Publications

Citizen-operated low-cost sensors for estimating outdoor particulate matter infiltration

Salamalikis, Vasileios; Hassani, Amirhossein; Zawadzki, Paweł; Bykuć, Sebastian; Castell, Nuria

Fine particulates observed indoors exhibit high variability, influenced by both indoor emission sources and the infiltration of outdoor particles through open spaces and the incomplete building insulation. This study examines the relationship between indoor and outdoor PM2.5 levels in Legionowo, Poland, using data from low-cost air quality sensors operated by citizens. The indoor PM2.5 was lower than outdoor levels (median PM2.5: 1.9–17.3 μg m–3 indoors and 6.7–27.9 μg m–3 outdoors), with occasional peaks attributed to potential indoor emission sources. Statistical analysis identified emission events—particularly during cooking and household-heating periods—occurring more frequently from October to April. During this period, nearly 17% of indoor PM2.5 measurements were attributed to indoor emission sources after 18:00 LT, representing a 7% increase compared to the May–September period. In the absence of indoor sources, outdoor particles accounted for 29% to 75% of indoor concentrations, highlighting the significance of infiltration. This research emphasizes how citizen-generated data using low-cost sensors, after post-processing, can provide decision-ready information as for example outdoor particles’ infiltration factors for each building. The knowledge of the infiltration factor enables the determination of the contribution of indoor and outdoor sources to each resident’s exposure to airborne PM. This information can help decision-makers in devising interventions such as prioritizing indoor ventilation, reducing indoor activities resulting in increased exposure, and addressing outdoor pollution sources.

Multi-year black carbon observations and modeling close to the largest gas flaring and wildfire regions in the Western Siberian Arctic

Popovicheva, Olga; Chichaeva, Marina; Evangeliou, Nikolaos; Eckhardt, Sabine; Diapouli, Evangelia; Kasimov, Nikolay

The influence of aerosols on the Arctic system remains associated with significant uncertainties, particularly concerning black carbon (BC). The polar aerosol station “Island Bely” (IBS), located in the Western Siberian Arctic, was established to enhance aerosol monitoring. Continuous measurements from 2019 to 2022 revealed the long-term effects of light-absorbing carbon. During the cold period, the annual average light-absorption coefficient was 0.7 ± 0.7 Mm−1, decreasing by 2–3 times during the warm period. The interannual mean showed a peak in February (0.9 ± 0.8 Mm−1) then 10 times the lower minimum in June and exhibited high variability in August (0.7 ± 2.2 Mm−1). An increase of up to 1.5 at shorter wavelengths from April to September suggests contribution from brown carbon (BrC). The annual mean equivalent black carbon (eBC) demonstrated considerable interannual variability, with the lowest in 2020 (24 ± 29 ng m−3). Significant difference was observed between Arctic haze and Siberian wildfire periods, with record-high pollution levels in February 2022 (110 ± 70 ng m−3) and August 2021 (83 ± 249 ng m−3). Anthropogenic BC contributed 83 % to the total for the entire study period, and gas flaring, domestic combustion, transportation, and industrial emissions dominated. During the cold season, > 90 % of surface BC was attributed to anthropogenic sources, mainly gas flaring. In contrast, during the warm period, Siberian wildfires contributed to BC concentrations by 48 %. In August 2021, intense smoke from Yakutian wildfires was transported at high altitudes during the region's worst fire season in 40 years.

Microplastic pellets in Arctic marine sediments: a common source or a common process?

Collard, France; Hallanger, Ingeborg G.; Philipp, Carolin; Herzke, Dorte; Schmidt, Natascha; Hotvedt, Ådne; Galtung, Kristin; Rydningen, Tom Arne; Litti, Lucio; Gentili, Giulia; Husum, Katrine

Plastic consumption is increasing, and millions of tonnes of plastic are released into the oceans every year. Plastic materials are accumulating in the marine environment, especially on the seafloor. The Arctic is contaminated with plastics, including microplastics (MPs, <5 mm) but occurrences, concentrations and fate are largely unknown. This study aimed at assessing whether MPs accumulate at greater water depths in the Barents Sea, and close to the Longyearbyen settlement, and at understanding the ubiquity and source of a specific type of collected pellets. Surface sediments were collected at seven stations around Svalbard with a box-corer, and three replicates were taken at each station. MPs were extracted through density separation with saturated saltwater. Many pellets were found, and their composition was assessed by pyrolysis-GC/MS. Procedural blanks were performed using field blanks as samples to assess the overall contamination. The composition of all extracted particles was then analysed by μRaman spectroscopy. On average, 3.61 ± 1.45 MPs/100 g (dw) were found. The sea ice station, after blank correction, was more contaminated and displaying a different profile than the other stations, and the deepest station did not show the highest MP concentrations but rather the opposite. Sediments close to Longyearbyen were not more contaminated than the other stations either. Dark pellets of similar aspect were found at all stations, raising the question about a possible common source or process. These pellets were made of several plastic polymers which varied in proportion for each pellet, suggesting a common process was at the origin of those pellets, potentially marine snow formation.

