bluuebunny/crossref_metadata_2025 huggingface dataset:
How to model all the plants on Earth?  An approach to managing system complexity in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). 
Authors: Fisher Rosie , Koven Charles , Knox Ryan , Needham Jessica , Lemiuex Gregory , Xu Chonggang , Foster Adrianna , Chitra-Tarak Rutuja , Robbins Zachary
Published: March 2024

The relationship between carbon dioxide emissions and their accumulation in the atmosphere is one of the most important elements of the function of the Earth system.  Exertion of control over the terrestrial carbon budget, via afforestation, reforestation, bioenergy production and other methods to enhance land carbon storage (biochar, enhanced weathering) all imply a need to forecast and understand the dynamics of these carbon stores as they evolve in changing atmospheric CO2 and climatic conditions. The dynamics of the carbon cycle, however, are notably complex and require comprehension of models representing the functioning of numerous coupled systems which must produce predictions under these no-analog conditions, and so must necessarily embed process understanding to allow for meaningful extrapolation into the future. Models of the terrestrial biosphere, often embedded in Earth system models, thus contain advanced representations of a large set of processes that are known to impact ecosystem carbon storage.  This complexity, however, has presented a substantial barrier to objective calibration using conventional statistical approaches, as the number of model parameters and the computational expense of the models means that comprehensive exploration of the parameter space is effectively unmanageable. Further, many ecosystem processes exhibit non-linear and threshold properties (notably, vegetation death, competitive interactions, fire thresholds) and thus are challenging for methods that assume linearity. Here we propose a method for decomposing the complexity of one such model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) that allows investigation, calibration and comprehension of individual parts of the system in isolation (driven by observed data fields). This ‘modular complexity’ approach allows the full complexity model to be run in a series of ‘modes’ that can operate as domain-specific models for, e.g. ecohydrology, community ecology, biogeochemistry etc. while also allowing the full complexity version to be used for higher order problems, such a predicting global vegetation dynamics under future climate scenarios.  We describe a series of investigations using FATES that illustrate the potential for this model decomposition approach and discuss the potential for further application of this philosophy.  
Comparative Analysis of Data Preprocessing Methods for Precise Orbit Determination
Authors: Andert Tom , Aigner Benedikt , Dallinger Fabian , Haser Benjamin , Pätzold Martin , Hahn Matthias
Published: March 2024

In Precise Orbit Determination (POD), employing proper methods for pre-processing tracking data is crucial not only to mitigate data noise but also to identify potential unmodeled effects that may elude the prediction model of the POD algorithm. Unaccounted effects can skew parameter estimation, causing certain parameters to assimilate the unmodeled effects and deviate from their true values. Therefore, enhancing the pre-processing of tracking data ultimately contributes to refining the prediction model.The Rosetta spacecraft, during its two-year mission alongside comet 67P/Churyumov-Gerasimenko, collected a substantial dataset of tracking data. In addition to this data, also tracking data from the Mars Express spacecraft, orbiting Mars since 2004, will serve as a use case to assess and compare diverse data pre-processing methods. Both traditional and AI-based techniques are explored to examine the impact of various strategies on the accuracy of orbit determination. This aims to enhance POD, thereby yielding a more robust scientific outcome.
The role of Dry Intrusions in the formation and intensification of Atmospheric Rivers impacting France
Authors: Weiss Kim Andreas , Gaspar Tomás , Pérez-Alarcón Albenis , Raveh-Rubin Shira , Pinto Joaquim G. , Ramos Alexandre M.
Published: March 2024

Extra-tropical storms over the North Atlantic often leads to socio-economic impacts over Western Europe, associated with strong winds and precipitation. Such storms can be associated with so-called Atmospheric Rivers (ARs) which are relatively narrow regions of concentrated water vapor (WV) and strong winds where intense horizontal moisture transport can take place. In turn, the moisture availability along the cyclone path and the ARs lifetime can be impacted by boundary layer processes. For example, the occurrence of Dry Intrusions (DI) associated with previous cyclones can strongly destabilize the planetary boundary layer (PBL) leading to enhanced moisture uptake over the ocean. This can support the formation and/or intensification of the ARs themselves.The objective of this study is to understand the influence of DI on the moisture uptake in the PBL and transport associated with ARs impacting France.With this aim, an adapted version of the detection algorithm developed by Ramos et al. (2015), was applied to ERA-5 reanalysis targeting events impacting the Atlantic coast of France. A total of 300 AR-events were detected over the extended winter (ONDJFM) spanning the years 1979 to 2023. Indeed, the most intense landfalling ARs are associated with intense precipitation and high wind speeds over western France.For a subset of these AR-events, occurring between 1992 and 2022, the Lagrangian FLEXPART model using ERA5-data was applied to calculate the moisture sources for these events. This approach allows for the tracking of air masses 10 days backward in time from the target region (5°W to 0.5°E and 43.75°N to 50°N). Additionally, the occurrence of DI outflows (from 1979 onward) was based on its Lagrangian detection in ERA5 to assign possible DI outflows overlapping with the source regions of moisture uptake.Our results suggest a relationship between the areas of DIs and moisture uptake, indicating the possibility of the DI exerting influence on the formation and intensification of ARs. Overall, this work serves as a preliminary investigation for the upcoming North Atlantic Waveguide, Dry Intrusion, and Downstream Impact Campaign (NAWDIC) recently endorsed by the World Weather Research Programme (WWRP).
The Carrington Map in H I Ly-alpha Passband Based on ASO-S/LST/SDI
Authors: Li Shuting , Feng Li , Ying Beili
Published: March 2024

