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Characterization of German SF6 Emissions
Sulfur hexafluoride (SF6) is a highly potent greenhouse gas with a Global Warming Potential (GWP) of 24,700 over 100 years and is globally mainly used as an electrical insulator in switchgear. Several measurement networks have tracked SF6 for many years and their European data reveal significant emissions in southern Germany. This study focuses on German SF6 emissions (2020–2023), using atmospheric measurements from 22 European sites, offering high spatial and temporal resolution for robust emission assessments. While German UNFCCC inventory bottom-up emission estimates report a major source of SF6 through the disposal of soundproof windows, the spatial distribution of German SF6 emissions derived on top-down inversion techniques (InTEM and Flexinvert+) reveals a different picture: The continuous pattern of high emissions from a particular region is responsible for one-third of total SF6 emissions in Germany. Despite this, total German SF6 emissions have decreased from 112 ± 26 t in 2020 to 89 ± 15 t in 2023 (InTEM), with estimates from all methods (both bottom-up and top-down) showing similar trends. Our findings suggest that the emissions from soundproof windows are overestimated, while industrial sources - particularly from SF6 production and recycling in the focus region - are likely underestimated.
2025
Abstract Methane is a powerful greenhouse gas with a shorter lifetime than carbon dioxide (CO 2 ), making it an important target for near‐term climate action. The Global Methane Pledge (GMP) aims to cut anthropogenic methane emissions by 30% from 2020 levels by 2030. Using an Earth system model with interactive CH 4 sources and sinks, we assess the Pledge's impact through 2050. Results show that current GMP commitments deliver only a 10% cut by 2030—well below the target. Only the maximum technically feasible reduction (MTFR) pathway can achieve the 30% goal. By 2050, current GMP commitments lowers methane concentrations by 3% relative to 2025, while MTFR achieves 8%. Both pathways slow warming slightly, avoiding about 0.1°C of global temperature rise, with the Arctic seeing the greatest benefits (up to 2°C less warming). Without wider participation, the GMP with current signatories will fall short of its targets and Paris Agreement goals.
2026
Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers
The instruments used for routine pollen monitoring are gradually changing from traditional impactors with manual data processing to automated pollen monitors using deterministic and/or machine-learning algorithms for data analysis. This manuscript compares pollen number concentration of Alnus sp., Betula sp., Corylus sp., and Poaceae measured by Hirst-type bioaerosol samplers and the SwisensPoleno automated bioaerosol monitor in Switzerland and Norway. Due to physical particle losses and the classification rate of the algorithms being well below unity, scaling factors had to be applied to the measurements of the SwisensPoleno to match those of the Hirst impactor. These scaling factors depended on the geographic location, i.e. differed significantly between Switzerland and Norway. The importance of adjusting the scaling factors according to the location of the monitoring network and the need for reporting the numerical values of these scaling factors in future scientific publications is emphasized.
2026
Urban areas experience elevated air pollution levels which pose significant health risks. Reducing exposure to poor air quality and mitigating the associated negative health impacts requires informed policy measures. This study advances urban air quality modelling by developing an air quality model (baseline model) and further integrating measurements from a network of low-cost sensors and regulatory monitors into the model output (data fusion model). The resulting data fusion model provides accurate air quality data in high spatiotemporal resolution. The data fusion model showed higher PM2.5 concentrations during evening hours and winter months, with a population-weighted exposure to PM2.5 almost twice as high as predicted by the baseline model during these months. The models exhibited different spatial patterns, with the data fusion model showing a shift in peak concentrations from the city centre to residential areas, where levels were up to 10 µg/m3 higher than the baseline model. These differences are likely attributable to an underestimation of residential emissions in the baseline model. While both models were FAIRMODE compliant, the data fusion model showed a reduced bias for most monitoring stations compared to the baseline model. The data fusion model enabled a more accurate assessment of existing policies, specifically those aimed at reducing urban air pollution from solid fuel burning. Moreover, by identifying locations and sectors which contribute significantly to high levels of PM2.5, the data fusion model supports the formation of targeted air quality policies. This enables cities to maximise reductions in air pollution and exposures, thereby safeguarding public health.
