Informing landfill emission reporting programs through remote sensing

Kate Howell, Tia Scarpelli, Riley Duren, Andrew Thorpe, Gregory Asner, Joseph Heckler, Roger Green, Halley Brantley, Daniel Cusworth 

Landfills are a significant source of global methane emissions. To quantify and track emissions from these sources over time, jurisdictions and industry experts generally use emission models that involve a variety of process and waste characterization assumptions. These models are used to assess landfill operational performance and establish emission reduction targets. Testing the assumptions of these models through atmospheric measurements is challenging due to the complex nature and variability of landfill emissions, and due to difficulty in observing a sufficient set of waste sites than span a diversity of operations, sizes, and management practices. In this study, using data from multi-season campaigns with airborne and satellite remote sensing instruments in both California and the Southeastern U.S., we quantify correlation between site-level variables (operational and environmental) and observed emissions, and use this information to guide observation-informed adjustments to emissions models, reconciling the two emission estimates. Through intensive airborne sampling at California sites, we quantify variability of site-level emissions throughout the study period, yet do not find a clear seasonal pattern to this variability. We show significant correlation between measured annual gas collection as reported to the United States Greenhouse Gas Reporting Program (GHGRP) and time-averaged emissions estimates derived from airborne remote-sensing data. Observed emissions are also strongly correlated with one of the GHGRP’s existing emission models but suggest that there may be a low, regionally variable, bias in the model compared to observations. We use gas collection efficiency, a key assumption to this model, as an adjustment factor to bring modeled emissions more in line with observations and test this methodology for reconciliation against varying monitoring schemes using satellite and airborne data. We find that observed and modeled emissions agree when reported collection efficiencies at individual landfills are replaced with optimized regional values (42% in the Southeast, 69% in California). These results demonstrate a pathway for top-down and bottom-up landfill emission reconciliation, helping to improve reporting and verification programs which are crucial for attributing progress towards sustainability goals.