Found 10456 publications. Showing page 416 of 419:
Plastic pollution monitoring programs use a wide array of methods, protocols, and analytical approaches, making it difficult for researchers and practitioners to determine which techniques to apply, where, and how. This lack of harmonisation across environmental compartments and plastic size classes has led to inconsistent data and limited comparability across studies. To address this, a systematic review of monitoring methods from 1960 to 2021 was conducted, encompassing both peer-reviewed and grey literature. Techniques were categorised into Reproducible Analytical Pipelines (RAPs), each comprising six core steps: survey design, sample collection, sample preparation, analytical detection, quantification, and data reporting. Each RAP was assessed using Technological Readiness Levels (TRLs) to evaluate maturity and suitability for standardised monitoring. The review revealed that while robust and repeatable methods exist, they are inconsistently applied. At the time of this review, atmospheric plastics was underrepresented, highlighting a critical gap in monitoring efforts. The findings underscore the urgent need for a global, objective framework to guide the selection and implementation of plastic pollution monitoring methodologies. This paper lays the foundation for such a framework by presenting a methodology to identify mature, reproducible methods and prioritise areas for further development. Future work should focus on harmonising protocols across compartments and size classes, improving transparency in data reporting, and building consensus around standardised practices to enable global comparability and policy relevance.
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Quantifying European SF6 emissions (2005-2021) using a large ensemble of atmospheric inversions
Sulfur hexafluoride (SF₆) is an extremely potent (GWP100 = 24,300) and long-lived greenhouse gas whose atmospheric concentrations continue to rise due to anthropogenic emissions. Europe represents a particularly relevant test case for investigating SF₆ emissions, as successive EU F-gas regulations over the past two decades have aimed to substantially reduce emissions. A key question is whether these regulatory measures are reflected in observed emission trends and whether reported national inventories are consistent with observation-based estimates. In this study, we quantify European SF₆ emissions for the period 2005–2021 using a large ensemble of atmospheric inversions with a strong focus on uncertainty characterization. Uncertainties are assessed using an extensive set of sensitivity tests in which key inversion parameters are systematically varied, while final uncertainties are quantified via a Monte Carlo ensemble that randomly samples combinations of these parameters. This allows us to identify the main sources of uncertainty and to evaluate the robustness of inferred emission trends.Our analysis focuses on countries with relatively dense observational coverage - the United Kingdom, Germany, France, and Italy - while also examining aggregated emissions for the EU-27. The inversion results reveal declining SF₆ emissions in all studied regions except Italy, broadly consistent with the timing of EU F-gas regulations (842/2006, 517/2014). In several countries, inferred emissions exceed reported national inventories, although the agreement generally improves in more recent years. At the EU-27 scale, emissions exhibit a pronounced decline between 2017 and 2018, coinciding with a marked reduction in emissions from southwestern Germany, suggesting regional actions were taken as the 2014 regulation took effect.Our sensitivity tests highlight the crucial role of dense and sustained atmospheric monitoring networks for robust inversion-based emission estimates. In particular, expansions of the UK observing system in 2012 and 2014 lead to significant reductions in emission uncertainties, demonstrating the importance of comprehensive observational networks in refining emission estimates.
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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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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.
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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.
