Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning-Reference-Cited by-同舟云学术

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Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning

Published:2024-03-22 Issue: Volume:12 Page:79
ISSN:
Container-title:Eurosensors 2023
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Author:

Domènech-Gil Guillem¹^ORCID,Duc Nguyen Thanh¹,Wikner J. Jacob²,Eriksson Jens³,Puglisi Donatella³^ORCID,Bastviken David¹

Affiliation:

1. Department of Thematic Studies and Environmental Change (TEMA M), Linköping University, S-581 83 Linköping, Sweden

2. GE HealthCare, Teknikringen 8, S-583 30 Linköping, Sweden

3. Department of Physics, Chemistry, and Biology (IFM), Linköping University, S-581 83 Linköping, Sweden

Publisher

MDPI

Link

https://www.mdpi.com/2504-3900/97/1/79/pdf

Reference3 articles.

1. Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.

2. The Global Methane Budget 2000–2017;Saunois;Earth Syst. Sci. Data,2020

3. Critical method needs in measuring greenhouse gas fluxes;Bastviken;Environ. Res. Lett.,2022

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