The Lyman-alpha (Lyα) Solar telescope (LST) is one of the three payloads onboard the Advanced Space-based Solar Observatory (ASO-S) mission. As one of the instruments of LST, the Solar Disk Imager (SDI) works in the Lyα waveband of 121.6 ± 10nm with a field of view (FOV) up to 1.2 R⊙. In this work, we construct the Carrington maps (or so-called synoptic map) of solar Lyα intensities based on full-disk images acquired by SDI. We present two versions of Carrington maps, one is synthesized once in a Carrington cycle, and the other is daily-updated. The former gives the general view of the emission distribution in Lyα during the whole CR, while the latter provides a better near-real-time measurement of the intensity. By establishing the relation between the emission of Lyα in 121.6 nm and He II in 30.4 nm, we provide a possible way to offset the observation gaps of SDI from AIA/304 channel. The radiometric calibration is performed using the cross-calibration method with GOES/EUVS data. To this end, the calibrated carrington map can be applied to constrain the incident emission to the corona in Lyα. Regarding this application, we propose an upgraded version of the carrington map to correct the emission variation caused by eruptions like coronal mass ejections (CMEs) and flares. The comparison between the SDI Carrington map and the magnetogram synoptic map serves as the final example of the application of SDI Carrington map in our work, the locations of different features in line-of-sight magnetic field and in Lyα are directly compared.
Snowpack Stability Prediction with Machine Learning
Authors: Kaltenborn Julia , Di Pietro Tiziano , Rolnick David , Schneebeli Martin
Published: March 2024

Snow avalanches are destructive hazards, impacting life and infrastructure in snow-covered mountain regions around the world. Avalanche warning and management are essential in those regions and involve a diversity of tasks, ranging from numerical modeling of regional avalanche danger, search and rescue of avalanche victims, to artificially triggering avalanches with explosives in a controlled way. Avalanche warning systems rely on in-situ point measurements of snowpack stability in conjunction with numerical models, to predict the avalanche risk at different exposition and height levels. Avalanche teams are performing those in-situ measurements involving various time-intensive tests, such as Rutschblock tests (RB) and manual snow profiles. There are faster measurement approaches, such as using the Snow Micro Pen (SMP), but snowpack stability scores cannot directly be derived from an SMP profile. Machine learning (ML) has supported and improved numerical modeling and forecasting of regional avalanche danger in multiple ways. However, the question if ML can assist in-situ assessments of snowpack stability, remains, to our knowledge, unanswered. In order to retrieve faster and more intuitive assessments, we are proposing to use ML to directly predict snowpack stability scores from SMP profiles. Our ML pipeline 1) is flexible and can be adapted for other measurement devices, 2) allows training to retrieve further physical parameters, and 3) allows the integration of explainability methods. Furthermore, we outline criteria relevant to collecting the data needed for training the ML model. For safe operational use, training on large datasets, uncertainty estimates, and explainability must be addressed. We believe that our approach represents a significant step towards assisting SMP practitioners in the analysis of snowpack stability. We also see the potential for coupling regional avalanche forecasting systems with in-situ snowpack stability estimates further down the line. If done correctly, we believe that ML can be a useful tool to assist in managing in-situ avalanche risk.
Extreme precipitation in catchments of Northern Peru under climate change: Insights from CMIP6 scenarios
Authors: Montenegro Gambini Julio Isaac , Alfaro Alejo Roberto , Perez Coro Maria , Castromonte Araujo David , Cusipuma Ayuque Magaly
Published: March 2024

Extreme precipitation events are occurring more frequently and with greater intensity due to climate change. According to previous studies, there appears to be a detectable pattern of extreme precipitation occurrences, with trends demonstrating notable regional variations, highlighting the complexity of local climate adaptations to global warming. It is predicted that both mean and extreme precipitation would rise and impact developing countries of South America. Here, Pacific drainge catchments of Northern Peru are historically prone to summer excessive rainfall that frequently caused severe floods with devastating impacts. Recognizing the escalating impact of extreme rainfall in the context of a changing climate, our research aims to examine the modifications in extreme precipitation variations and trends under under different Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The NEX-GDDP-CMIP6 dataset was used, considering daily rainfall data up to 2100. Our methodology involves a detailed statistical analysis of the CMIP6 multi-model ensemble, enabling us to extract robust signals of change in extreme precipitation indices across different studied catchments. Changes in key extreme precipitation indices, such as the maximum amount of precipitation in 5 consecutive days (Rx5day), the annual total precipitation from wet days (PRCPTOT), and the simple daily intensity index (SDII) are examined. It is expected that our research insights will make a significant contribution to the knowledge of how regional hydrological extremes in Peruvian Pacific drainage catchments are projected to alter as a result of climate change. Our findings can be used for decision support and the planning of appropriate adaptation strategies for hydro-meteorological disasters, hydropower, and agriculture sectors.
Multitechnical approach for the reconstruction of coastal changes: the case of Cala del Cefalo (southern Italy)
Authors: Fasciglione Giovanni , Sorrentino Alessia , Mattei Gaia , Pappone Gerardo , Aucelli Pietro Patrizio Ciro
Published: March 2024

