Publications

Evolving trends in application of low-cost air quality sensor networks: challenges and future directions

Abstract Low-cost air quality sensors (LCS) are increasingly used to complement traditional air quality monitoring yet concerns about their accuracy and fitness-for-purpose persist. This scoping review investigates topics, methods, and technologies in the application of LCS networks in recent years that are gaining momentum, focusing on LCS networks (LCSN) operation, drone-based and mobile monitoring, data fusion/assimilation, and community engagement. We identify several key challenges remaining. A major limitation is the absence of unified performance metrics and cross-validation methods to compare different LCSN calibration and imputation techniques and meta-analyses. LCSN still face challenges in effectively sharing and interpreting data due to a lack of common protocols and standardized definitions, which can hinder collaboration and data integration across different systems. In mobile monitoring, LCS siting, orientation, and platform speed are challenges to data consistency of different LCS types and limit the transferability of static calibration models to mobile settings. For drone-based monitoring, rotor downwash, LCS placement, flight pattern, and environmental variability complicate accurate measurements. In integrating LCS data with air quality models or data assimilation, realistic uncertainty quantification, ideally at the individual measurement level, remains a major obstacle. Finally, citizen science initiatives often encounter motivational, technological, economic, societal, and regulatory barriers that hinder their scalability and long-term impact.

Machine Learning Prediction of Student Satisfaction on Indoor Air Quality and Thermal Environment in a Norwegian Secondary School

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.

Scene-to-Scene classification variability in spatiotemporal glacier surface facies mapping in Svalbard

Sex and Gender Dimensions in Hazard and Risk Assessment of Engineered Nanomaterials

The knowledge on hazards and risks connected to human exposure to engineered nanomaterials (ENMs) is still very limited, despite several decades of research and regulatory efforts at the international level. In particular, sex/gender‐related responses to such exposure have not been clearly articulated so far in any of the existing guidance documents or regulatory relevant opinions provided to the parties involved in the risk assessment and risk management of ENMs. We aimed to demonstrate the relevance of the sex/gender dimension for the characterization of the risks and hazards associated with ENMs by analyzing existing scientific data on sex‐related differences in response to ENMs exposure. This was achieved by performing an extensive review of in vivo mammalian toxicity studies published in PubMed and Web of Science databases. Further analysis was performed only for data reported in publications that satisfied scientific quality criteria assessed using the GUIDEnano approach. Finally, we demonstrated the importance of the sex/gender dimension for safety testing of ENMs in the future and provided recommendations on how to include the sex/gender dimension in toxicity testing of ENMs to ensure precise, transparent, and reliable conclusions in the process of hazard and risk assessments. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine

Fant helseskadelige kjemikalier på barnerom

Rostkowski, Pawel (interview subject); Aasen, Kristine Ramberg (journalist)

Health inequalitites

Seminar focused on health inequality arranged by the project Healthy Choices and the Social gradient.

Evaluating the ecological and societal impact of urban rain gardens in Lublin, Poland

Designing an ethical and explainable automatic coaching (eCoach) system for community based, persuasive recommendations

Abstract This study introduces a community-focused eCoach recommendation system aimed at enhancing physical activity by leveraging demographic data, wearable sensor inputs, and machine learning algorithms to generate both individual and community-based activity recommendations using advice-based collaborative filtering. Existing eCoaching systems largely focus on personalized feedback without incorporating social reinforcement or group-level motivation, creating a gap in leveraging community influence for sustained health behaviors. Our system combines real-time activity tracking through wearable sensors and advice-based collaborative filtering to deliver adaptive recommendations. We collected data from 31 participants (16 using MOX2-5 sensors and 15 from a public Fitbit-based dataset), targeting daily activity levels to generate actionable guidance. Through decision tree classification and SHAP-based interpretability, we achieved a model accuracy of 99.8%, with F1, precision, recall, and MCC metrics confirming robustness across both balanced and imbalanced datasets. Ethical considerations, including privacy, bias mitigation, and informed consent, were integral to our design and implementation. Limitations include potential biases due to sample size and data imbalances, suggesting the need for future validation on independent datasets. This system demonstrates the potential to integrate with real-world healthcare initiatives, offering trust, transparency, and user engagement opportunities to meet public health objectives.

Environmental pollutants in the terrestrial and urban environment 2024

Samples from the urban terrestrial environment in the Oslo area were analysed for metals and a large number of organic environmental pollutants. The selected sample types that were analysed were soil, earthworm, fieldfare and sparrowhawk eggs, liver samples of brown rat, red fox and badger and blood serum from dog. Biomagnification potential was estimated based on detected data for relevant predator-prey pairs.

NILU

Activities of the Aerosol CClouds and Trace gases European Research Infrastructure (ACTRIS) Expert Group on Satellite Cal/Val

Evaluation of EarthCARE Level 2 Product Uncertainties - AOD Uncertainties in the Arcitc

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