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Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
City-produced and transported black carbon: Synergy of in-situ optical measurements and modeling
The implementation of air pollution mitigation strategies requires not only high-quality continuous measurements of pollutants but also proper definitions of ways to differentiate between transported and locally produced contributions, as only the latter can be effectively reduced by authorities. To address this issue, we propose a new approach for partitioning monitored black carbon (BC) concentrations into city-produced (urban) and transported fractions using a combination of measured and modeled data. Two simultaneous measurement campaigns (warm season 2022 and cold season 2022/23) were conducted in two urban environments: Vilnius (Lithuania) and Warsaw (Poland). In the cold season in Warsaw, BC mass concentration was 90% higher than in the warm season, while in Vilnius, an increase of 44% was observed, as compared to the warm season. Aerosol optical properties showed more complex aerosol mixtures of dust, BC and brown carbon (BrC) during the cold season, forming larger particles. Single scattering albedo (SSA) anti-correlated with BCFF, proving that fossil fuel (FF) combustion contributes to the warming effect in both cities. A positive correlation between the population density of the emission areas of transported BC and the BC mass concentrations in Vilnius and Warsaw was found. The impact of transported BC on the local BC levels in the cities was of % and % in the cold season and of % and % in the warm season for Warsaw and Vilnius, respectively. Thus, the approach of BC partitioning showed that in the cold season, the two cities suffered from worse air quality, in part due to more transported BC.
2026
This study investigates the contamination of both ingested plastics and plasma of northern fulmars (Fulmarus glacialis) with benzotriazole UV stabilisers (BUVSs) in Kongsfjorden and Isfjorden, Svalbard. Ingested plastics were collected from fulmars in 1997, 2009, 2013, 2020 and 2021. Additionally, plasma samples were collected specifically in 2020. BUVSs, including UV-320, UV-326, UV-327, UV-328 and UV-329, were detected in both ingested plastics and plasma, suggesting a potential for transfer from plastics to the bloodstream. However, additional studies are required to confirm such a transfer mechanism. BUVSs were detected as early as 1997 in ingested plastics, highlighting the potential long-term exposure of fulmars in Svalbard. UV-326, UV-328 and total BUVS concentrations in ingested plastics increased significantly between 1997 and 2021, but likely due to outliers. In plasma, there was no significant correlation between any of BUVS concentrations and the mass of ingested plastics except for UV-327, although relying on only three values above LOD. This study represents a first step in investigating the multiple exposures of fulmars, and more generally seabirds, to plastic and plastic related chemicals and their potential ecotoxicological risks. More specifically we recommend further studies extracting microplastics from seabirds to perform additional quantification of BUVSs or other additives to provide available datasets and deeper understanding of leaching from plastics and temporal trends.
2026
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.
2026
Efficacy of individual and combined terrestrial and marine carbon dioxide removal
Abstract Limiting global temperature rise below 2°C requires significant reduction in greenhouse gas emissions and likely large-scale carbon dioxide removal (CDR). This study assesses the CO2 sequestration and efficacy of two CDR approaches, Bioenergy with Carbon Capture and Storage (BECCS) and Ocean Alkalinity Enhancement (OAE), applied individually and in combination. Using the Norwegian Earth System Model (NorESM2-LM), simulations were designed to ramp up deployment of BECCS and OAE, to an additional area of 5.2 million km² by 2100 for bioenergy feedstock for BECCS, and a CaO deployment rate of approximately 2.7 Gt/year for OAE within the exclusive economic zones of Europe, the United States and China. The combined land-ocean CDR simulation revealed a largely additive carbon removal effect. Over 2030-2100, OAE sequestered 7 ppm of CO 22 with an accumulated 82.3 Gt CaO, achieving a CDR effectiveness of 0.08 ppm (~ 0.17 PgC) per Gt CaO, while BECCS reduced 16 ppm of CO2, with CDR effectiveness of 3.1 ppm per million km² of bioenergy crops. Together, the carbon removal achieved by BECCS and OAE corresponds to anthropogenic CO₂ emissions of 5.4 Gt CO₂/year by 2100, slightly more than 60% of current global transport sector emissions. Notably, the efficiency of BECCS and OAE alone was unaffected by their concurrent deployment. Nevertheless, simulations revealed distinct non- linear interactions, such as declines in land and soil carbon sinks in the combined scenario. Furthermore, all simulations show negligible effects on the global annual mean temperature. These results highlight near-additive CDR responses even under net-negative emissions, but feedback on land and ocean carbon sinks must be considered when designing CDR portfolios. This study provides new insights into CDR portfolio design and Earth system feedback under an overshoot scenario, highlighting both their potential and the need for continued emissions cuts and supportive policies.
