Publikasjoner

Bygger hytte av plast: – Dette er bra for miljøet

Herzke, Dorte (intervjuobjekt); Kolve, André Rajan; Ellefsen, Vegard Unger (journalister)

Bye-bye, two degree target? Recent development in observed CO2 and CH4: Do we see signs of reductions? NILU F

Myhre, C.L.

Buried and forgotten: Plastic contamination in an ancient deep-sea fish lineage

Ferreira, Guilherme V.B.; Schmidt, Natascha; Justino, Anne K.S.; Fudge, Douglas S.; Lucena-Frédou, Flávia; Eduardo, Leandro N.; Mincarone, Michael M.

Building-related symptoms in school environment: Predictability using machine learning approach

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

Building-related symptoms (BRS) are commonly experienced by students in schools and are potentially affecting academic performance and health. Even though indoor environment quality (IEQ) measurements indicated fair conditions, students still perceived discomfort that led to symptoms, highlighting the necessity of collecting user-feedback about IEQ-complaints. This study aimed to predict and understand the prevalence of BRS (headache, tiredness, cough, dry eyes-hands) experienced by students in classrooms using machine-learning (ML) approach based on measurement data, building factors, and prevalence of IEQ-complaints. We collected measurement data (from indoor and outdoor climate), building factors, and user-feedback by students via online-platform across three sampled classrooms each campaign during three consecutive school semesters. Significant input variables for ML were pre-selected using statistical tests. ML models were evaluated based on accuracy metrics and SHAP analysis for input interpretation. Models using measurement data alone performed poorly (testing R² <50 %) to predict prevalence of BRS, whereas adding building factors and prevalence of IEQ-complaints increased accuracy (R² up to 95 %) of prediction of BRS with lower RMSE. In addition, interpretation from SHAP analysis showed IEQ-complaints especially related with indoor air quality (e.g., heavy air, dust & dirt, and dry air) as significant contributors for predicting prevalence of BRS. We conclude that the framework of combining objective measurements with occupant-reported complaints can be reliable, interpretable predictions of symptom prevalence. This study is limited by single-school setting, health confounders, and symptoms verification. Future research may contribute to exploring wider set of input variables, applicability, and variation of complaints preference.

Building and evaluating sensor-based Citizens' Observatories for improving quality of life in cities.

Castell, N.; Lahoz, W.; Schneider, P.; Hoiskar, B.A.; Grossberndt, S.; Naderer, C.; Robinson, J.; Kocman, D.; Horvat, M.; Bartonova, A.

BTEX fra vannrenseanlegg ved Mongstad raffineri. Spredningsberegninger utslipp til luft

Berglen, Tore Flatlandsmo; Markelj, Miha

NILU

Bruk av sensorsystemer for måling av utendørs luftforurensning – muligheter og begrensninger

Høiskar, Britt Ann Kåstad

Bruk av luftkvalitet.info. NILU F

Høiskar, B.A.K.

Bruk av fjernmålinger for overvåkning av luft og atmosfære, muligheter og begrensninger

Aas, Wenche; Stebel, Kerstin; Schneider, Philipp; Evangeliou, Nikolaos; Svendby, Tove Marit; Berglen, Tore Flatlandsmo; Myhre, Cathrine Lund; Kylling, Arve; Pisso, Ignacio; Fjæraa, Ann Mari

Bronopol, resorcinol, m-cresol and triclosan in the Nordic environment. TemaNord, 2007:585

Dye, C.; Schlabach, M.; Green, J.; Remberger, M.; Kaj, L.; Palm Cousins, A.; Brorström-Lunden, E.

Brominated flame retardants. Oxidative stress and cell death using cerebellar granule cells and granulocytes.

Fonnum, E.; Reistad, T.,, Mariussen, E.

Brominated flame retardants in the European chemicals policy of REACH-Regulation and determination in materials.

Kemmlein, S.; Herzke, D.; Law, R.J.