Efficient Methane Monitoring with Low-Cost Chemical Sensors and Machine Learning

We present a method to monitor methane at atmospheric concentrations with errors in the order of tens of parts per billion. We use machine learning techniques and periodic calibrations with reference equipment to quantify methane from the readings of an electronic nose. The results obtained demonstrate versatile and robust solution that outputs adequate concentrations in a variety of different cases studied, including indoor and outdoor environments with emissions arising from natural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-cost sensor system networks for greenhouse gas monitoring.