Fant 10464 publikasjoner. Viser side 2 av 419:
2026
2026
Climate change is expected to intensify the frequency and severity of droughts across Europe,
posing significant risks to agricultural production and the food systems that depend on it. As drought
severity can range widely within one country, estimates of supply chains with higher spatial
resolution are needed to characterize drought risk exposure. This study develops an inter-regional
(NUTS-2) version of the Food and Agriculture Biomass Input Output (FABIO)[1] model to quantify
the exposure of European food trade to drought risk under current and future climate conditions.
Drought exposure is quantified by linking regional agricultural trade flows to Copernicus
satellite-based soil moisture anomaly data.
Input output (IO) analysis has been widely used to study supply-chain dependencies, resource
use, and environmental pressures embedded in production and trade. The development of
multi-regional input output (MRIO) databases, which combine national IO tables into a single
balanced global framework, has enabled such analyses at the global scale. Prominent examples
include EXIOBASE[2], Eora[3], and FIGARO[4], which have been applied to assess environmental
impacts embodied in trade. However, while these databases provide global coverage, their
national-level resolution limits their ability to capture spatially heterogeneous climate risks such as
droughts, which often vary substantially within countries. Subnational or interregional IO tables are
rarely published due to data and resource constraints, creating a critical gap for climate risk
assessments that require finer spatial detail.
This study extends FABIO[1], an established physical MRIO framework for food, feed, and biomass
flows, into an interregional European model. The interregional FABIO disaggregates national food
trade into NUTS2-level trade using regional crop and livestock production, regional demand, and
reported trade flows. The FABIO database is regionalized following two main steps, similarly to
Ouyang et al[5]. First, international trade is estimated using reported subnational trade data where
available, subject to the constraints that regional exports and imports do not exceed supply and
demand, and that national totals remain consistent with the original FABIO data. Second, domestic
interregional flows are estimated using the CHARM approach in combination with a gravity model[6].
The model distinguishes 67 sectors and 332 NUTS 2 regions.
Using this framework, drought exposure is assessed by linking regional agricultural trade with
satellite-based drought intensity metrics, considering both historical events and projected future
conditions. The results show that accounting for trade significantly increases estimated drought
exposure of food consumption in Northern Europe compared with production-based assessments.
Exposure varies strongly across food categories; non-essential commodities are generally more
exposed than staple foods. Trade can both amplify and buffer drought impacts, depending on the
geographic extent of the drought and the availability of alternative sourcing regions.
Overall, this study demonstrates the value of combining interregional input output modeling with
satellite-based climate indicators to assess the vulnerability of European food trade to droughts. The
results reveal the exposure to drought risk in regions and support the design of diversification and
resilience strategies in agricultural supply chains. The annual resolution of the model, consistent
with input output tables and available trade data, cannot capture dynamics such as monthly stock
variations that can be critical for drought impacts, and merits further research. Beyond drought risk
assessment, the interregional FABIO framework can be applied to other spatially explicit
environmental questions, including consumption-based analyses of nitrogen emissions, land use,
and other local pressures.
2026
2026
Parametric life cycle assessment of asphalt pavements with probabilistic lifetimes and traffic
This study combines established methods – parametric LCA, probabilistic pavement lifetime modelling, traffic-volume characterization, and Monte Carlo simulation – into a service-based decision-support framework for asphalt pavement assessment. The framework evaluates environmental impacts not only per unit mass, but also in relation to pavement service life and traffic exposure, expressed as impact/(yr·AADT). Results indicate that binder type, asphalt plant efficiency, and transport distances are dominant contributors to environmental impacts. Incorporating probabilistic lifetimes and traffic loads reshapes environmental rankings, revealing that longer lifetimes do not always equate to lower burdens. These findings provide data-driven insights for reducing the environmental impacts of road construction and maintenance.
2026
For the first time, we present long-term, ongoing atmospheric measurements of 1,2-dichloroethane (DCE, CH2ClCH2Cl) from the Advanced Global Atmospheric Gases Experiment (AGAGE) and National Oceanic and Atmospheric Administration (NOAA) global monitoring networks. DCE is an industrially produced, very short-lived chlorinated substance (Cl-VSLS) that has the potential to contribute chlorine to the stratosphere and cause ozone depletion. Compared to other Cl-VSLS, DCE is produced in higher volumes for its primary use as a feedstock in polyvinyl chloride (PVC) manufacture. This production has sustained annual mean mole fractions at the Earth's surface of between 5 and 10 ppt during 2017–2023, making it the third most abundant Cl-VSLS after dichloromethane and chloroform. In this study we estimate mean global emissions for 2017–2023 of 453 ± 185 Gg yr−1 using the AGAGE observations, and 525 ± 209 Gg yr−1 using the NOAA observations. We also use AGAGE measurements to estimate regional emissions for northwest Europe (2.06 [1.31, 2.65] Gg yr−1) and California (0.23 [0, 0.37] Gg yr−1), two domains with sufficient observational coverage to enable this approach. Our global emissions estimates are consistent (within uncertainties) with the only previously published estimate by Hossaini et al. (2024), whereas our regional emissions estimates are at least an order of magnitude smaller than those in that study. This suggests global total emissions may be well constrained, but their spatial distribution remains uncertain.
