Fant 9896 publikasjoner. Viser side 395 av 396:
2025
Road traffic externalities are an important consequence of land-use and transport interactions and may be especially induced by their inefficient combinations. In this study, we integrate land-use, transport and emission modelling tools (the LUTEm framework) to assess how suburban expansion vs. inward densification scenarios influence journey parameters, road network performance and traffic emissions. Case-study simulations for Warsaw (Poland) underscore the negative consequences of suburban sprawl development, which are hardly mitigated by additional land-use or transport interventions, such as rebalancing of population-workplace distribution or road capacity reductions. On the other side, compact city development lowers global traffic congestion and emissions, but can also raise the risks of traffic externalities in central city area unless complemented with further interventions such as improved public transport attractiveness. This study aims to enrich the understanding of how integrating the land-use development and transport interventions can ultimately influence travel parameters and reduce urban road traffic externalities.
Elsevier
2025
2025
Indian Land Carbon Sink Estimated from Surface and GOSAT Observations
The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO2) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO2 fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO2 exchanges independently, and it obtains CO2 fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr−1 using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr−1 by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO2 fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency.
MDPI
2025
Forurensning i Arktis kan være opptil 71 ganger høyere i løpet av sommeren sammenlignet med vinteren
Norges forskningsråd
2025
Havforskningsinstituttet
2025
Enniatins (ENNs) and beauvericin (BEA) are cyclic hexadepsipeptide fungal metabolites which have demonstrated antibiotic, antimycotic, and insecticidal activities. The substantial toxic potentials of these mycotoxins are associated with their ionophoric molecular properties and relatively high lipophilicities. ENNs occur extensively in grain and grain-derived products and are considered a food safety issue by the European Food Safety Authority (EFSA). The tolerable daily intake and maximum levels for ENNs in humans and animals remain unestablished due to key toxicological and toxicokinetic data gaps, preventing full risk assessment. Aiming to find critical data gaps impeding hazard characterization and risk evaluation, this review presents a comprehensive summary of the existing information from in vitro and in vivo studies on toxicokinetic characteristics and cytotoxic, genotoxic, immunotoxic, endocrine, reproductive and developmental effects of the most prevalent ENN analogues (ENN A, A1, B, B1) and BEA. The missing information identified showed that additional studies on ENNs and BEA have to be performed before sufficient data for an in-depth hazard characterisation of these mycotoxins become available.
Springer
2025
2025
Air Quality and Healthy Ageing: Predictive Modelling of Pollutants using CNN Quantum-LSTM
The concept of healthy ageing is emerging and becoming a norm to achieve a high quality of life, reducing healthcare costs and promoting longevity. Rapid growth in global population and urbanisation requires substantial efforts to ensure healthy and supportive environments to improve the quality of life, closely aligned with the principles of healthy ageing. Access to fundamental resources which include quality healthcare services, clean air, green and blue spaces plays a pivotal role in achieving this goal. Air quality, in particular, is a critical factor in achieving healthy ageing targets. However, it necessitates a global effort to develop and implement policies aimed at reducing air pollution, which has severe implications for human health including cognitive impairment and neurodegenerative diseases, while promoting healthier environments such as high quality green and blue spaces for all age groups. Such actions inevitably depend on the current status of air pollution and better predictive models to mitigate the harmful impact of emissions on planetary health and public health. In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. The performance of the proposed model is analysed on five key pollutants including fine Particulate Matter PM2.5, Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulphur Dioxide (SO2), sourced from air quality monitoring station in Northern Ireland, UK. The effectiveness of the proposed model is evaluated by comparing its performance with its equivalent classical counterpart using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate the superiority of the proposed model, achieving a performance gain of up to 14% and validating its robustness, efficiency and reliability by leveraging t.
IEEE (Institute of Electrical and Electronics Engineers)
2025
We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.
Springer Nature
2025
2025
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
Wood building materials can be a source of volatile organic compounds (VOCs) in the indoor environment and increasing focus is put on classification and regulation of the use of wood building materials in Europe. The main wood related VOCs such as monoterpenes rarely pose adverse health effects for humans, but as analytical procedures become more sensitive new hazardous VOCs are detected in low concentration. There is a need for comprehensive identification of VOCs emitting from different wood building materials for indoor use. This study performed a first semi-quantitative non-target and suspect screening of VOC emissions from three important wood-based building materials in Europe. Air samples collected from emission chambers were analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and resulting mass spectra were classified into confidence groups. A total of 84, 133 and 197 compounds were found to emit from cross-laminated timber, untreated spruce panel and untreated pine panel, respectively. Pine panel was found to emit a higher number of VOCs as well as higher concentrations of most VOCs compared to the spruce building materials. Several new VOCs were detected in the emission profile of pine and spruce. However, they were mostly structurally similar to previously reported wood VOCs. Two compounds of concern emitting from all three wood building materials were furfural and (E)-2-octenal, as these have been classified as group 2 carcinogen and potent eye irritant, respectively.
Elsevier
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.
Elsevier
2025