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In Switzerland, regulatory requirements mandated that spraying drones undergo a testing procedure, which included assessing their transversal spray distribution using a patternator. Various stakeholders have repeatedly expressed uncertainty as to whether these static measurements correctly reflect the real dynamic spray distribution or whether this type of measurement falsifies the spray pattern. Taking these uncertainties into account, the present study compared the static patternator method with dynamic field measurements using water-sensitive and filter paper. The three methods showed good agreement in the results. In contrast to the other two methods, the measurements on the patternator showed the lowest variability, which shows that hovering at the same point probably causes some compensation in the distribution. Different nozzles (flat fan, hollow cone, air induction nozzles) produce quite similar distribution patterns in all three measurement methods, whereby the injector nozzle with the largest droplets produced the best transverse distribution with a coefficient of variation of less than 10 %. • Spray drones can achieve regular transversal distribution with coefficients of variation less than 10 %. • Patternator is well suited to measure the transversal volume distribution of the spray liquid. • Collectors (water sensitive and filter paper) show the variability of the distribution during the flight. [ABSTRACT FROM AUTHOR]

[Display omitted] • The "containmentFoam" package is extended to simulate particle-laden flows. • The URANS-CRW approach is applied to natural convection turbulent flows. • Systematic validation is performed against the DIANA experimental and LES data. • Particle deposition, airborne concentration and the distribution are assessed. The Continuous Random Walk (CRW) model based on the normalized Langevin equation is often used to model the turbulent dispersion of particles wherein the flow field is computed using the RANS models. It has shown good accuracy in predicting overall particle deposition and the particle velocity statistics in forced convection turbulent flows. In the present paper, the CRW model is extended to predict particle transport in a natural convection flow (Ra 10 9 ). The validation is performed against the DIANA experiments performed at the Paul Scherrer Institute, Switzerland. The mean flow field is obtained systematically using the URANS approach (k- ω SST). The fluid near-wall turbulent statistics are obtained using correlations derived from the DNS data of Sebilleau et al. (2018). The results show that the CRW model is capable of predicting the particle deposition and distribution with reasonable accuracy for both 2.5 and 1 μ m particle sizes. [ABSTRACT FROM AUTHOR]

The main danger to which backcountry skiers are exposed are dry slab avalanches triggered by skiers themselves. In this paper we present a new probabilistic method to assess the avalanche risk when planning a ski tour: SLABS (S creening the L ikelihood of A valanches on B ackcountry S ki tours). The SLABS method is the first fully statistically derived probabilistic method for backcountry skiing. We used a data set with GPS (Global Positioning System) tracks of backcountry ski tours (57.8 thousand km) and a data set of 1250 accidents recorded in Switzerland during the last 20 winter seasons. A GAM (Generalised Additive Model) with binomial link discriminating between accidents and non-accidents was fitted to these data. As predictors the model uses a non-linear relation with the slope angle, and a linear relation with the danger level, the elevation and the aspect. Conditions with elevated and high risk were defined such that the accident prevention rate is 80% and 60%, respectively. A comparison of the SLABS method with the Graphical Reduction Method (GRM), the Professional Reduction Method (PRM) and the Quantitative Reduction Method (QRM) shows that it offers more freedom of movement for a given accident prevention rate. It is the first time that probabilistic methods are compared in terms of their trade-off between accident prevention and freedom of movement. Because the SLABS method is not suited for mental arithmetic it is implemented in the website www.skitourenguru.ch. This website evaluates on a daily basis the avalanche risk along thousands of backcountry tours throughout the Alps to help skiers plan their next tour. For a single slope the risk assessment of SLABS provides a starting point that has to be supplemented with local observations. [Display omitted] • A Generalised Additive Model was fitted to both backcountry accident and travel data. • A new probabilistic method for avalanche risk during backcountry skiing was developed. • Maximum slope angle, danger level, elevation and aspect are combined in a risk score. • The trade-off between prevention and freedom is better than for existing methods. • Implemented in www.skitourenguru.ch the new method helps skiers plan their tours. [ABSTRACT FROM AUTHOR]

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation. • A novel framework is proposed to vectorise and classify roads from historical maps. • Deep CNNs are utilized to systematically and accurately extract roads. • Road classification and vectorization are achieved simultaneously by symbol painting. • Visual and quantitative assessments validate the effectiveness and robustness of the framework. [ABSTRACT FROM AUTHOR]

Carbon dioxide removal (CDR) is recognized as an important tool for addressing residual emissions and achieving net-zero emission targets. While some have cautioned that a focus on CDR in policy processes may lead to delayed efforts to mitigate emissions, others have argued that such concerns are unwarranted. Nevertheless, the circumstances under which CDR could help or delay emissions mitigation in given contexts remain unclear. This paper explores the emerging discourse on CDR in Switzerland. We examined how the CDR community legitimizes CDR and limits its scope, and what the implications are for emissions mitigation. Switzerland is home to growing businesses in CDR and has pioneered the implementation of international offsetting projects under Article 6.2 of the Paris Agreement. We found that numerous promises help legitimize and attract interest in CDR. Actors use discursive strategies and rules to limit CDR and avoid disappointment in its contribution to climate mitigation. The idea that emission reduction should prevail over removal is promoted accordingly, which ironically helps legitimize the CDR idea yet dodges the question of how much removal is possible and for balancing which emissions. Superficial engagement with the issue is reinforced by the normalization of inflated promises and the sentiment that the mitigation deterrence rhetoric erodes trust in CDR. We argue that this can contribute to mitigation delays by evading the debate on what it is possible to remove and taking resources from alternative measures. We recommend a thorough discussion to examine the risks and the implementation of rules that minimize them. • Mitigation deterrence discourse leads to efforts to avoid substitution dynamics. • "Reductions first" legitimizes CDR without coordinating emission residuals. • Inflated promises of CDR may reduce resources for more realistic measures. • Attention to CDR dynamics is required to help achieve comprehensive decarbonization. [ABSTRACT FROM AUTHOR]