Coastal environments undergo continuous changes due to the complex interactions of geological, oceanographic, climatic, and anthropic processes. In the last centuries, human activities have deeply altered the natural balance of coastal areas, making coastal evolution studies crucial for planning strategies related to prevention, preservation, and restoration, particularly in areas with a relevant role in biodiversity conservation.The aim of this research is the evaluation of recent morphological and environmental changes that occurred since the second half of the 20th century along the sandy stretch belonging to the Site of Community Importance (SCI) “Spiaggia del Mingardo e Scoglio di Cala del Cefalo” in the Campania region (Southern Italy), near the mouth of the Mingardo River. Through an integrated GIS analysis of topographic maps, aerial and satellite photos, and high-resolution data (a photogrammetric survey carried out with an aerial drone in April 2023), a retreating trend of both the shoreline and the dune system was observed. In particular, a decrease in the retreating trend of the shoreline has been detected since 2004, while the mean erosion rate is equal to -0.354m/y for the period between 2011 and2016, approximately 12 cm more than the previous time span. On the other hand, the retreat of the dune system over the whole period amounted to 40 m, 35 m, and 27 min the northern, middle, and southern areas respectively, while the highest retreat has been detected for the period between 2012-2016. This coincides with the results obtained for the shoreline between 2011 and 2016. Since one of the strongest storm surges occurred in 2014, this result demonstrates the great influence of storm surges on the state of conservation of the coast, as testified by detected wash-over fans and strong degradation of the vegetation cover. Therefore, the resulting forcing factors acting in the area are the winter wave regime, which can cause flooding and consequent dragging of sediment offshore, and the draining of the Mingardo River. Another relevant aspect is the intense anthropization of the area, especially during the summer season.To conclude, this work provides evidence of the high impact of storm surges on beach and dune environments, showing the importance of an integrated approach for the analysis of coastal dynamics in a fast-evolving world, where human presence could strongly interfere with natural processes. The potential erosion risk is crucial information for sustainable management of the coast, also taking into account the expected increase in storm surge frequency and magnitude strictly related to climate change.
Integration of socio-economic aspects in the development and holistic assessment of national CDR portfolios
Authors: Witting Maximilian , Gulde Felix
Published: March 2024

A broad range of different mitigation measures are necessary to achieve immediate and drastic emissions reductions in all sectors called for in the Paris Agreement. These targets underscore the need for Carbon Dioxide Removal (CDR) measures, also known as negative emissions, since hard-to-abate residual emissions are very likely to remain until the end of the century. Even though CDR options are urgently needed, it remains unclear how the implementation will affect future land use and the associated provision of food, material, and other ecosystem services. While the knowledge about biogeophysical and biogeochemical potentials of land-based CDR and their impacts on the earth system (e.g., on water, soil carbon) is increasing, there is still a lack of understanding about how the socio-economic context influences these aspects.
The interdisciplinary research project STEPSEC (Scrutinizing the feasibility of TErrestrial CDR Potentials under Socio-Ecological Constraints) therefore investigates the feasibility of the land-based CDR options BECCS (Bioenergy with Carbon Capture and Storage), forest management and afforestation/reforestation under socio-ecological constraints in Germany. As part of the project, three participatory stakeholder workshops are conducted in order to develop:
1) qualitative and quantitative CDR-related visions of the future land system in Germany, and
2) narratives on different land-use-related Shared Socioeconomic Pathways (SSPs) at national level.
In a second step, their implications are combined with climate change scenarios using a newly developed agent-based model (CRAFTY-DE) to comprehensively simulate the development of the German land system. CRAFTY-DE uses different types of agents with specific land management behaviours for different land use types. The developed SSPs are incorporated as socio-economic location factors in the form of quantifiable indicators. The natural suitability of the land surface for different land use types and CDR options is based on the results of dynamic vegetation and biodiversity models. CRAFTY-DE delivers potential pathways of land use development for Germany.
In a last step, the feasibility and desirability of these pathways are assessed against socio-ecological and ethical context conditions using a newly developed integrated and multidimensional assessment framework. The assessment results provide input for decision making of various stakeholders.
The talk will focus on the challenges and opportunities through the inclusion of social sciences in environmental science modelling.
A Comprehensive Tropospheric Pollutant Concentration Database for the Mediterranean Basin: Addressing Data Gaps and Enhancing Air Quality Assessment
Authors: Sánchez Jiménez Francisco , Segado-Moreno Leandro , Raluy-López Eloisa , García-Fernández Ester , Jiménez-Guerrero Pedro , Montávez Juan Pedro
Published: March 2024

