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Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix
This article presents a new way to determine odor nuisance based on the proposed odor air quality index ([Formula: see text]), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268730/ https://www.ncbi.nlm.nih.gov/pubmed/35807428 http://dx.doi.org/10.3390/molecules27134180 |
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author | Dobrzyniewski, Dominik Szulczyński, Bartosz Gębicki, Jacek |
author_facet | Dobrzyniewski, Dominik Szulczyński, Bartosz Gębicki, Jacek |
author_sort | Dobrzyniewski, Dominik |
collection | PubMed |
description | This article presents a new way to determine odor nuisance based on the proposed odor air quality index ([Formula: see text]), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed [Formula: see text]. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model. |
format | Online Article Text |
id | pubmed-9268730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92687302022-07-09 Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix Dobrzyniewski, Dominik Szulczyński, Bartosz Gębicki, Jacek Molecules Article This article presents a new way to determine odor nuisance based on the proposed odor air quality index ([Formula: see text]), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed [Formula: see text]. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model. MDPI 2022-06-29 /pmc/articles/PMC9268730/ /pubmed/35807428 http://dx.doi.org/10.3390/molecules27134180 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dobrzyniewski, Dominik Szulczyński, Bartosz Gębicki, Jacek Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title | Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title_full | Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title_fullStr | Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title_full_unstemmed | Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title_short | Determination of Odor Air Quality Index (OAQI(I)) Using Gas Sensor Matrix |
title_sort | determination of odor air quality index (oaqi(i)) using gas sensor matrix |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268730/ https://www.ncbi.nlm.nih.gov/pubmed/35807428 http://dx.doi.org/10.3390/molecules27134180 |
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