Apportionment and Inventory Optimization of Agriculture and Energy Sector Methane Emissions Using Multi-Month Trace Gas Measurements in Northern Colorado

Abstract

Quantifying sector-resolved methane fluxes in complex emissions environments is challenging yet necessary to improve emissions inventories and guide policy. Here, we separate energy and agriculture sector emissions using a dynamic linear model analysis of methane, ethane, and ammonia data measured at a Northern Colorado site from November 2021 to January 2022. By combining these sector-apportioned observations with spatially resolved inventories and Bayesian inverse methods, energy and agriculture methane fluxes are optimized across the study's ∼850 km2 sensitivity area. Energy sector fluxes are synthesized with previous literature to evaluate trends in energy sector methane emissions. Optimized agriculture fluxes in the study area were 3.5× larger than inventory estimates; we demonstrate this discrepancy is consistent with differences in the modeled versus real-world spatial distribution of agricultural sources. These results highlight how sector-apportioned methane observations can yield multi-sector inventory optimizations in complex environments.