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An interlaboratory comparison (ILC) was conducted for levoglucosan, mannosan, and galactosan, as widely used organic tracers for assessing biomass burning aerosol in ambient air. Organized as part of the European research infrastructure ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) activities the OrGanic Tracers and Aerosol Constituents - Calibration Centre (OGTAC-CC) distributed aliquots from three ambient PM2.5 filter samples and two prepared aqueous standard solutions to ten research laboratories across Europe, each using its own analytical protocol. Overall agreement was good for the ambient filter samples, with relative standard deviations relative to the general mean of 14% for levoglucosan, 22% for mannosan, and 33% for galactosan. Individual measurement accuracy, expressed as mean percentage error, ranged from −33% to 13% for levoglucosan, −51% to 15% for mannosan, and −54% to 42% for galactosan. Laboratory performance was also assessed using z-scores, showing that despite methodological diversity, nearly all results were classified as acceptable. This ILC provides a timely snapshot of current European laboratory capability for key biomass burning tracers. The joint intercomparison study demonstrates the readiness of European laboratories to provide harmonized levoglucosan measurements at a continental scale, meeting the comparability needs arising from the inclusion of levoglucosan in the revised EU Ambient Air Quality Directive (AAQD), and supporting requirements across European (Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP), ACTRIS) and national monitoring networks.
2026
Biogenic volatile organic compound (BVOC) emissions from European vegetation are a major precursor of tropospheric ozone and remain a key uncertainty in regional air-quality modelling. We present two high-resolution (0.1° × 0.1°) European BVOC emission datasets developed within the EU SEEDS project aimed at supporting scientific development within Copernicus Atmospheric Monitoring Service (CAMS). The datasets include BVOC species consistent with the RACM chemical mechanism and are generated by coupling the SURFEX land surface model with the MEGAN3.0 emission model.Emissions based on two land surface model simulations were analysed: (i) an open-loop SURFEX simulation available for 2018–2022, and (ii) a data-assimilation simulation in which satellite leaf area index (LAI) observations are assimilated, available for 2018–2020. In both cases, SURFEX is configured to allow vegetation phenological responses to meteorological variability, enabling a realistic representation of phenology. Evaluation against independent datasets shows that both simulations capture temporal variability in LAI and root-zone soil moisture, with improved skill in the analysis configuration.Given its importance for atmospheric chemistry, we focus on isoprene emissions. Interannual and seasonal variability in isoprene emissions is shown to be primarily driven by LAI variability, with specific events (e.g. summer 2019) linked to drought-induced vegetation stress simulated by SURFEX. Daily variability in isoprene emissions is evaluated against in-situ online isoprene concentration measurements at eight western European sites, revealing moderate to strong correlations across most site-year combinations. Comparisons with other bottom-up European isoprene inventories show that SURFEX-MEGAN3.0 emissions lie between the lower CAMS-GLOB-BIOv3.1 and higher MEGAN-MACC estimates, with differences in seasonality attributable largely to the underlying LAI datasets.These results highlight the important role of vegetation phenology, particularly LAI variability, in controlling BVOC emissions on monthly to interannual timescales, and demonstrate the added value of an Earth-system approach for BVOC emission modelling in support of air-quality assessments.ReferencesHamer, . D., Markelj, M., Rojas-Munoz, O., Bonan, B., Calvet, J.-C., Marécal, V., Guenther, A., Trimmel, H., Vallejo, I., Eckhardt, S., Sousa Santos, G., Sindelarova, K., Simpson, D., Schmidbauer, N., and Tarrasón, L.: Two Biogenic Volatile Organic Compound Emission Datasets over Europe Based on Land Surface Modelling and Satellite Data Assimilation, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-442, in review, 2025.
