Bayesian inversion methods for quantifying diffuse methane emissions with open-path laser measurements: Enabling landscape-scale tracking of distributed sources

Quantifying methane emissions from distributed, non-point sources remains a critical barrier to effective mitigation in the waste, agriculture, and natural systems sectors. Unlike oil and gas infrastructure that produces well-defined plumes from point sources at the local scale, emissions from landfills, wetlands, and agricultural operations are often diffuse, cover large spatial extents (100s–1000s m²), and produce weaker local enhancements. Despite potential difficulties in quantification, these sources remain globally significant contributors to the methane budget and require accurate characterization for effective mitigation. The next generation of emissions quantification requires new measurement approaches specifically designed for distributed sources.  

We present a Bayesian inversion framework designed to quantify distributed, area-source fluxes from open-path laser spectroscopy concentration measurements. This method simultaneously retrieves spatially-resolved surface fluxes and time-varying background concentrations. We eliminate the common practice of discarding measurements when unenhanced background concentrations are unavailable by relaxing the constraint of requiring explicit background subtraction or favorable wind conditions. This approach enables quantification in systems characterized by networks of diffuse and nearby sources. By accumulating observations over extended periods (hours to weeks), the method achieves sensitivity to weak signals below traditional detection thresholds while providing detailed uncertainty diagnostics through the Bayesian framework. These diagnostics inform how well our measurements constrain inferred emission rates, providing actionable guidance for observing system design and establishing emission detection thresholds. 

We demonstrate these methods with continuous multi-month observations at an active municipal solid waste landfill, revealing linkages between operational activities and emission patterns. Combining new inversion methods with open-path laser measurements enables reliable mitigation tracking in the distributed-source sectors where closing the gap between atmospheric observations and inventory estimates is most critical.