Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis
Sudhanshu Pandey, John R. Worden, Daniel Cusworth, Daniel J. Varon, Matthew D. Thill, Daniel Jacob, K. W. Bowman
Abstract
High Resolution Image Download MS PowerPoint Slide Surface emissions of atmospheric trace gases like methane are typically inferred through two methodologies: plume detection and area-scale estimation. Integrating these methods can enhance emission monitoring but remains challenging due to irregular sampling, variable detection sensitivities, and differing spatial resolutions among plume-detecting instruments. In this study, we develop a theoretical framework to link plume-scale and area-scale emission estimates for regions with dense point-source emissions. Our analysis demonstrates that the spatial resolution of plume-detecting instruments influences the observed distribution of plume emission rates. Empirical tests using oil and gas emissions data from the Permian Basin reveal a robust linear relationship between summed gridded plume emission rates and area-scale estimates. After accounting for variability in sampling of the plume detectors, area-scale estimates derived from TROPOMI flux inversions strongly correlate with weekly plume sums ( R 2 > 0.94, P < 0.005). We also assess the feasibility of using plume data to inform area-scale estimates within a Bayesian assimilation framework and find that plume assimilation improves the constant EDF inventory, bringing it into agreement with independent TROPOMI-derived emission estimates. This work highlights that, given sufficient sampling and favorable observational conditions, plume observations from aircraft, satellites, and in situ instruments can inform and enhance area-scale methane emission estimates, particularly within the oil and gas sector.