Mapping human-nature archetypes to guide global biodiversity, food security and land use policy

Sietz, Diana; Niamir, Aidin; Müller, Daniel; Hickler, Thomas; Kanemoto, Keiichiro; Moran, Daniel Dean; Thonicke, Kirsten

Reconciling biodiversity conservation, food security, and sustainable agriculture at global scale requires a clear understanding of regional social-ecological opportunities and challenges. This understanding helps untap regional contributions to better achieve global policy targets, such as those framed in the Kunming-Montreal Global Biodiversity Framework (GBF). Yet previous global syntheses of social-ecological interlinkages remain limited in thematic and spatial detail, restricting the discussion of regional contributions and targeted policy implementation. Here, we present 25 human-nature archetypes derived from clustering of global social-ecological data revealing regional opportunities and challenges for meeting global policy targets. Our results differentiate regions with large conservation opportunities from those well suited for ecological restoration or ecological intensification. They highlight the widespread need for improving governance to enhance food security and re-design agricultural systems. Overall, our analysis supports international and national decision makers in tailoring GBF targets to regional specificities in order to more effectively achieve global sustainability goals.

High resolution GDP modelling for climate risk assessments with an application to coastal flooding in Norway

Barre, Francis Isidore; Bouman, Evert Alwin; Simpson, Matthew James Ross; Borck, Hilde Sande; Hertwich, Edgar; Moran, Daniel Dean

An important prerequisite for accurately characterizing economic exposure from climate change at the national scale is a spatial inventory of economic activity and value creation. Current options for such inventories are limited, being either spatially precise but economically bounded sector-specific or owner-specific datasets, or gridded gross domestic product (GDP) products with coarse spatial resolution and inadequate sectoral resolution. To address these limitations, we develop a map of national GDP with high spatial and sectoral resolution. We stress this with meter-scale flood hazard maps to characterize GDP at risk from flooding. We further couple this to a macroeconomic input–output analysis to use the new sectoral resolution to estimate the scope of indirect economic exposure to flood at a national scale.

Filling the Gaps in PFAS Detection: Integrating GC-MS Non-Targeted Analysis for Comprehensive Environmental Monitoring and Exposure Assessment

Newton, Seth R.; Bowden, John A.; Charest, Nathaniel; Jackson, Stephen R.; Koelmel, Jeremy P.; Liberatore, Hannah K.; Lin, Ashley M.; Lowe, Charles N.; Nieto, Sofia; Pollitt, Krystal J. Godri; Robuck, Anna R.; Rostkowski, Pawel; Townsend, Timothy G.; Wallace, M. Ariel Geer; Williams, Anthony John

Matemballasje og farlige stoffer i sirkulærøkonomi

Rostkowski, Pawel

Environmental sustainability of urban expansion: Implications for transport emissions, air pollution, and city growth

Lopez-Aparicio, Susana; Grythe, Henrik; Drabicki, Arkadiusz; Chwastek, Konrad; Tobola, Kamila; Górska-Niemas, Lidia; Kierpiec, Urszula; Markelj, Miha; Strużewska, Joanna; Kud, Bartosz; Santos, Gabriela Sousa

This study examines the environmental impacts of urban growth in Warsaw since 2006 and models the implications of future urban development for traffic pollutant emissions and pollution levels. Our findings demonstrate that, over the past two decades, urban sprawl has resulted in decreases in accessibility to public transport, social services, and natural areas. We analyse CO2 traffic emissions, NO2 concentrations, and population exposure across urban areas in future scenarios of further sprawling or alternative compacting land-use development. Results indicate that a compact future scenario reduces transport CO2 emissions and urban NO2 levels, though increases in population density raise exposure to air pollution. A sprawl future scenario increases CO2 and NOx emissions due to longer commutes and congestion, and NO2 levels increase up to 25% in parts of the city. Several traffic abatement strategies were simulated, and in all simulations a compact city consistently yields the largest reductions in CO2 emissions and NO2 levels, implying that the best abatement strategy for combating negative consequences of sprawl is to reduce sprawling. In both city layouts, network-wide improvements of public transport travel times gave significantly reduced emissions. Combined, our findings highlight the importance of co-beneficial urban planning strategies to balance CO2 emissions reduction, and air pollution exposure in expanding cities.

