Ground-based remote sensing of methane - estimating emissions on facility and regional scales

Atmospheric methane (CH4) causes the second largest radiative forcing of the long living greenhouse gases. The methane concentration in the Earth's atmosphere increased by a factor of roughly 2.5 since 1750. The quantication of methane sources is crucial to understand the underlying carbon cycle and hence, the impact of anthropogenic emissions on the global changing climate. Methane emissions from coal production are one of the main sources of anthropogenic CH4 in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish area of the Upper Silesian Coal Basin (USCB) as the main part of it. During the coal mining process, methane is emitted from the coal bed and vented through exhaust shafts to keep the mine safe for workers. Different inventories estimate the emission of the USCB between 344 kt a-1 (EUROSTAT, 2020b) and 720 kt a-1 (Janssens-Maenhout et al., 2017). However, recent studies (Luther et al., 2019; Kostinek et al., 2020; Fiehn et al., 2020) show a general agreement with the E-PRTR inventory, which suggests 466 kt a-1 (E-PRTR 2014) for the USCB. During the Carbon dioxide and Methane Mission 2018 (CoMet), five portable, ground-based, direct sun-viewing Fourier transform infrared spectrometers (FTS) are deployed in the USCB. One instrument is mounted on a truck to perform stop-and-go measurements downwind of single facilities by crossing the emitted methane plumes in 1 to 10 km distance. With a mass balance approach making use of wind information from three co-deployed 3D wind lidars, the emissions of the coal mine ventilation shafts are estimated ranging from 6 ± 1 kt a-1 for a single shaft to 109±33 kt a-1 for a small group of shafts. Wind-related relative errors on the emission estimates typically amount to 20% for the mobile instrument approach. The other four FTS are deployed in the four cardinal directions around the USCB in approx. 50 km distance to the center of the basin. The upwind instrument measures the background methane information from which the downwind observations are deducted to receive regional methane enhancements. WRF (Weather Research and Forecast) model runs with assimilated 3D wind lidar data feed a Lagrangian particle dispersion model (FLEXPART) to simulate the methane distribution. The residuals between simulated and measured enhancements are minimized with a Phillips-Tikhonov regularized, non-negative least squares approach using the E-PRTR inventory data as a-priori information. The regularization parameters are graphically chosen via L-curve determination. Atmospheric variability is expressed through an ensemble of different model runs, each with altered, basic meteorological parameters. One of six case studies agree with the E-PRTR estimates. The other five case studies suggest 1.4 to 3 times higher emissions than reported by the E-PRTR. The errors introduced by the model ensemble range between 10% and 32%. The functional principle of the mobile mass balance method and the model approach based on stationary network observations could thus be demonstrated. With general errors amounting to 20%, the two methods may be applied to verify instantaneous emissions on facility scale as well as on regional scale

Das Königreich Adiabene zwischen Parthern undRömern

In diesem Beitrag werden die Beziehungen zwischen den Königen der Adiabene – einem Gebiet im heutigen Nordirak um die Stadt Erbil – und den Römern näher untersucht. Es zeigt sich, dass die adiabenischen Könige trotz der Zugehörigkeit ihres Reiches zum parthischen Staatsverband zeitweilig auf die Interessen des Römischen Reiches Rücksicht nahmen bzw. nehmen mussten.This article examines more closely the relations between the kings of Adiabene – an area in the North of modern Iraq around the city of Arbil – and the Romans. It reveals that the kings of Adiabene at times took into consideration the interests of the Roman Empire, despite forming part of the Parthian Empire, in part because they had to

Ground-based remote sensing of methane - estimating emissions on facility and regional scales

