Fractionation of Methane Isotopologues during Preparation for Analysis from Ambient Air

Preconcentration of methane (CH4) from air is a critical sampling step in the measurement of singly and doubly substituted isotopologue ratios. We demonstrate the potential for isotope fractionation during preconcentration onto and elution from the common trapping material HayeSep-D and investigate its significance in laser spectroscopy measurements. By altering the trapping temperature for adsorption, the flow direction of CH4 through the trap and the time at which CH4 is eluted during a desorption temperature ramp, we explain the mechanisms behind fractionation affecting δ13C(CH4) and δ2H(CH4). The results highlight that carbon isotope fractionation is driven by advection and diffusion, while hydrogen isotope fractionation is driven by the interaction of CH4 with the adsorbing material (tending to smaller isotopic effects at higher temperatures). We have compared the difference between the measured isotope ratio of sample gases (compressed whole air and a synthetic mixture of CH4 at ambient amount fraction in an N2 matrix) and their known isotopic composition. An open-system Rayleigh model is used to quantify the magnitude of isotopic fractionation affecting measured δ13C(CH4) and δ2H(CH4), which can be used to calculate the possible magnitude of isotopic fractionation given the recovery percentage. These results provide a quantitative understanding of isotopic fractionation during the sample preparation of CH4 from ambient air. The results also provide valuable insights applicable to other cryogenic preconcentration systems, such as those for measurements that probe the distribution of rarer isotopologues

Supporting the creation of hybrid museum experiences

This paper presents the evolution of a tool to support the rapid prototyping of hybrid museum experiences by domain professionals. The developed tool uses visual markers to associate digital resources with physical artefacts. We present the iterative development of the tool through a user centred design process and demonstrate its use by domain experts to realise two distinct hybrid exhibits. The process of design and refinement of the tool highlights the need to adopt an experience oriented approach allowing authors to think in terms of the physical and digital “things” that comprise a hybrid experience rather than in terms of the underlying technical components

Supporting the design of network-spanning applications

In this case study, we describe our use of ECT, a tool intended to simplify the design and development of network-spanning applications. We have used ECT throughout the course of a two-year collaboration, which has involved individuals with expertise in a variety of fields, including interaction design and computer systems engineering. We describe our experiences with this tool, with a particular focus on its emerging role in helping us to structure our collaboration. We conclude by presenting lessons that we have learned, and by suggesting future directions for the development of tools to support the design of network-spanning applications

Detectability of COVID-19 global emissions reductions in local CO2 concentration measurements

It is estimated that global anthropogenic carbon dioxide (CO2) emissions reduced by up to 12% at the start of 2020 compared to recent years due to the COVID19 related downturn in economic activity. Despite the large decrease in CO2 emissions, no reduction in the trend in background atmospheric CO2 concentrations has been detected. So, how long would it take for sustained COVID-19 CO2 emission reductions to be detected in daily and monthly averaged local CO2 concentration measurements? CO2 concentration measurements for 5 measurement sites in the UK and Ireland are combined with meteorological numerical weather prediction data to build statistical models that can predict future CO2 concentrations. It is found that 75% of the observed daily variability can be explained by these simple models. Emission reduction scenario experiments using these simple models illustrate that large daily and seasonal variability in local CO2 concentrations precludes the rapid emergence of a detectable signal. COVID-19 magnitude emissions reductions would only be detectable in the daily CO2 concentrations after at least 38 months and in monthly CO2 concentrations after 11 months of sustained reductions. For monthly CO2 concentrations the time of emergence is similar for all sites since the seasonal variability is largely driven by non-local fluxes of CO2 between the terrestrial biosphere and the atmosphere. The COVID-19 CO2 anthropogenic emissions reductions are similar in magnitude to those that are required to meet the Paris Agreement target of keeping global temperatures below 2◦C. This study demonstrates that, using measurements alone, there will be a considerable lag between changes in global anthropogenic emissions and a detected signal in local CO2 concentration trends. Thus, there is likely to be a delay of several years between changes in policy designed to meet CO2 anthropogenic emissions targets and our ability to detect the impact of these policies on CO2 concentrations using atmospheric measurements alone

The Implementation of Recommender Systems for Mental Health Recovery Narratives: Evaluation of Use and Performance