2026
2026
2026
2026
South Asia is a major contributor to global methane (CH4) emissions, yet its emissions remain poorly constrained, limiting targeted mitigation. Current bottom-up inventories do not consistently capture the magnitude and seasonality of CH4 emissions in this region, particularly during the monsoon. Here we quantify South Asian CH4 emissions for 2020 using column observations from a satellite instrument (TROPOMI), a Lagrangian transport model (FLEXPART), and a Bayesian inversion system (FLEXINVERT+). We estimate a posteriori emission of 73.0 ± 0.7 Tg yr−1 for South Asia, including 35.6 ± 0.5 Tg yr−1 for India and 13.2 ± 0.4 Tg yr−1 for Bangladesh. Agriculture and wetlands contribute substantially to the regional budget, with the flux increments coincident with rice-growing areas and inundated lowlands. The inversion indicates pronounced monsoon-modulated seasonality in South Asia: posterior fluxes are higher than the prior by about 19.3 Tg CH4 (an increase of ∼ 70 %) during June–September and lower during January–May by ∼ 46 %. Localized enhancements seen over the lower Indus Basin align with runoff patterns, while the seasonal peaks here are absent in inventories. By resolving monsoon seasonality with satellite constraints, our results point towards key uncertainties in the South Asian CH4 budget and underscore the need for process-based, seasonally responsive inventories to inform mitigation strategies and reconcile bottom-up and top-down estimates.
2026
Contextual recommendation modeling in eCoaching with machine learning, X-AI, and semantic ontology
Physical activities can be divided into indoor and outdoor activities. While outdoor activities offer enjoyable fitness opportunities, they are often limited by weather conditions. Unfavorable weather conditions such as cold, rain, fog, or snow can significantly reduce physical activity levels, posing risks such as heat stress, dehydration, or cold-related injuries. To address these challenges, we have developed the concept of an automated eCoaching system that provides personalized activity recommendations based on real-time weather data. Our system uses an algorithm to annotate, process, and classify the collected data, generating tailored suggestions for indoor or outdoor exercise. This information is semantically represented using an Ontology framework. We have conducted a comprehensive study by collecting weather data for 18 months from thirteen cities in southern Norway. Furthermore, we have developed rules to determine the appropriate activity types corresponding to different weather conditions. The classification performance of the system has been rigorously evaluated using metrics such as accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC). Remarkably, the decision tree classifier achieved an accuracy of 99.1%. To increase interpretability, we used local model-independent interpretable explanations (LIME) to explain individual predictions. The consistency of the Ontology model has been verified using inference, providing a reliable semantic representation and efficient rule-based recommendation modeling. In addition, we have developed various test cases of the system to evaluate eCoaching recommendations under different weather scenarios. This approach provides users with accurate and contextually relevant guidance, promoting continuous physical activity regardless of external weather conditions.
2026
Bisphenol A (BPA) alternatives are increasingly used in the manufacture of industrial and consumer products, following regulatory restrictions on BPA. However, insufficient safety data on these substitutes raise concern as regards potential regrettable substitutions. Under the EU Partnership for the Assessment of Risks from Chemicals (PARC), Work Package 5 (WP5) addresses this challenge by applying a human-relevant tiered hazard assessment strategy grounded on OECD test guidelines as first tier and expanding the battery to include NAMs (New Approach Methodologies). Eight BPA alternatives were prioritized for studies addressing key toxicological endpoints, namely, endocrine disruption (ED), developmental neurotoxicity (DNT), immunotoxicity, genotoxicity and carcinogenicity and metabolic fate examination (detoxification vs. potential bioactivation), to enable early identification of biological activity and support cross-endpoint prioritization. This manuscript describes the structure and implementation of the testing framework. This integrated testing strategy proposes a structured approach to identify substances of potential concern, guide targeted higher-tier studies, and support regulatory prioritization. PARC WP5 framework is testing whether coordinated NAM-based methods may contribute to next-generation risk assessment and help prevent regrettable substitutions among BPA alternatives for rapid regulatory adoption. Detailed experimental results will be reported separately upon completion of the project.
2026