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Recent climate change mitigation strategies rely on the reduction of methane (CH4) emissions. Carbon and hydrogen isotope ratio (δ13CCH4 and δ2HCH4) measurements can be used to distinguish sources and thus to understand the CH4 budget better. The CH4 emission estimates by models are sensitive to the isotopic signatures assigned to each source category, so it is important to provide representative estimates of the different CH4 source isotopic signatures worldwide. We present new measurements of isotope signatures of various, mainly anthropogenic, CH4 sources in Europe, which represent a substantial contribution to the global dataset of source isotopic measurements from the literature, especially for δ2HCH4. They improve the definition of δ13CCH4 from waste sources, and demonstrate the use of δ2HCH4 for fossil fuel source attribution. We combined our new measurements with the last published database of CH4 isotopic signatures and with additional literature, and present a new global database. We found that microbial sources are generally well characterised. The large variability in fossil fuel isotopic compositions requires particular care in the choice of weighting criteria for the calculation of a representative global value. The global dataset could be further improved by measurements from African, South American, and Asian countries, and more measurements from pyrogenic sources. We improved the source characterisation of CH4 emissions using stable isotopes and associated uncertainty, to be used in top-down studies. We emphasise that an appropriate use of the database requires the analysis of specific parameters in relation to source type and the region of interest. The final version of the European CH4 isotope database coupled with a global inventory of fossil and non-fossil δ13CCH4 and δ2HCH4 source signature measurements is available at https://doi.org/10.24416/UU01-YP43IN (Menoud et al., 2022a).

We developed and field-tested an unmanned aerial vehicle (UAV)-based active AirCore for atmospheric mole fraction measurements of CO2, CH4, and CO. The system applies an alternative way of using the AirCore technique invented by NOAA. As opposed to the conventional concept of passively sampling air using the atmospheric pressure gradient during descent, the active AirCore collects atmospheric air samples using a pump to pull the air through the tube during flight, which opens up the possibility to spatially sample atmospheric air. The active AirCore system used for this study weighs ∼ 1.1 kg. It consists of a ∼ 50 m long stainless-steel tube, a small stainless-steel tube filled with magnesium perchlorate, a KNF micropump, and a 45 µm orifice working together to form a critical flow of dried atmospheric air through the active AirCore. A cavity ring-down spectrometer (CRDS) was used to analyze the air samples on site not more than 7 min after landing for mole fraction measurements of CO2, CH4, and CO. We flew the active AirCore system on a UAV near the atmospheric measurement station at Lutjewad, located in the northwest of the city of Groningen in the Netherlands. Five consecutive flights took place over a 5 h period on the same morning, from sunrise until noon. We validated the measurements of CO2 and CH4 from the active AirCore against those from the Lutjewad station at 60 m. The results show a good agreement between the measurements from the active AirCore and the atmospheric station (N = 146; RCO22: 0.97 and RCH42: 0.94; and mean differences: ΔCO2: 0.18 ppm and ΔCH4: 5.13 ppb). The vertical and horizontal resolution (for CH4) at typical UAV speeds of 1.5 and 2.5 m s−1 were determined to be ±24.7 to 29.3 and ±41.2 to 48.9 m, respectively, depending on the storage time. The collapse of the nocturnal boundary layer and the buildup of the mixed layer were clearly observed with three consecutive vertical profile measurements in the early morning hours. Besides this, we furthermore detected a CH4 hotspot in the coastal wetlands from a horizontal flight north to the dike, which demonstrates the potential of this new active AirCore method to measure at locations where other techniques have no practical access.

There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH4-emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH4-emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h-1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h-1 on 21 August 2017. Results show that CH4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH4-emission rates from strong point sources with high accuracy.

There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm-interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH.sub.4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH.sub.4 -emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH.sub.4 -emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h.sup.-1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h.sup.-1 on 21 August 2017. Results show that CH.sub.4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH.sub.4 -emission rates from strong point sources with high accuracy.

There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH4-emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH4-emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h−1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h−1 on 21 August 2017. Results show that CH4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH4-emission rates from strong point sources with high accuracy.

Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Supersonic helium beams are used in a wide range of applications, for example surface scattering experiments and, most recently, microscopy. The high ionization potential of neutral helium atoms makes it difficult to build efficient detectors. Therefore, it is important to develop beam sources with a high centre line intensity. Several approaches for predicting the centre line intensity exist, with the quitting surface model incorporating the largest amount of physical dependencies. However, until now only a limited amount of experimental data has been available. Here we present a study where we compare the quitting surface model with an extensive set of experimental data. In the quitting surface model the source is described as a sphere from where the particles leave in a molecular flow determined by Maxwell-Boltzmann statistics. We use numerical solutions of the Boltzmann equation to determine its properties. The centre-line intensity is then calculated using an analytical integral. This integral can be reduced to two cases, one which assumes a continuously expanding beam until the skimmer aperture, and another which assumes a quitting surface placed before the aperture. We compare the two cases to experimental data with a nozzle diameter of 10 micron, skimmer diameters ranging from 4 micron to 390 micron, a source pressure range from 2 to 190 bar, and nozzle-skimmer distances between 17.3 mm and 5.3 mm. To support the two analytical approaches, we have also performed equivalent ray tracing simulations. We conclude that the quitting surface model predicts the centre line intensity well for skimmers with a diameter larger than 120 micron when using a beam expanding until the skimmer aperture. For the case of smaller skimmers the trend is correct, but the absolute agreement not as good. We propose several explanations for this, and test the ones that can be implemented analytically.

