Fant 10464 publikasjoner. Viser side 406 av 419:
2025
2025
2025
Statusrapport 2025. Nasjonalt referanselaboratorium for luftkvalitetsmålinger
Denne rapporten oppsummerer oppgavene til Nasjonalt referanselaboratorium for luftkvalitetsmålinger (NRL), delkontrakt 1b, for første halvår 2025.
NILU
2025
2025
MIKRONOR 2024 Monitoring of microplastics and tyre wear particles in the Norwegian environment
The 2024 MIKRONOR campaign, coordinated by NIVA and NILU on behalf of the Norwegian Environment Agency, signifcantly expanded the national monitoring framework for microplastics (MPs) to encompass diverse environmental compartments, including surface waters (Oslofjord and Lake Mjøsa), urban runoff, marine sediments, atmospheric deposition, and coastal beach sediments. Urban stormwater runoff was identifed as a predominant source of MPs, particularly tyre wear particles (TWP). Sediment samples from stormwater traps in Oslo exhibited high TWP concentrations up to 240 mg/g, constituting approximately 25% of the total sediment mass. Corresponding runoff water samples revealed MP concentrations as high as 733 ± 142 particles/L, indicating substantial episodic fuxes of MPs into receiving aquatic or marine systems. Inner Oslofjord sediments contained 0.6–3.5 % TWP by mass, confrming the high levels found in 2023. Microplastic concentrations in surface waters were generally low, ranging from 0 to 0.6 MP/m³. However, two hydrodynamic accumulation zones within the Oslofjord exhibited anomalously high concentrations, with levels approximately two orders of magnitude greater than outside the accumulation zones. One net tow recovered >7,000 fragments of expanded polystyrene, highlighting localized retention. Atmospheric deposition peaked in urban Sofenbergparken (1514 µg/m²/d; 68 % TWP) and showed a clear urban-to-remote gradient. Beach sediments at Akerøya remained low in MPs, with most samples below detection limits. The findings highlight urban runoff, especially TWP, as a dominant source to the Oslofjord, and reveal critical hotspots in both water and air pathways.
Norsk institutt for vannforskning (NIVA)
2025
2025
Poor Indoor Environmental Quality (IEQ) in schools significantly impacts students’ well-being, learning capabilities, and health. Perceived dissatisfaction rates (PD%) among students often remain high, even when indoor environmental variables appear well-controlled. This study aims to predict perceived dissatisfaction rates (PD%) across multi-domain environmental factors—thermal, acoustic, visual, and indoor air quality (IAQ)—using machine learning (ML) models. The research integrates sensor-based environmental measurements, outdoor weather data, building parameters, and 1437 student survey responses collected from three classrooms in a Norwegian school across multiple seasons. Statistical tests were used to pre-select relevant input variables, followed by the development and evaluation of multiple ML algorithms. Among the tested ML models, Random Forest (RF) demonstrated the highest predictive accuracy for PD%, outperforming multi-linear regression (MLR) and decision trees (DT), with R² values up to 0.91 for overall IEQ dissatisfaction (PDIEQ%). SHAP analysis revealed key predictors: CO₂ levels, VOCs, humidity, temperature, solar radiation, and room window orientation. IAQ, thermal comfort, and acoustic environment were the most influential factors affecting students' perceived well-being. Despite limitations as implementation in building level scale, the study demonstrates the feasibility of deploying predictive ML models under real-world constraints for improving IEQ monitoring system. The findings support practical strategies for adaptive indoor environmental management, particularly in educational settings, and provide a replicable framework for future research. Future research can expand to other climates, buildings, measurements, occupant levels, and ML training optimization.
2025
2025
2025
2025
Machine learning for mapping glacier surface facies in Svalbard
Glaciers are dynamic and highly sensitive indicators of climate change, necessitating frequent and precise monitoring. As Earth observation technology evolves with advanced sensors and mapping methods, the need for accurate and efficient approaches to monitor glacier changes becomes increasingly important. Glacier Surface Facies (GSF), formed through snow accumulation and ablation, serve as valuable indicators of glacial health. Mapping GSF provides insights into a glacier's annual adaptations. However, satellite-based GSF mapping presents significant challenges in terms of data preprocessing and algorithm selection for accurate feature extraction. This study presents an experiment using very high-resolution (VHR) WorldView-3 satellite data to map GSF on the Midtre Lovénbreen glacier in Svalbard. We applied three machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)—to explore the impact of different image preprocessing techniques, including atmospheric corrections, pan sharpening methods, and spectral band combinations. Our results demonstrate that RF outperformed both ANN and SVM, achieving an overall accuracy of 85.02 %. However, nuanced variations were found for specific processing conditions and can be explored for specific applications. This study represents the first clear delineation of ML algorithm performance for GSF mapping under varying preprocessing conditions. The data and findings from this experiment will inform future ML-based studies aimed at understanding glaciological adaptations in a rapidly changing cryosphere, with potential applications in long-term spatiotemporal monitoring of glacier health.
2025
Geoengineering skulle kunna skapa ett mer orättvist klimat
Världens klimatsystem hänger ihop, så geoengineering för bättre klimat i en region kan påverka andra regioner negativt. Men vem styr och tar beslut om ...
2025
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
2025