Global emissions of fluorinated greenhouse gases until 2050: technical mitigation potentials and cost

The anthropogenic fluorinated (F-gases) greenhouse gas emissions have increased significantly in recent years and are estimated to rise further in response to increased demand for cooling services and the phase out of ozonedepleting substances (ODS) under the Montreal Protocol. F-gases (HFCs, PFCs and SF6) are potent greenhouse gases, with a global warming effect up to 22,800 times greater than carbon dioxide (CO2). This study presents estimates of current and future global emissions of F-gases, their technical mitigation potential and associated costs for the period 2005 to 2050. The analysis uses the GAINS model framework to estimate emissions, mitigation potentials and costs for all major sources of anthropogenic F-gases for 162 countries/regions, which are aggregated to produce global estimates. For each region, 18 emission source sectors with mitigation potentials and costs were identified. Global F-gas emissions are estimated at 0.7 Gt CO2eq in 2005 with an expected increase to about 3.6 Gt CO2eq in 2050. There are extensive opportunities to reduce emissions by over 95 percent primarily through replacement with existing low GWP substances. The initial results indicate that at least half of the mitigation potential is attainable at a cost of less than 20C per t CO2eq, while almost 90 percent reduction is attainable at less than 100C per t CO2eq. Currently, several policy proposals have been presented to amend the Montreal Protocol to substantially curb global HFC use. We analyze the technical potentials and costs associated with the HFC mitigation required under the different proposed Montreal Protocol amendments

First ice core records of NO3− stable isotopes from Lomonosovfonna, Svalbard

Samples from two ice cores drilled at Lomonosovfonna, Svalbard, covering the period 1957–2009, and 1650–1995, respectively, were analyzed for NO3− concentrations, and NO3− stable isotopes (δ15N and δ18O). Post-1950 δ15N has an average of (−6.9 ± 1.9) ‰, which is lower than the isotopic signal known for Summit, Greenland, but agrees with values observed in recent Svalbard snow and aerosol. Pre-1900 δ15N has an average of (4.2 ± 1.6) ‰ suggesting that natural sources, enriched in the 15 N-isotope, dominated before industrialization. The post-1950 δ18O average of (75.1 ± 4.1) ‰ agrees with data from low and polar latitudes, suggesting similar atmospheric NOy (NOy = NO + NO2 + HNO3) processing pathways. The combination of anthropogenic source δ15N and transport isotope effect was estimated as −29.1 ‰ for the last 60 years. This value is below the usual range of NOx (NOx = NO + NO2) anthropogenic sources which is likely the result of a transport isotope effect of –32 ‰. We suggest that the δ15N recorded at Lomonosovfonna is influenced mainly by fossil fuel combustion, soil emissions and forest fires; the first and second being responsible for the marked decrease in δ15N observed in the post-1950s record with soil emissions being associated to the decreasing trend in δ15N observed up to present time, and the third being responsible for the sharp increase of δ15N around 2000

Carbon in global waste and wastewater flows – its potential as energy source under alternative future waste management regimes

This study provides a quantification of the maximum energy that can be generated from global waste and wastewater sectors in the timeframe to 2050, as well as of the potential limitations introduced by different future waste and wastewater management regimes. Results show that considerable amounts of carbon are currently stored in waste materials without being recovered for recycling or made available for energy generation. Future levels of energy recovery when maintaining current states of waste and wastewater management systems are contrasted with those that can be attained under a circular system identified here as a system with successful implementation of food and plastic waste reduction policies, maximum recycling rates of all different types of waste streams, and once the recycling capacity is exhausted, incineration of remaining materials to produce energy. Moreover, biogas is assumed to be produced from anaerobic codigestion of food and garden wastes, animal manure, and anaerobically treated wastewater. Finally, we explore the limits for energy generation from waste and wastewater sources should the efficiency of energy recovery be pushed further through development of existing technology. We find that global implementation of such an ideal system could increase the relative contribution of waste and wastewater sources to global energy demand from 2% to 9% by 2040, corresponding to a maximum energy potential of 64 EJ per year. This would however require widespread adoption of policies and infrastructure that stimulate and allow for large-scale waste prevention and separation, as well as highly advanced treatment processes. Giving priority to such efforts would enable circularity of the waste-energy system

Non-CO2 greenhouse gas emissions in the EU-28 from 2005 to 2050: GAINS model methodology

