Estimating point source emissions using CO2 and CH4 lidar observations

Wolff, Sebastian (2025): Estimating point source emissions using CO2 and CH4 lidar observations. Dissertation, LMU München: Fakultät für Physik

Abstract

International climate agreements aim to reduce anthropogenic greenhouse gas (GHG) emissions over the coming decades. A substantial share of these emissions originates from localized point sources, such as power plants and coal mines. As emissions potentially become smaller, quantification methods must become more precise and robust in order to reliably detect these changes. Satellite observations are well suited for this task due to their global coverage, but most current missions rely on passive sensors that measure reflected sunlight. Their capabilities are therefore limited under clouds, over dark surfaces, and during night or polar winter. Active remote sensing with Integrated Path Differential Absorption (IPDA) lidar overcomes these constraints. This dissertation employs the airborne IPDA lidar system CHARM-F to quantify CO2 and CH4 emissions from point sources using data from the 2018 CoMet campaign in Europe. Two case studies of increasing complexity demonstrate its capabilities. Case Study I investigates the isolated CO2 plume of the Jänschwalde coal-fired power plant. Using the cross-sectional flux method, I derive an average emission of 20.3 ± 7.9 Tg/a, which agrees within uncertainty with annually reported inventory data. The variability between overflights mainly results from turbulence-induced inhomogeneities in plume propagation and exceeds the formal measurement uncertainty. Individual overflights achieve flux estimates with uncertainties of only 8 – 10 %. Based on simulations, I show that these turbulence-driven limitations can be circumvented by conducting nighttime flights under stable conditions, where even a single instantaneous flux measurement reaches an accuracy of about 95 %. Case Study II addresses multiple CH4 emissions from coal mine ventilation shafts in the Upper Silesian Coal Basin. To disentangle overlapping plumes, I develop a novel inversion-driven clustering approach: by combining automated diagnostics with expert judgment, individual shafts aggregate into 13 emission clusters. Posterior emission estimates are obtained for each cluster, yielding a total basin-wide emission of 570 ± 78 kt/a, which is 16 % higher than officially reported inventory data. These results are consistent with independent observation-based studies, which highlights the robustness of the methodology despite transport and background uncertainties. Together, these case studies show that airborne IPDA lidar provide reliable and independent emission estimates for both isolated and clustered point sources. The findings underline their value for validating inventories, reducing reporting uncertainties, and guiding the design of future airborne and spaceborne missions such as MERLIN, thereby strengthening the global framework for GHG monitoring.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: CO2 and CH4, point sources, airborne lidar, bayesian inversion, WRF
• Themengebiete: 500 Naturwissenschaften und Mathematik ; 500 Naturwissenschaften und Mathematik > 530 Physik
• Fakultäten: Fakultät für Physik
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 16. Dezember 2025
• 1. Bericht­erstatter:in: Rapp, Markus