Technical note: Facilitating the use of low-cost methane (CH<sub>4</sub>) sensors in flux chambers – calibration, data processing, and an open source make-it-yourself logger

Abstract

Abstract. A major bottleneck regarding the efforts to better quantify greenhouse gas fluxes, map sources and sinks, and understand flux regulation, is the shortage of low-cost and accurate-enough measurement methods. The studies of methane (CH4) – a long-lived greenhouse gas increasing rapidly but irregularly in the atmosphere for unclear reasons, and with poorly understood source-sink attribution – suffer from such method limitations. This study present new calibration and data processing approaches for use of a low-cost CH4 sensor in flux chambers. Results show that the change in relative CH4 levels can be determined at rather high accuracy in the 2–700 ppm range, with modest efforts of collecting reference samples in situ, and without continuous access to expensive reference instruments. These results open for more affordable and time-effective measurements of CH4 in flux chambers. To facilitate such measurements, we also provide a description for building and using an Arduino logger for CH4, carbon dioxide (CO2), humidity, and temperature.

Technical note: Facilitating the use of low-cost methane (CH<sub>4</sub>) sensors in flux chambers – calibration, data processing, and an open-source make-it-yourself logger

“…The Python code needs modifications for use with other data, and the CH 4 sensor data cannot represent results from other sensors as sensor-specific calibration is needed. The Arduino code for the CH 4 -CO 2 -RH-T logger described in the Supplement is available at http://urn.kb.se/resolve?urn=urn:nbn: se:liu:diva-162780 (Bastviken and Duc, 2020).…”

Diel variability of methane emissions from lakes

“…In case of missing temperature and RH for a given AFC (due to K33 ELG sensor malfunction), values of temperature and RH from the closest AFC and closest in time were used, assuming the same temperature and RH in both chambers. Afterward, each CH 4 sensor was calibrated separately by following a procedure described in detail in Bastviken et al (45) using background air concentrations under different humidity and well mixed atmospheric conditions (wind speed higher >2 m/s) as reference data. Finally, including volume and area of the chamber, CH 4 flux (μmol•m −2 •h −1 ) was calculated as a relative change in CH 4 levels in a chamber over time.…”

Continuous methane concentration measurements at the Greenland ice sheet–atmosphere interface using a low-cost, low-power metal oxide sensor system

“…Based on existing knowledge of the expected air temperature variations at the in situ sampling point at the GrIS (Christiansen and Jørgensen, 2018), the humidity calibration was only carried out at a single temperature in this study. However, variations in the ambient air temperature are also expected to have a linear scaling effect for the type MOS system tested in this study (Bastviken et al, 2020;van den Bossche et al, 2017).…”

“…Future tests should aim to investigate if the differences between the results from the laboratory and field calibration can be minimized by using the same type of datalogger and identical power supply (FX rechargeable lithium ion battery pack) both in the laboratory and in the field. Results from this type of test could reveal if field calibration for each individual MOS system is needed, similar to the approach in Bastviken et al (2020), or if batch calibrations of several identical MOS systems can be performed in the laboratory without the need for timeconsuming field calibration.…”

“…One of the main obstacles previously reported concerning the use of MOSs for monitoring of gases in ambient air is the possible effect of variations in air temperature and humidity in the sampling environment (Bastviken et al, 2020;Eugster et al, 2019;Masson et al, 2015;Sohn et al, 2008). Different approaches exist to compensate for this potential measurement error and are related to post-correcting for variations in temperature and humidity, based on either generic temperature and humidity dependency curves supplied by the sensor company (Eugster and Kling, 2012).…”