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2026
The Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) is the European Research Infrastructure Consortium (ERIC) dedicated to short-lived atmospheric constituents and clouds, supporting fundamental research and excellence in Earth system observation. ACTRIS produces high-quality, integrated long-term datasets in the field of atmospheric sciences and provides services tailored for scientific and technological use, including access to instrumented observational platforms. To enhance the availability, usability, and scientific exploitation of these datasets across disciplines and user communities, the ACTRIS Data Centre (DC) develops a range of user-oriented services, among which the ACTRIS Virtual Research Environment (VRE) plays a central role. The ACTRIS VRE enables efficient discovery, access, and scientific analysis of long-term observational data from ACTRIS National Facilities as well as other ground based observational sites as e.g. EMEP, EARLINET, Cloudnet and GAW. It facilitates analyses such as calculation of climatologies, long-term trend assessments, and the combination of datasets within the ACTRIS domain. The VRE is developed in collaboration between the ACTRIS DC and the ACTRIS-Norway community and is designed to serve both data producers and data users, ranging from infrastructure operators to researchers and students, across a wide range of atmospheric research applications. This presentation demonstrates the use of the ACTRIS VRE through selected notebook-based examples of higher-level data analysis and highlights the collaborative scientific efforts underlying its development. Data access within the VRE is based on the ACTRIS metadata REST API. ACTRIS datasets are provided in CF-compliant NetCDF format and are accessible through both streaming services (OPeNDAP) and direct HTTPS download. This approach enables flexible, reproducible, and programmatic data use, supporting interoperability with commonly used analysis tools and workflows. In collaboration with the ACTRIS-Norway community, the VRE includes several examples combining datasets for long time series analysis, the exploration of climatologies, and the investigation of trends. Selected examples are presented and discussed, with particular focus on the combination of FLEXPART footprint products and black carbon source apportionment data, developed within the EU project ATMO-ACCESS, together with observed equivalent black carbon measurements at several ACTRIS National Facilities. Additional higher-level analysis examples include single scattering albedo (SSA), ultrafine particle number concentrations (UFPs), and PM₁ source-related metrics from wood burning and traffic. These examples highlight how ACTRIS data can be applied to both climate-relevant and air-quality-focused research questions. Beyond scientific analysis, the ACTRIS VRE also serves as a platform for education and capacity building. Introductory notebooks demonstrate programmatic access to data and metadata and illustrate best practices for scientific analysis. The VRE has been used in ACTRIS training courses, ACTRIS Week, ITINERIS training workshops, and dedicated events at NILU, including collaborations with EUMETSAT, highlighting its role as a reusable training and demonstration environment. Community contributions to the example library are encouraged through an open GitHub repository, fostering collaborative development and reuse. The ACTRIS Virtual Research Environment is openly accessible at https://data.actris.eu/vre.
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ML-based data fusion of model, satellite, and ground observations for 1-km PM2.5 mapping over Europe
2026
Bioaerosols interact with society and environment in a multi-faceted way. Information about biological aerosols in the atmosphere is at high demand for medical practitioners and allergy sufferers, climate change researchers, agriculture and forestry industries, air quality forecasters, a variety of information added-value businesses, and many other stakeholders. However, the monitoring practices established over 70 years ago and barely changed since then are country-specific, with varying data availability and usage policy. These roadblocks slow down cross-disciplinary research and development of measures to understand and, upon necessity, control societal and environmental impacts of bioaerosols.A series of technological breakthroughs during last 10 years introduced a variety of automatic particle counters capable of bioaerosol monitoring in real time. They paved the way to the volunteering consolidation of European aerobiologists to establish the EUMETNET AutoPollen Programme (www.autopollen.net), laid down the foundation for the bioaerosol monitoring infrastructure with the EU Horizon SYLVA project (A SYstem for reaL-time obserVation of Aeroallergens, https://sylva.bioaerosol.eu), initiated developments of European standards and guidelines for the automatic bioaerosol measurements with the EURAMET project BioAirMet, and started the European standardization effort with CEN WG 39.The new technologies allow to observe bioaerosol concentration in real time, analyze vertical concentration profiles via