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Measuring Dissolved Methane in Aquatic Ecosystems Using An Optical Spectroscopy Gas Analyzer

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Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring.

Jonathan Silberstein¹, Matthew Wellbrook², Michael Hannigan¹

¹Department of Mechanical Engineering, University of Colorado at Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

Low-cost sensors effectively screen for fugitive methane emissions from oil and gas wells. A random forest model showed high correlation and low error, accurately predicting 80% of methane spikes.

Keywords:

low-cost sensors mobile monitoring model calibration oil and gas well emissions quantification screening tools

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Area of Science:

Environmental Science
Atmospheric Chemistry
Sensor Technology

Background:

Mobile monitoring of oil and gas emissions using low-cost sensors (LCSs) is an understudied area.
Fugitive methane emissions from well sites pose environmental concerns.

Purpose of the Study:

To assess the efficacy of LCSs as a screening tool for mobile monitoring of fugitive methane emissions.
To evaluate the performance of LCSs against a reference-grade methane monitor.

Main Methods:

Colocation of an array of LCSs (XPOD) with a reference methane monitor (Aeris Ultra) on a mobile vehicle.
Calibration of LCS data using a bootstrap and aggregated random forest model.
Analysis of model performance, including correlation, error, and prediction of methane spikes.

Main Results:

A high correlation (r = 0.719) and low experimental error (RMSD = 0.3673 ppm) were found between LCS and reference methane concentrations.
The random forest model outperformed multilinear regression and artificial neural networks.
The model predicted 80% of methane spikes above 2.5 ppm with a 35% false response rate.

Conclusions:

LCSs, when calibrated with a random forest model, are effective screening tools for mobile methane emission monitoring.
Model performance remained robust even with reduced calibration data (down to 50%).
Further development can improve the accuracy and reduce false positives for LCS-based methane detection.