The Mediterranean basin is a region particularly vulnerable to atmospheric pollution. At the tropospheric level, pollutants such as ozone (O3), nitrogen dioxide (NO2), nitrogen monoxide (NO), and particulate matter (PM10 and PM2.5) are particularly harmful. Therefore, it is necessary to have observational air quality databases of sufficient completeness and quality to be able to analyze and extract valuable information regarding the mitigation of air pollution on the population, environment and economy. In this regard, what the scientific literature provides is that existing air quality databases have notable weaknesses such as lack of inclusion of data from areas close to emission sources, exclusion of cities whose population does not exceed a threshold (Schwela et al., 2020) or the challenge of comparing values due to the presentation of air quality information using city-specific air quality indices (Baldasano et al., 2003). This study presents a database of tropospheric pollutant concentrations in the Mediterranean basin for the last two decades. The database was constructed from pollution records acquired from thousands of automated air quality stations throughout the European region, through AirBase, provided by the European Environmental Agency (EEA) through the European Air Quality Portal. The data were evaluated using a rigorous quality control process that included detecting manipulation errors, verifying consistency and coherence limits, and assessing spatio-temporal coherence. The analysis of the database revealed that ozone measurements are the most complete and consistent. Particulate matter stations exhibit more localized behavior, as isolated pollution spikes are more common. With regard to nitrogen oxides, a downward trend in tropospheric pollution has been observed in recent years. In particular, 3323 measurement stations have been treated for O3, 4727 for PM10, 2317 for PM2.5, 3446 for NO and 4933 for NO2. The quality control employed allows to have available air quality records sufficiently dense and robust for further analysis, achieving a homogenization that allows to reduce the weaknesses presented by other databases. In addition, it is intended in further research to extend these records to a higher spatial resolution by means of interpolation methods. References Baldasano J. M., Valera E., Jim ́enez P. (2003). Air quality data from large cities. Science of the Total Environment 307 (1-3), 141–165.Schwela D. H., Haq G., et al. (2020). Strengths and weaknesses of the who global ambient air quality database. Aerosol and Air QualityResearch 20(5), 1026–1037
Digital soil analysis and mapping using in-situ Vis-NIR spectroscopy – Challenges and future perspectives
Authors: Csibi Akos-Etele , Sanden Hans , Baykalov Pavel , Pereira Ruth , Cachada Anabela , Rewald Boris , Perry David
Published: March 2024

The use of Vis-NIR spectroscopy in digital soil mapping is emerging as a fast, viable option to provide spatial and temporal information on specific soil parameters that serve as good indicators for soil health. While MIR spectroscopy tends to be a much more reliable (high-precision) tool for different soil properties estimations, currently only NIR can be adapted for rapid in-situ soil surveys.The Subterra Green device, developed by “S4 Mobile Laboratories”, equipped with a Visible and an FTIR spectrometer can optimally capture spectra until 90 cm underground down to a 1 cm resolution. With a carefully selected sampling pattern, a survey of several hectares can be conducted in a matter of few days as a single insertion takes about 2-6 minutes.One scope of the PHENET project is to carry out soil surveys in different locations with varying soil types, from the humid continental zones of Austria to the temperate oceanic climate of Portugal. This will be done by creating models which are verified with laboratory biochemical analysis of soil samples. Previous scientific resource concluded that some soil properties like the soil water content or texture can have a major effect on the recorded spectra, so when building up a database for machine learning models from different site surveys (with unique spatial and temporal conditions) a lot of external factors should be taken into consideration and pre-processing techniques selected, like external parameter orthogonalization or calibration spiking for creating an accurately predicting model for soil parameters prediction. The aim is to provide estimations of soil organic carbon and nitrogen stocks as well as interpolated maps in different soil depths. Being able to do fast and highresolution soil maps using in-situ Vis-NIR soil spectroscopy makes it possible to improve precision agriculture and monitor soil properties over space and time.
Insights into China's Air Quality: WRF-Chem and SHERPA Analysis for Effective Air Quality Solutions
Authors: Xu Jiayu , Clappier Alain , Zhang Lin , Thunis Philippe , Pisoni Enrico , Ye Xingpei
Published: March 2024