Is Antarctica Greening?

Colesie, Claudia; Gray, Andrew Møller; Walshaw, Charlotte V.; Bokhorst, Stef; Kerby, Jeffrey T.; Jawak, Shridhar Digambar; Sancho, Leopoldo G.; Convey, Peter

Stochastic and deterministic processes in Asymmetric Tsetlin Machine

Elmisadr, Negar; Belaid, Mohamed-Bachir; Yazidi, Anis

This paper introduces a new approach to enhance the decision-making capabilities of the Tsetlin Machine (TM) through the Stochastic Point Location (SPL) algorithm and the Asymmetric Steps technique. We incorporate stochasticity and asymmetry into the TM's process, along with a decaying normal distribution function that improves adaptability as it converges toward zero over time. We present two methods: the Asymmetric Probabilistic Tsetlin (APT) Machine, influenced by random events, and the Asymmetric Tsetlin (AT) Machine, which transitions from probabilistic to deterministic states. We evaluate these methods against traditional machine learning algorithms and classical Tsetlin (CT) machines across various benchmark datasets. Both AT and APT demonstrate competitive performance, with the AT model notably excelling, especially in complex datasets.

Hazard assessment of nanomaterials using in vitro toxicity assays: Guidance on potential assay interferences and mitigating actions to avoid biased results

Rundén-Pran, Elise

Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection

Ghose, Debasish; Chatterjee, Ayan; Balapuwaduge, Indika A.M.; Lin, Yuan; Dash, Soumya P.

Stress is a common human reaction to demanding circumstances, and prolonged and excessive stress can have detrimental effects on both mental and physical health. Heart rate variability (HRV) is widely used as a measure of stress due to its ability to capture variations in the time intervals between heartbeats. However, achieving high accuracy in stress detection through machine learning (ML), using a reduced set of statistical features extracted from HRV, remains a significant challenge. In this study, we aim to address these challenges by proposing lightweight ML models that can effectively detect stress using minimal HRV features and are computationally efficient enough for IoT deployment. We have developed ML models incorporating efficient feature selection techniques and hyper-parameter tuning. The publicly available SWELL-KW dataset has been utilized for evaluating the performance of our models. Our results demonstrate that lightweight models such as k-NN and Decision Tree can achieve competitive accuracy while ensuring lower computational demands, making them ideal for real-time applications. Promisingly, among the developed models, the k-nearest neighbors (k-NN) algorithm has emerged as the best-performing model, achieving an accuracy score of 99.3% using only three selected features. To confirm real-world deployability, we benchmarked the best model on an 8 GB NVIDIA Jetson Orin Nano edge device, where it retained 99.26% accuracy and completed training in 31 s. Furthermore, our study has incorporated local interpretable model-agnostic explanations to provide comprehensive insights into the predictions made by the k-NN-based architecture.

Revisjon av indikatorer for tilstandsvurdering av miljø og økosystem i norske havområder — Gruppen for overvåking av de marine økosystemene

Skern-Mauritzen, Mette; Andersson, Ingvild; Arneberg, Per; Sanchez-Borque, Jorge; Christensen, Kai Håkon; Danielsen, Ida Kristin; Ersvik, Mihaela; Frantzen, Sylvia; Frie, Anne Kirstine Højholt; Frigstad, Helene; Grøsvik, Bjørn Einar; Gundersen, Kjell; Hanssen, Sveinn Are; Heimstad, Eldbjørg Sofie; Husa, Vivian; Jensen, Henning; Jensen, Louise Kiel; Johansson, Josefina; Johnsen, Hanne; Leiknes, Øystein; Lindeman, Ingunn Hoel; Lorentsen, Svein-Håkon; Meeren, Gro Ingleid van der; Moe, Øyvind Grøner; Mørk, Herdis Langøy; Nesse, Steinar; Anker-Nilsen, Tycho; Bohlin-Nizzetto, Pernilla; Nordgård, Ida Kessel; Pettersson, Lasse; Roland, Rune; Schøyen, Merete; Skjerdal, Hilde Kristin; Stene, Kristine Orset; Thorsnes, Terje; Vee, Ida; Wasbotten, Ingar

Havforskningsinstituttet