Submission date :
2019-08-30T15:11:45Z
2019-09-02T13:00:42Z
2019-10-28T07:29:43Z
2023-01-04T08:07:47Z
2019-09-02T13:00:42Z
2019-10-28T07:29:43Z
2023-01-04T08:07:47Z
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