remote-sensing, perform metagenomic analysis of bioaerosols with the 3rd generation DNA sequencing technique, and combine these observations with atmospheric composition models. Newly established regional networks have been connected to regional atmospheric composition models, which assimilate the real-time regional data to improve the forecasts. It changes the existing paradigm of bioaerosol observations as the new monitoring networks involve large-scale data handling infrastructure, which also includes numerical models as an interface between the different technologies and a bridge to users of information.The new observations heavily rely on sophisticated technologies, such as high-resolution image analysis, holography, multi-band scatterometry and fluorescence spectrometry, lidar-based remote sensing, and nanotechnology for DNA sequencing. A particle recognition task, the key challenge for the new devices, is solved via machine learning approaches. Technological complexity of the new instruments and large amounts of raw data they produce have been recognized, and a European-scale solution has been proposed by AutoPollen/SYLVA. AutoPollen is being converted into a EUMETNET operational programme with the SYLVA infrastructure as its technological backbone. The programme, with support of Copernicus Atmosphere Monitoring Service (https://atmosphere.copernicus.eu), ACTRIS aerosol monitoring network, and other stakeholders, will become operational from 2027. The central processing system will be hosted by Finnish Meteorological Institute with support of MeteoSwiss, Technical University of Munich, and all SYLVA partners. The pre-operational work of AutoPollen/SYLVA started already in 2025, owing to the efforts of the SYLVA consortium, its sister projects and collaborators. The programme is open for all European (and from outside Europe) groups performing automatic bioaerosol monitoring. AutoPollen offers technological and organizational support, community-developed bioaerosol monitoring solutions, and a motivated team of experts advancing the relevant research and applications.
2026
The AlphaEarth Foundations model, recently released in Google Earth Engine as annual satellite embeddings, provides a new way to work with multi-sensor Earth observation data. Each 10-m pixel is summarized as a 64-dimensional vector that captures the yearly trajectory of surface conditions using information learned from optical, radar, LiDAR, and other datasets, including climatic model outputs and digital terrain data. Rather than representing physical measurements directly, these embeddings condense complex spatial and temporal patterns into compact descriptors that can be used as inputs for machine-learning regression models. This allows researchers to explore environmental patterns—such as air quality—that are influenced by geographical, environmental, and meteorological conditions in cities.In this study, we evaluate whether these annual embeddings, represented as 64 bands (A00–A63), can describe spatial patterns of urban NO₂ without explicitly supplying additional land-use, meteorological, or emission datasets. We present first results from two contrasting environments: Quito, a high-altitude Andean basin in Ecuador, and Essen, a dense urban–industrial region in western Germany. Models trained only with the embedding bands and ground-based NO₂ observations reproduce meaningful spatial gradients in both cities, suggesting that the embeddings encode attributes relevant to emission intensity, urban structure, and pollutant dispersion.These early results highlight the potential of foundation-model satellite embeddings as lightweight, scalable predictors for urban air-quality analyses. They also show how these embeddings can be combined with advanced AI-based regression models, offering a new option for studying air pollution patterns in cities where data availability is often limited by the small number of air-quality monitoring stations.
2026
Low-cost sensor (LCS) networks can complement sparse regulatory monitoring, but their value depends on robust integration strategies that preserve data quality while exploiting dense spatial sampling. Here we assess the added value of incorporating validated LCS PM2.5 observations into the S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) machine learning framework (Shetty et al., 2024, 2025) to generate continental-scale, 1 km resolution surface PM2.5 fields across Central Europe. Two integration strategies are evaluated for 2021–2022 within a stacked XGBoost architecture driven by satellite aerosol optical depth, meteorological predictors, and CAMS regional forecasts: a) using LCS data as an additional training target (LCST), and b) using LCS information as a model input feature (LCSI) via an inverse-distance-weighted spatial convolution layer that encodes local sensor influence. Relative to a baseline trained only on official monitoring stations, LCSI yields consistent performance gains, with RMSE reductions of ~15–20% in urban areas, whereas LCST provides less consistent improvement. The resulting high-resolution mapping product achieves skill comparable to the CAMS regional reanalysis, often considered as a modelling “gold standard” for European