08 Jul 2022
08 Jul 2022
Status: this preprint is currently under review for the journal AMT.

Reconstruction of high-frequency methane atmospheric concentration peaks from measurements using metal oxide low-cost sensors

Rodrigo Andres Rivera Martinez1, Diego Santaren1, Olivier Laurent1, Gregoire Broquet1, Ford Cropley1, Cécile Mallet2, Michel Ramonet1, Adil Shah1, Leonard Rivier1, Caroline Bouchet3, Catherine Juery4, Olivier Duclaux4, and Philippe Ciais1 Rodrigo Andres Rivera Martinez et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 2Université de Versailles Saint-Quentin, UMR8190 – CNRS/INSU, LATMOS-IPSL, Laboratoire Atmosphères Milieux, Observations Spatiales, Quartier des Garennes, 11 Boulevard d’Alembert, 78280 Guyancourt, France
  • 3SUEZ - Smart & Environmental Solutions; Tour CB21, 16 place de l’Iris, 92040 La Défense France
  • 4TOTAL Energies - Raffinage chimie, Laboratoire Qualité de l’Air – 69360 SOLAIZE FRANCE

Abstract. Detecting and quantifying CH4 gas emissions at industrial facilities is important goal for being able to reduce these emissions. The nature of CH4 emissions through 'leaks' is episodic and spatially variable, making their monitoring a complex task, being partly addressed by atmospheric surveys with various types of instruments. Continuous records are preferable to snapshot surveys for monitoring a site, and one solution would be to deploy a permanent network of sensors. Deploying such a network with research-level instruments being expensive, low-cost and low-power sensors could be a good alternative. However, low cost entails usually lower accuracy and the existence of sensors drifts and cross-sensitivity to other gases and environmental parameters. Here we present four tests conducted with two types of Figaro TGS sensors on a laboratory experiment. The sensors were exposed to ambient air and peaks of CH4 concentrations. We assembled four chambers, each containing one TGS sensor of each type. The first test consisted in comparing parametric and non-parametric models to reconstruct the CH4 peaks signal from observations of the voltage variations of TGS sensors. The obtained relative accuracy is higher than 10 % to reconstruct the maximum amplitude of peaks (RMSE ≤ 2 ppm). Polynomial regression and multilayer perceptron (MLP) models gave the highest performances for one type of sensor (TGS 2611C, RMSE = 0.9 ppm) and for the combination of two sensors (TGS 2611C + TGS 2611E, RMSE = 0.8 ppm) with a training set size of 70 % of the total observations. In the second test, we compared the performance of the same models with a reduced training set. To reduce the size of the training set, we have employed a stratification of the data into clusters of peaks that allowed us to keep the same model performances with only 25 % of the data to train the models. The third test consisted of detecting the effects of age in the sensors after six months of continuous measurements. We observed performance degradation through our models, of between 0.6 and 0.8 ppm. In the final test, we assessed the capability of a model to be transferred between chambers on the same type of sensor, and found that it is possible to transfer models only if the target range of variation of CH4 is similar to the one on which the model was trained.

Rodrigo Andres Rivera Martinez et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-200', Anonymous Referee #1, 24 Aug 2022
  • RC2: 'Comment on amt-2022-200', Anonymous Referee #2, 05 Oct 2022

Rodrigo Andres Rivera Martinez et al.

Rodrigo Andres Rivera Martinez et al.


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Short summary
A network of low cost sensors is a good alternative to improve the detection of fugitive CH4 emissions. In this study, we present the results of 4 tests conducted with 2 types of Figaro sensors, which were assembled on four chambers in a laboratory experiment: a comparison of five models to reconstruct the CH4 signal; a strategy to reduce the training set size; a detection of age effects in the sensors; and a test of the capability to transfer a model between chambers on the same type of sensor.