2026
Maritime sector pathways toward net-zero emissions within global energy scenarios
Abstract The maritime sector’s transition toward decarbonization cannot occur in isolation, rather it will be tied to broader transformations in energy, economic, and societal systems. Yet, most existing studies often overlook this integrated perspective, focusing primarily on sector-specific strategies without considering broader societal changes and energy availability on a global scale. To address this gap, this study integrates the MariTeam ship emission model into the MESSAGEix-GLOBIOM integrated assessment framework. Through this approach, we assess how climate scenarios may influence the maritime sector’s trajectory toward achieving net-zero emissions by 2050, in line with the International Maritime Organization (IMO) targets. Our findings indicate that action before 2030 is crucial and it can be achieved through combining four key solutions: improvements in energy efficiency, biofuels, liquefied hydrogen, and ammonia. Furthermore, the results suggest that the maritime sector could have access to enough renewables to achieve substantial emissions reductions with increase in final product costs ranging from 2 to 30% (interquartile range) with variations across products and regions. On average, cost increases are estimated at 10.2% for Global North countries and 13.3% for Global South countries. This analysis highlights the urgency and scale of transformation required for the maritime industry to meet the IMO’s net-zero ambitions and align with broader global sustainability goals.
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
Gastrointestinal image classification with GIDNet CNN model and non-linear Tansh activation function
2026
This study investigates the contamination of both ingested plastics and plasma of northern fulmars (Fulmarus glacialis) with benzotriazole UV stabilisers (BUVSs) in Kongsfjorden and Isfjorden, Svalbard. Ingested plastics were collected from fulmars in 1997, 2009, 2013, 2020 and 2021. Additionally, plasma samples were collected specifically in 2020. BUVSs, including UV-320, UV-326, UV-327, UV-328 and UV-329, were detected in both ingested plastics and plasma, suggesting a potential for transfer from plastics to the bloodstream. However, additional studies are required to confirm such a transfer mechanism. BUVSs were detected as early as 1997 in ingested plastics, highlighting the potential long-term exposure of fulmars in Svalbard. UV-326, UV-328 and total BUVS concentrations in ingested plastics increased significantly between 1997 and 2021, but likely due to outliers. In plasma, there was no significant correlation between any of BUVS concentrations and the mass of ingested plastics except for UV-327, although relying on only three values above LOD. This study represents a first step in investigating the multiple exposures of fulmars, and more generally seabirds, to plastic and plastic related chemicals and their potential ecotoxicological risks. More specifically we recommend further studies extracting microplastics from seabirds to perform additional quantification of BUVSs or other additives to provide available datasets and deeper understanding of leaching from plastics and temporal trends.
2026
Polluted air is a major global health risk factor, yet the chemical composition and toxicity of airborne gases and particles remain underexplored due to their complexity and difficulties in sampling. We recently introduced how polydimethylsiloxane (PDMS) foam─or silicone foam─can be synthesized for passive air sampling, enabling simple and cost-effective nontarget chemical profiling of indoor air. Here, we demonstrate expanded applications, indoors and outdoors, with commercial PDMS-foam, including for: (i) wide-scope target analysis of >220 priority substances by quantitative liquid- and gas chromatography-high-resolution mass spectrometry, (ii) microscopic characterization and nontarget profiling of accumulated fine particles, and (iii) effect-guided discovery of harmful substances, combining toxicological data with nontarget analysis in silico. Median method quantification limits were 0.12 ng/mL, 90% of target analytes had absolute recoveries between 70 and 130%, and hazardous substances were discovered, including ethylene glycols, insecticides, and UV filters. Microscopy revealed the accumulation of abundant fine particles, and the automated characterization of the fluorescent fraction revealed that most were <4 μm. Extracts from outdoor samples reduced human lung cell viability, and multivariate modeling flagged families of potentially toxic substances in a virtual effect-directed analysis. PDMS-foam disks require field calibration to determine their linear sampling rate(s), but current results and applications establish PDMS-foam as a multimodal passive sampler, enabling integrated chemical quantitation, toxicological analysis, and molecular discovery in air.
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
Organ-specific in vitro models for prediction of hazard assessment of nanomaterials
Organ-specific multicellular in vitro models are used to mimic the lung-blood-brain axis, and to assess the nanomaterials (NMs) safety in humans. We employed a triculture lung model, a whole-blood model, an astrocytes-neurons coculture to examine health outcomes by three cerium dioxide (CeO2) NMs and silver (Ag) nanowires. Endpoints included cytotoxicity, gene expression, genotoxicity, inflammatory markers at the air–liquid interface (ALI), complement activation, and secondary toxicity in astrocytes-neurons coculture. Post-exposure, CeO2–3.5 nm high-dose decreased cell viability, no DNA damage was detected. At epithelial-macrophages interface, CeO2–50 nm upregulated surfactant protein A (SPA), cell surface death receptor (FAS), and heme oxygenase-1 (HMOX1), whereas CeO2–3.5 nm downregulated SPA. Ag-nanowires upregulated HMOX1, macrophage inflammatory protein-1β (MIP-1β), granulocyte colony-stimulator factor (G-CSF), chemokine C-X-C-motif ligand 1 (CXCL1). At endothelial side, CeO2–50 nm and − 3.5 nm, and Ag-nanowires upregulated HMOX1. In whole-blood model, CeO2–3.5 nm high-dose reduced terminal complement complex (TCC) proteins, while CeO2–50 nm and Ag-nanowires increased them. Nanomaterials activated CD11b+ on granulocytes and monocytes. Ag-nanowires conditioned-medium (CM) on astrocytes-neurons coculture, decreased cell viability. CeO2–50 nm CM upregulated IL1β, NFκB, and HMOX1. Overall, CeO₂–3.5 nm exhibits lung toxicity; CeO₂–50 nm CM triggers inflammatory response and Ag-nanowires CM may induce cytotoxicity in brain cells.
2026
This study assesses trends in the total ozone column (TOC) and the atmospheric factors influencing ozone variability at three Antarctic stations (Marambio, Troll/Trollhaugen, and Concordia) from 2007 to 2023. Ground-based TOC measurements were used, supplemented by satellite observations from the Ozone Monitoring Instrument on NASA's Aura satellite. TOC trends were derived using a multiple linear regression model provided by the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) project. The selected LOTUS model was able to explain 94 %–97 % of the TOC variability at all three stations. The regression analysis showed that ozone variability at these stations is mainly driven by the lower stratospheric temperature, eddy heat flux, and the Quasi-Biennial Oscillation. A statistically significant increasing trend was found at the Marambio station (3.43 ± 3.22 DU per decade), while statistically insignificant trends were detected at the other two stations. Using MERRA-2 reanalyses, the LOTUS model was applied to each grid point in the 40–90° S region, which effectively illustrates the spatial distribution of the impacts of individual predictors. It was found that warmer conditions in the Antarctic stratosphere in September 2019 caused TOC to be up to 100 DU higher than normal, especially over East Antarctica. The results improve understanding of regional TOC trends and how the Antarctic ozone layer responds to changes in ozone-depleting substances.
2026
Exceptional high AOD over Svalbard in summer 2019: a multi-instrumental approach
In the summer of 2019, the Arctic region registered exceptionally high aerosol optical depth (AOD) values over Svalbard, linked to intense biomass burning (BB) and volcanic activity across the Northern Hemisphere. This study presents a comprehensive, multi-instrumental analysis of the aerosol conditions in and around Ny-Ålesund (Spitsbergen, Norway), combining data from ground-based sun-photometry, in-situ observations, active remote sensing (ground-based and on satellite), and atmospheric dispersion modelling (FLEXPART). Despite high AOD was observed during all the period, three different aerosol events are identified in the atmospheric column (6–10 July, 25–28 July, and 6–17 August). In contrast, in-situ surface stations only recorded significant aerosol load during 5–9 July, 30 August, and 12 September, suggesting that most of the aerosol particles remained above the boundary layer. Lidar and photometric observations revealed the presence of spherical, weakly absorbing Accumulation-mode particles (with effective radii between 0.1 and 0.2 µm) in both the troposphere and stratosphere, with persistent layers extending above 10 km. Simulations carried out with FLEXPART correlate well with the measurements, attributing the observed aerosol events to multiple sources, including Siberian and North American wildfires, the Raikoke (Russia) volcanic eruption, and anthropogenic pollution. While the simulations show a contribution from volcanic aerosols, the contribution from biomass-burning aerosols in the upper troposphere and lower stratosphere were likely more significant under the atmospheric conditions of summer 2019. Overall, the aerosol radiative impact during this long-lasting event was substantial, with a mean reduction in direct solar radiation of approximately −74 W m−2 during July and August. This work shows how the use of dispersion modelling together with multiple observation sources allows to achieve a more complete description of the atmospheric aerosol events and contributes to a better understanding of the overall picture.
2026
A multi-year analysis of aerosol optical depth (AOD, τ) and Ångström exponent (α) was conducted using ground-based photometer data from 15 Arctic and 11 Antarctic sites. Extending the dataset of (Tomasi et al., 2015) through December 2024, the study incorporates stellar and lunar photometric observations to fill data gaps during the polar night. Daily mean values of τ at 0.500 µm and α (0.440–0.870 µm) were used to derive monthly means and seasonal histograms. In the Arctic, persistent haze events in winter and early spring lead to peak τ values. A decreasing trend in Arctic τ suggests the impact of European emission regulations, while biomass-burning aerosols are becoming more significant. In Antarctica, τ increases from the plateau to the coast. Fine-mode aerosols dominate in summer-autumn, while coarse-mode particles are more prevalent in winter-spring. Shipborne photometer data align well with ground-based measurements, confirming the reliability of mobile observations. Trend analyses using the Mann-Kendall test and Theil-Sen regression indicate a significant negative trend in τ at Andenes (−2.43 % per year), likely driven by reduced anthropogenic emissions. Antarctic stations such as Syowa and South Pole show positive trends (+3.84 % and +3.54 % per year), though these are subject to uncertainties from data limitations and instrument changes. This work contributes to the Polar-AOD network (https://polaraod.net/, last access: 15 May 2025), enhancing the understanding of aerosol variability and long-term trends in polar regions while promoting open data access for the scientific community.
2026
Abstract Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet) , which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM) , is proposed to enhance the model’s learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.
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
Surveys in Norwegian schools showed that some students experienced health problems, such as headaches or concentration issues which have been linked to indoor environment quality (IEQ). This research investigates the relationship between measured IEQ and students’ perceived IEQ as user-feedback in one lower secondary school. This study explores the factors contributing to the connection with certain parameters such as carbon dioxide (CO2), volatile organic compounds (VOC), and temperature levels with perceived IEQ. Despite achieving good IEQ levels according to standards, there is a notable discrepancy between measured IEQ and how students perceive the air quality. Two classrooms served by a demand-controlled ventilation system were monitored with IEQ measurement sensors and online questionnaires were given individually to students in each classroom. This enables to provide real-time students’ perception of indoor air and room temperature quality. Measurement results showed IEQ are of good quality, but students’ responses on perceived IEQ vary and showed over 25% are dissatisfied, indicating mixed feelings and dissatisfaction about perceived IEQ. Future research should focus on refining ventilation systems to bridge the gap between measured and perceived IEQ.
2025
A Global Compendium of Nature-based Solutions in Small-Medium Islands
Small and medium-sized islands (SMI) combine high ecological value with limited resources and vulnerability to climatic and environmental risks. Nature-based solutions (NbS) can contribute to addressing some of these challenges, but studies on the uptake and effectiveness of NbS in SMI remain scattered, with few systematic syntheses. Here, we introduce the SMI-NbS compendium, a comprehensive and open-access dataset compiling 280 NbS case studies implemented across SMI worldwide, developed through a systematic review of published and grey literature. Each SMI-NbS case study includes information on the location, NbS category, ecosystem types, societal challenges addressed, associated co-benefits, and links to the United Nations’ Sustainable Development Goals (SDGs). The SMI-NbS compendium provides practical information on NbS implementation and identifies current research trends and gaps, such as the dominance of ecological and climate-focused NbS, with limited integration of other socio-economic challenges, thereby supporting further research and enabling knowledge exchange across the science-policy-practice interface to inform sustainable development pathways in SMI.
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
Circulating MicroRNAs in Cord Blood to Predict Attention-Deficit/Hyperactivity Disorder Diagnosis
Background
There are large knowledge gaps in the etiology of attention-deficit/hyperactivity disorder (ADHD), and although it is a prevalent and highly heritable neurodevelopmental disorder, diagnosis can be challenging. We aimed to assess the association of circulating blood plasma microRNAs (miRNAs) at birth with ADHD for use as biomarker candidates and build an miRNA-based prediction model.
Methods
Our study population consisted of 206 children with ADHD (33.0% female), 207 control children (33.8% female), and their parents from the MoBa (Norwegian Mother, Father, and Child Cohort Study). Expression levels of 51 selected miRNAs in plasma from children’s cord blood at birth and from both parents during early pregnancy were quantified by quantitative polymerase chain reaction and tested for association with children’s ADHD diagnosis and ADHD symptom scores based on ratings by parents.
Results
Seven miRNAs were differentially expressed at birth in children with ADHD and control children (false discovery rate < .05), and 31 had a statistically significant linear relationship with parent-rated ADHD symptom score at 8 years. A 19-miRNA ADHD prediction model achieved good discrimination in the test population (area under the receiver operating curve = 0.959, accuracy = 0.893). Functional analysis for the 19-miRNA prediction set revealed involvement in several highly relevant pathways, e.g., dopaminergic synapse, circadian rhythm, and axon guidance. We also found that parental miRNA expression levels significantly associated with children’s ADHD diagnoses and/or ADHD symptoms scores.
Conclusions
We showed that expression levels of circulating miRNAs at birth may be used to predict increased risk of ADHD diagnosis, and our 19-miRNA set should be included in future efforts to develop a biomarker panel.
2025
Building-related symptoms in school environment: Predictability using machine learning approach
Building-related symptoms (BRS) are commonly experienced by students in schools and are potentially affecting academic performance and health. Even though indoor environment quality (IEQ) measurements indicated fair conditions, students still perceived discomfort that led to symptoms, highlighting the necessity of collecting user-feedback about IEQ-complaints. This study aimed to predict and understand the prevalence of BRS (headache, tiredness, cough, dry eyes-hands) experienced by students in classrooms using machine-learning (ML) approach based on measurement data, building factors, and prevalence of IEQ-complaints. We collected measurement data (from indoor and outdoor climate), building factors, and user-feedback by students via online-platform across three sampled classrooms each campaign during three consecutive school semesters. Significant input variables for ML were pre-selected using statistical tests. ML models were evaluated based on accuracy metrics and SHAP analysis for input interpretation. Models using measurement data alone performed poorly (testing R² <50 %) to predict prevalence of BRS, whereas adding building factors and prevalence of IEQ-complaints increased accuracy (R² up to 95 %) of prediction of BRS with lower RMSE. In addition, interpretation from SHAP analysis showed IEQ-complaints especially related with indoor air quality (e.g., heavy air, dust & dirt, and dry air) as significant contributors for predicting prevalence of BRS. We conclude that the framework of combining objective measurements with occupant-reported complaints can be reliable, interpretable predictions of symptom prevalence. This study is limited by single-school setting, health confounders, and symptoms verification. Future research may contribute to exploring wider set of input variables, applicability, and variation of complaints preference.
2025
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
2026
Worldwide, edible fish are well studied for plastic occurrence. Microplastic (MP) occurrence in edible tissues raises concern for the organism’s health, but also on food safety. In the Arctic region, MP occurrence in other tissues than the digestive tract of fish has not been published yet. Plastic-related chemicals such as UV stabilisers (UVS) were also scarcely studied in Arctic biota. Our objectives were to (1) provide data on MP occurrence in Atlantic cod (Gadus morhua) fillets, (2) quantify UVS in the same fillets, and (3) provide estimations of both MP and UVS intake by Norwegian and European consumers through cod consumption. Twenty individuals were collected in the Barents Sea, south-west of Svalbard. They were dissected onboard and a piece of fillet was used for the extraction of MPs in the lab. Particles’ identification was performed by µRaman spectroscopy. MPs were found in 45 % of the fillets, with an average of 0.25 MP/g ww. Only one UV stabiliser (UV-326) was detected, in four fillets. Based on consumption data of cod, an average Norwegian man and woman would ingest 20.8 and 33.5 MPs weekly, respectively. Considering a European diet, a weekly intake of 8 MPs and a yearly intake of 403 MPs through cod consumption was calculated. The impacts of MP exposure to humans are unknown. Through this study, rather than to raise potential risks of consuming fish, we aimed to trigger further research on microplastics occurrence in seafood, i.e. in the edible tissues of aquatic animals.
2025
Spatial and temporal assessment of soil degradation risk in Europe
Soil degradation threatens agricultural productivity and ecosystem resilience across Europe, yet spatially consistent assessments of its intensity and drivers remain limited. In this study, we used Soil Degradation Proxy (SDP), that integrates four key indicators of soil degradation, including erosion rate, soil pH, electrical conductivity, and organic carbon content, to quantify soil degradation risk. Using over 38,000 LUCAS topsoil observations and a machine learning model trained on climate, land cover, topographic, soil parent material properties, and spectral variables, we map annual SDP values between years 2000 to 2022 across Europe. Results show soil degradation risk is highest in southern Europe, especially in intensively managed and sparsely vegetated landscapes. Over the past two decades, approximately 7.1% of land area across the EU and the UK has experienced increasing degradation risk (most notably across Eastern Europe), with rainfed croplands emerging as the most affected land cover type. Land cover is the most influential driver, modulating effects of climatic variables such as precipitation and temperature on SDP. This data-driven framework provides a consistent and scalable approach for monitoring soil degradation risk and offers actionable insights to support targeted conservation and EU-wide policy implementation.
2025