An electronic nose for the detection and discrimination of environmental pollutant gases in the aglomeration of the city of meknes

  • The ambient air quality around residential areas is influenced by industrial objects, including industrial sewage, livestock farming and landfill sites. These sites are generating malodours or toxic gases involving degradation of ambient air quality, which may constitute a risk in human health if maximum emission limits are exceeded. Therefore, appropriate tools allowing detection of harmful or bad odorous, subsequently contributing to a reduction of odour nuisance are greatly needed. The aim of this study was to demonstrate the capability of an electronic nose E-nose to discriminate various gas samples collected from six different sites from the agglomeration of Meknès city corresponding to municipal landfill, in the city at 2 km of landfill, industrial estate wastewater, traffic road, and sheep breeding. The investigations were carried out with an E-nose system based on an array of six-commercial MQ sensors. Further, a pattern recognition technique known as Principal ComponentThe ambient air quality around residential areas is influenced by industrial objects, including industrial sewage, livestock farming and landfill sites. These sites are generating malodours or toxic gases involving degradation of ambient air quality, which may constitute a risk in human health if maximum emission limits are exceeded. Therefore, appropriate tools allowing detection of harmful or bad odorous, subsequently contributing to a reduction of odour nuisance are greatly needed. The aim of this study was to demonstrate the capability of an electronic nose E-nose to discriminate various gas samples collected from six different sites from the agglomeration of Meknès city corresponding to municipal landfill, in the city at 2 km of landfill, industrial estate wastewater, traffic road, and sheep breeding. The investigations were carried out with an E-nose system based on an array of six-commercial MQ sensors. Further, a pattern recognition technique known as Principal Component Analysis (PCA), Linear Discriminent Analysis (LDA), and Support Vector Machines (SVMs) was implemented to study the discrimination capability of the sensor array. PCA results demonstrate excellent discriminating ability of the dataset with a score of 99.47 %. Additionally, another measurement database containing 12 air atmospheric samples was projected on the previously built PCA model to check the stability of the E-nose. The LDA was applied to the same dataset and showed a good discrimination between the ambient air samples of the six sites. Furthermore, SVMs technique was also used to build a classifier and reached a score of 100 % success rate in the recognition of the analysed samples. The obtained results of six areas demonstrate the increasing interests and the applicability of E-noses for ambient air quality classification of six areas caused by emitted decomposed organic matters.zeige mehrzeige weniger

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Metadaten
Autor*innen:M. Moufid
Koautor*innen:Carlo Tiebe, N. El Bari, Thomas Hübert, B. Bouchikhi
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2017
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Electronic nose; Environmental analysis; Gas sensor; Malodour detection; Pattern recognition methods
Veranstaltung:Eighth International Workshop on Biosensors for Food Safety and Environmental Monitoring
Veranstaltungsort:Rabat, Morocco
Beginndatum der Veranstaltung:12.10.2017
Enddatum der Veranstaltung:14.10.2017
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:17.10.2017
Referierte Publikation:Nein
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