2026
Urban Living Labs as Inter- and Transdisciplinary Arenas for Sustainability Planning Research
The transition towards sustainable societies necessitates inter- and transdisciplinary knowledge, particularly in urban planning, where diverse knowledge traditions are crucial for decision-making. Despite this, planning practices often remain entrenched in institutional and legal frameworks that hinder the integration of multiple ways of knowing and undervalue lay knowledge. Researcher-led urban innovation processes are increasingly adopting experimental approaches for the multi-stakeholder co-creation of knowledge, addressing urban challenges through interdisciplinary approaches. This article addresses the interdisciplinary collaboration between researchers in experimental urban planning processes by examining a research project that focused on participatory environmental co-monitoring and planning for urban air quality in Nordic contexts. The study builds a bridge between theories of interdisciplinarity, urban experimentation, and planning theory. By presenting urban living labs (ULLs) as arenas for co-learning that integrate scientific and lay knowledge, the article explores how planning researchers can facilitate mutual learning and navigate the micropolitics of knowledge co-production. We develop the concept of cross-disciplinary unknowns to highlight the dynamics and challenges in research teams with diverse epistemological backgrounds. We argue that an explicit and structured approach for explicating epistemological differences can facilitate the detection of unreflected knowledge retention between disciplines.
2026
Organic aerosol (OA) is a major component of atmospheric particulate matter (PM), affecting both human health and climate. However, high-resolution estimates of OA exposure needed for exposure analysis remain scarce. Here, we integrate a chemical transport model (CAMx) with a random forest (RF) machine learning approach to bias-correct and downscale daily OA concentrations across Europe. CAMx OA simulations at ∼15 km resolution show moderate agreement with observations (r = 0.55). By combining these outputs with high-resolution land-use data and training the RF model on ∼48,000 daily OA measurements from 137 sites, prediction accuracy improved (r = 0.65), with ∼l5% reduction in root mean square error. The resulting maps provide European daily OA concentrations at ∼250 m resolution for alternate years from 2011 to 2019. The model captures key spatial features, including elevated OA in the Po Valley, Southeastern, and Central Europe, as well as intracity variations due to local hotspots. Seasonal analysis reveals higher concentrations in winter, while long-term trends indicate a general decline in OA levels. Exposure estimates show that half of the European population experiences OA levels above 3 µg/m3, and ∼50 million people are exposed to more than 5 µg/m3, which is the current guideline level recommended by the world health organization for total PM2.5. These high-resolution OA maps offer vital critical support for epidemiological research and air quality policy.
2026
New Approach Methodologies (NAMs) are gaining significant momentum globally to reduce animal testing and enhance the efficiency and human relevance of chemical safety assessment. Even with substantial EU commitment from regulatory agencies and the academic community, the full regulatory adoption of NAMs remains a distant prospect. This challenge is further complicated by the fact that the academic world, oriented toward NAMs development, and regulatory agencies, focused on practical application, frequently operate in separate spheres. Addressing this disconnect, the present paper, developed within the European Partnership for the Assessment of Risks from Chemicals (PARC), provides a clear overview of both the available non-animal tests and current evaluation practices for genotoxic and carcinogenic hazard assessment, while simultaneously highlighting existing regulatory needs, gaps, and challenges toward greater human health protection and the replacement of animal testing through NAMs adoption.
The analysis reveals a complex landscape: while the EU is deeply committed to developing and adopting NAMs, as outlined in its Chemical Strategy for Sustainability and supported by initiatives like PARC, prescriptive regulations such as Classification, Labelling and Packaging (CLP) and Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) still heavily mandate in vivo animal data for hazard classification, particularly for germ cell mutagenicity and carcinogenicity. This reliance creates a “too-short-blanket-problem,” where efforts to reduce animal testing may impact human health protection because of the current in vivo-based classification criteria. In contrast, sectors such as cosmetics and certain European Food Safety Authority (EFSA)-regulated products demonstrate greater flexibility toward progressive integration of NAMs. While the deep mechanistic understanding of genotoxicity and carcinogenicity has significantly advanced the integration of alternatives to animal tests into regulatory chemical hazard assessment, their broader and full implementation faces considerable challenges due to both scientific complexities (i.e., the development and validation of fit-for-purpose NAMs) and existing legislative provisions.
2026
2026
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.
2026
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
2026
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.
2026
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.
2026
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.
2026