Calibration and field deployment of low-cost sensor network to monitor underground pipeline leakage

Recent technological advances in methane detection have improved leak detection and repair. However, current methods to reliably measure methane concentrations rely on expensive instruments or demand significant labor input. There is interest in using affordable methane sensors that are responsive to ppmv level changes in methane concentrations in both urban and rural environments for monitoring underground natural gas pipeline leaks. This is especially relevant for situations where potentially significant leaks cannot be repaired immediately or smaller leaks that require long-term monitoring and further evaluation. In this work, a low-cost sensor unit, equipped with a metal oxide sensor, was designed, built, and calibrated over a wide range of methane concentrations and environmental conditions in preparation for field application. A network of these sensors was then installed at the test site and used to measure methane concentrations at ground level above known sub-surface natural gas emissions which emulated underground gas pipeline leaks. This low-cost sensor network measured over 4 days total for the two different known leakage rates. Results demonstrate that the sensors can continuously measure relative methane variability for extended periods but require calibration for a wide range of temperature and humidity conditions to properly determine absolute gas (i.e., methane) concentrations. When a regression analysis was conducted to evaluate the effects of meteorological parameters on methane concentration, air temperature and wind speed have strong impacts on the concentration. Overall, the network approach allows improved identification of leak location and monitoring of underground natural gas leaks. •Low-cost sensors can monitor underground natural gas pipelines for leak detection.•Meteorological effects on low-cost sensor were included to improve gas prediction.•Calibrated sensors showed acceptable accuracy and stability.•A network of low-cost sensors can enhance spatiotemporal coverage.