Publications

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.

Preparation and certification of a reference material on PCBs in pig fat and its application in quality control in monitoring laboratories during the Belgian

Bester, K.; de Vos, P.; Le Guern, L.; Harbeck, S.; Hendrickx, F.; Kramer, G.N.; Linsinger, T.; Mertens, I.; Schimmel, H.; Sejeroe-Olsen, B.; Pauwels, J.; De Poorter, G.; Rimkus, G.G.; Schlabach, M.

Prenatal exposure to DDT in malaria endemic region following indoor residual spraying and in non-malaria coastal regions of South Africa.

Channa, K.; Röllin, H.B.; Nøst, T.H.; Odland, J.Ø.; Sandanger, T.M.

Preliminary Results of Air Pollution Monitoring and Effects on the surface of Cultural Heritage Buildings at case study sites: Oslo, Florence, Cologne, Krakow, Salé and Bilbao. NILU F

Krupinska, B.; Lopez-Aparicio, S.; Bonazza, A.

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

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

Preliminary evaluation of the relationship between IAQ and demand controlled ventilation.

Lopez-Aparicio, S.; Vogt, M.; Hak, C.; Dauge, F. R.; Holøs, S.; Mysen, M.

Preliminary assessment report on the spatial mapping of air quality trends for Europe. ETC/ACC Tecnical paper, 2008/3

Denby, B.; Sundvor, I.; de Smet, P.; de Leeuw, F.

Preliminary assessment of air quality in Norway according to preliminary draft on EU air quality directive on heavy metals and BaP. NILU OR

Hagen, L.O.

Preliminary assessment of air quality in Norway (Phase 1), according to the new EU air quality directives. NILU OR

Larssen, S.; Hagen, L.O.; Tønnesen, D.

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.

Elsevier