Detection of Methane Leaks via a Drone-Based System for Sustainable Landfills and Oil and Gas Facilities: Effect of Different Variables on the Background-Noise Measurement

Giuseppe Tassielli (), Lucianna Cananà and Miriam Spalatro
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Giuseppe Tassielli: Department of Physics, University of Bari “Aldo Moro”, Via Orabona 4, 70124 Bari, Italy
Lucianna Cananà: Ionian Department of Law, Economics and Environment, University of Bari “Aldo Moro”, Via Lago Maggiore Ang. Via Ancona, 74121 Taranto, Italy
Miriam Spalatro: Department of Physics, University of Bari “Aldo Moro”, Via Orabona 4, 70124 Bari, Italy

Sustainability, 2024, vol. 16, issue 17, 1-20

Abstract: In recent years, thanks to the great diffusion of drone technology and the development of miniaturized sensors that can be connected to drones, in order to increase the sustainability of landfills and oil and gas facilities, interest in finding methane leaks and quantifying the relative flow has grown significantly. This operation requires the methane background concentration to be subtracted from the calculations. Therefore, in order to proceed with a right estimate of CH 4 flows emitted, the possibility of correctly measuring or estimating the background level becomes crucial. The present work intends to illustrate the effects of different variables on the background-noise measurement in a drone-based system that uses a tunable diode laser absorption spectrometer (TDLAS). The methodology used is that of field testing; the data acquisition campaign consisted of the execution of 80 flights during which different flight variables (drone speed, flight altitude) were tested; the flights were repeated in different weather and climate conditions both during the same day and in different periods of the year. Different surfaces, similar to those found in landfill or natural gas sites, were also tested. In some of the field trials, a controlled methane release test was performed in order to verify how much the quantification of the methane flow can vary depending on the background level used. The results of the different field trials highlighted the best conditions under which to measure methane emissions with a TDLAS sensor in order to minimize the number of outliers: flight altitude not exceeding 15 m above ground level; the drone speed appears to have less impact on the results, however, it is optimal between 1 and 2 ms −1 ; a very sunny day produces much higher methane background levels than a cloudy one. The type of surface also significantly affects the measurement of background noise. Finally, tests conducted with a controlled methane release highlighted that different levels of background have a significant impact on the estimation of the methane flux emitted.

Keywords: drone; UAS; methane; fugitive emissions; TDLAS; landfill; climate change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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