Air quality in China has been significantly improved over the past decade. However, many areas still face challenges with air pollutants such as PM2.5 and ozone. More than a quarter of the 339 cities regularly exceed the Chinese air quality standards and all of them exceed the latest World Health Organization guidelines, despite compliance efforts in reducing sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon emissions. Continuous and deep improvements in air quality need a more in-depth quantitative assessment to understand the spatial scales (urban, regional, and national) of air pollution sources and to clarify the sequence of precursors and emission sectors that contribute to these pollutants. Here we combine the WRF-Chem model sensitivity simulations and the Screening for High Emission Reduction Potentials for Air quality (SHERPA) tool to address these challenges, particularly focusing on PM2.5. We first assess the chemical regimes of secondary inorganic aerosols (SIA) formation by simulating emission reduction scenarios for three main precursors (SO2, NOx, NH3). We find that NH3 predominantly controls SIA formation over 60% of China during cold seasons and NOx- or SO2-senesitive grids only dominate in four warm months (April to July). The spatial distributions of the chemical regimes throughout the year show a distinct demarcation between eastern NH3-NOx-controlled area and western NH3-SO2(-NOx)-controlled area. Sichuan Basin and Henan Province, however, are NOx-sensitive throughout the year. We then train the source-receptor relationships in SHERPA using the baseline and sensitivity simulation outputs of WRF-Chem. SHERPA can well reproduce the responses of PM2.5 concentrations to the emission changes of all precursors in China. Our results show that for yearly average PM2.5, local actions of precursor emission reduction at the urban scale are effective for most cities and mitigations in agricultural sector are important. Our study stresses the essential role of NH3 to further PM2.5 mitigation strategies of whole China.
AI and physical models for air quality monitoring at urban scale with PRISMA hyperspectral data
Authors: De Santis Davide , Sasidharan Sarathchandrakumar T. , Di Giacomo Marco , Bencivenni Gianmarco , Del Frate Fabio , Curci Gabriele , Amarillo Ana Carolina , Barnaba Francesca , Di Liberto Luca , Pasqualini Ferdinando , Bassani Cristiana , Scifoni Silvia , Casadio Stefano , Cofano Alessandra , Cardaci Massimo , Licciardi Giorgio
Published: March 2024

The challenge of air pollution and its impact on human health is a significant concern in contemporary society. The PRIMARY (PRIsma for Monitoring AiR quality) research project aims to leverage the capabilities of the Italian Space Agency's PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission to enhance air quality monitoring, particularly in urban areas. The project seeks to utilize PRISMA's hyperspectral data for detailed qualitative and quantitative insights into atmospheric aerosol content and composition in urban environments, crucial for understanding the environmental and health impacts of particulate matter. PRISMA's decametric spatial resolution and the project's use of artificial intelligence address limitations in spatial resolution and the complexity of the inverse problem in satellite-based characterization of particulate matter.
In the context of the PRIMARY project, which deals with high-dimensional hyperspectral data, feature extraction before inversion modeling presents challenges, especially when employing machine learning techniques like neural networks (NNs). Dimensionality reduction addresses this challenge by using feature extraction. Comparative evaluation on a PRISMA dataset for Rome showed variable performance between PCA and NN models in compressing and reconstructing the original vector. Additionally, a synthetic dataset was generated to train the algorithm for atmospheric aerosol composition recognition, relying on a sufficiently large number of aerosol profile examples. The Copernicus Atmosphere Monitoring (CAMS) service, specifically its global atmospheric composition forecast product, was chosen as the primary data source. The FlexAOD code, a post-processing tool, was adapted to read CAMS data and obtain aerosol optical properties for input into LibRadtran, a radiative transfer model, used to generate PRISMA-like synthetic data. The resulting dataset, obtained through automated processes, serves as training data for neural networks. To provide validation for the PRIMARY products, the project planned dedicated measurement campaigns in Rome (autumn 2022) and Milan (from winter to summer 2023).

 The PRIMARY project is co-funded by the Italian Space Agency (ASI – “Tor Vergata” University of Rome Agreement n. 2022-3 U.0); the project is part of the ASI’s program “PRISMA Scienza”.
Multi scale surface temperature monitoring (by UAS and Satellite) on quiescent Volcanoes.
Authors: Bellucci Sessa Eliana , Avino Rosario , Avvisati Gala , Belviso Pasquale , Buongiorno Maria Fabrizia , Caputo Teresa , Carandente Antonio , Fabbrocino Silvia , Rabuffi Federico , Silvestri Malvina , Peluso Rosario , Marotta Enrica
Published: March 2024

The study of thermal anomalies linked to volcanic activity is an indispensable tool to understand the state of volcanoes and for their monitoring. Over time, the tools for studying anomalies have improved over time and from this perspective, unmanned aerial systems (UAS) have made it possible to bridge the gap between space-based and terrestrial remote sensing data. UAS provide very high resolution spatial data, which allows the detection of thermal anomalies of even smaller extent and with lower temperatures.We made a comparison in different areas between UAS and satellites such as Pisciarelli, Monte Nuovo, Biancane and Solfatara.Furthermore, the continuous monitoring of the Pisciarelli area has allowed us to understand the best method for acquiring data such as the flight plan, mosaicking, analyzes for comparisons with other satellite systems and for the future calculation of the heat flow.
Soil microbial responses to nanoplastic particles investigated in transparent soil micromodels
Authors: Hammer Edith C. , Mafla-Endara P. Micaela , Beech Jason , Ohlsson Pelle
Published: March 2024

