• Treffer 4 von 17
Zurück zur Trefferliste

Assessment of outdoor odor emissions from polluted sites using simultaneous thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS), electronic nose in conjunction with advanced multivariate statistical approaches

  • Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits aPoor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.zeige mehrzeige weniger

Volltext Dateien herunterladen

  • Moufid_et_al_Atmospheric_Environment.pdf
    eng

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Anzahl der Zugriffe auf dieses Dokument
Metadaten
Autor*innen:M. Moufid, B. Bouchikhi, Carlo Tiebe, Matthias BartholmaiORCiD, N. El Bari
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Atmospheric Environment
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.1 Sensorik, mess- und prüftechnische Verfahren
Verlag:Elsevier Ltd.
Jahrgang/Band:256
Erste Seite:118449
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Electronic nose; Multivariate analysis; Outdoor odor emissions; PLS regression; TD-GC-MS
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik
Umwelt
Umwelt / Sensorik
DOI:10.1016/j.atmosenv.2021.118449
ISSN:1352-2310
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:12.05.2021
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:14.06.2021
Einverstanden
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.