air-quality assessment, and in some evaluations surpasses it, with lower annual mean absolute error (2.68 vs 3.32 µg m⁻³) (Shetty et al., 2026). This demonstrates that a data-fusion ML approach including LCS information can deliver reanalysis-level performance at 1 km resolution while requiring only modest computational resources compared with running full chemical transport model reanalyses, enabling rapid updates and scalable deployment. SHAP-based attribution further suggests that LCSI improves the model’s ability to capture localized pollution variability, while performance degrades where sensor density is low, limiting representation of inter-urban transport.Although demonstrated in Europe, the underlying methodology, namely combining globally available satellite products and meteorology with quality-controlled LCS networks in a computationally efficient ML framework, has potential to strengthen air-quality assessment also in resource-limited settings where regulatory infrastructure is scarce. A requirement for this is that appropriate sensor calibration/validation workflows are in place and equitable partnerships support sustainable sensor deployment and data stewardship. Shetty, S., Schneider, P., Stebel, K., Hamer, P. D., Kylling, A., and Koren Berntsen, T.: Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning, Remote Sens. Environ., 312, 114321, https://doi.org/10.1016/j.rse.2024.114321, 2024.Shetty, S., Hamer, P. D., Stebel, K., Kylling, A., Hassani, A., Berntsen, T. K., and Schneider, P.: Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast, Environ. Res., 264, 120363, https://doi.org/10.1016/j.envres.2024.120363, 2025.Shetty, S., Hassani, A., Hamer, P. D., Stebel, K., Salamalikis, V., Berntsen, T. K., Castell, N., and Schneider, P.: Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring, Environ. Res., 291, 123558, https://doi.org/10.1016/j.envres.2025.123558, 2026.
2026
The growing adoption of low-cost sensors (LCSs) has significantly enhanced environmental monitoring by enabling widespread, community-driven data collection, particularly in regions requiring dense monitoring, and in regions with limited or no reference instrumentation. Increased public awareness and demand for dense environmental monitoring have resulted in extensive air quality and meteorological datasets from diverse sources. However, the integration of such datasets into regulatory frameworks and large-scale environmental monitoring remains challenging due to persistent issues related to data quality, standardization, and interoperability. To address these challenges, the FILTER (Framework for Improving Low-cost Technology Effectiveness and Reliability) approach developed by Hassani et al. (2025) provides a suite of algorithms to harmonize, quality-check, flag, and perform in-situ corrections on crowdsourced PM2.5 LCS datasets. While FILTER was initially designed and validated for static PM2.5 sensors, it has since been extended to address data quality challenges associated with the dynamics of mobile and wearable sensing. Across both static and mobile LCS platforms, FILTER employs a unified processing pipeline that generates measurement-level quality flags based on multiple statistical tests, to quantify the reliability of each observation. Quality control (QC) includes statistical tests to: (a) assess physical measurement consistency (range validity test), (b) detect flatline behavior (constant value test), and (c) identify abnormal patterns (spatiotemporal outlier detection test) using both historical trends and spatial comparisons with neighboring LCSs. Beyond these mandatory QC steps, more advanced statistical procedures incorporate relative (spatial correlation test) and absolute (spatial similarity test) comparisons with nearby LCSs, higher-quality instruments, and reference monitoring stations. For mobile and wearable sensing, FILTER has been specifically adapted to support pairwise comparisons between mobile sensors and comparisons with higher-accuracy nodes, accounting for operation under dynamic environmental and operational conditions. In this context, statistical comparisons are evaluated during rendezvous events, that is, periods in which the mobile sensor and a higher-accuracy node provide temporally coincident measurements. The modified framework retains the core principles of transparency, scalability, and sensor independence, while introducing additional steps to address motion-related artifacts, intermittent time series, and location-specific uncertainties. FILTER is developed in the open-source R environment. Its modular and hierarchical design allows flexible adaptation of quality control and correction workflows based on data availability, the spatiotemporal characteristics of LCS networks, and application-specific requirements. By improving data reliability and usability, FILTER enables crowdsourced LCS datasets to serve as a reliable complement to official monitoring networks for air quality management, urban- and regional-scale modeling, and policymaking. References Hassani, A., Salamalikis, V., Schneider, P., Stebel, K., and Castell, N.: A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2. 5 data across Europe, J. Environ. Manage., 380, 125100, 2025.