Micro- and nanoplastics have become very common pollutants of soil ecosystems, yet their impact on soil microorganisms remains poorly understood. We exposed a model soil bacterium (Pseudomonas putida) and a model soil fungus (Coprinopsis cinerea) to different concentrations of nanosized polystyrene beads in microfluidic soil chips. The transparent micromodels allowed us to perform direct investigation of the effect of beads on abundance of the microbes and on interactions of individual cells with the nanobeads. Growth of both the bacteria and the fungi was reduced by the exposure to nanoplastics, along with a reduction in bacterial enzymatic activity. Nanobeads were strongly attracted to fungal hyphae, causing a high concentration of beads along the first hyphae to enter a pore space, and thus freeing the surrounding from a large proportion of the beads. We also found plastic particle accumulation along fungal hyphae in setups with soil inocula.  Chips with soil inocula also allowed us to investigate direct interactions of microbes with plastic particles, and particle aggregation under the influence of the microbe-affected soil solution over time. These studies contribute to our understanding of direct toxicity effects and interactions of nanoplastics and soil microbes.
Layered by water: Impact excavation processes on terrestrial analogues for Mars. 
Authors: Wilk Jakob , Agarwal Amar
Published: March 2024

Comparative planetological studies show that the excavation of rock debris during impact processes and their distribution (ejecta) around impact craters are strongly influenced by rheology and volatile content of the affected crust [1]. The formation of so-called Double-layered ejecta (DLE) craters on mars specifically is linked to the presence of water-ice in the Martian subsurface (see review by [2]). Although morphological features, in particular the ejecta blankets, have largely been destroyed for most of the terrestrial impact structures (e.g., [3]), recent studies have shown that at least Lonar crater in India [4], Ries crater in Germany [5] and Bosumtwi crater in Ghana [6, 7] possess ejecta ramparts, similar to martian DLE impact craters.We test this hypothesis of DLE-like craters on earth by examining ejecta features, such as radial grooves, of Ries and Lonar crater. For this study, we combine geophysical (GPR and ERT), analysis of micro-fracture networks and photogrammetric (drone photogrammetry) techniques in key locations of the Ries and Lonar crater.As a result of from processed DEMs an apparent thickening of the ejecta deposits are observable in distal parts of the Ries and Lonar craters, similar to rampart structures of martian impact craters [5, 7]. Geophysical measurements suggest the presence of lateral striations in the morphometric contact of the Ries crater ejecta layers. We intend to construct an area-wide reconstruction of the ejecta boundary layer also for Lonar. The formation of martian-like DLE craters on earth could help to substantiate analogoues to constrain boundary conditions for DLE craters on other planets, specifically in aformentioned volatile content of the affected crust. Literature: [1] Osinski et al. 2011 Earth and Planetary Science Letters 310, 167–181. [2] Weiss and Head 2018 Meteoritics & Planet. Science 53, 741-777. [3] Hergarten and Kenkmann 2015 Earth and Planetary Science Letters 425, 187–192. [4] Maloof et al. 2009 Geological Society of America Bulletin 122:109–126. [5] Sturm et al. 2013 Geology, 41, 531-534. [6] Baratoux et al. 2019 Meteoritics & Planetary Science 54, 2541–2556. [7] Wulf et al. 2019 Earth and Planetary Science Letters 506, 209–220.
The 5-year trend of PM1 and PM2.5 mass concentrations and their carbon content at an urban background site in Zagreb, Croatia
Authors: Godec Ranka , Šilović Hujić Martina , Gluščić Valentina , Pehnec Gordana
Published: March 2024

