Efficient Methane Monitoring with Low-Cost Chemical Sensorsand Machine Learning

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

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Naturresursteknik -- Fjärranalysteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Environmental Engineering -- Remote Sensing (hsv//eng)

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