Santiago Bravo

In the last decade, there has been a significant rise in interest in anthropogenic litter and plastic pollution in the marine environment. The recently proposed Integrated Marine Debris Observing System is expected to encompass three main components: in situ observations, remote sensing, and numerical modeling (Maximenko et al. 2019).In this work, we show the model-based outcome from systematic observations of floating marine macro litter in the Western Mediterranean and Adriatic seas. Regular ship-based observations of marine litter in the Mediterranean provide unique field data generated by complex hydrodynamic conditions and uncertain pollution sources. The observed floating macro litter distributions 2023–2024 were tracked for 150 days (Liubartseva et al., 2018) using the Parcels Lagrangian model (Delandmeter and van Sebille, 2019). Simulations were implemented using the plasticparcels python package, which is specifically designed for plastic transport and dispersion (Denes and van Sebille, 2024). The drift of virtual particles was forced by the sea surface currents provided by the Copernicus Marine Service reanalysis (Escudier et al., 2021) and by 10-m wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis (Hersbach et al., 2023). Turbulent diffusion was represented as a 2D random walk process.Monthly maps of litter occurrence frequency showed the areas where the litter was most/least likely to be transported. These maps can be superposed with the distributions of the Mediterranean megafauna, such as cetaceans and sea turtles, to conduct the associated risk assessment.A dedicated web-based application was developed to track the fate of beached litter under the absorbing boundary conditions. This analytical tool allows interactive analysis of the histograms and spatial distributions of beached virtual particles. The results can be compared with vulnerable coastlines as turtle nesting sites to evaluate the risk of contact.This work was carried out within the framework of the Life CONCEPTU MARIS Project (LIFE20 NAT/IT/001371). References Delandmeter, P., van Sebille, E., 2019. The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584.Denes, M.C., Van Sebille, E., 2024. Plasticparcels: A python package for marine plastic dispersal simulations and parameterisation development using parcels. J. Open Source Softw. 9, 7094.Escudier, R., Clementi, E., Cipollone, A., et al., 2021. A high resolution reanalysis for the Mediterranean Sea. Front. Earth Sci. 9, 1060.Hersbach, H., Comyn-Platt, E., Bell, B., et al., 2023. ERA5 post-processed daily statistics on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store.Liubartseva, S., Coppini, G., Lecci, R., Clementi, E., 2018. Tracking plastics in the Mediterranean: 2D Lagrangian model. Mar. Pollut. Bull. 129, 151–162.Maximenko, N., Corradi, P., Law, K.L., et al., 2019. Toward the Integrated Marine Debris Observing System. Front. Mar. Sci. 6, 447.

