Found 2736 publications. Showing page 11 of 274:
Car tire particles and their additives: biomarkers for recent exposure in marine environments
Car tire particles represent an important category of microplastics that is difficult to alleviate. The particles stem from abrasion during driving, so-called tire wear particles (TWPs), down-cycled end-oflife tire granulate, popular as low-cost infill on sports fields, or degradation products from discarded tires. The material contains a variety of additives and chemical residues from the manufacturing process, including metals, especially high concentrations of zinc, polycyclic aromatic hydrocarbons (PAHs), and benzothiazoles, but also para-phenylenediamines (PPDs) and numerous other organic chemicals. In urbanized areas, TWPs are emitted from roads, and granulates disperse from artifical sports fields and other urban surfaces to the environment, suggesting that runoff to coastal systems is likely and a route of exposure to marine organisms. Recent experimental studies show tire rubber
particles in marine animals from different functional groups in addition to uptake of tire-related organic chemicals into biological tissues. These include bivalves, crabs, and fish, representing different body sizes, marine habitats, and feeding modes, and thus varying exposure scenarios. Our findings from GC-HRMS SIM chromatography demonstrate that different marine species ingest tire rubber particles, and that several tire additives are taken up into tissues post-ingestion. Although the organic chemicals do not seem to bioaccumulate, they are specific and bioavailable chemicals in tire materials. Mapping of tire rubber particle distributions in coastal systems, dose-response toxicity
testing and risk assessments of environmental concentrations are thus warranted, also with a view to potential trophic transfer and implications for human health.
2025
2025
Ensuring a healthy and comfortable indoor environment in schools is essential for student well-being and academic performance. The purpose of this study is to investigate the factors influencing students’ satisfaction with indoor air quality (IAQ) and thermal comfort in classrooms. To address this, one year-long measurements were conducted across multiple classrooms in a Norwegian secondary school, collecting data on indoor climate (CO₂, VOC levels, temperature, relative humidity, and air pressure) along with outdoor climate variables (temperature, humidity, and solar radiation). Additional room-specific data, including orientation, floor level, and ventilation system specifications, were also considered. An online feedback system was used to gather 1,473 real-time student responses on satisfaction levels. Supervised machine learning (ML) models were developed to assess the importance of these parameters in predicting perceived indoor comfort: IAQ perceptions and thermal environmental perceptions. Results showed ML models effectively predicted student dissatisfaction, achieving accuracy greater than 80% when environmental and building parameters were considered simultaneously. The findings emphasized that dissatisfaction with indoor conditions is driven by multiple interacting factors of measured variables and building parameters single independent variables. SHAP analysis provided valuable interpretability, revealing how variations in environmental conditions collectively impact students' perceived comfort. This comprehensive approach demonstrates the practical potential of ML-based IEQ monitoring systems, suggesting that schools can proactively improve indoor conditions through targeted interventions informed by real-time predictions.
2025
Seminar focused on health inequality arranged by the project Healthy Choices and the Social gradient.
2025