IntroductionRepetitive head impacts (RHI) in sports may represent a risk factor for long-term cognitive and neurological sequelae. Recent studies have identified an association between playing football at the top level and an elevated risk of cognitive impairment and neurodegenerative disease. However, these were conducted on men, and there is a knowledge gap regarding these risks in female athletes. This study aims to investigate the effect of head impacts on brain health in female former top-level football players.Methods and analysisThis is a prospective cohort study, enroling female former football players and top-level athletes from sports without an inherent risk of RHI. All participants are born in 1980 or earlier. We plan to perform follow-up assessments at least three times over 20 years.The protocol includes neurocognitive assessments, self-reported neurocognitive outcomes, neurological examination, advanced brain MRI, and fluid biomarkers.Ethics and disseminationThe study has been approved by the South-East Regional Ethics Committee for Medical Research in Norway (2023/178330) and the Norwegian Agency for Shared Services in Education and Research (SIKT). A Data Protection Impact Assessment was developed by the research group and approved by SIKT and the Norwegian School of Sport Sciences. We will disseminate the results through peer-reviewed publications, academic conference presentations and webinars. We will communicate with the public and key stakeholders in football worldwide to inform and promote the development and implementation of potential preventive measures based on our study findings.

Background The long-term prognosis after cognitive behavioral therapy (CBT) in outpatient groups for panic disorder and agoraphobia is not well known. The purpose of this study was to assess long-term outcomes in terms of psychological health, health-related quality of life (HRQoL), quality of life (QoL) and treatment satisfaction after CBT for panic disorder and agoraphobia. Methods The sample consisted of 68 patients (61% response rate), who were assessed at pretreatment; at the start and end of treatment; and after 3 months, after 1 year, and over the long term (M = 24 years; SD = 5.3; range: 12 to 31 years). The main outcome was the total score on the Phobic Avoidance Rating Scale (PARS-total). At long-term follow-up, HRQoL was measured with the RAND-12 questionnaire, and QoL was measured with two questions from the “Study on European Union Statistics on Income and Living Conditions”. Patient experiences and treatment satisfaction were assessed by the Generic Short Patient Experiences Questionnaire. A marginal longitudinal model was applied to study the main outcome. Results The effect size of the long-term change (mean change/ pooled SD) in the PARS-total score was (− 1.6, p  < 0.001) and was stable over time. A PARS-total score reduction of 50% was found in 98% of patients at the long-term follow-up. The patients’ HRQoL and QoL were similar to the expected scores for the general Norwegian population. Of the patients, 95% reported high to very high satisfaction with the CBT, and 93% reported large treatment benefits. Conclusions To the best of our knowledge, this study has the longest follow-up after group CBT for panic disorder and agoraphobia, showing a good prognosis in ≥93% of the participating patients.

Fractional flow reserve (FFR) or non-hyperaemic pressure ratios are recommended to assess functional relevance of intermediate coronary stenosis. Both diagnostic methods require the placement of a pressure wire in the coronary artery during invasive coronary angiography. Quantitative flow ratio (QFR) is an angiography-based computational method for the estimation of FFR that does not require the use of pressure wires. We aimed to investigate whether a QFR-based diagnostic strategy yields a non-inferior 12-month clinical outcome compared with an FFR-based strategy. FAVOR III Europe was a multicentre, randomised, open-label, non-inferiority trial comparing a QFR-based with an FFR-based diagnostic strategy for patients with intermediate coronary stenosis. Enrolment was performed in 34 centres across 11 European countries. Patients aged 18 years or older with either chronic coronary syndrome or stabilised acute coronary syndrome, and with at least one intermediate non-culprit stenosis (40–90% diameter stenosis by visual estimate; referred to here as a study lesion), were randomly assigned (1:1) to the QFR-guided or the FFR-guided group. Randomisation was done using a concealed web-based system and was stratified by diabetes and presence of a left anterior descending coronary artery study lesion. The primary endpoint was a composite of death, myocardial infarction, and unplanned revascularisation at 12 months. The predefined non-inferiority margin was 3·4% and the primary analysis was performed in the intention-to-treat population. The trial was registered with ClinicalTrials.gov (NCT03729739) and long-term follow-up is ongoing. Between Nov 6, 2018, and July 21, 2023, 2000 patients were enrolled and randomly assigned to the QFR-guided strategy (1008 patients) or the FFR-guided strategy (992 patients). The median age was 67·3 years (IQR 59·9–74·7); 1538 (76·9%) patients were male and 462 (23·1%) were female. Median follow-up time was 365 days (IQR 365–365). At 12 months, a primary endpoint event had occurred in 67 (6·7%) patients in the QFR group, and in 41 (4·2%) patients in the FFR group (hazard ratio 1·63 [95% CI 1·11–2·41]). The event proportion difference was 2·5% (90% two-sided CI 0·9–4·2). The upper limit of the 90% CI exceeded the prespecified non-inferiority margin of 3·4%. Therefore, QFR did not meet non-inferiority to FFR. A total of 18 (1·8%) patients in each group experienced an adverse procedural event, the most frequent being procedure-related myocardial infarction, which occurred in ten (1·0%) patients in the QFR group and seven (0·7%) in the FFR group. One patient in the QFR group died in relation to the index procedure. The results of the FAVOR III Europe trial do not support the use of QFR if FFR is available to guide revascularisation decisions in patients with intermediate coronary stenosis. This finding could have implications for current clinical guidelines recommending QFR for this purpose. Medis Medical Imaging Systems and Aarhus University.