Atmospheric methane (CH4) causes the second largest radiative forcing of the long living greenhouse gases. The methane concentration in the Earth's atmosphere increased by a factor of roughly 2.5 since 1750. The quantication of methane sources is crucial to understand the underlying carbon cycle and hence, the impact of anthropogenic emissions on the global changing climate. Methane emissions from coal production are one of the main sources of anthropogenic CH4 in the atmosphere. Poland is the largest hard coal producer in the European Union with the Polish area of the Upper Silesian Coal Basin (USCB) as the main part of it. During the coal mining process, methane is emitted from the coal bed and vented through exhaust shafts to keep the mine safe for workers. Different inventories estimate the emission of the USCB between 344 kt a-1 (EUROSTAT, 2020b) and 720 kt a-1 (Janssens-Maenhout et al., 2017). However, recent studies (Luther et al., 2019; Kostinek et al., 2020; Fiehn et al., 2020) show a general agreement with the E-PRTR inventory, which suggests 466 kt a-1 (E-PRTR 2014) for the USCB. During the Carbon dioxide and Methane Mission 2018 (CoMet), five portable, ground-based, direct sun-viewing Fourier transform infrared spectrometers (FTS) are deployed in the USCB. One instrument is mounted on a truck to perform stop-and-go measurements downwind of single facilities by crossing the emitted methane plumes in 1 to 10 km distance. With a mass balance approach making use of wind information from three co-deployed 3D wind lidars, the emissions of the coal mine ventilation shafts are estimated ranging from 6 ± 1 kt a-1 for a single shaft to 109±33 kt a-1 for a small group of shafts. Wind-related relative errors on the emission estimates typically amount to 20% for the mobile instrument approach. The other four FTS are deployed in the four cardinal directions around the USCB in approx. 50 km distance to the center of the basin. The upwind instrument measures the background methane information from which the downwind observations are deducted to receive regional methane enhancements. WRF (Weather Research and Forecast) model runs with assimilated 3D wind lidar data feed a Lagrangian particle dispersion model (FLEXPART) to simulate the methane distribution. The residuals between simulated and measured enhancements are minimized with a Phillips-Tikhonov regularized, non-negative least squares approach using the E-PRTR inventory data as a-priori information. The regularization parameters are graphically chosen via L-curve determination. Atmospheric variability is expressed through an ensemble of different model runs, each with altered, basic meteorological parameters. One of six case studies agree with the E-PRTR estimates. The other five case studies suggest 1.4 to 3 times higher emissions than reported by the E-PRTR. The errors introduced by the model ensemble range between 10% and 32%. The functional principle of the mobile mass balance method and the model approach based on stationary network observations could thus be demonstrated. With general errors amounting to 20%, the two methods may be applied to verify instantaneous emissions on facility scale as well as on regional scale

Second-harmonic generation in vortex-induced waveguides

We study the second-harmonic generation and localization of light in a reconfigurable waveguide induced by an optical vortex soliton in a defocusing Kerr medium. We show that the vortex-induced waveguide greatly improves conversion efficiency from the fundamental to the second harmonic field.Comment: 3 pages, 4 figures, submitted to Optics Letter

Temperature dependent spatial oscillations in the correlations of the XXZ spin chain

We study the correlation $$ for the XXZ chain in the massless attractive (ferromagnetic) region at positive temperatures by means of a numerical study of the quantum transfer matrix. We find that there is a range of temperature where the behavior of the correlation for large separations is oscillatory with an incommensurate period which depends on temperature.Comment: 4 pages, REVTEX, 6 table

Cactaceae at Caryophyllales.org- A dynamic online species-level taxonomic backbone for the family

This data paper presents a largely phylogeny-based online taxonomic backbone for the Cactaceae compiled from literature and online sources using the tools of the EDIT Platform for Cybertaxonomy. The data will form a contribution of the Caryophyllales Network for the World Flora Online and serve as the base for further integration of research results from the systematic research community. The final aim is to treat all effectively published scientific names in the family. The checklist includes 150 accepted genera, 1851 accepted species, 91 hybrids, 746 infraspecific taxa (458 heterotypic, 288 with autonyms), 17,932 synonyms of accepted taxa, 16 definitely excluded names, 389 names of uncertain application, 672 unresolved names and 454 names belonging to (probably artificial) named hybrids, totalling 22,275 names. The process of compiling this database is described and further editorial rules for the compilation of the taxonomic backbone for the Caryophyllales Network are proposed. A checklist depicting the current state of the taxonomic backbone is provided as supplemental material. All results are also available online on the website of the Caryophyllales Network and will be constantly updated and expanded in the future. Citation: Korotkova N., Aquino D., Arias S., Eggli U., Franck A., Gómez-Hinostrosa C., Guerrero P. C., Hernández H. M., Kohlbecker A., Köhler M., Luther K., Majure L. C., Müller A., Metzing D., Nyffeler R., Sánchez D., Schlumpberger B. & Berendsohn W. G. 2021: Cactaceae at Caryophyllales.org- A dynamic online species-level taxonomic backbone for the family.-Willdenowia 51: 251-270. Version of record first published online on 31 August 2021 ahead of inclusion in August 2021 issue. Data published through: Http://caryophyllales.org/cactaceae/Checklis

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

Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources of anthropogenic methane (CH$_{4}$) emissions in Europe. Here, we report on CH$_{4}$emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pairwise upwind–downwind observations of the column-average dry-air mole fractions of CH$_{4}$ (XCH$_{4}$) by a network of four portable, ground-based, sun-viewing Fourier transform spectrometers of the type EM27/SUN operated during the CoMet campaign in May–June 2018. The EM27/SUN instruments were deployed in the four cardinal directions around the USCB approximately 50 km from the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips–Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the emissions averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the emissions averaging kernels is essential to detect correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH$_{4}$ a$^{-1}$ for the southeastern part of the USCB and between 414 and 790 kt CH$_{4}$a$^{-1}$ for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 % and 36 %, dominated by the error contribution from uncertain wind fields