Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning
We present a method to monitor methane at atmospheric concentrations with errors in the order of tens of parts per billion. We use machine learning techniques and periodic calibrations with reference equipment to quantify methane from the readings of an electronic nose. The results obtained demonstr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/97/1/79 |
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| author | Guillem Domènech-Gil Nguyen Thanh Duc J. Jacob Wikner Jens Eriksson Donatella Puglisi David Bastviken |
| author_facet | Guillem Domènech-Gil Nguyen Thanh Duc J. Jacob Wikner Jens Eriksson Donatella Puglisi David Bastviken |
| author_sort | Guillem Domènech-Gil |
| collection | DOAJ |
| description | We present a method to monitor methane at atmospheric concentrations with errors in the order of tens of parts per billion. We use machine learning techniques and periodic calibrations with reference equipment to quantify methane from the readings of an electronic nose. The results obtained demonstrate versatile and robust solution that outputs adequate concentrations in a variety of different cases studied, including indoor and outdoor environments with emissions arising from natural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-cost sensor system networks for greenhouse gas monitoring. |
| format | Article |
| id | doaj-art-b382021dce3d4de2b7a7e69fabf4bd52 |
| institution | DOAJ |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj-art-b382021dce3d4de2b7a7e69fabf4bd522025-08-20T02:57:00ZengMDPI AGProceedings2504-39002024-03-019717910.3390/proceedings2024097079Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine LearningGuillem Domènech-Gil0Nguyen Thanh Duc1J. Jacob Wikner2Jens Eriksson3Donatella Puglisi4David Bastviken5Department of Thematic Studies and Environmental Change (TEMA M), Linköping University, S-581 83 Linköping, SwedenDepartment of Thematic Studies and Environmental Change (TEMA M), Linköping University, S-581 83 Linköping, SwedenGE HealthCare, Teknikringen 8, S-583 30 Linköping, SwedenDepartment of Physics, Chemistry, and Biology (IFM), Linköping University, S-581 83 Linköping, SwedenDepartment of Physics, Chemistry, and Biology (IFM), Linköping University, S-581 83 Linköping, SwedenDepartment of Thematic Studies and Environmental Change (TEMA M), Linköping University, S-581 83 Linköping, SwedenWe present a method to monitor methane at atmospheric concentrations with errors in the order of tens of parts per billion. We use machine learning techniques and periodic calibrations with reference equipment to quantify methane from the readings of an electronic nose. The results obtained demonstrate versatile and robust solution that outputs adequate concentrations in a variety of different cases studied, including indoor and outdoor environments with emissions arising from natural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-cost sensor system networks for greenhouse gas monitoring.https://www.mdpi.com/2504-3900/97/1/79methaneelectronic noseenvironmental monitoringmachine learninggas sensorslow-cost |
| spellingShingle | Guillem Domènech-Gil Nguyen Thanh Duc J. Jacob Wikner Jens Eriksson Donatella Puglisi David Bastviken Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning Proceedings methane electronic nose environmental monitoring machine learning gas sensors low-cost |
| title | Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning |
| title_full | Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning |
| title_fullStr | Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning |
| title_full_unstemmed | Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning |
| title_short | Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning |
| title_sort | efficient methane monitoring with low cost chemical sensors and machine learning |
| topic | methane electronic nose environmental monitoring machine learning gas sensors low-cost |
| url | https://www.mdpi.com/2504-3900/97/1/79 |
| work_keys_str_mv | AT guillemdomenechgil efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning AT nguyenthanhduc efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning AT jjacobwikner efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning AT jenseriksson efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning AT donatellapuglisi efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning AT davidbastviken efficientmethanemonitoringwithlowcostchemicalsensorsandmachinelearning |