Electronic Nose for Improved Environmental Methane Monitoring

• Published in: Environmental science & technology Vol. 58; no. 1; pp. 352 - 361
• Main Authors: Domènech-Gil, Guillem, Duc, Nguyen Thanh, Wikner, J. Jacob, Eriksson, Jens, Påledal, Sören Nilsson, Puglisi, Donatella, Bastviken, David
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 09.01.2024
• Subjects: Atmospheric models; Climate change; Climate change mitigation; Electronic noses; Emission measurements; Emissions control; Energy and Climate; Environment models; Greenhouse effect; Greenhouse gases; In situ measurement; Low cost; Machine learning; Methane; Monitoring; Pressure sensors; Sensors; low-cost; machine learning; gas sensors; greenhouse gas
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.3c06945

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.