Characterizing High Spatiotemporal Resolution Methane Emissions from an EPA Landfill Testbed Site

Landfill methane emissions are a large portion of anthropogenic methane fluxes, but these estimates are also highly uncertain. Much of this uncertainty is due to a mismatch in scale between reporting methods and various types of observations of methane emissions. Reported landfill methane emissions and subsequent inventories are cumulative annual budgets, derived from first-order decay models or gas capture and collection efficiency models. In contrast, satellite and aircraft-based observations of methane provide measurement-based emission flux estimates but are infrequent snapshots in time. Satellite observations are either fine spatial resolution but with worse detection limits and use plume-modeling assuming a point-source emission, or they have better detection limits but coarser resolution, for which it can be difficult to isolate specific sources of methane within sub-pixel (e.g. < 12 km) heterogeneity. Landfills can act as both point-sources and area-sources of methane, and methane fluxes from landfills can be highly variable in space and time. In this study, we aim to reduce uncertainty in landfill methane emissions by comparing multiple measurement techniques at high spatiotemporal resolution. Beginning in July 2024 we used eddy covariance and footprint modeling to characterize spatiotemporal patterns in methane fluxes from an Environmental Protection Agency (EPA) testbed landfill in Apex, NC. These flux measurements were paired with two ground-based low-cost methane sensors located 500-m downwind of the testbed landfill site. From these sensors we calculated landfill methane emissions using backward Lagrangian stochastic transport (bLst) models at 5 to 30 minute intervals. We investigate potential drivers of landfill methane emissions, such as meteorological conditions and landfill activities. We evaluate contributions to variability and uncertainties in emissions from both eddy covariance and bLst methods, with implications for methane budgets derived from inventory reporting or satellite observations.