Evaluation of Hydrocarbon Soil Pollution Using E-Nose

The possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten ara...

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Main Authors: Andrzej Bieganowski, Grzegorz Józefaciuk, Lidia Bandura, Łukasz Guz, Grzegorz Łagód, Wojciech Franus
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/8/2463
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spelling doaj-aa45aa36c459444caa8f190261f675322020-11-24T21:49:15ZengMDPI AGSensors1424-82202018-07-01188246310.3390/s18082463s18082463Evaluation of Hydrocarbon Soil Pollution Using E-NoseAndrzej Bieganowski0Grzegorz Józefaciuk1Lidia Bandura2Łukasz Guz3Grzegorz Łagód4Wojciech Franus5Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, PolandInstitute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, PolandFaculty of Civil Engineering and Architecture, Lublin University of Technology, Nadbystrzycka 40, 20-618 Lublin, PolandFaculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, PolandFaculty of Environmental Engineering, Lublin University of Technology, Nadbystrzycka 40B, 20-618 Lublin, PolandFaculty of Civil Engineering and Architecture, Lublin University of Technology, Nadbystrzycka 40, 20-618 Lublin, PolandThe possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten arable soils, belonging to various soil groups from the World Reference Base (WRB), were investigated. The measurements were performed on soils that were moistened to field capacity, polluted separately with both hydrocarbons, and then allowed to dry slowly over a period of 180 days. The volatile fingerprints differed throughout the course of the experiment, and, by its end, they were similar to those of the unpolluted soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that the e-nose results could be used to detect soil contamination and distinguish between pollutants and contamination levels.http://www.mdpi.com/1424-8220/18/8/2463e-nosehydrocarbonpollutionsoil
collection DOAJ
language English
format Article
sources DOAJ
author Andrzej Bieganowski
Grzegorz Józefaciuk
Lidia Bandura
Łukasz Guz
Grzegorz Łagód
Wojciech Franus
spellingShingle Andrzej Bieganowski
Grzegorz Józefaciuk
Lidia Bandura
Łukasz Guz
Grzegorz Łagód
Wojciech Franus
Evaluation of Hydrocarbon Soil Pollution Using E-Nose
Sensors
e-nose
hydrocarbon
pollution
soil
author_facet Andrzej Bieganowski
Grzegorz Józefaciuk
Lidia Bandura
Łukasz Guz
Grzegorz Łagód
Wojciech Franus
author_sort Andrzej Bieganowski
title Evaluation of Hydrocarbon Soil Pollution Using E-Nose
title_short Evaluation of Hydrocarbon Soil Pollution Using E-Nose
title_full Evaluation of Hydrocarbon Soil Pollution Using E-Nose
title_fullStr Evaluation of Hydrocarbon Soil Pollution Using E-Nose
title_full_unstemmed Evaluation of Hydrocarbon Soil Pollution Using E-Nose
title_sort evaluation of hydrocarbon soil pollution using e-nose
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-07-01
description The possibility of detecting low levels of soil pollution by petroleum fuel using an electronic nose (e-nose) was studied. An attempt to distinguish between pollution caused by petrol and diesel oil, and its relation to the time elapsed since the pollution event was simultaneously performed. Ten arable soils, belonging to various soil groups from the World Reference Base (WRB), were investigated. The measurements were performed on soils that were moistened to field capacity, polluted separately with both hydrocarbons, and then allowed to dry slowly over a period of 180 days. The volatile fingerprints differed throughout the course of the experiment, and, by its end, they were similar to those of the unpolluted soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that the e-nose results could be used to detect soil contamination and distinguish between pollutants and contamination levels.
topic e-nose
hydrocarbon
pollution
soil
url http://www.mdpi.com/1424-8220/18/8/2463
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AT łukaszguz evaluationofhydrocarbonsoilpollutionusingenose
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