Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling

Abstract

Abstract. Abundant mining and industrial activities located in the Upper Silesian Coal Basin (USCB) lead to large emissions of the potent greenhouse gas (GHG) methane (CH4). The strong localization of CH4 emitters (mostly confined to known coal mine ventilation shafts) and the large emissions of 448/720 kt CH4 yr−1 reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) and the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) make the USCB a prime research target for validating and improving CH4 flux estimation techniques. High-precision observations of this GHG were made downwind of local (e.g. single facilities) to regional-scale sources (e.g. agglomerations) in the context of the CoMet 1.0 campaign in early summer 2018. A Quantum Cascade/Interband Cascade Laser (QCL/ICL) based spectrometer adapted for airborne research was deployed aboard the German Aerospace Centers (DLR) Cessna 208B to sample the planetary boundary layer (PBL) in situ. Regional CH4 emission estimates for the USCB are derived using a model approach including assimilated wind soundings from three ground-based Doppler lidars. Although retrieving estimates for individual emitters is difficult using only single flights, due to sparse data availability, the combination of two flights allows for exploiting different meteorological conditions (analogous to a sparse tomography algorithm) to establish confidence on facility level estimates. Emission rates from individual sources are not only needed for unambiguous comparisons between bottom-up and top-down inventories but become indispensable if (independently verifiable) sanctions are to be imposed on individual companies emitting GHGs. An uncertainty analysis is presented for both the regional scale and facility level emission estimates. We find instantaneous emission estimates of 452/442 ± 78/75 kt CH4 yr−1 for the morning/afternoon flight of June 6th, 2018. The derived emission rates coincide (±2 %) with annual-average inventorial data from E-PRTR 2017 albeit they are distinctly lower (−37 %/−40 %) than values reported in EDGAR v4.3.2. Discrepancies in available emission inventories could potentially be narrowed down with sufficient observations using the method described herein to bridge the gap between instantaneous emission estimates and yearly averaged inventories.

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

“…In our precursor study (Luther et al, 2019), we used stop-and-go measurements of the column-average dry-air mole fractions of CH 4 (XCH 4 ) by a mobile, ground-based Fourier Transform Spectrometer (FTS) to evaluate the mining emissions of individual ventilation facilities, and found similar emissions as suggested by the E-PRTR inventory. The total USCB emission estimates of Fiehn et al (2020) and Kostinek et al (2020), based on airborne in situ measurements, are in broad agreement with the E-PRTR data for single flights. Using airborne imager data, Krautwurst et al (2021) found some discrepancies between their estimates and the E-PRTR inventory for small groups of ventilation facilities.…”

“…Our simulations of methane dispersion in the USCB partition into two steps largely adopting the basic setup reported by Kostinek et al (2020): first (Sect. 3.1), the wind fields are modeled by a two domain WRF setup including assimilation of the wind lidar observations.…”

“…We calculate differences between pairs of upwind and downwind observations to determine enhancements (∆XCH 4 ) in our XCH 4 records attributable to sources within the USCB. Developing on the model setup of Kostinek et al (2020), we use the Flexible Lagrangian particle dispersion model (FLEXPART) together with wind fields from the Weather Research and Forecasting model (WRF) constrained by three wind lidars to translate the observed XCH 4 enhancements into emission estimates for groups of coal mine ventilation shafts. To this end, we set up an inverse estimation scheme based on Phillips-Tikhonov regularization.…”

Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar

“…Whenever sunlight is needed to perform the measurement, less turbulent conditions, for example in the morning after sunrise or winter, should be preferred. Further, it should be pointed out that, with a lidar, cross-sectional plume measurements can also be performed over water bodies whose detrimental reflective properties often impede the use of passive remote sensing (Gerilowski et al, 2015;Krautwurst et al, 2021;Larsen and Stamnes, 2006). Therefore, plumes from offshore installations can also be addressed with this approach.…”

“…The CoMet campaign saw the deployment of a suite of airborne instruments to measure atmospheric CH 4 and CO 2 , alongside a variety of ground-based instruments. In particular, the synergetic use of active remote sensing (lidar) (Amediek et al, 2017;Wildmann et al, 2020), passive spectrometry (Krautwurst et al, 2021;Luther et al, 2019), and in situ measurements (Fiehn et al, 2020;Gałkowski et al, 2021;Kostinek et al, 2020) supported by modeling activities (Chen et al, 2020;, as well as the validation of existing (e.g., Sentinel-5P, GOSAT, Greenhouse gases Observing SATellite) and the preparation of upcoming (e.g., MERLIN, MEthane Remote sensing Lidar missioN) GHG satellite missions, was aimed at.…”