2026
Hydrofluoroolefins (HFOs) are important synthetic compounds replacing other halocarbons in phase-down from usage (e.g., as refrigerants, propellants, foam blowing). Little is known about their atmospheric abundance, distribution and trends, nor about their emissons. Here, we report atmospheric observations of the widely used HFO-1234yf (2,3,3,3-tetrafluoroprop-1-ene), and HFO-1234ze(E) (E-1,3,3,3-tetrafluoroprop-1-ene), and the hydrochlorofluoroolefin (HCFO) HCFO-1233zd(E) (E-1-chloro-3,3,3-trifluoroprop-1-ene) observed as part of the Advanced Global Atmospheric Gases Experiment (AGAGE) network. Over the observational period 2011–2025, pollution events have grown in magnitude and frequency at sites which are influenced by regional emissions, while remote stations show first appearances of these substances. By 2024/2025 winter peak mole fractions in background northern hemisphere air have reached ∼ 0.25 ppt (picomol mol−1, parts-per-trillion in dry air) for HFO-1234yf and HFO-1234ze(E) and ∼ 0.45 ppt for HCFO-1233zd(E). Using European observations and the inverse modeling frameworks InTEM, ELRIS, and RHIME we determine emission trends and regional distributions. For Northwest Europe, emissions of HFO-1234yf increased steadily and rapidly from <0.1 Gg yr−1 in 2014 to 1.50 [1.23–1.74, range of 16–84 percentile] Gg yr−1 by 2023, presumably due to its introduction in mobile air conditioning and stationary refrigeration. HFO-1234ze(E) emissions were low during 2014–2017, followed by a rapid increase in 2018/2019, potentially due its introduction as an aerosol propellant, after which they increased more slowly to 0.96 [0.82–1.13] Gg yr−1 by 2023. HCFO-1233zd(E) emissions are derived from 2017 onward, showing a steady increase from 0.15 [0.07–0.23] to 1.04 [0.93–1.15] Gg yr−1 in 2023.
2026
Franzefoss Husøya Kristiansund. Målinger av ammoniakk NH3 og flyktige organiske forbindelser VOC
NILU
2026
The Filter Inlet for Gases and AEROsols coupled to a Chemical Ionization Mass Spectrometer (FIGAERO–CIMS) can be used to derive volatility of atmospheric aerosol by using the temperature at thermogram maximum signal (Tmax). For complex ambient particle matrices, Tmax of an individual compound often varies, for reasons not fully elucidated. Here, we apply machine learning to study the relation between Tmax of levoglucosan (C6H10O5), a common tracer to identify the influence of biomass burning (BB) in ambient air, and a set of atmospheric and instrumental parameters for an ambient year-long FIGAERO–CIMS data set measured in the Arctic. Using three different modeling approaches, namely, multiple linear regression (MLR), random forest (RF) regressor, and XGBoost regressor, we find that the mass loading on the FIGAERO filter has the highest relevance for variation in Tmax of levoglucosan. On the basis of these results, we suggest controlling the mass collected on the filter for continuous online measurement with the FIGAERO–CIMS if quantitative volatility information is to be gained. More generally, we demonstrate the usefulness of machine learning approaches for characterization of instrumental backgrounds in complex ambient or laboratory data.
