The calibration and deployment of a low-cost methane sensor

Riddick, Stuart N.; Mauzerall, Denise L.; Celia, Michael A.; Allen, Grant; Pitt, Joseph; Kang, Mary; Riddick, John C.

doi:10.1016/j.atmosenv.2020.117440

Cited by 32 publications

(57 citation statements)

References 10 publications

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“…They concluded that ANN would outperform linear models if other driving variables were included to the model. Riddick et al [36] conducted an experiment to investigate the potential of Figaro ® TGS 2600 to measure CH 4 mixing ratios in ranges between 2 and 10 ppm, assess the long term measurements over 3 months and estimate the emissions from a natural gas point source. Calibration of the sensor was derived from a non linear relationship giving the best agreement with the reference measurements when computing the time averaged concentration with a uncertainty of ±0.01 ppm.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…Moreover, the same authors suggest a regular calibration of the sensor since they found a drift of the sensor signal over time. Compared with studies that identified better results for low methane concentrations [16,22], it is understood that it would be necessary to apply the sensor for longer periods of time at methane concentrations not exceeding 10 ppm, in order to obtain a more specific curve for low concentrations.…”

Section: Application Of the Low-cost System In Indoor Atmospheres Of A Wastewater Treatment Plantmentioning

confidence: 99%

“…Methane is present in the atmosphere at low concentrations, with a global background concentration around 1.8 ppm [3], and studies accomplished in Washington [20] and Boston [21] identified values of methane in urban areas varying between 2.5 and 90 ppm. The same sensor was used by Eugster and Kling [22] for measurements of methane concentrations in Toolik Lake, Alaska, where the sensor was able to identify concentrations of atmospheric methane below 2 ppm; and by Riddick et al [16] to measure methane concentrations at one onshore gas terminal, identifying methane concentrations from 1.82 to 5.40 ppm.…”

Section: System Architecturementioning

confidence: 99%

“…Calibration is also a fundamental part of the sensor application. One study [16] found that at low methane concentrations the signal from different TGS2600 sensors tend to vary for the same concentration, so that the application of a generalized calibration becomes unfeasible for obtaining reliable data. Moreover, the same authors suggest a regular calibration of the sensor since they found a drift of the sensor signal over time.…”

Section: Application Of the Low-cost System In Indoor Atmospheres Of A Wastewater Treatment Plantmentioning

confidence: 99%

“…In this work, the potential of a low-cost sensor, the TGS2600 from Figaro, was studied when incorporated into a portable system for methane monitoring at two distinct environments-in barns and in indoor atmospheres of a wastewater treatment plant. Although several papers present the use of low-cost gas sensors [15][16][17] in different situations, commercially available low-cost sensors, mainly used in the detection of low concentrations of air pollutants, need more improvements and specific strategies for their reliable application. Thus, this work also covers the technical aspects related to the application of the semiconductor sensor TGS2600 for the detection of low concentrations of methane, discussing its limitations and the necessary details for a better field application, providing relevant information for the use of this type of sensor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications

Furst

Feliciano

Frare

et al. 2021

Sensors

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Methane is a major greenhouse gas and a precursor of tropospheric ozone, and most of its sources are linked to anthropogenic activities. The sources of methane are well known and its monitoring generally involves the use of expensive gas analyzers with high operating costs. Many studies have investigated the use of low-cost gas sensors as an alternative for measuring methane concentrations; however, it is still an area that needs further development to ensure reliable measurements. In this work a low-cost platform for measuring methane within a low concentration range was developed and used in two distinct environments to continuously assess and improve its performance. The methane sensor was the Figaro TGS2600, a metal oxide semiconductor (MOS) based on tin dioxide (SnO2). In a first stage, the monitoring platform was applied in a small ruminant barn after undergoing a multi-point calibration. In a second stage, the system was used in a wastewater treatment plant together with a multi-gas analyzer (Gasera One Pulse). The calibration of low-cost sensor was based on the relation of the readings of the two devices. Temperature and relative humidity were also measured to perform corrections to minimize the effects of these variables on the sensor signal and an active ventilation system was used to improve the performance of the sensor. The system proved to be able to measure low methane concentrations following reliable spatial and temporal patterns in both places. A very similar behavior between both measuring systems was also well noticeable at WWTP. In general, the low-cost system presented good performance under several environmental conditions, showing itself to be a good alternative, at least as a screening monitoring system.

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Real-time indoor sensing of volatile organic compounds during building disinfection events via photoionization detection and proton transfer reaction mass spectrometry

Ding,

Lu,

Jiang

et al. 2023

Building and Environment

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The calibration and deployment of a low-cost methane sensor

Cited by 32 publications

References 10 publications

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

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

A Portable Device for Methane Measurement Using a Low-Cost Semiconductor Sensor: Development, Calibration and Environmental Applications

Real-time indoor sensing of volatile organic compounds during building disinfection events via photoionization detection and proton transfer reaction mass spectrometry

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