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Preliminary assessments under the 4th daughter directive. ETC/ACC Technical paper 2007/10
2007
Preliminary assessment report on the spatial mapping of air quality trends for Europe. ETC/ACC Tecnical paper, 2008/3
2008
2003
2014
2012
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
2005
2019
2008
2019
The blood enzyme glutamate-oxaloacetate transaminase (GOT) has been postulated as an effective therapeutic to protect the brain during stroke. To demonstrate its potential clinical utility, a new human recombinant form of GOT (rGOT) was produced for medical use.
We tested the pharmacokinetics and evaluated the protective efficacy of rGOT in rodent and non-human primate models that reflected clinical stroke conditions.
We found that continuous intravenous administration of rGOT within the first 8 h after ischemic onset significantly reduced the infarct size in both severe (30%) and mild lesions (48%). Cerebrospinal fluid and proteomics analysis, in combination with positron emission tomography imaging, indicated that rGOT can reach the brain and induce cytoprotective autophagy and induce local protection by alleviating neuronal apoptosis.
Our results suggest that rGOT can be safely used immediately in patients suspected of having a stroke. This study requires further validation in clinical stroke populations.
2024
2014
2012
2023
2024
2020
Potential improvements on benzo(a)pyrene (BaP) mapping. ETC/ACM Technical Paper, 2016/3
2016
This report examines the impact of air pollution from residential wood combustion on health in the Nordic countries.Residential wood combustion is a major contributor to premature deaths and health issues. The number of premature deaths is expected to decrease from 1,600 in 2019 to 1,200 by 2030, with health costs dropping from EUR 3.2 bn. to EUR 2.5 bn. This improvement is due to fewer and newer, less polluting appliances, and better energy efficiency in homes.
Two additional scenarios for 2030 reflecting national differences were evaluated.
Technology Scenario: Faster replacement of old appliances, reducing premature deaths by 190 and health costs by EUR 390 mil.
Zone-Based Scenario: Bans in densely populated areas, reducing premature deaths by 240 and health costs by EUR 510 mil.
Mitigation in densely populated areas offers greater health benefits than national-level efforts.
Nordic Council of Ministers
2025
Potential environmental impact of bromoform from Asparagopsis farming in Australia
To mitigate the rumen enteric methane (CH4) produced by ruminant livestock, Asparagopsis taxiformis is proposed as an additive to ruminant feed. During the cultivation of Asparagopsis taxiformis in the sea or in terrestrially based systems, this macroalgae, like most seaweeds and phytoplankton, produces a large amount of bromoform (CHBr3), which contributes to ozone depletion once released into the atmosphere. In this study, we focus on the impact of CHBr3 on the stratospheric ozone layer resulting from potential emissions from proposed Asparagopsis cultivation in Australia. The impact is assessed by weighting the emissions of CHBr3 with its ozone depletion potential (ODP), which is traditionally defined for long-lived halocarbons but has also been applied to very short-lived substances (VSLSs). An annual yield of ∼3.5 × 104 Mg dry weight is required to meet the needs of 50 % of the beef feedlot and dairy cattle in Australia. Our study shows that the intensity and impact of CHBr3 emissions vary, depending on location and cultivation scenarios. Of the proposed locations, tropical farms near the Darwin region are associated with the largest CHBr3 ODP values. However, farming of Asparagopsis using either ocean or terrestrial cultivation systems at any of the proposed locations does not have the potential to significantly impact the ozone layer. Even if all Asparagopsis farming were performed in Darwin, the CHBr3 emitted into the atmosphere would amount to less than 0.02 % of the global ODP-weighted emissions. The impact of remaining farming scenarios is also relatively small even if the intended annual yield in Darwin is scaled by a factor of 30 to meet the global requirements, which will increase the global ODP-weighted emissions up to ∼0.5 %.
2022
Arctic-breeding geese acquire resources for egg production from overwintering and breeding grounds, where pollutant exposure may differ. We investigated the effect of migration strategy on pollutant occurrence of lipophilic polychlorinated biphenyls (PCBs) and protein-associated poly- and perfluoroalkyl substances (PFASs) and mercury (Hg) in eggs of herbivorous barnacle geese (Branta leucopsis) from an island colony on Svalbard. Stable isotopes (δ13C and δ15N) in eggs and vegetation collected along the migration route were similar. Pollutant concentrations in eggs were low, reflecting their terrestrial diet (∑PCB = 1.23 ± 0.80 ng/g ww; ∑PFAS = 1.21 ± 2.97 ng/g ww; Hg = 20.17 ± 7.52 ng/g dw). PCB concentrations in eggs increased with later hatch date, independently of lipid content which also increased over time. Some females may remobilize and transfer more PCBs to their eggs, by delaying migration several weeks, relying on more polluted and stored resources, or being in poor body condition when arriving at the breeding grounds. PFAS and Hg occurrence in eggs did not change throughout the breeding season, suggesting migration has a greater effect on lipophilic pollutants. Pollutant exposure during offspring production in Arctic-breeding migrants may result in different profiles, with effects becoming more apparent with increasing trophic levels.
2019
2011
Potato plant disease detection: leveraging hybrid deep learning models
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the “Potato Leaf Disease Dataset”, which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06, representing an 11.43 improvement over the results of the previous study. These results highlight the effectiveness of the hybrid model in complex agricultural settings and its potential to improve potato plant disease detection and identification.
BioMed Central (BMC)
2025