Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis

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 (R² > 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.