Found 10488 publications. Showing page 404 of 420:
2025
Transboundary particulate matter, photo-oxidants, acidifying and eutrophying components
Norwegian Meteorological Institute
2025
2025
2025
Biomass burning emission analysis based on MODIS
We assessed the biomass burning (BB) smoke aerosol optical depth (AOD) simulations of 11 global models that participated in the AeroCom phase III BB emission experiment. By comparing multi-model simulations and satellite observations in the vicinity of fires over 13 regions globally, we (1) assess model-simulated BB AOD performance as an indication of smoke source–strength, (2) identify regions where the common emission dataset used by the models might underestimate or overestimate smoke sources, and (3) assess model diversity and identify underlying causes as much as possible. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background non-BB aerosol, such as in boreal forest regions and over South America and southern hemispheric Africa. The comparison is inconclusive where the total AOD is low, as in many agricultural burning areas, and where the background is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss rate from each model. The results point to a need for increased numbers of available BB cases for study in some regions and especially to a need for more extensive regional-to-global-scale measurements of aerosol loss rates and of detailed particle microphysical and optical properties; this would both better constrain models and help distinguish BB from other aerosol types in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations.
2025
Målinger av SO2 i omgivelsene til Elkem Carbon. Kalenderår 2024
På oppdrag fra Elkem Carbon AS har NILU utført målinger av SO2 i omgivelsene til Elkem Carbon i Kristiansand. Målingene ble utført med SO2-monitor i boligområdet på Fiskåtangen (Konsul Wilds vei). I tillegg ble SO2 målt med passive prøvetakere ved 3 steder rundt bedriften. Rapporten dekker målinger i perioden 1. januar – 31. desember 2024. Norske grenseverdier for luftkvalitet (SO2) ble overholdt ved Konsul Wilds vei for alle midlingsperioder (årsmiddel, vintermiddel, døgnmiddel og timemiddel). To døgnmiddelverdier var over nedre vurderingsterskel (50 µg/m3). Passive luftprøver viste at Fiskåveien, rett sør for bedriften, var det mest belastede stedet i måleperioden.
NILU
2025
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