| Summary: | Continued developments in instrumentation and modeling have driven progress in monitoring methane (CH<sub>4</sub>) emissions at a range of spatial scales. The sites that emit CH<sub>4</sub> such as landfills, oil and gas extraction or storage infrastructure, intensive livestock farms account for a large share of global emissions, and need to be monitored on a continuous basis to verify the effectiveness of reductions policies. Low cost sensors are valuable to monitor methane (CH<sub>4</sub>) around such facilities because they can be deployed in a large number to sample atmospheric plumes and retrieve emission rates using dispersion models. Here we present two tests of three different versions of Figaro<sup>®</sup> TGS tin-oxide sensors for estimating CH<sub>4</sub> concentrations variations, at levels similar to current atmospheric values, with a sought accuracy of 0.1 to 0.2 ppm. In the first test, we characterize the variation of the resistance of the tin-oxide semi-conducting sensors to controlled levels of CH<sub>4</sub>, H<sub>2</sub>O and CO in the laboratory, to analyze cross-sensitivities. In the second test, we reconstruct observed CH<sub>4</sub> variations in a room, that ranged from 1.9 and 2.4 ppm during a three month experiment from observed time series of resistances and other variables. To do so, a machine learning model is trained against true CH<sub>4</sub> recorded by a high precision instrument. The machine-learning model using 30% of the data for training reconstructs CH<sub>4</sub> within the target accuracy of 0.1 ppm only if training variables are representative of conditions during the testing period. The model-derived sensitivities of the sensors resistance to H<sub>2</sub>O compared to CH<sub>4</sub> are larger than those observed under controlled conditions, which deserves further characterization of all the factors influencing the resistance of the sensors.
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