The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emission and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large scale deployment of methane analyzers across the millions of emission sites is prohibitively expensive and lower-cost instrumentation has been recently developed as an alternative.  Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy.  To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH₄ERS) has been developed and comprises four commercially available sensing technologies: Metal Oxide (MOx,); Non-dispersion Infrared (NDIR); Integrated Infrared (INIR); and Tunable Diode Laser Absorption Spectrometer (TDLAS).  WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC) and this study describes the integration of these sensors to a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH₄ERS unit, report initial sensor response, and describe future research goals, while future work will use data gathered by multiple WATCH₄ERS units to 1. better understand the cost-benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large‐scale methane monitoring strategies.   Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm.  The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensor cannot respond fast enough to report concentrations that change on a sub-minute time frame.  The NDIR sensor reported linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart.  The TDLAS sensor reported all changes in concentration but remains prohibitively expensive.  Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions and the WATCH₄ERS units will be deployed in real-world environments to investigate the utility of each in the future.

The WATCH₄ERS comprises five individual methane sensors (two MOx sensors, NDIR sensor, INIR sensor and a TDLAS) that stream data to a single laptop PC via a powered USB hub.  Data are read into the PC using Python code and data are stored locally.  

The code required to run the WATCH₄ERS can be found at https://github.com/stuartnriddick/AMMMU.git.  Three Arduino UNO’s are used to interface the MOx sensors, the ’46 Hawk (TDLAS sensor) and the environment monitoring sensor (DHT22) to the data logging PC through a standard USB port. For the TGS2600 and TGS2611 metal oxide sensors, an Arduino UNO along with an Adafruit ADS1115 ADC interfaces the sensors and the code “Riddick MOX Arduino Code.ino” controls the sampling of these sensors and the transfer of the data to the PC.  The ’46 Hawk sensor does not have any digital output and the data from this instrument is extracted by a technique known as “data sniffing” where code run by the monitoring Arduino interrogates the ’46 Hawk LCD and extracts the values displayed on the LCD.  Sniffing the data transmitted to the LCD screen of the ’46 Hawk requires “Hawk Arduino Code.ino” downloaded to the Arduino UNO connected to it.  The other two sensors, the INIR and the NDIR both have serial RS232 interfaces and these outputs are converted using RS232 to USB converter modules plugged directly into the USB Hub. The Python code that collects data from all the sensors and writes them to a file is “Ammu Python Code.py”.