Fine particulate matter, such as PM2.5 and PM1, is a harmful air pollutant from natural and human sources. It impacts the climate and causes respiratory issues. It's classified as a Group 1 carcinogen, and it's the most harmful form of air pollution, causing heart and lung disease and premature death. Aerosols' composition and toxicity vary depending on their source and atmospheric chemistry. The carbon fraction of particulate matter, consisting of both elemental and organic carbon, may cause adverse health effects due to its reductive potential and organic species. It also contributes to global warming due to its radiative properties. Carbonaceous material is a substantial part of particulate matter, with primary organic carbon coming from incomplete combustion and degradation of carbon-containing products. Secondary organic carbon forms through the condensation of organic carbon gases from organic material combustion, fuel evaporation, or natural volatile organic compounds emission.The objective of this study was to identify and compare the concentrations of elemental carbon (EC) and organic carbon (OC) in PM2.5 and PM1 particles during different seasons and years in order to detect any potential trends of individual pollutants in PM at a single site in Zagreb. Continuous monitoring of mass concentrations of PM1 and PM2.5 particles, along with their carbon content, was carried out for a period of five years, starting from January 1st, 2018, and ending on December 31st, 2022, at an urban background site located in the northern part of Zagreb. Daily samples of PM1 and PM2.5 particle fractions were gathered on pre-fired quartz fiber. Their mass concentrations were determined gravimetrically, while carbon content was analyzed for OC, TC, WSOC, and EC. This was done using the thermal-optical transmittance method, following the EUSAAR_2 protocol. The measurements taken over 5 years showed that the concentration of carbon species in PM1 and PM2.5 varied seasonally. The concentration of carbon pollutants was observed to be higher during cold periods, while it was lower during warmer periods of the 5-year campaign. A weak downward trend in PM1, PM2.5, and OC mass concentrations was observed during the studied period, while EC mass concentrations showed a slight increasing trend. ACKNOWLEDGEMENTS: These measurements were conducted within the internal scientific project of the Institute for Medical Research and Occupational Health “Organic content of PM1 particle fraction” (PI: R. Godec). The results of this research were obtained using the facilities and equipment funded within the European Regional Development Fund project KK.01.1.1.02.0007 "Research and Education Centre of Environmental Health and Radiation Protection – Reconstruction and Expansion of the Institute for Medical Research and Occupational Health".
Spatiotemporal evolution and renewable energy potential in coal regions in transition
Authors: Pyrgaki Konstantina , Krassakis Pavlos , Karavias Andreas , Zarogiannis Theodoros , Zygouri Evangelia , Mpatsi Anna , Koukouzas Nikolaos
Published: March 2024

The WINTER project, funded by the EU, aims to develop a web GIS interactive platform that will be used as a tool for the management of coal regions in transition. The platform will guide and engage stakeholders by sharing best practices and addressing transition challenges in pilot regions at different transition stages.  The applied methodology involved a three-step process: 1) developing a geodatabase to import and standardize geospatial datasets; 2) training and implementing a Machine Learning (ML) approach [1]; and 3) identifying and quantifying land cover (LC) changes from 2018 to 2021. Particularly in Western Macedonia (Greece), the Amynteo mine, illustrated a green transition (Figure 1), converting mining areas to bare soil, vegetation and water bodies, indicating strong reclamation potential. In contrast, the Ptolemaida mine, still operational, illustrated minimal land cover changes. In Poland and specifically, in Konin region results highlighted mining expansion, affecting agricultural and wetland areas. On the other hand, the Kazimierz mine, which is already at a closure phase, exhibited a significant green transition, with a marked increase in vegetation land cover. The following step was the assessment for the potential for Renewable Energy Source (RES) implementation utilizing, open-source geospatial datasets, considering factors like elevation/slope, wind speed, solar radiation, and land cover/land use were used. Scenarios were designed to identify preliminary suitable areas for Photovoltaic (PV) and Wind Parks (WP) installations. In Western Macedonia, potential sites were identified adjacent to the Ptolemaida mine limits, with a significant area suitable for PV parks. In Konin, the analysis within mine boundaries revealed similar suitability for PV and WP, showing the highest potential for RES implementation. Specifically, in Western Macedonia, the potentially suitable areas for PV was higher, up to 34% of the total studied area, in contrast to the 12%-18% range observed in Konin's mines. Additionally, the potentially suitable sites for WP in Western Macedonia seem to be related due to geomorphological differences, whereas in Konin, the suitability analysis based on results within the boundaries of the open-pit mine.The present study has received funding from the Research Fund for Coal and Steel—2020, under grant agreement No. 101057228 (WINTER).[1] Krassakis, P.; Karavias, A.; Nomikou, P.; Karantzalos, K.; Koukouzas, N.; Kazana, S.; Parcharidis, I. Geospatial Intelligence and Machine Learning Technique for Urban Mapping in Coastal Regions of South Aegean Volcanic Arc Islands. Geomatics 2022, 2, 297-322. https://doi.org/10.3390/geomatics2030017
Sensitivity analysis with external stochastic forcings for robust calibration : application to a water and pesticide transfer model
Authors: Lauvernet Claire , Radisic Katarina , Vidard Arthur
Published: March 2024

