Articles | Volume 16, issue 13
https://doi.org/10.5194/amt-16-3391-2023
https://doi.org/10.5194/amt-16-3391-2023
Research article
 | 
05 Jul 2023
Research article |  | 05 Jul 2023

Characterising the methane gas and environmental response of the Figaro Taguchi Gas Sensor (TGS) 2611-E00

Adil Shah, Olivier Laurent, Luc Lienhardt, Grégoire Broquet, Rodrigo Rivera Martinez, Elisa Allegrini, and Philippe Ciais

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Revised manuscript accepted for AMT
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Cited articles

Baer, D. S., Paul, J. B., Gupta, M., and O'Keefe, A.: Sensitive absorption measurements in the near-infrared region using off-axis integrated-cavity-output spectroscopy, Appl. Phys. B, 75, 261–265, https://doi.org/10.1007/s00340-002-0971-z, 2002. 
Barsan, N., Koziej, D., and Weimar, U.: Metal oxide-based gas sensor research: How to?, Sensor. Actuat. B-Chem., 121, 18–35, https://doi.org/10.1016/j.snb.2006.09.047, 2007. 
Bastviken, D., Nygren, J., Schenk, J., Parellada Massana, R., and Duc, N. T.: Technical note: Facilitating the use of low-cost methane (CH4) sensors in flux chambers – calibration, data processing, and an open-source make-it-yourself logger, Biogeosciences, 17, 3659–3667, https://doi.org/10.5194/bg-17-3659-2020, 2020. 
Casey, J. G., Collier-Oxandale, A., and Hannigan, M.: Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors, Sensor. Actuat. B-Chem., 283, 504–514, https://doi.org/10.1016/j.snb.2018.12.049, 2019. 
Chakraborty, S., Sen, A., and Maiti, H. S.: Selective detection of methane and butane by temperature modulation in iron doped tin oxide sensors, Sensor. Actuat. B-Chem., 115, 610–613, https://doi.org/10.1016/j.snb.2005.10.046, 2006. 
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Short summary
As methane (CH4) contributes to global warming, more CH4 measurements are required to better characterise source emissions. Hence, we tested a cheap CH4 sensor for 338 d of landfill sampling. We derived an excellent CH4 response model in a stable environment. However, different types of air with the same CH4 level had diverse sensor responses. We characterised temperature and water vapour response but could not replicate field sampling. Thus, other species may cause sensor interactions.