The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH4 Variations around Background Concentration

Abstract

Continued developments in instrumentation and modeling have driven progress in monitoring methane (CH4) emissions at a range of spatial scales. The sites that emit CH4 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 (CH4) 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® TGS tin-oxide sensors for estimating CH4 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 CH4, H2O and CO in the laboratory, to analyze cross-sensitivities. In the second test, we reconstruct observed CH4 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 CH4 recorded by a high precision instrument. The machine-learning model using 30% of the data for training reconstructs CH4 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 H2O compared to CH4 are larger than those observed under controlled conditions, which deserves further characterization of all the factors influencing the resistance of the sensors.

Determining methane mole fraction at a landfill site using the Figaro Taguchi gas sensor 2611-C00 and wind direction measurements

“…30 Nevertheless, TGS resistance is highly sensitive to water mole fraction ([H 2 O]) and ambient temperature (T) variations. 29,[31][32][33] Furthermore, TGS resistance may behave erratically following sharp [H 2 O] changes. 34 TGS resistance is also inuenced by supply voltage.…”

“…28 We focus here on the Figaro Taguchi Gas Sensor (TGS) range, with an ultimate objective of measuring [CH 4 ] with a sufficient parts-per-million level accuracy to detect CH 4 emissions from facility-scale sources. Unlike many SMO sensors, the TGS can operate in ambient humidity conditions 29 and, in contrast, functions abnormally in dry conditions. 30 Nevertheless, TGS resistance is highly sensitive to water mole fraction ([H 2 O]) and ambient temperature ( T ) variations.…”

“…Sources producing large [CH 4 ] spikes above the background may have lower [CH 4 ] accuracy requirements. 54…”

“…58 For example, several studies have conducted TGS field characterisations, using a high-precision reference instrument (HPR) for model training and refinement. 38,47,48,54,55 Most of these previous studies modelled [CH 4 ] directly using raw TGS resistance, without deriving a baseline resistance representative of sampling at a fixed (predefined) reference [CH 4 ] level. The ability to derive a robust baseline TGS resistance at a reference [CH 4 ] level, and from field sampling, would be a valuable asset in estimating [CH 4 ] at a desired ±1 ppm [CH 4 ] accuracy, by taking the resistance ratio between measured TGS resistance and this reference resistance.…”

Electronic Nose for Improved Environmental Methane Monitoring

“…While laboratory calibrations with only CH 4 as the gas giving sensor response yield “clean” calibration curves, outdoor use suffers from large interferences from, e.g., water vapor (H 2 O­(g)), and multidimensional calibration is needed for reliable use in outdoor environments . It has also been challenging to reach high-performance CH 4 measurements (e.g., resolving small changes around concentrations as low as the ambient atmospheric CH 4 concentrations) under variable H 2 O­(g) concentrations with single sensors in spite of advanced data processing approaches including machine learning. , Here, we approach this challenge by a combination of multivariable calibration and using an electronic nose (e-nose) approach with integrated multivariate analysis of simultaneous data from multiple sensors in low-cost sensor systems (LCSSs) designed to monitor CH 4 concentrations.…”