Background:Recommender systems help narrow down a large range of items to a smaller, personalized set. NarraGive is a first-in-field hybrid recommender system for mental health recovery narratives, recommending narratives based on their content and narrator characteristics (using content-based filtering) and on narratives beneficially impacting other similar users (using collaborative filtering). NarraGive is integrated into the Narrative Experiences Online (NEON) intervention, a web application providing access to the NEON Collection of recovery narratives.Objective:This study aims to analyze the 3 recommender system algorithms used in NarraGive to inform future interventions using recommender systems for lived experience narratives.Methods:Using a recently published framework for evaluating recommender systems to structure the analysis, we compared the content-based filtering algorithm and collaborative filtering algorithms by evaluating the accuracy (how close the predicted ratings are to the true ratings), precision (the proportion of the recommended narratives that are relevant), diversity (how diverse the recommended narratives are), coverage (the proportion of all available narratives that can be recommended), and unfairness (whether the algorithms produce less accurate predictions for disadvantaged participants) across gender and ethnicity. We used data from all participants in 2 parallel-group, waitlist control clinical trials of the NEON intervention (NEON trial: N=739; NEON for other [eg, nonpsychosis] mental health problems [NEON-O] trial: N=1023). Both trials included people with self-reported mental health problems who had and had not used statutory mental health services. In addition, NEON trial participants had experienced self-reported psychosis in the previous 5 years. Our evaluation used a database of Likert-scale narrative ratings provided by trial participants in response to validated narrative feedback questions.Results:Participants from the NEON and NEON-O trials provided 2288 and 1896 narrative ratings, respectively. Each rated narrative had a median of 3 ratings and 2 ratings, respectively. For the NEON trial, the content-based filtering algorithm performed better for coverage; the collaborative filtering algorithms performed better for accuracy, diversity, and unfairness across both gender and ethnicity; and neither algorithm performed better for precision. For the NEON-O trial, the content-based filtering algorithm did not perform better on any metric; the collaborative filtering algorithms performed better on accuracy and unfairness across both gender and ethnicity; and neither algorithm performed better for precision, diversity, or coverage.Conclusions:Clinical population may be associated with recommender system performance. Recommender systems are susceptible to a wide range of undesirable biases. Approaches to mitigating these include providing enough initial data for the recommender system (to prevent overfitting), ensuring that items can be accessed outside the recommender system (to prevent a feedback loop between accessed items and recommended items), and encouraging participants to provide feedback on every narrative they interact with (to prevent participants from only providing feedback when they have strong opinions)

The increasing atmospheric burden of the greenhouse gas sulfur hexafluoride (SF<sub>6</sub>)

We report a 40-year history of SF6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr-1 in 2008 and over the past decade emissions have continued to increase by about 24% to 9.04±0.35 Gg yr-1 in 2018. We show that changing patterns in SF6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF6-insulated switchgear, circuit breakers, and transformers. The large bank of SF6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF6-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5-5 Gg yr-1 (1990-2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36% and 3.1 %, respectively, of our global SF6 emissions estimate.NASA (Grant NAG5-12669, NNX07AE89G and NNX11AF17G)NOAA (Contract RA-133R-15-CN-0008

Quantifying fossil fuel methane emissions using observations of atmospheric ethane and an uncertain emission ratio

We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a spatially and temporally variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane–ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95 % uncertainty interval 0.26–0.29) Tg yr−1 from fossil fuel sources and 2.06 (1.99–2.15) Tg yr−1 from non-fossil fuel sources, during the period 2015–2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15 % and up to 35 % when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion

Combining top-down and bottom-up approaches to evaluate recent trends and seasonal patterns in UK N2O emissions

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modeling. Atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (UK) and Republic of Ireland are used to derive monthly N2O emissions for 2013–2022 using two different inverse methods. We find mean UK emissions of 90.5 ± 23.0 (1σ) and 111.7 ± 32.1 (1σ) Gg N2O yr−1 for 2013–2022, and corresponding trends of −0.68 ± 0.48 (1σ) Gg N2O yr−2 and −2.10 ± 0.72 (1σ) Gg N2O yr−2, respectively, for the two inverse methods. The UK National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 ± 1.7 (1σ) Gg N2O yr−1 across this period, which is 22%–51% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up UK Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top-down (atmospheric data-based) and bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation

Impact of receiving recorded mental health recovery narratives on quality of life in people experiencing non-psychosis mental health problems (NEON-O Trial):updated randomised controlled trial protocol

Background: Mental health recovery narratives are first-person lived experience accounts of recovery from mental health problems, which refer to events or actions over a period of time, and which include elements of adversity or struggle, and also self-defined or observable strengths, successes, or survival. Recorded recovery narratives are those presented in invariant form, including text, audio, or video. In a previous publication, we presented a protocol for three pragmatic trials of the Narrative Experiences Online (NEON) Intervention, a web application recommending recorded recovery narratives to participants. The aim of the definitive NEON Trial was to understand whether the NEON Intervention benefitted people with experience of psychosis. The aim of the smaller NEON-O and NEON-C trials was to evaluate the feasibility of conducting definitive trials of the NEON Intervention with people (1) experiencing non-psychosis mental health problems and (2) who informally care for others experiencing mental health problems. An open recruitment strategy with a 60-week recruitment period was developed. Recruitment for the NEON Trial and NEON-O Trial targeted mental health service users and people not using mental health services. The NEON Trial recruited to time and target. The NEON-O Trial achieved its target in 10 weeks. Analysis considered by a Programme Steering Committee after the target was achieved demonstrated a definitive result could be obtained if the trial was adapted for recruitment to continue. The UK Health Research Authority approved all needed amendments following ethical review. Purpose of this article: To describe the decision-making process for amending the NEON-O Trial and to describe amendments made to the NEON-O Trial to enable a definitive result. The article describes amendments to the aims, objectives, design, power calculation, recruitment rate, process evaluation design, and informed consent documents. The extended NEON-O Trial adopts analysis principles previously specified for the NEON Trial. The article provides a model for other studies adapting feasibility trials into definitive trials. Trial registration: All trials prospectively registered. NEON Trial: ISRCTN11152837. Registered on 13th August 2018. NEON-C Trial: ISRCTN76355273. Registered on 9th January 2020. NEON-O Trial: ISRCTN63197153. Registered on 9th January 2020. The NEON-O Trial ISRCTN was updated when amendments were approved. Amendment details: NOSA2, 30th October 2020