Fant 10456 publikasjoner. Viser side 415 av 419:
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
2026
An inter-comparison of inverse models for estimating European CH4 emissions
Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH4) emissions across scales from global to local, combining observations with atmospheric transport models. This study uses the dense network of in situ stations of the Integrated Carbon Observation System (ICOS) to explore how well in situ data can constrain European CH4 emissions. Following the concept of inter-comparison studies of the atmospheric tracer transport model inter-comparison Project (TransCom), a CH4 inverse inter-comparison modeling study has been performed, focusing on Europe for the period 2006–2018. The aim is to investigate the capability of inverse models to deliver consistent flux estimates at the national scale and evaluate trends in emission inventories, using a detailed dataset of CH4 emissions described and presented here for first time.
Study participants were asked to perform inverse modelling computations using a common database of a priori CH4 emissions and in-situ observations as specified in a protocol. The participants submitted their best estimates of CH4 emissions for the 27 European Union (EU-27) member states, the United Kingdom (UK), Switzerland, and Norway. Results were collected from 9 different inverse modelling systems, using 7 different global and regional transport models. The range of outcomes allows us to assess posterior emission uncertainty, accounting for transport model uncertainty and inversion design decisions, including a priori emission and model-data mismatch uncertainty.
This paper presents inversion results covering 15 years, that are used to investigate the seasonality and trends of CH4 emissions. The different inversion systems show a range of a posteriori emission adjustments, pointing to factors that should receive further attention in the design of inversions such as optimising background mole fractions. Most inverse models increase the seasonal cycle amplitude, by up to 400 Gg month−1, with the largest adjustments to the a priori emissions in Western and Eastern Europe. This might be due to underestimation of emissions from wetlands during summer or the importance of seasonality in other microbial sources, such as landfills and waste water treatment plants. In Northern Europe, absolute flux adjustments are comparatively small, which could imply that the emission magnitude is relatively well captured by the a priori, though the lower station density could contribute also.
Across Europe, the inverse models yield a similar decreasing trend in CH4 emissions compared to the a priori emissions (−12.3 % instead of −9.1 %) from 2006 to 2018. While both the a priori and the a posteriori trend for the EU-27 are statistically significant from zero, their difference is not. On a subregional scale, the differences between a posteriori and a priori trends are more statistically significant over regions with more in-situ measurement sites, such as over Western and Southern Europe.
Uncertainties in the a priori anthropogenic emissions, such as in the agriculture sector (cows, manure), or waste sector (microbial CH4 emissions), but also in the a priori natural emissions, e.g. wetlands, might be responsible for the discrepancies between the a priori and a posteriori emission shift in the trends in Western, Eastern and Southern Europe.
Our results highlight the importance of improving the inversion setup, such as the treatment of lateral boundary conditions and the model representation of measurement sites, to narrow the uncertainty ranges further. The referenced dataset related to the analysis and figures are available at the ICOS portal: https://doi.org/10.18160/KZ63-2NDJ (Ioannidis et al., 2025).
2026
A pan-European spatial inventory of agricultural land degradation
Agricultural land degradation is a contemporary reality that increasingly threatens food security and socio-economic stability in Europe and worldwide. Monitoring and controlling this environmental problem are complicated missions, considering that land degradation generally occurs as multiple processes in agricultural environments, which have not yet been thoroughly investigated as an integrated multi-process and multi-scale inventory in Europe. Here we developed a detailed multi-scale (continental to sub-regional) inventory of 12 key agricultural land degradation pathways in Europe, including water erosion, wind erosion, soil organic carbon loss, soil salinization, soil acidification, soil compaction, soil nutrient imbalances, soil pollution via pesticides, soil pollution via heavy metals, vegetation degradation, groundwater decline, and aridity. Using various and (generally) high-resolution geospatial datasets of land degradative pathways, which were mapped at critical levels and statistically explored as a spatial footprint at various territorial levels, we highlighted a complex geographical pattern of agricultural degradation across Europe. Our findings revealed that continental agricultural environments are between 1 and 52 % affected by critical levels of individual degradative processes. Essentially, our results highlighted that soil pollution via pesticides (which impacts 52 % of Europe's evaluated agricultural area), soil nutrient imbalances (39 %), soil pollution via heavy metals (31 %), aridity (25 %), water erosion (15 %), and soil compaction (15 %) are the largest threats to European agriculture. Furthermore, using a Land Multi-degradation Index that integrates the critical conditions of all degradative processes, we emphasized that 31 % of pan-European agricultural landscapes are impacted by significant multi-degradation (lands simultaneously affected by at least three co-occurring processes). This general picture of agricultural degradation becomes however increasingly heterogeneous towards the more detailed (national to sub-regional) territorial levels, according to the multiple maps (52) and statistics provided in this unprecedented integrated inventory, which has the potential to support various land degradation-related policies in Europe.
