CH4 emission estimates from an active landfill site inferred from a combined approach of CFD modelling and in situ FTIR measurements

Globally, the waste sector contributes to nearly a fifth of anthropogenic methane emitted to the atmosphere and is the second largest source of methane in the UK. In recent years great improvements to reduce those emissions have been achieved by installation of methane recovery systems at landfill sites and subsequently methane emissions reported in national emission inventories have been reduced. Nevertheless, methane emissions of landfills remain uncertain and quantification of emission fluxes is essential to verify reported emission inventories and to monitor changes in emissions. Here we present a new approach for methane emission quantification from a complex source like a landfill site by applying a Computational Fluid Dynamics (CFD) model to calibrated in situ measurements of methane as part of a field campaign at a landfill site near Ipswich, UK, in August 2014. The methane distribution for different meteorological scenarios is calculated with the CFD model and compared to methane mole fractions measured by an in situ Fourier Transform Infrared (FTIR) spectrometer downwind of the prevailing wind direction. Assuming emissions only from the active site, a mean daytime flux of 0.83 mg m−2 s−1, corresponding to 53.26 kg h−1, was estimated. The addition of a secondary source area adjacent to the active site, where some methane hotspots were observed, improved the agreement between the simulated and measured methane distribution. As a result, the flux from the active site was reduced slightly to 0.71 mg m−2 s−1 (45.56 kg h−1), at the same time an additional flux of 0.32 mg m−2 s−1 (30.41 kg h−1) was found from the secondary source area. This highlights the capability of our method to distinguish between different emission areas of the landfill site, which can provide more detailed information about emission source apportionment compared to other methods deriving bulk emissions

A measurement-based verification framework for UK greenhouse gas emissions: an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) project

We describe the motivation, design, and execution of the Greenhouse gAs Uk and Global Emissions (GAUGE) project. The overarching scientific objective of GAUGE was to use atmospheric data to estimate the magnitude, distribution, and uncertainty of the UK greenhouse gas (GHG, defined here as CO₂, CH₄, and N₂O) budget, 2013–2015. To address this objective, we established a multi-year and interlinked measurement and data analysis programme, building on an established tall-tower GHG measurement network. The calibrated measurement network comprises ground-based, airborne, ship-borne, balloon-borne, and space-borne GHG sensors. Our choice of measurement technologies and measurement locations reflects the heterogeneity of UK GHG sources, which range from small point sources such as landfills to large, diffuse sources such as agriculture. Atmospheric mole fraction data collected at the tall towers and on the ships provide information on sub-continental fluxes, representing the backbone to the GAUGE network. Additional spatial and temporal details of GHG fluxes over East Anglia were inferred from data collected by a regional network. Data collected during aircraft flights were used to study the transport of GHGs on local and regional scales. We purposely integrated new sensor and platform technologies into the GAUGE network, allowing us to lay the foundations of a strengthened UK capability to verify national GHG emissions beyond the project lifetime. For example, current satellites provide sparse and seasonally uneven sampling over the UK mainly because of its geographical size and cloud cover. This situation will improve with new and future satellite instruments, e.g. measurements of CH₄ from the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5P. We use global, nested, and regional atmospheric transport models and inverse methods to infer geographically resolved CO₂ and CH₄ fluxes. This multi-model approach allows us to study model spread in a posteriori flux estimates. These models are used to determine the relative importance of different measurements to infer the UK GHG budget. Attributing observed GHG variations to specific sources is a major challenge. Within a UK-wide spatial context we used two approaches: (1) Δ¹⁴CO₂ and other relevant isotopologues (e.g. δ¹³CCH₄) from collected air samples to quantify the contribution from fossil fuel combustion and other sources, and (2) geographical separation of individual sources, e.g. agriculture, using a high-density measurement network. Neither of these represents a definitive approach, but they will provide invaluable information about GHG source attribution when they are adopted as part of a more comprehensive, long-term national GHG measurement programme. We also conducted a number of case studies, including an instrumented landfill experiment that provided a test bed for new technologies and flux estimation methods. We anticipate that results from the GAUGE project will help inform other countries on how to use atmospheric data to quantify their nationally determined contributions to the Paris Agreement

GraFLAP - LMS-unabhängige Bewertung von JFLAP-Aufgaben

Bisher wurde die automatisierte Bewertung von Übungsaufgaben in LON-CAPA angeboten und mit mehreren Werkzeugen umgesetzt, darunter der JFLAP-Wrapper, der die Grundlage dieser Arbeit bildet. Daraus soll ein vollständiges eigenstehendes Programm erarbeitet werden, dass auch an andere Lernmanagementsysteme angebunden werden kann. Dabei erhält es den neuen Namen GraFLAP. Dazu wurden die Bewertungsprozesse im JFLAP-Wrapper zusammen gelegt und eine neue Schnittstelle nach ProFormA-2.1-Standard ergänzt. Außerdem sollte die Wartbarkeit verbessert werden, sodass zukünftige weiterführende Arbeiten erleichtert werden. Dazu wurden neue Datenstrukturen und Prozesse integriert, unter anderem ein einheitlicher Build-Prozess mit Maven und automatisierte Tests mit JUnit. GraFLAP bietet nun eine standardisierte Schnittstelle, übernimmt alle Bewertungsprozesse und ist so nun vollständig unabhängig von Lernmanagementsystemen

Ganglioside GM2 is substrate for a sialidase in MDCK cells

GM1 ganglioside carrying a fluorescent fatty acid in substitution of the natural one, has been administered to cultured Madin-Darby canine kidney (MDCK) cells for different pulse times (0.5^24 h), and its metabolic fate was followed. The fluorescent GM2, asialo-GM2, asialo-GM1 and ceramide were the only detectable metabolites. The complete absence of fluorescent GM3 is consistent with the presence in these cells of a sialidase working on GM1 and GM2 gangliosides. After treatment of the cells with chloroquine the fluorescent GM1 remained essentially undegraded, indicating a catabolic proces- sing at lysosomal level