Publikasjoner

Growth in Production and Environmental Deposition of Trifluoroacetic Acid Due To Long‐Lived CFC Replacements and Anesthetics

Abstract Trifluoroacetic acid (TFA) is a persistent pollutant with potential long‐term effects on the environment and on health. Recent studies using ice core records report large increases (up to tenfold) in Arctic TFA deposition since the 1970s, and trends suggest long‐lived chlorofluorocarbon (CFC) replacements may be a major source. Here, we use a chemical transport model to examine the global TFA budget arising from CFC replacements–hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs)–and inhalation anesthetics. Global TFA deposition from these sources increased ∼3.5‐fold from 6.8 (5.9–7.6) Gg/yr in 2000 to 21.8 (18.6–25.0) Gg/yr in 2022, with cumulative deposition reaching 335.5 Gg. We find HCFC‐123, HCFC‐124, and HFC‐134a account for most modeled TFA production and that long‐lived CFC replacements account for virtually all of the observed Arctic deposition trend. At lower latitudes, our analysis supports the recent emergence of hydrofluoroolefins (HFOs) as a TFA source. We conclude that increased TFA monitoring is required.

Kenya gir meg håp og inspi­rasjon

Muri, Helene (intervjuobjekt)

Evolution of Near‐Term Atmospheric Methane and Associated Temperature Response Under the Global Methane Pledge: Insights From an Earth System Model

Abstract Methane is a powerful greenhouse gas with a shorter lifetime than carbon dioxide (CO 2 ), making it an important target for near‐term climate action. The Global Methane Pledge (GMP) aims to cut anthropogenic methane emissions by 30% from 2020 levels by 2030. Using an Earth system model with interactive CH 4 sources and sinks, we assess the Pledge's impact through 2050. Results show that current GMP commitments deliver only a 10% cut by 2030—well below the target. Only the maximum technically feasible reduction (MTFR) pathway can achieve the 30% goal. By 2050, current GMP commitments lowers methane concentrations by 3% relative to 2025, while MTFR achieves 8%. Both pathways slow warming slightly, avoiding about 0.1°C of global temperature rise, with the Arctic seeing the greatest benefits (up to 2°C less warming). Without wider participation, the GMP with current signatories will fall short of its targets and Paris Agreement goals.

Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers

The instruments used for routine pollen monitoring are gradually changing from traditional impactors with manual data processing to automated pollen monitors using deterministic and/or machine-learning algorithms for data analysis. This manuscript compares pollen number concentration of Alnus sp., Betula sp., Corylus sp., and Poaceae measured by Hirst-type bioaerosol samplers and the SwisensPoleno automated bioaerosol monitor in Switzerland and Norway. Due to physical particle losses and the classification rate of the algorithms being well below unity, scaling factors had to be applied to the measurements of the SwisensPoleno to match those of the Hirst impactor. These scaling factors depended on the geographic location, i.e. differed significantly between Switzerland and Norway. The importance of adjusting the scaling factors according to the location of the monitoring network and the need for reporting the numerical values of these scaling factors in future scientific publications is emphasized.

Integrating Low-Cost Sensors with Dispersion Modelling for High-Resolution Insights into Urban Air Quality

Urban areas experience elevated air pollution levels which pose significant health risks. Reducing exposure to poor air quality and mitigating the associated negative health impacts requires informed policy measures. This study advances urban air quality modelling by developing an air quality model (baseline model) and further integrating measurements from a network of low-cost sensors and regulatory monitors into the model output (data fusion model). The resulting data fusion model provides accurate air quality data in high spatiotemporal resolution. The data fusion model showed higher PM2.5 concentrations during evening hours and winter months, with a population-weighted exposure to PM2.5 almost twice as high as predicted by the baseline model during these months. The models exhibited different spatial patterns, with the data fusion model showing a shift in peak concentrations from the city centre to residential areas, where levels were up to 10 µg/m3 higher than the baseline model. These differences are likely attributable to an underestimation of residential emissions in the baseline model. While both models were FAIRMODE compliant, the data fusion model showed a reduced bias for most monitoring stations compared to the baseline model. The data fusion model enabled a more accurate assessment of existing policies, specifically those aimed at reducing urban air pollution from solid fuel burning. Moreover, by identifying locations and sectors which contribute significantly to high levels of PM2.5, the data fusion model supports the formation of targeted air quality policies. This enables cities to maximise reductions in air pollution and exposures, thereby safeguarding public health.

State of MRV for marine CDR

Gastrointestinal image classification with GIDNet CNN model and non-linear Tansh activation function

Silicone-Foam Passive Air Samplers for Combined Target and Nontarget Chemical Profiling and Toxicity Assessment of Airborne Exposomes

Polluted air is a major global health risk factor, yet the chemical composition and toxicity of airborne gases and particles remain underexplored due to their complexity and difficulties in sampling. We recently introduced how polydimethylsiloxane (PDMS) foam─or silicone foam─can be synthesized for passive air sampling, enabling simple and cost-effective nontarget chemical profiling of indoor air. Here, we demonstrate expanded applications, indoors and outdoors, with commercial PDMS-foam, including for: (i) wide-scope target analysis of >220 priority substances by quantitative liquid- and gas chromatography-high-resolution mass spectrometry, (ii) microscopic characterization and nontarget profiling of accumulated fine particles, and (iii) effect-guided discovery of harmful substances, combining toxicological data with nontarget analysis in silico. Median method quantification limits were 0.12 ng/mL, 90% of target analytes had absolute recoveries between 70 and 130%, and hazardous substances were discovered, including ethylene glycols, insecticides, and UV filters. Microscopy revealed the accumulation of abundant fine particles, and the automated characterization of the fluorescent fraction revealed that most were <4 μm. Extracts from outdoor samples reduced human lung cell viability, and multivariate modeling flagged families of potentially toxic substances in a virtual effect-directed analysis. PDMS-foam disks require field calibration to determine their linear sampling rate(s), but current results and applications establish PDMS-foam as a multimodal passive sampler, enabling integrated chemical quantitation, toxicological analysis, and molecular discovery in air.

