Estimating Local CH4 Emissions in the Upper Silesian Coal Basin Using Inverse Modelling

Abstract

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH4 concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH4 emissions originates from point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favorable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for the future satellite mission MERLIN. CHARM-F measures weighted vertical column mixing ratios of CO2 and CH4 below the aircraft. In spring 2018, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH4 emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH4 exhaust plumes can overlap. This makes simple mass balance approaches to determine the emission rates of single shafts in a direct manner using for instance the cross-sectional flux method, difficult. Therefore, we apply inverse modelling to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH4 emissions forward in space and time, samples the simulated CH4 concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates and their uncertainties.