Publications

Precipitation events over North China in August 2010: observations, monthly forecasting, and intra-seasonal variability of wave-trains across Eurasia. NILU PP

Orsolini, Y.J.; Zhang, L.; Peters, D.H.W.; Fraedrich, K.; Zhu, X.; Schneidereit, A.; van den Hurk, B.

Precursor in Arctic oscillation for the East Asian January temperature and its relationship with stationary planetary waves: Results from CMIP5 models

Li, Shuo; He, Shengping; Li, Fei; Wang, Huijun

Predicting Future Condition and Conservation Costs from Modelling Improvements to the Indoor Environment: The Monumental Munch-Paintings in the University of Oslo’s Aula Assembly Hall

Grøntoft, Terje; Stoveland, Lena Porsmo; Frøysaker, Tine

The aim of this work was to assess how improvements to the indoor environment could affect the future condition, frequency and costs of major conservation-cleaning campaigns on the monumental paintings (1909–1916) by Edvard Munch, centrally located in the Aula assembly hall of the University of Oslo. A lower soiling rate is expected to reduce the need for frequent and major cleaning campaigns. Estimations were performed using the freely available NILU-EnvCul web-model. The conservation of these large, mostly unvarnished, oil paintings is challenging, and it is important to understand the potential benefits of preventive conservation measures. The results from the model suggested benefits from preventive conservation in protecting the paintings, and as a cost-efficient strategy to reduce the soiling and cleaning frequency. The model results indicated that an improvement in the indoor air quality in the Aula, of 50–80% as compared to the 1916–2009 average, would increase the time until the next similar major conservation cleaning campaign from approximately 45 years to between about 85 and 165 years. This should give a 45–70% reduction in the respective conservation costs. This saving was probably initiated by improvements in the recent past, before the last Aula campaign in 2009–11.

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.

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.

Preface: Supplement on ELOISE II.

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

Preliminary Analysis of Aerosol Size Distribution at Col Margherita

Rossetti, Claudia; Favaro, Eleonora; Barbaro, Elena; Feltracco, Matteo; Gambaro, Andrea; Giovannini, Lorenzo; Doglioni, Giorgio; Cassiani, Massimo; Paolantiono, Marco Di; Rajput, Akanksha; Zardi, Dino; Cairns, Warren Lee Raymond

Preliminary assessment of air quality in Norway (Phase 2), according to the new EU air quality directives. NILU OR

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

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

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

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

Barrett, K.

Preliminary in vitro investigation of the biological effects of key tire chemicals

Longhin, Eleonora Marta; Honza, Tatiana; Hudecova, Alexandra Misci; McFadden, Erin; Rundén-Pran, Elise

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 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.