Organ-specific in vitro models for prediction of hazard assessment of nanomaterials

Organ-specific multicellular in vitro models are used to mimic the lung-blood-brain axis, and to assess the nanomaterials (NMs) safety in humans. We employed a triculture lung model, a whole-blood model, an astrocytes-neurons coculture to examine health outcomes by three cerium dioxide (CeO2) NMs and silver (Ag) nanowires. Endpoints included cytotoxicity, gene expression, genotoxicity, inflammatory markers at the air–liquid interface (ALI), complement activation, and secondary toxicity in astrocytes-neurons coculture. Post-exposure, CeO2–3.5 nm high-dose decreased cell viability, no DNA damage was detected. At epithelial-macrophages interface, CeO2–50 nm upregulated surfactant protein A (SPA), cell surface death receptor (FAS), and heme oxygenase-1 (HMOX1), whereas CeO2–3.5 nm downregulated SPA. Ag-nanowires upregulated HMOX1, macrophage inflammatory protein-1β (MIP-1β), granulocyte colony-stimulator factor (G-CSF), chemokine C-X-C-motif ligand 1 (CXCL1). At endothelial side, CeO2–50 nm and − 3.5 nm, and Ag-nanowires upregulated HMOX1. In whole-blood model, CeO2–3.5 nm high-dose reduced terminal complement complex (TCC) proteins, while CeO2–50 nm and Ag-nanowires increased them. Nanomaterials activated CD11b+ on granulocytes and monocytes. Ag-nanowires conditioned-medium (CM) on astrocytes-neurons coculture, decreased cell viability. CeO2–50 nm CM upregulated IL1β, NFκB, and HMOX1. Overall, CeO₂–3.5 nm exhibits lung toxicity; CeO₂–50 nm CM triggers inflammatory response and Ag-nanowires CM may induce cytotoxicity in brain cells.

Exceptional high AOD over Svalbard in summer 2019: a multi-instrumental approach

In the summer of 2019, the Arctic region registered exceptionally high aerosol optical depth (AOD) values over Svalbard, linked to intense biomass burning (BB) and volcanic activity across the Northern Hemisphere. This study presents a comprehensive, multi-instrumental analysis of the aerosol conditions in and around Ny-Ålesund (Spitsbergen, Norway), combining data from ground-based sun-photometry, in-situ observations, active remote sensing (ground-based and on satellite), and atmospheric dispersion modelling (FLEXPART). Despite high AOD was observed during all the period, three different aerosol events are identified in the atmospheric column (6–10 July, 25–28 July, and 6–17 August). In contrast, in-situ surface stations only recorded significant aerosol load during 5–9 July, 30 August, and 12 September, suggesting that most of the aerosol particles remained above the boundary layer. Lidar and photometric observations revealed the presence of spherical, weakly absorbing Accumulation-mode particles (with effective radii between 0.1 and 0.2 µm) in both the troposphere and stratosphere, with persistent layers extending above 10 km. Simulations carried out with FLEXPART correlate well with the measurements, attributing the observed aerosol events to multiple sources, including Siberian and North American wildfires, the Raikoke (Russia) volcanic eruption, and anthropogenic pollution. While the simulations show a contribution from volcanic aerosols, the contribution from biomass-burning aerosols in the upper troposphere and lower stratosphere were likely more significant under the atmospheric conditions of summer 2019. Overall, the aerosol radiative impact during this long-lasting event was substantial, with a mean reduction in direct solar radiation of approximately −74 W m−2 during July and August. This work shows how the use of dispersion modelling together with multiple observation sources allows to achieve a more complete description of the atmospheric aerosol events and contributes to a better understanding of the overall picture.

Investigating climate change impacts on contaminant exposure in the Arctic using the Nested Exposure Model

A hybrid CNN-transformer model with adaptive activation function for potato leaf disease classification

Abstract Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet) , which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM) , is proposed to enhance the model’s learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.

Boreal forests at risk: Absence of climate perspectives in current management policies

Boreal forests influence climate both biogeochemically through carbon uptake and biogeophysically through evapotranspiration, turbulent fluxes and albedo, and are in turn impacted by climate through biotic and abiotic damages. This systematic literature review and qualitative narrative policy review and analysis aims to get a better insight into the discrepancy between policy and science on forestry action to mitigate climate warming in high latitude jurisdictions. We identify climate effects on and from forests with corresponding management options in a systematic review of scientific literature following PRISMA guidelines. These results were combined with a qualitative policy review and analysis to identify the climate and forestry policies from all boreal-to-Arctic jurisdictions and determine how (many of) these climate effects ended up in forest and climate policy. There is mounting evidence that in boreal regions, albedo-driven warming can partially offset, and in some contexts be comparable to, carbon-driven cooling; the balance varies by season, forest type and disturbance history. However, although all analysed jurisdictions (Alaska, Canada, European Union, Sweden, Finland, Iceland, Norway and Russia) recognise the forests' role in carbon uptake, none recognise the albedo effect, and none translate these climate effect into binding regulatory measures. Nor do most of the jurisdictions take into account possible risk of climate-related damages. This might lead to ineffective and even adverse forest and climate measures. Our study emphasises a need for more evidence-based and comprehensive climate and forestry policies and regulations, along with a proactive approach to adopting these measures swiftly.