This report presents the GAINS model methodology for the 2016 Reference scenario for emissions of non-CO2 greenhouse gases (GHGs), mitigation potentials and costs in the EU-28 with projections to 2050. The non-CO2 emission scenarios form part of the work under the EUCLIMIT2 project1. The project aims at producing projections for all emissions of GHGs in the EU-28 consistent with the macroeconomic and population projections presented in EC/DG ECFIN (2015). Four modelling groups were involved in the work: PRIMES (National Technical University of Athens), CAPRI (Bonn University), GLOBIOM (IIASA-ESM program) and GAINS (IIASA-MAG program). This report focuses on describing the methodology of the GAINS model for the estimation of the non-CO2 GHGs, i.e., methane (CH4), nitrous oxide (N2O) and three groups of fluorinated gases (F-gases) viz. hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6). The report is structured as follows. Section 2 presents the general GAINS methodology for estimating draft non-CO2 greenhouse gas emissions for EU-28. Sections 3, 4 and 5 describe in detail the methodology applied for estimation of emissions by source for CH4, N2O and Fgases, respectively. Finally, Section 6 provides a comparison between emissions reported by member states to the UNFCCC for years 2005 and 2010 and the emissions estimated by the GAINS model for the same years

Does using a foreign language reduce mental imagery?

In a recent article, Hayakawa and Keysar (2018) propose that mental imagery is less vivid when evoked in a foreign than in a native language. The authors argue that reduced mental imagery could even account for moral foreign language effects, whereby moral choices become more utilitarian when made in a foreign language. Here we demonstrate that Hayakawa and Keysar's (2018) key results are better explained by reduced language comprehension in a foreign language than by less vivid imagery. We argue that the paradigm used in Hayakawa and Keysar (2018) does not provide a satisfactory test of reduced imagery and we discuss an alternative paradigm based on recent experimental developments

How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements

Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R2=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10–16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical-experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response

Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image

Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of the femurs and pelvises in sets 1 and 2, respectively. The 3D shape of the femur was reconstructed from the DXA image by matching the SAMs with the DXA images. The orientation and modes of variation of the SAMs were adjusted to minimize the sum of the absolute differences between the projection of the SAMs and a DXA image. The mesh quality and the location of the SAMs with respect to the manually placed control points on the DXA image were used as additional constraints. Then, finite element (FE) models were built from the reconstructed shapes. Mean point-to-surface distance between the reconstructed shape and CT image was 1.0mm for cadaver femurs in set 1 (leave-one-out test) and 1.4mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185mg/cm3 for set 1 and set 3 respectively. The generation of the SAM and the limitation of using only one 2D image were found to be the most significant sources of errors in the shape reconstruction. The noise in the DXA images had only small effect on the accuracy of the shape reconstruction. DXA-based FE simulation was able to explain 85% of the CT-predicted strength of the femur in stance loading. The present method can be used to accurately reconstruct the 3D shape and internal density of the femur from 2D DXA images. This may help to derive new information from clinical DXA images by producing patient-specific FE models for mechanical simulation of femoral bone mechanics

Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments

Computed tomography (CT)-based finite element (FE) models may improve the current osteoporosis diagnostics and prediction of fracture risk by providing an estimate for femoral strength. However, the need for a CT scan, as opposed to the conventional use of dual-energy X-ray absorptiometry (DXA) for osteoporosis diagnostics, is considered a major obstacle. The 3D shape and bone mineral density (BMD) distribution of a femur can be reconstructed using a statistical shape and appearance model (SSAM) and the DXA image of the femur. Then, the reconstructed shape and BMD could be used to build FE models to predict bone strength. Since high accuracy is needed in all steps of the analysis, this study aimed at evaluating the ability of a 3D FE model built from one 2D DXA image to predict the strains and fracture load of human femora. Three cadaver femora were retrieved, for which experimental measurements from ex vivo mechanical tests were available. FE models were built using the SSAM-based reconstructions: using only the SSAM-reconstructed shape, only the SSAM-reconstructed BMD distribution, and the full SSAM-based reconstruction (including both shape and BMD distribution). When compared with experimental data, the SSAM-based models predicted accurately principal strains (coefficient of determination >0.83, normalized root-mean-square error <16%) and femoral strength (standard error of the estimate 1215 N). These results were only slightly inferior to those obtained with CT-based FE models, but with the considerable advantage of the models being built from DXA images. In summary, the results support the feasibility of SSAM-based models as a practical tool to introduce FE-based bone strength estimation in the current fracture risk diagnostics

FUNCTION DRIVEN ASSESSMENT OF MANUFACTURING RISKS IN CONCEPT GENERATION STAGES

Decisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives