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Nitrogen dioxide (NO2) is a well-known air pollutant, mostly elevated by car traffic in cities. To date, small, reliable, cost-efficient multipollutant sensors with sufficient power and accuracy for community-based atmospheric studies are still lacking. The HAPADS (highly accurate and autonomous programmable platforms for providing air pollution data services) platforms, developed and tested in real conditions, can be a possible approach to solving this issue. The developed HAPADS platforms are equipped with three different NO2 sensors (7E4-NO2–5, SGX-7NO2, MICS-2711 MOS) and a combined ambient air temperature, humidity, and pressure sensor (BME280). The platforms were tested during the driving test, which was conducted across various roads, including highways, expressways, and national and regional routes, as well as major cities and the countryside, to analyse the environmental conditions as much as possible (Poland, 2024). The correlation coefficient r was more than 0.8, and RMSE (root mean squared error) was in the 3.3–4.3 μg/m3 range during the calibration process. The results obtained during the driving tests showed R2 of 0.9–1.0, which proves the ability of HAPADS platforms to work in the hard environmental conditions (including high rain and snow, as well as sun and a wide range of temperatures and humidity).
2026
Physics-Informed Deep Learning for Wind Downscaling over Oslo
Running a numerical weather model such as WRF at kilometre or sub-kilometre grid spacing over a regional domain is computationally expensive. We present physics-informed deeplearning models that ingest a single 9km WRF wind field and simultaneously predict two finer-scale wind fields at 3 km and 1 km resolution via dual decoder heads. Four representative architectures are benchmarked-Deep Residual U-Net (DeepRU), DEVINE, a bespoke 3-D Transformer, and a Fourier Neural Operator (FNO)-each trained with divergence-free, vorticity, and Navier-Stokes residual constraints plus Charbonnier and gradient perceptual losses. We train and validate our models on the city of Oslo for the year 2018. DeepRU achieves R2=0.94 (RMSE =0.050) at 3km and R2=0.89(RMSE=0.065) at 1 km. DEVINE, Transformer 3-D, and FNO yield 3 km scores of 0.91−0.93, with 1km scores lower by 0.02−0.08, illustrating the increased difficulty of finer-scale reconstruction. Physicsinformed losses improve all models compared to MSE-only baselines, and the residual architecture (DeepRU) remains most effective for this dual-scale task.
2025
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
Ensuring data quality, completeness, and interoperability is crucial for progressing safety research, Safe-and-Sustainable-by-Design approaches, and regulatory approval of nanoscale and advanced materials. While the FAIR (Findable, Accessible, Interoperable, and Re-usable) principles aim to promote data re-use, they do not address data quality, essential for data re-use for advancing sustainable and safe innovation. Effective quality assurance procedures require (meta)data to conform to community-agreed standards. Nanosafety data offer a key reference point for developing best practices in data management for advanced materials, as their large-scale generation coincided with the emergence of dedicated data quality criteria and concepts such as FAIR data. This work highlights frameworks, methodologies, and tools that address the challenges associated with the multidisciplinary nature of nanomaterial safety data. Existing approaches to evaluating the reliability, relevance, and completeness of data are considered in light of their potential for integration into harmonized standards and adaptation to advance material requirements. The goal here is to emphasize the importance of automated tools to reduce manual labor in making (meta)data FAIR, enabling trusted data re-use and fostering safer, more sustainable innovation of advanced materials. Awareness and prioritization of these challenges are critical for building robust data infrastructures.
2025
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
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
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
This paper reviews results published by the International Co-operative Programme on Effects on Materials including Historic and Cultural Monuments (ICP Materials) with emphasis on those obtained after the turn of the century. Data from ICP Materials come from two main sources. The first is through exposures of materials and collection of environmental data in a network of atmospheric exposure test sites mainly distributed across Europe. Corrosion of carbon steel has continued to decrease during the period 2000–2020 but corrosion of zinc only up until 2014, and the trend in zinc corrosion is only visible when examining four-year data. Surface recession of limestone as well as soiling of modern glass show no decreasing trend during 2000–2020. The second is through case studies performed at heritage sites across Europe. Risk analysis of corrosion and soiling for twenty-six sites indicate that currently soiling is a more significant maintenance trigger than corrosion. Costs for maintaining heritage sites are substantial and costs attributable to air pollution is estimated from 40% to as much as 80% of the total cost. Future directions of the program are work on effects of particulate matter, improving the scientific basis for the work, and making the monitoring data publicly available.
2025