Publikasjoner

Presentation of a VOC data submission tool under EBAS.

Reimann, S.; Claude, A.; Hill, M.; Fjæraa, A.M.; Plass-Dülmer, C.

Presentasjon av NILU for Romerike batteriverk

Guerreiro, Cristina; Bogra, Shelly

Presentasjon av Framsenterets forskningsprogram, innledning

Heimstad, Eldbjørg Sofie

Presence and weekly concentrations of Q1, 2,4-dibromo- and 2,4,6-tribromoanisole, and 2,4,6-tribromophenol in comparison to HCH isomers in air from Southern Norway (2003).

Melcher, J.; Schlabach, M.; Andersen, M.S.

Prenatal exposure to persistent organic pollutants and child overweight/obesity at 5-year follow-up: A prospective cohort study

Lauritzen, Hilde Brun; Larose, Tricia L; Øien, Torbjørn; Sandanger, Torkjel M; Odland, Jon Øyvind; Bor, Margot van de; Jacobsen, Geir Wenberg

Background Prenatal exposure to persistent organic pollutants (POPs), may influence offspring weight gain. More prospective epidemiological studies are needed to compliment the growing body of evidence from animal studies. Methods Serum from 412 pregnant Norwegian and Swedish women participating in a Scandinavian prospective cohort study were collected in 1986–88, and analyses of two perfluoroalkyl substances (PFASs) and five organochlorines (OCs) were conducted. We used linear and logistic regression models with 95% confidence intervals (CIs) to evaluate the associations between maternal serum POP concentrations at 17–20 weeks of gestation and child overweight/obesity (body mass index (BMI) ≥ 85th percentile) at 5-year follow-up. Results were further stratified by country after testing for effect modification. We also assessed potential non-monotonic dose-response (NMDR) relationships. Results In adjusted linear models, we observed increased BMI-for-age-and-sex z-score (β = 0.18, 95% CI: 0.01–0.35), and increased triceps skinfold z-score (β = 0.15, 95% CI: 0.02–0.27) in children at 5-year follow-up per ln-unit increase in maternal serum perfluorooctane sulfonate (PFOS) concentrations. We observed increased odds for child overweight/obesity (BMI ≥ 85th percentile) for each ln-unit increase in maternal serum PFOS levels (adjusted OR: 2.04, 95% CI: 1.11–3.74), with stronger odds among Norwegian children (OR: 2.96, 95% CI: 1.42–6.15). We found similar associations between maternal serum perfluorooctanoate (PFOA) concentrations and child overweight/obesity. We found indications of NMDR relationships between PFOS and polychlorinated biphenyl (PCB) 153 and child overweight/obesity among Swedish children. Conclusion We found positive associations between maternal serum PFAS concentrations and child overweight/obesity at 5-year follow-up, particularly among Norwegian participants. We observed some evidence for NMDR relationships among Swedish participants.

Preliminary results of the ACTRIS ACSM intercomparison study at the SIRTA French Atmospheric Supersite in the region of Paris. NILU PP

Crenn, V.; Frölich, R.; Sciare, J.; Croteau, P.L.; Favez, O.; Verlhac, S.; Belis, C.A.; Aas, W.; Äijälä, M.; Artiñano, B.; Baisnée, D.; Baltensprenger, U.; Bonnaire, N.; Bressi, M.; Canagaratna, M.; Canonaco, F.; Carbone, C.; Cavalli, F.; Coz, E.; Cubison, M.J.; Gietl, J.K.; Green, D.C.; Gros, V.; Heikkinen, L.; Lunder, C.; Minguillón, M.C.; Mocnik, G.; O'Dowd, C.D.; Ovadnevaite, J.; Petit, J-E.; Petralia, E.; Poulain, L.; Prevôt, A.S.H.; Priestman, M.; Riffault, V.; Ripoll, A.; Sarda-Estève, R.; Slowik, J.G.; Setyan, A.; Jayne, J.T.

Preliminary results from the evaluation of the impact of bioethanol buses on urban air quality.

Lopez-Aparicio, S.; Hak, C.; Schmidbauer, N.; Dye, C.; Manø, S.

Preliminary results from air quality modelling in Dakar. NILU OR

Guerreiro, C.; Vo, D.T.

Preliminary assessments under the 4th daughter directive. ETC/ACC Technical paper 2007/10

Barrett, K.

Preface: Supplement on ELOISE II.

Pirrone, N.; Pacyna, J.M.; Munthe, J.; Barth, H.

Predictors of per- and polyfluoroalkyl substances in a maternal population from Northern Norway.

Berg, V.; Nøst, T.H.; Huber, S.; Rylander, C.; Hansen, S.; Odland, J.Ø.; Sandanger, T.M.

Predicting the student's perceptions of multi-domain environmental factors in a Norwegian school building: Machine learning approach

Alam, Azimil Gani; Bartonova, Alena; Høiskar, Britt Ann Kåstad; Fredriksen, Mirjam; Sharma, Jivitesh; Mathisen, Hans Martin; Yang, Zhirong; Gustavsen, Kai; Hart, Kent; Fredriksen, Tore; Cao, Guangyu

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.