A new method for quantifying methane emitted from a landfill using a drone-mounted TDLAS sensor

In waste landfills, as well as in the oil and gas sectors, the drive toward greater sustainability has intensified efforts to detect methane leaks. These efforts aim not only to curb the release of a potent greenhouse gas but also to enhance fuel recovery. Environmental monitoring plays a crucial role in assessing climate impacts and other environmental effects associated with the operation of production sites and facilities. Additionally, it contributes to a better understanding of production processes, enables more efficient resource allocation, and helps anticipate and mitigate environmental risks. Currently, most monitoring and data collection methods rely on a mix of ground-based measurements, sensor-equipped manned aircraft, and satellite observations. Each method, however, faces spatial and temporal limitations. In this context, Unmanned Aerial Systems (UAS), commonly known as drones, offer significant potential to enhance environmental monitoring. Equipped with advanced sensors, UAS can bridge the gap between ground measurements and conventional aerial remote sensing by providing high-resolution spatial data over large areas with notable savings in both time and cost. For effective leak detection and flow estimation, drone-mounted sensors must be precisely calibrated to suit the specific characteristics of the sensor employed. In this study, methane detection was conducted using a tunable diode laser absorption spectrometer (TDLAS) mounted on a drone. The collected data were processed using a mass balance approach to estimate methane flow rates. This work presents the key features of the model developed to quantify biogas emissions from landfills. By analyzing how various factors affect background noise in measurements, the study identifies optimal setup conditions for reliably detecting and quantifying methane leaks. The methodology is based on field testing through a series of drone survey campaigns carried out under different environmental conditions. These campaigns involved multiple flights with varying parameters such as drone speed and altitude, conducted under a range of weather and seasonal conditions. Tests were also performed over various surfaces. In some cases, controlled methane releases were carried out to assess how different background levels impact methane flow estimation using the mass balance method. The findings were used to develop a practical intervention method for measuring methane emissions across extensive areas like landfills. The method was then applied to multiple landfill sites, and results underscored the critical role of certain variables—particularly wind speed and direction—in minimizing uncertainty in emission estimates.