2026
Biogenic volatile organic compound (BVOC) emissions from vegetation represent a major source of volatile compounds globally and play an important role as precursors for tropospheric ozone. Understanding their emissions is therefore crucial for quantifying the impact of ozone on air quality. We present two datasets of biogenic volatile organic compound emissions that cover the European modelling domain of the Copernicus Atmospheric Monitoring Service at a resolution of 0.1° × 0.1° to support the study of European scale air quality. The compounds included in the dataset follow the VOCs included in the regional atmospheric chemistry model mechanism (RACM). The datasets were produced within the framework of the EU's SEEDS project. We produced each dataset by coupling modelling output variables from the SURFEX land surface model with the MEGAN3.0 BVOC emission model. In one instance, the SURFEX model was run in free-running mode, which we term the open-loop (OL) and in the other case we assimilated satellite observations of leaf area index (LAI), which we term the analysis. The OL and analysis land surface model outputs form the basis for each emission dataset that are called SURFEX-MEGAN3.0 OL (https://doi.org/10.7910/DVN/LAUVTU, Hamer et al., 2025a) and SURFEX-MEGAN3.0 analysis (https://doi.org/10.7910/DVN/69G1FX, Hamer et al., 2025b), respectively. The OL dataset is available over a five-year period from 2018–2022 and the analysis dataset is available over the three-year period 2018–2020. SURFEX was run for both the OL and analysis simulations in a configuration that allowed simulated vegetation to respond to variations in meteorology over time to more realistically track vegetation phenology. Evaluation of the land surface model output LAI and root-zone soil moisture (RZSM) showed that the OL and analysis simulations had good skill at tracking temporal changes in both variables, with the analysis performing better in each instance. We perform a variety of evaluations on the isoprene emissions specifically given the importance of this compound for atmospheric chemistry. We evaluated the temporal variability of isoprene emissions in both datasets and found that the majority of the interannual and monthly variability was linked to variability in LAI that in specific cases, like the summer of 2019, could be linked to drought impacts on vegetation growth simulated by SURFEX. We evaluated the daily temporal variability of the OL and analysis isoprene emission datasets against in-situ online observations of isoprene concentrations at 8 sites in western Europe and found moderate to strong correlation between the emissions and observations in almost all location-year pairings. We also evaluated the OL and analysis emission datasets against other published bottom-up isoprene emission datasets over the same European domain used in this study. We found that the SURFEX-MEGAN3.0 OL and analysis isoprene emission datasets lie between the minimum (CAMS-GLOB-BIOv3.1) and maximum (MEGAN-MACC) published emission datasets based on bottom-up approaches. Furthermore, we were able to attribute differences in seasonality between SURFEX-MEGAN3.0 and other emission inventories to differences in the temporal variability of the underlying LAI dataset used to compile them. Overall, our findings show the importance of variability in LAI in controlling isoprene emissions on monthly to annual timescales. Combining this with the demonstrated skill of the emissions in evaluation with independent data, this points towards the value of an Earth-system approach to BVOC emission modelling.
2026
Urban nature-based solutions (NBS) are increasingly deployed to restore ecosystems, regulate microclimates, support biodiversity, and enhance wellbeing. Yet many remain short-lived: once installation and early monitoring end, maintenance budgets shrink, responsibilities become unclear, and socio–ecological performance declines. The EU BiodivNBS NatureScape project addresses this overlooked post-implementation phase by examining how NBS are cared for, governed, and experienced over time in seven European cities – Oslo, Dublin, Riga, Milan, Lisbon, Lublin, and St. Gallen.To strengthen long-term sustainability, NatureScape establishes Transformation Labs (T-Labs) at demonstration sites, including rain gardens in Lublin; community gardens in Oslo, Riga, Milan, and St. Gallen; school gardens in Lisbon; and goat-grazing vegetation management in Dublin. These T-Labs function as practice-based innovation spaces where municipal authorities, researchers, and community groups jointly observe socio–ecological dynamics, identify stewardship challenges, and co-develop adaptive responses. The approach extends conventional living labs by focusing on long-term socio–ecological change and governance arrangements that support NBS persistence.NatureScape integrates baseline assessments across five forms of capital (natural, social, human, manufactured, financial) with participatory workshops, PPGIS, citizen science, and systems tools such as causal loop diagrams and multi-criteria assessments. This mixed-methods design enables analysis of NBS as dynamic systems shaped by interactions between ecological conditions, institutions, and community practices.Early findings from Oslo, Riga, Lublin and St. Gallen reveal recurrent barriers: unclear responsibilities after project funding ends, limited resources for routine care and climate adaptation, insecure land tenure, weak alignment with municipal strategies, and uneven community participation. In St. Gallen, expectations to expand activities, actors, or spatial scope further increase complexity and demand stronger management capacities.This study presents the NatureScape framework for post-implementation NBS governance and demonstrates how T-Labs can: (i) shift perceptions of NBS from temporary projects to living infrastructures requiring continuous care; (ii) clarify and redistribute responsibilities and resources for long-term stewardship; and (iii) provide structured settings where new forms of cooperation and valuation can be tested and embedded in policy. Embedding co-maintenance and co-stewardship as core practices can help cities move beyond pilot projects toward durable, multifunctional NBS aligned with EU and global biodiversity frameworks and targets.