Produced water (PW), the largest waste stream from offshore oil and gas production, is continuously discharged into the marine environment after treatment, introducing polycyclic aromatic hydrocarbons (PAHs), heavy metals, and other contaminants [5], [2]. Accurate prediction of the fate and transport of these substances requires modeling both the initial discharge dynamics and long-range dispersion. We present a coupled near- and-far-field modeling framework that integrates the Texas A&M Oilspill/Outfall Calculator (TAMOC) with OpenDrift's ChemicalDrift module to simulate PW discharges from offshore platforms.TAMOC's Bent Plume Model (BPM) simulates the nearfield dynamics of PW discharge. The BPM solves conservation equations for mass, momentum, and buoyancy considering ambient stratification and cross flow conditions, capturing the initial dilution and trajectory of the plume [6], [4], [3]. The key outputs transferred to the far-field model include the plume terminal location and the dilution, which are used to initialize the Lagrangian particle seeding in ChemicalDrift.ChemicalDrift, a Lagrangian chemical-fate and transport model integrated within the open-source OpenDrift framework, simulates the subsequent long-range advection and dispersion of contaminants [1]. The model tracks advection and diffusion by ocean currents, interaction with surface wind and turbulent mixing. Critically, it implements dynamic partitioning of contaminants between dissolved, particle-bound, and sediment phases, along with their degradation and volatilization. Temperature and salinity dependencies on chemical processes are formulated, enabling enhanced fate predictions in varying oceanographic conditions.This coupled system is being deployed within the Integrated Monitoring System for the Italian Ministry of the Environment and Energy Security (SIM MASE) for operational monitoring of PW discharges from Italian offshore platforms. The framework enables direct integration of regulatory thresholds from Italian legislation (D.Lgs. 172/2015) and EU Directive 2013/39/EU on priority substances in water policy. Model outputs flag exceedances of Environmental Quality Standards, enabling users to identify when and where contaminant concentrations surpass legal limits, according to the simulations. By separating contributions from multiple platforms and providing spatiotemporal concentration fields, the modeling chain offers decision-makers a tool for compliance assessment and sustainable management of offshore activities.References1. Aghito, Manuel, et al. "ChemicalDrift 1.0: an open-source Lagrangian chemical-fate and transport model for organic aquatic pollutants." Geoscientific Model Development 16.9 (2023): 2477-2494.2. Beyer, Jonny, et al. "Environmental effects of offshore produced water discharges: A review focused on the Norwegian continental shelf." Marine environmental research 162 (2020): 105155.3. Dissanayake, Anusha L., Jonas Gros, and Scott A. Socolofsky. "Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow." Environmental Fluid Mechanics 18.5 (2018): 1167-1202.4. Gros, Jonas, et al. "Petroleum dynamics in the sea and influence of subsea dispersant injection during Deepwater Horizon." Proceedings of the National Academy of Sciences 114.38 (2017): 10065-10070.5. Neff, Jerry, Kenneth Lee, and Elisabeth M. DeBlois. "Produced water: overview of composition, fates, and effects." Produced water: Environmental risks and advances in mitigation technologies (2011): 3-54.6. Socolofsky, Scott A., et al. "Texas A&M Oilspill Calculator (TAMOC) modeling suite for subsea spills." Proceedings of the thirty-eighth AMOP technical seminar. Ottawa: Environment Canada, 2015.

Here we present the development of SIM MASE (https://sim.mase.gov.it/portalediaccesso/), an integrated operational platform designed for the Italian Ministry of Environment. Our focus was to develop an integrated set of applications to monitor and mitigate marine pollution in the so-called Vertical 3. The system bridges the gap between complex numerical modeling and stakeholder usability.Surface hydrocarbon slicks (either oil spill or produced water slicks) are identified by processing satellite images using a neural network, specifically trained for the recognition and semantic segmentation of these events. This object-based approach allows for accurate spatial characterization providing not only their location but also detailed geometric information (area, perimeter, shape), proving robust even in the presence of noise, variable weather and sea conditions, and confounding phenomena.The hydrocarbon slicks masks generated by the detection module constitute the initial input or validation for a suite of numerical models that simulate the spatiotemporal evolution of the slicks over time.The modeling takes into account the main environmental forces, such as marine currents, wind, etc., allowing for the prediction of the trajectory, dispersion, and potential impact area. Similarly, the system integrates models dedicated to the dispersion of produced water, allowing for a joint assessment of different scenarios. For oil spill modeling we have integrated TAMOC and MEDSLIK-II, allowing users to perform subsurface oil spill modeling and follow its drift in the surface. While produced water modeling we have coupled TAMOC and ChemicalDrift, following the same concept. In the platform there is also available a system to check Hazard, Vulnerability and Risk maps generated from millions of simulations run on HPC systems.The platform's architecture is based on a containerized environment that ensures high portability, scalability, and reproducibility of the models, facilitating their use in an operational context.The user interface is designed to allow institutional stakeholders to independently launch simulations and consult the results, transforming raw satellite data into forecasts and risk maps to support surveillance and rapid response activities.SIM MASE demonstrates a successful transition from academic modeling to an operational decision-support system, providing the Italian Ministry with a robust tool for the long-term protection of Mediterranean marine ecosystems.

