A methane sensor’s suitability cannot be judged by a single accuracy figure listed in its product specifications. Safety alarms, methane measurement, and emissions estimation have different requirements for range and representativeness. Chapter 8 of Technology and Solutions for Demonstrating Carbon Reduction treats sensor selection as a question of intended use and installation conditions.
Define the concept and scope of application first
Required performance starts with the expected concentration range and the size of the change that needs to be distinguished. Check whether response time matches the data-collection interval and how the effects of temperature and humidity changes will be assessed. Specification figures must be read within their stated conditions of application. In long-term operation, calibration and inspection methods can drive costs. Determine whether field calibration is possible, whether the device must be returned for inspection, and how data from a period when an anomaly is found will be handled. The equipment purchase and the plan for maintaining data quality should be evaluated together.
Measurement (M) produces data. It involves deciding what to observe, where, how often, and with which equipment, and then carrying out the measurements. Its outputs are raw data and the metadata needed to interpret those data, such as calibration history, equipment specifications, installation conditions, and missing intervals.
A reduction estimate needs both metrological traceability and calculation traceability. Measurement values are linked to a reference through a documented calibration chain and uncertainty, while activity data, emission factors, and models are traced to the final result through their sources, versions, and conditions of application. Not every emissions reduction comes from a single direct measurement. Reviewers should check not only whether the calculation can be reproduced, but also whether the data and methods used are appropriate for the claim.
Concentration is the proportion of a gas in the air, while an emission rate is the mass emitted per unit of time. Measuring or estimating emissions requires linking concentration and flow rate with a measurement model suited to the purpose. Even if concentration falls, the mass emission rate cannot be assumed to move in the same direction if airflow has increased. When converting an emission rate over time into emissions, the measurement period and treatment of missing intervals must also be addressed.
In mechanically ventilated barns, check actual airflow changes caused by static pressure and operating status, as well as contamination and wear, rather than relying only on the fans’ rated capacity. For natural ventilation, examine wind direction and speed, temperature differences, and the condition of openings. Assess within the measurement model how much uncertainty in estimated ventilation rates affects the emission rate.
A practical path forward is to accumulate concentration observations together with reference emission-rate measurements, and link these to ventilation, weather, animal counts, and feed information. Prediction error should be assessed on farms and time periods not used to develop the model, and the scope of application should be stated. Even a large volume of simultaneous observations does not automatically increase model reliability if they lack representativeness or share a common bias.
Catalytic combustion (pellistor). This method uses the heat generated when combustible gas reacts on a catalyst, and requires oxygen. It is mainly used for safety purposes, such as measuring concentration as a percentage of the lower explosive limit (%LEL); whether its specifications are sufficient to quantify small ppm differences in livestock settings must be checked separately. Response may be reduced by poisoning or inhibition and environmental effects such as dust, so regular inspection is needed.
Conditions and limitations of field application
Suppose an instrument designed to warn of combustible-gas hazards is being used to quantify low-concentration differences. Check whether its display units, resolution, and drift fit the analytical purpose. Even when the device is functioning normally, it may not be able to distinguish the changes that matter. Measurement principles and product-specific performance should be examined separately.
Non-dispersive infrared (NDIR) uses methane’s absorption of infrared light. It does not have oxygen dependence or catalyst poisoning of the kind associated with catalytic combustion, but it is not free from optical contamination or the effects of temperature, pressure, and moisture. Do not assume it is suitable for long-term farm monitoring based on its operating principle alone. For each product, check whether its detection limit, repeatability, and drift are adequate in relation to the expected concentration and the difference between indoor and outdoor concentrations.
Set calibration intervals based on the use environment, the manufacturer’s procedures, actual drift records, and required uncertainty. Even equipment that does not support field calibration can be managed using a combination of periodic comparison with a reference instrument, return-to-lab calibration, and checks with test gas. What matters is not a uniform interval, but records that support performance between calibrations and rules for handling data when an anomaly occurs.
When comparing sensors, check measurement range and resolution, cross-sensitivity to other gases, response time, and drift together. Environmental conditions such as temperature, humidity, and dust, as well as calibration requirements, should also be reviewed. If the purpose is to distinguish small concentration differences, examine by product and operating mode whether the device’s display units and uncertainty fit that purpose. The name of a sensor principle cannot stand in for these conditions.
Evidence needed for investment review
Investors should review evidence that required performance has been confirmed in the relevant environment, rather than simply whether a sensor is available. They should check whether the effects of dust, moisture, temperature changes, and maintenance methods on data quality have been evaluated. Being able to detect something is different from being able to quantify it for the intended purpose.
AI evaluation data should be separated according to the intended use. To predict future operations, consider chronological ordering; to deploy at other farms, consider separating data by farm. Also distinguish the data used to select the model from those reserved for the final performance check. Compare with a simple baseline model, and examine not only average error but also bias by farm and season and the conditions under which the model fails. This is necessary to explain its actual scope of use.
Data lineage is the path showing which raw data and transformations produced a final figure. Record unit conversions, corrections, filtering, and aggregation rules along with the equipment and channel and the time of generation. Also retain who ran and approved each process and what was changed later, and why. The fact that raw data remain available and the fact that a qualified reviewer can recheck the processing are conditions that should be examined together.
Criteria to keep in view when making a decision
Sensor selection connects purpose and concentration range with field conditions and the cost of quality control. Specification comparisons become meaningful only after defining the size of the change that must actually be distinguished. A measurement plan is complete only when post-installation inspection and recordkeeping are included.
About the author
Bongjun Lee graduated from the Graduate School of Management of Technology (MOT) at Sogang University. He spent seven years at Infitron Co., Ltd. and 20 years at Honeywell, working for a total of 27 years in gas measurement in service engineering, technical sales, and channel management. He is now the founder of AI Safety Korea Co., Ltd., where he is researching livestock methane monitoring and measurement, reporting, and verification (MRV) systems using sensors, AI, and data analytics.