Environmental and hydrological models have become important decision-making tools. The PESHMELBA model [1] is a pesticide transfer model used to simulate and compare possible land-use planning scenarios in order to identify developments that reduce the impact of pesticides in surface waters.When calibrating environmental models, one of the first steps is a sensitivity analysis [2]. However, this analysis can vary for different realizations of the external conditions (such as rainfall, evapotranspiration, or date of pesticide application) under which the model operates. Indeed, even the external conditions as an uncontrollable stochastic quantity means that the hydrological model itself becomes stochastic. Sobol indices can then be seen as random variables [3], where randomness is given by their dependence on rainfall.In this study, we calculate the sensitivity indices on two examples: first, on soil hydrodynamic parameters on a vineyard plot in PESHMELBA under several realizations of the same type of rainfall, corresponding to events measured on the field. Second, on a omore complex situation considering the whole watershed and pesticides output variables. The stochasticity of that second case comes from the difference between the rainfall and the date of pesticide application, whicih is a key unknown in pesticide transfer simulations.We show that the hierarchy of input parameters varies according to the forcings used. In particular, heavier rainfall mainly activates processes in the deep saturated horizon, involving parameters governing saturated soil properties (water content at saturation, for example), which is not the case for lighter rainfall, for which PESHMELBA is essentially influenced by unsaturated soil parameters.The aim of this work is to take this dependency into account within the sensitivity analysis and to propose a global indice which is valid condidering the forcings uncertainties.[1] Rouzies, E., Lauvernet, C., Barachet, C., Morel, T., Branger, F., Braud, I., & Carluer, N. (2019). From agricultural catchment to management scenarios: A modular tool to assess effects of landscape features on water and pesticide behavior. Science of The Total Environment, 671, 1144–1160. https://doi.org/10.1016/j.scitotenv.2019.03.060[2] Mai, J. (2023). Ten strategies towards successful calibration of environmental models. Journal of Hydrology, 620, 129414. https://doi.org/10.1016/j.jhydrol.2023.129414[3] Hart, J. L., Alexanderian, A., & Gremaud, P. A. (2017). Efficient Computation of Sobol' Indices for Stochastic Models. SIAM Journal on Scientific Computing, 39(4), A1514–A1530. https://doi.org/10.1137/16M106193X
Modelling the climate response following idealized long-lasting high latitude volcanic eruptions: The stratospheric response and resulting implications for North Atlantic surface weather
Authors: Guðlaugsdóttir Hera , Peings Yannick , Zanchettin Davide , Magnúsdóttir Guðrún
Published: March 2024

Given the fact that many Icelandic volcanic systems are on the verge of an eruption, producing some of the largest volcanic eruptions over the past millennia, e.g., Öræfajökull, Bárðabunga, Grímsvötn and the Katla system, it is important to be able to predict potential changes in Northern Hemisphere (NH) climate variability in the following years after an NH eruption in due time. Recent volcanic activity in Iceland, e.g., Holuhraun 2014-2015 and Reykjanes/Geldingadalur 2021-2023, further demonstrates this urgency.With the aim to contribute to improving the forcasting and adaptation strategies for the North Atlantic region we, as a first step, forced an Earth System Model (CESM1.2.2) with an idealized long-lasting high-latitude volcanic eruption to quantify i) the response within the stratospheric polar vortex and ii) the resulting response within the coupled climate system in the Northern Hemisphere (NH) by assessing the first 15 years following the eruption focusing on the winter (DJF) response. Here results will be presented showing evidence of sudden stratospheric warming events and a deceleration of the stratospheric polar vortex occurring in the second and third post-volcanic winter. This is identified in the temperature and zonal winds at 50hPa as a result of the large modelled surface cooling in the NH where Eliassen-Palmer wave flux calculations further support these findings. The strong stratospheric response identified further influences surface climate throughout the continental NH in the first 5 years following this event via the NAO. Our result suggest that two competing mechanisms are at work during these first years, partly explaining this long-lasting short-term response. The long-term impact is identified as a change in regional surface temperature and sea ice variability as well as a general strengthening of the AMOC, reaching a maximum in winter 2 and remaining positive throughout the run.
Variations in stratospheric aerosol layer and aerosol microphysical processes following the 2021 La Soufrière eruption: insights from in situ and satellite observations
Authors: Li Yaowei , Pedersen Corey , Dykema John , Vernier Jean-Paul , Wrana Felix , von Savigny Christian , Vattioni Sandro , Pandit Amit , Stenke Andrea , Asher Elizabeth , Thornberry Troy , Todt Michael , Bui ThaoPaul , Dean-Day Jonathan , Keutsch Frank
Published: March 2024

Stratospheric aerosol plays an important role in Earth's radiative budget and in heterogeneous chemistry. Volcanic eruptions modulate the stratospheric aerosol layer by injecting particles and particle precursors like sulfur dioxide (SO2) into the stratosphere. The eruption of La Soufrière in April 2021 resulted in two distinct enhanced aerosol layers in the tropical lower stratosphere. These layers emerged approximately 3–4 weeks after the eruption, specifically at altitudes of 18 km (∼400 K) and 21 km (∼490 K), as observed through CALIOP/CALIPSO measurements. The lower plumes dispersed to higher latitudes in the Northern Hemisphere, while the upper plume exhibited restricted poleward transport. From June to August 2021 and May to July 2022, the NASA ER-2 high-altitude aircraft and balloon-borne instruments extensively sampled the stratospheric aerosol layer over the continental United States during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) mission. These in situ aerosol measurements provide detailed insights into the number concentration, size distribution, and spatiotemporal variations of particles within volcanic plumes. Notably, aerosol surface area density and number density in 2021 were enhanced by a factor of 2–4 between 380–500 K potential temperature compared to the 2022 DCOTSS observations, which were minimally influenced by volcanic activity. Within the volcanic plume, the observed aerosol number density exhibited significant meridional and zonal variations, while the mode and shape of aerosol size distributions did not vary. The La Soufrière eruption led to an increase in the number concentration of small particles (