Hydrogen is considered one of the key pillars of an effective decarbonization strategy of the energy sector; however, the potential of hydrogen as an electricity storage medium is debated. This paper investigates the role of hydrogen as an electricity storage medium in an electricity system with large hydropower resources, focusing on the Swiss electricity sector. Several techno-economic and climate scenarios are considered. Findings suggest that hydrogen storage plays no major role under most conditions, because of the large hydropower resources. More specifically, no hydrogen storage is installed in Switzerland if today's values of net-transfer capacities and low load-shedding costs are assumed. This applies even to hydrogen-favorable climate scenarios (dry years with low precipitation and dam inflows) and economic assumptions (high learning rates for hydrogen technologies). In contrast, hydrogen storage is installed when net-transfer capacities between countries are reduced below 30% of current values and load-shedding costs are above 1,000 EUR/MWh. When installed, hydrogen is deployed in a few large-scale installations near the national borders. [Display omitted] • High-fidelity models of Swiss electricity sector and hydrogen storage technologies. • No significant hydrogen storage is required in Switzerland under most scenarios. • Large hydropower resources reduce the need for hydrogen storage significantly. • Hydrogen storage becomes relevant with reduced electricity net transfer capacities. • Hydrogen storage is used for high self-sufficiency ratios to avoid load-shedding. [ABSTRACT FROM AUTHOR]

Residential buildings have a substantial impact on global environmental impacts. In this paper we present a model that assesses impacts from materials (embodied impacts) as well as from heating (operational impacts) for all residential buildings in Switzerland. Our model builds on an existing high-resolution spatio-temporal building stock model, which estimates the space heating demand of buildings, based on building property and site data. In this work we additionally include the impacts of materials extraction, production, transportation, and disposal of building components (floors, roofs, walls, ceilings and windows). Our model comes with a site- and building-specific default parametrization for all buildings, but it is developed as a tool that allows architects and planners to adapt building and site data with specific information to increase the accuracy of the environmental assessment of the current buildings and future building-management scenarios. We applied the model to ten Swiss buildings and assessed a selection of building-management scenarios to illustrate the application of the tool. The results show that our model estimates fairly accurate values of thermal and ventilation heat transmittance, operational space heating demand, and operational and embodied climate-change impacts for the considered buildings. The detailed modeling results for the ten case study buildings outperformed the simplified version of the model with generic default values for material building composition (using age-grouped building archetypes), emphasizing the importance of adding detailed material information in building life cycle assessments. Therefore, a focus should be placed on obtaining accurate material inventories of buildings in future research. The here-developed model allows for integration of such detailed material inventories, overwriting the provided default values. • Tool provides assessment of embodied and operational impacts of the entire Swiss residential building stock. • Tool estimates close to accurate space heating demands of buildings. • Tool is equipped with generic default building and site data. • Tool allows overwriting generic values with data inputs from building stakeholders, which is demonstrated in the case study of 10 buildings. [ABSTRACT FROM AUTHOR]

Agricultural food production is both affected by and contributing to climate change. At the global scale, agri-food systems are responsible for one-third of total greenhouse gas emissions. With progressing climate change, the risks of crop failure increase. Thus, an urgent need is to reduce emissions from food systems while increasing their resilience to climate change. Enormous untapped potentials to achieve these dual goals lie in transforming agri-food systems towards more diverse, plant-based, and regional food production systems. In this paper, we present an innovative approach for identifying climate-adapted alternative food crops that could (1) help to diversify existing cropping systems and thus increase their climate resilience and can be (2) nutritious elements of plant-based regional diets with reduced emissions. The approach builds on the model ecocrop to select food crops that could benefit from regionally projected changes in climate. The model-based analysis is complemented with a literature review to examine the ecocrop results for their plausibility and provide a broader assessment of potentials for cultivation, utilization, and nutritional values of model-selected crops. The approach is applied to Switzerland, where we identify eight alternative crops with the potential to increase climate resilience while contributing to healthy human diets of regional consumers with benefits for climate mitigation (almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage). The literature review indicated that the increasing demand for many of these crops suggests great potential for regional marketing of crop products. The results produced in this study provide an initial guide for researchers and innovative farmers interested in experimenting with alternative crops in Switzerland, thus promoting climate-smart food system transformation from the production side. Using our unbiased bottom-up screening approach, we identified climate-adapted alternative crops that can provide essential nutrients, cover nutritional gaps in Switzerland, diversify existing production systems, and improve sustainability. [Display omitted] • Innovative bottom-up screening approach identifies climate-adapted alternative crops. • Almond, pecan, sesame, durum wheat, quinoa, lentil, lupine, and borage are identified. • Identified crops provide essential nutrients and cover nutritional gaps in Switzerland. • Selected crops can diversify existing production systems to improve sustainability. [ABSTRACT FROM AUTHOR]