2026
Dust in the Arctic: feedbacks and interactions between climate change, aeolian dust and ecosystems
Dust in the Arctic is an emerging topic related to climate and environmental impacts. The United Nations (UN) General Assembles and the UN Coalition to Combat Desertification (UNCCD) have reiterated that the global frequency, intensity, and duration of Sand and Dust Storms (SDS) have increased in the last decade and that SDS have natural and human causes that can be exacerbated by desertification, land degradation, drought, biodiversity loss, and climate change. UNCCD and FAO have also highlighted that emerging SDS source areas have been associated with the warming of the Arctic and high latitude regions, the seasonal or permanent drying of inland waters and river deltas, or are following large-scale deforestation and wildfires, or even the ploughing of a single field. Loss of snow cover, retreat of glaciers, and increase in drought intensity due to climate change can lead to surface conditions that increase the likelihood of creation, continuation and expansion of SDS source areas.Climatic feedback mechanisms and ecosystem impacts related to dust in the Arctic include direct radiative forcing (absorption and scattering), indirect radiative forcing (via clouds and cryosphere), semi-direct effects of dust on meteorological parameters, effects on atmospheric chemistry, as well as impacts on terrestrial, marine, freshwater, and cryosphere ecosystems. Here we give an overview of our recent understanding on dust emissions and their long-range transport routes, deposition, and ecosystem effects in the Arctic as presented in Meinander et al. (2025), part of the series of review papers of the Arctic Council Working Group AMAP (Arctic Monitoring and Assessment Program) and CAFF (Conservation of Arctic Flora and Fauna), where the target audience is the scientific community focusing on the Arctic. Additional audiences include policy advisers and other staff in environmental-related ministries.We conclude that the multiple mechanisms related to dust emissions, transport and deposition both cool and warm the climate system, with an uncertain net effect. Dust plays a significant role in terrestrial and aquatic ecosystems, e.g., by providing nutrients, and with impacts on the availability of light and water. Due to Arctic warming, HLD dust emissions can be expected to increase. The contributions of LLD and HLD complicates the interpretation of how much different sources contribute to the dust loadings and corresponding temporal and spatial deposition patterns. Another challenge is that low latitude dust source emissions of road and agricultural dust is barely characterized.Reference:Meinander O, Uppstu A, Dagsson-Waldhauserova P, Groot Zwaaftink C, Juncher Jørgensen C, Baklanov A, Kristensson A, Massling A and Sofiev M (2025). Dust in the arctic: a brief review of feedbacks and interactions between climate change, aeolian dust and ecosystems. Front. Environ. Sci. Sec. Interdisciplinary Climate Studies, Volume 13 – 2025. doi: 10.3389/fenvs.2025.1536395. CAFF-special issue.
2026
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
City-produced and transported black carbon: Synergy of in-situ optical measurements and modeling
The implementation of air pollution mitigation strategies requires not only high-quality continuous measurements of pollutants but also proper definitions of ways to differentiate between transported and locally produced contributions, as only the latter can be effectively reduced by authorities. To address this issue, we propose a new approach for partitioning monitored black carbon (BC) concentrations into city-produced (urban) and transported fractions using a combination of measured and modeled data. Two simultaneous measurement campaigns (warm season 2022 and cold season 2022/23) were conducted in two urban environments: Vilnius (Lithuania) and Warsaw (Poland). In the cold season in Warsaw, BC mass concentration was 90% higher than in the warm season, while in Vilnius, an increase of 44% was observed, as compared to the warm season. Aerosol optical properties showed more complex aerosol mixtures of dust, BC and brown carbon (BrC) during the cold season, forming larger particles. Single scattering albedo (SSA) anti-correlated with BCFF, proving that fossil fuel (FF) combustion contributes to the warming effect in both cities. A positive correlation between the population density of the emission areas of transported BC and the BC mass concentrations in Vilnius and Warsaw was found. The impact of transported BC on the local BC levels in the cities was of % and % in the cold season and of % and % in the warm season for Warsaw and Vilnius, respectively. Thus, the approach of BC partitioning showed that in the cold season, the two cities suffered from worse air quality, in part due to more transported BC.