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The ISLAS2020 field campaign during February and March 2020 set out to obtain a unique dataset describing the Arctic water cycle using stable water isotope (SWI) observations. Our observation strategy focused on measuring evaporation, deposition, and precipitation, all of which are commonly sub-grid scale processes in numerical weather and climate models. Uncertain parameterizations for these processes can lead to compensating errors, which can go unnoticed; however, evaporation and precipitation can also be investigated with SWIs, as they are an integrated tracer for processes that atmospheric moisture has undergone. The campaign can be divided into two efforts: a localised field experiment in Ny-Ålesund focused on evaporation and deposition, and a larger precipitation collection network distributed around the Nordic Seas. The Ny-Ålesund field experiment lasted three weeks, from 23 February to 15 March 2020, with temperatures reaching below −30 °C. During these weeks, we obtained near-surface, high-resolution (approx. 20 cm) SWI profiles at two deployment sites. Using a newly developed profiling system, we measured SWI gradients in the lowermost 5 and 2 m over fjord water and snow-covered tundra, respectively. These profiles are complemented by fiber-optic distributed sensing (FODS) columns and ambient conditions from nearby meteorological stations. The FODS columns supply continuous, high-resolution (2 cm or finer) temperature profiles above both locations, whereas the meteorological stations provide information on wind speed and direction. We also made a short deployment to the Zeppelin mountain observatory (472 ma.s.l.) for measurements of the isotopic signal in the free-troposphere. Additionally, numerous water samples from the snowpack in and around Ny-Ålesund were taken, in addition to daily fjord water samples from Kongsfjorden. These samples provide the context for the surface conditions under which profiles were collected. Isotopic connections on the synoptic scale are achieved by linking Ny-Ålesund observations with precipitation sampling at locations across the European Arctic, namely Longyearbyen, Tromsø, Andenes, Ålesund, and Bergen. The resulting dataset provides comprehensive insight into the Arctic hydrological cycle and can facilitate the study of phase change processes and transport of water vapour into and out of the Svalbard region. Datasets from the field campaign are publicly available at the PANGAEA data repository (https://doi.org/10.1594/PANGAEA.971241, Seidl et al., 2024).
2026
Moss as an environmental factor
Did you know that stairstep moss can be used as a sampler for air pollution? Researchers at NILU have collected this kind of moss on several occasions and examined it for metals and other pollutants.
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Uncertainties associated with Lagrangian transport in greenhouse gas flux estimates
Atmospheric Lagrangian particle dispersion models (LPDMs) are commonly combined with Bayesian inversion/optimization methods to infer emission fluxes across spatial scales from local to global. These tools are central to monitoring greenhouse gases, especially CO₂, CH₄, and N₂O. However, uncertainties in flux estimates arise from multiple sources: prior flux information, representation of the background atmospheric composition, statistical model choices (including hyperparameters and error covariance assumptions), and errors in atmospheric transport. In this presentation, we describe current uncertainty quantification activities linked to ongoing projects (e.g. EYE-CLIMA). We will discuss the use of meteorological ensemble simulations to assess transport related uncertainty and explore connections with dynamical systems tools and common assumptions such as Gaussian errors. Emphasis will be placed on high-resolution transport modelling applications.
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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
As climate change impacts intensify across Europe and globally, societies are confronted with increasingly frequent and severe hazards that challenge public health, urban livability, and environmental sustainability. While adaptation measures are urgently needed to cope with current and near-term climate risks, it is becoming increasingly evident that mitigation efforts are essential to ensure a resilient and sustainable future. Too often, however, adaptation and mitigation strategies are planned and implemented in isolation, within sectoral silos, overlooking their potential interdependencies, synergies, and co-benefits. This contribution draws on the on-going experience and perspectives of the EU-funded healthRiskADAPT project, which addresses climate-related health risks by explicitly linking adaptation and mitigation pathways across multiple hazards.The project adopts a broad and integrated perspective that combines existing technical solutions, nature-based interventions, and engagement strategies, with a strong emphasis on co-benefits for health and well-being in the face of climate hazards namely heatwaves, air pollution including wildfire emission, and pollen. Central to this framework is the use of cost–benefit and co-benefit analyses to support decision-makers in identifying, prioritizing, and implementing measures that maximize societal resilience while delivering climate resilience solutions, considering natural based solutions (e.g., greening) as well as technical solutions (e.g., smart-buildings, do-it-yourself air purifier devices, evaporative cooling, high efficiency filtering). Beyond technical assessments, the healthRiskADAPT project recognizes that increasing resilience requires engagement beyond institutional actors. Social solutions such as education, awareness-raising, and capacity building at the stakeholder level are considered essential components of effective climate strategies. The contribution therefore also explores participatory formats and stakeholder engagement approaches designed to enhance understanding of climate-related health risks and support the co-design of locally relevant policies and interventions.By presenting the project’s methodological pathways, tools, and engagement strategies, this contribution illustrates how integrated adaptation–mitigation planning can be operationalized in practice. It highlights the value of moving beyond sector-specific solutions toward systemic approaches that acknowledge complex interdependencies between climate, environment, health, and society. Ultimately, the contribution aims to demonstrate how such integrated frameworks can support cities and regions in developing more coherent, evidence-based, and socially inclusive climate policies, strengthening resilience in the face of a changing climate.
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