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Electronic Nose for Improved Environmental Methane Monitoring
Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-9036-0856
Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Integrated Circuits and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-2144-6795
Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering. (Sensor and Actuator Systems)
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2024 (English)In: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 58, p. 352-361Article in journal (Refereed) Published
Sustainable development
Climate Improvements
Abstract [en]

Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R-2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.

Place, publisher, year, edition, pages
AMER CHEMICAL SOC , 2024. Vol. 58, p. 352-361
Keywords [en]
greenhouse gas; machine learning; gas sensors; low-cost
National Category
Environmental Engineering Earth and Related Environmental Sciences Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-200180DOI: 10.1021/acs.est.3c06945ISI: 001139523100001PubMedID: 38126254OAI: oai:DiVA.org:liu-200180DiVA, id: diva2:1827317
Note

Funding: Swedish Research Council FORMAS [2018-01794]; Swedish Research Council (Vetenskapsradet) [2016-04829, 2022-03841, 2021-0016, 725546]; European Research Council under the European Union [2017-00635]; Swedish Infrastructure for Ecosystem Science (SITES); Program SITES Water

Available from: 2024-01-12 Created: 2024-01-12 Last updated: 2024-01-24

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Domènech-Gil, GuillemNguyen, Thanh DucWikner, JacobEriksson, JensPuglisi, DonatellaBastviken, David

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Domènech-Gil, GuillemNguyen, Thanh DucWikner, JacobEriksson, JensPuglisi, DonatellaBastviken, David
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Tema Environmental ChangeFaculty of Arts and SciencesFaculty of Science & EngineeringIntegrated Circuits and SystemsSensor and Actuator Systems
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