2026
Urban Living Labs as Inter- and Transdisciplinary Arenas for Sustainability Planning Research
The transition towards sustainable societies necessitates inter- and transdisciplinary knowledge, particularly in urban planning, where diverse knowledge traditions are crucial for decision-making. Despite this, planning practices often remain entrenched in institutional and legal frameworks that hinder the integration of multiple ways of knowing and undervalue lay knowledge. Researcher-led urban innovation processes are increasingly adopting experimental approaches for the multi-stakeholder co-creation of knowledge, addressing urban challenges through interdisciplinary approaches. This article addresses the interdisciplinary collaboration between researchers in experimental urban planning processes by examining a research project that focused on participatory environmental co-monitoring and planning for urban air quality in Nordic contexts. The study builds a bridge between theories of interdisciplinarity, urban experimentation, and planning theory. By presenting urban living labs (ULLs) as arenas for co-learning that integrate scientific and lay knowledge, the article explores how planning researchers can facilitate mutual learning and navigate the micropolitics of knowledge co-production. We develop the concept of cross-disciplinary unknowns to highlight the dynamics and challenges in research teams with diverse epistemological backgrounds. We argue that an explicit and structured approach for explicating epistemological differences can facilitate the detection of unreflected knowledge retention between disciplines.
2026
2026
2026
Detection of ozone recovery in the Arctic from ground-based measurements
Contrary to the Antarctic, where ozone recovery has been observed for about a decade, the detection of positive ozone trends in the Arctic remains challenging due to higher natural variability of ozone in that region. Using a merging of long-term ozone data from Fourier transform infrared spectrometers, ozonesondes, and Dobson and Brewer spectrophotometers, we present regional long-term trends (2000–2024) for total, stratospheric and tropospheric ozone. First, ground-based measurements are cross-compared to two satellite data sets (MEGRIDOP and IASI-CDR). This enables the detection of drifts in ground-based data sets we further exclude from our study. We then use a representativeness study based on CAMS re-analysis data to define regions for which representative trends with reduced uncertainties are obtained by combining data sets from different instruments and stations. Annual and seasonal trends are calculated using a multiple linear regression technique involving a set of proxies that represent physical processes influencing the natural ozone variability. Annual trends indicate increasing total ozone over the Arctic, and are statistically significant over Canada and Reykjavik (+2.1 % per decade) and North-West Europe (Harestua and Lerwick, +0.7 % per decade). Ozone recovery is also observed over Canada in the mid-stratosphere (+2.0 % per decade) and over the North Pole region (Canada and Ny-Ålesund) in the upper stratosphere (+2.1 % per decade to +3.8 % per decade). By analyzing the sensitivity of the ozone trends to the proxies, we observe a slow down of the expected ozone recovery, especially in the lower stratosphere, due to stratospheric cooling (−0.6 % per decade) and to the increase of volume of polar stratospheric clouds (−0.8 % per decade).
2026
Modeling soil solution electrical conductivity across Europe
Soil salinization, referring to the excessive accumulation of soluble salts in soils, adversely influences nutrient cycling, biodiversity, soil structure, crop production, soil health, and ecosystem functioning. Accurately assessing soil salinity via electrical conductivity (EC) is key to mitigating its impacts. Thus, developing predictive tools for soil EC at regional and continental scales is essential for sustainable soil management. Here, we apply machine learning models to predict soil EC in the European Union (EU) and United Kingdom (UK) soils using different environmental factors like soil, climate, topography, and satellite data as predictors. The model is trained by ≈40,000 soil EC data points from the 2015 and 2018 Land Use/Cover Area Frame Survey data (LUCAS) surveys, complemented by the EC observations from World Soil Information Services (WoSIS) dataset. To improve the model performance, a forward feature selection technique was used resulting in selection of 17 covariates out of initially 34 predictors. The final selected XGBoost model achieved R2 values of 0.68, 0.6, and 0.63 for the training, internal testing, and independent validation datasets, respectively. For the year 2018, we estimate ≈21.7 Mha of EU + UK land exceeds an EC of 0.6 dS/m (at a 1:5 soil to water ratio, the so-called EC1:5). This estimate should be interpreted as elevated predicted EC1:5, rather than a direct estimate of soils meeting protosalic diagnostic criteria. The output of the predictive model consists of a gridded dataset that illustrates the spatial distribution of EC1:5 throughout the study area for the year 2018, along with an associated uncertainty map with a spatial resolution of 1 km.
