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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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 |
work_keys_str_mv |
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