2026
This study presents insights from the EU Biodiversa+ NatureScape project (2025–2028). The project offers a new perspective for understanding nature-based solutions (NBS) in cities by focusing on the post-implementation phase, in which environmental justice in urban planning is put to the test.In recent years, cities have increasingly pursued NBS in urban development projects such as community gardens, green roofs, and temporary green spaces to support biodiversity while simultaneously improving human well-being. Despite growing recognition of NBS in urban planning, their potential for cities' socio-ecological transformation remains constrained by overlooked post-implementation challenges. While the planning and implementation of NBS already receive considerable attention, critical dimensions of environmental justice – distributive equity, accessibility, and procedural justice for continuous public participation and stakeholder engagement – become apparent only in the post-implementation phase. This phase is characterized by dynamic interactions between social and ecological components, shaping whether NBS are consolidated and sustained in ways that contribute in the long term to transformative effects and environmental justice, or whether they instead undermine these aims.NatureScape addresses this critical transition and its challenges in urban planning. Through transformation laboratories (T-Labs) in seven cities (Oslo, Dublin, Riga, Milan, Lisbon, Lublin, and St. Gallen), the research team explores two central questions: (1) What enablers and barriers in urban planning shape the post-implementation stewardship of urban NBS? (2) What governance mechanisms, strategies, and measures lead to the successful integration of urban NBS into urban planning to unfold their transformative potential for biodiversity-positive transitions and environmental justice?Initial findings from the T-Labs reveal crucial barriers. The post-implementation phase is often reduced to technical maintenance. Insufficient incorporation of NBS into urban planning is associated with fragmented institutions and responsibilities, weak strategic and instrumental anchoring, financial insecurity, and the erosion of institutional and political support.The project identifies interconnected governance mechanisms that could successfully integrate NBS into urban planning: adaptive planning processes, institutional anchoring that fosters shared ownership among stakeholders, co-management approaches with formal agreements, public planning frameworks, and institutional structures that support integrated action. Together, these mechanisms highlight stewardship as a pivotal principle for achieving just and biodiversity-positive urban futures.
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The Fire Modeling Intercomparison Project (FireMIP) for CMIP7
Fire is a global phenomenon and a key Earth system process. Extreme fire events have increased in recent years, and fire frequency and intensity are projected to rise across most regions and biomes, posing substantial challenges for ecosystems, the carbon cycle, and society. The Fire Model Intercomparison Project (FireMIP), launched in 2014, has advanced global fire modeling in Dynamic Global Vegetation Models (DGVMs) and improved understanding of fire's local and direct drivers and its local impacts on vegetation and land carbon budgets through land offline simulations (i.e., uncoupled from the atmosphere). We now bring FireMIP into Coupled Model Intercomparison Project Phase 7 (CMIP7) to: (1) evaluate fire simulations in state-of-the-art fully coupled Earth system models (ESMs); (2) assess fire regime changes in the past, present, and future, and identify their primary natural and anthropogenic forcings and causal pathways within the Earth system, including the associated uncertainties; and (3) quantify the impacts of fires and fire changes on climate, ecosystems, and society across Earth system components, regions, and timescales, and elucidate the underlying mechanisms. FireMIP in CMIP7 will advance the fire and fire-related modeling in fully coupled ESMs, and provide a quantitative, comprehensive, and process-based understanding of fire's role in the Earth system by using models that incorporate critical climate feedbacks and CMIP7 multi-model, multi-initial-condition, and multi-scenario ensemble. This protocol paper presents the motivation, scientific questions, experimental design and rationale, model inputs and outputs, and recommended analysis framework for FireMIP in CMIP7, providing guidance to Earth system modeling teams conducting simulations and informing communities studying fire, climate change, and climate solutions.
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