2026
Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.
2026
Kan bli mer av dette – nå advarer forskere
Nye kriterier for emballasje peker på mer bruk av resirkulert plast. Men slik plast er ikke uproblematisk.
2026
2026
Hydrofluoroolefins (HFOs) are important synthetic compounds replacing other halocarbons in phase-down from usage (e.g., as refrigerants, propellants, foam blowing). Little is known about their atmospheric abundance, distribution and trends, nor about their emissons. Here, we report atmospheric observations of the widely used HFO-1234yf (2,3,3,3-tetrafluoroprop-1-ene), and HFO-1234ze(E) (E-1,3,3,3-tetrafluoroprop-1-ene), and the hydrochlorofluoroolefin (HCFO) HCFO-1233zd(E) (E-1-chloro-3,3,3-trifluoroprop-1-ene) observed as part of the Advanced Global Atmospheric Gases Experiment (AGAGE) network. Over the observational period 2011–2025, pollution events have grown in magnitude and frequency at sites which are influenced by regional emissions, while remote stations show first appearances of these substances. By 2024/2025 winter peak mole fractions in background northern hemisphere air have reached ∼ 0.25 ppt (picomol mol−1, parts-per-trillion in dry air) for HFO-1234yf and HFO-1234ze(E) and ∼ 0.45 ppt for HCFO-1233zd(E). Using European observations and the inverse modeling frameworks InTEM, ELRIS, and RHIME we determine emission trends and regional distributions. For Northwest Europe, emissions of HFO-1234yf increased steadily and rapidly from <0.1 Gg yr−1 in 2014 to 1.50 [1.23–1.74, range of 16–84 percentile] Gg yr−1 by 2023, presumably due to its introduction in mobile air conditioning and stationary refrigeration. HFO-1234ze(E) emissions were low during 2014–2017, followed by a rapid increase in 2018/2019, potentially due its introduction as an aerosol propellant, after which they increased more slowly to 0.96 [0.82–1.13] Gg yr−1 by 2023. HCFO-1233zd(E) emissions are derived from 2017 onward, showing a steady increase from 0.15 [0.07–0.23] to 1.04 [0.93–1.15] Gg yr−1 in 2023.
2026
This study presents results from an Intensive Measurement Period (IMP2022) conducted during the European heatwave of July 2022, focusing on ozone, volatile organic compounds (VOCs), and carbonaceous aerosols at 31 sites across Europe. The episode featured persistent high-pressure systems, record-breaking temperatures, widespread ozone exceedances and concurrent atmospheric new particle formation and growth events. Coordinated measurements and chemistry transport modelling were used to examine the spatial variability of ozone, VOC composition, and secondary organic aerosol (SOA) formation under extreme meteorological conditions. Oxygenated VOCs (O-VOCs) constituted the largest fraction of total measured VOC mixing ratios, followed by non-methane hydrocarbons (NMHCs) and aromatics, with contributions from both anthropogenic and biogenic sources. Sensitivity simulations indicate that ozone formation was predominantly NOx-limited across most regions during IMP2022. However, the highest ozone peaks occurred under conditions of elevated NOx in combination with enhanced BVOC emissions. In contrast, SOA formation was slightly enhanced under low-NOx conditions and reduced in elevated NOx. Isoprene, aliphatic NMHCs, and O-VOCs dominated the ozone formation potential, while aromatics and monoterpenes were major contributors to SOA potential. Model simulations indicated that higher NOx concentrations can reduce SOA formation by about 10 %. The campaign also highlighted observational gaps underscoring the need for broader and higher-resolution VOC monitoring across Europe. Overall, further reductions in NOx emissions, alongside targeted control of key anthropogenic VOCs, would benefit air quality under future climate extremes.
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