Motivation: Fish sounds are integral to a variety of ecological functions, including reproduction, predator–prey interactions and recruitment, with ever‐growing interest in their relationships to anthropogenic impacts and applications for passive acoustic monitoring. Until recently, however, fish sound production data were often not easily accessible, limiting research, management and public awareness. FishSounds.net launched in 2021 to compile and disseminate global fish sound production information and recordings. Here, we describe the subsequent FishSounds version releases 2.0, 2.1, 2.2, 3.0 and 3.1 (cumulatively referred to as Versions 2 and 3). We updated the core dataset to include any fish species studied for sound production up until the year 2023. We added over 1000 new fish sound recordings, collated from FishSounds contributors or Cornell University's Macaulay Library. Connections with FishBase and the World Register of Marine Species were strengthened to improve the species information provided on FishSounds and facilitate data sharing. We also created several interactive visualisation tools, including a dendrogram and map view, to allow users to explore trends in known fish sound production. These updates have made FishSounds now the largest catalogue of fish sound production knowledge, utilised by over 17,000 users annually and featuring 1252 fish species studied across 1013 references as well as 1304 recordings. Main Types of Variables Contained: Fish sound production information compiled from the scientific literature, representative fish sound recordings with associated metadata and supporting images and species data drawn from other repositories. Spatial Location and Grain: Global. Time Period and Grain: 1874–2023. Major Taxa and Level of Measurement: Fishes (Agnatha, Chondrichthyes, Sarcopterygii, Actinopterygii). All sound production information and most recordings have species‐level identification, with any others identified to the lowest possible taxonomic level. Software Format: The complete database is presented on FishSounds.net , with versioned image, audio, tabular and text files in a Borealis data repository.

On 15 December 2024, a severe storm in the Kerch Strait led to catastrophic incidents involving two Russian oil tankers, Volgoneft-212 and Volgoneft-239. The Volgoneft-212 broke apart, spilling approximately 4,900 tonnes of mazut into the Black Sea, while Volgoneft-239, damaged and aground, leaked an additional 2,400 tonnes.To assess and mitigate the environmental impact, simulations were initiated immediately using the Medslik-II oil spill model. These simulations utilized analysis and forecast data from the Copernicus Marine Service (Black Sea currents and sea surface temperature) and ECMWF-IFS winds provided by the Italian Air Force Meteorological Service. Four operational bulletins were generated during the first week, informed by evolving observations and reliable event reports. These reports were sent to authorities and environmental entities, explaining the constraints of the simulation and the expected forecast.On 18 December at 03:00 UTC, COSMO-SkyMed satellite imagery, distributed and processed by e-GEOS (a Telespazio and Italian Space Agency Company) (based on COSMO-SkyMed satellites by Agenzia Spaziale Italiana and Ministero della Difesa) detected an oil slick near the Kerch Strait. Comparative analysis revealed strong agreement between Medslik-II simulations and satellite observations in both shape and trajectory, validating the model's accuracy during the initial response phase. Forecasts continued until 21 December, predicting a reversal of current patterns that would transport oil westward. This forecast aligned with subsequent reports of oil pollution as far as Sevastopol, approximately 250 kilometers from the spill's origin.Post-event analyses incorporated satellite imagery and media reports to refine simulations and assess long-term impacts. These efforts highlight the importance of integrating operational modeling, remote sensing, and reliable field data for real-time decision-making and post-incident analysis. Lessons learned from the Kerch Strait accident can serve as pathways to enhance oil spill response strategies and mitigate environmental risks in future maritime emergencies.

WITOIL (Where Is The Oil http://www.witoil.com), has been developed to maintain emergency management in case of an oil spill accident. Currently more and more initiatives are integrating traditional modeling systems into Digital Twins (DT’s) and in this context it was done the same for oil spill simulations. The usage of DT systems enables advanced environmental management and emergency response, since they provide close to real-time monitoring, simulation, and analysis, offering tools to predict and manage complex scenarios effectively. WITOIL is containerized through Docker, encapsulating Python workflows, integration scripts, and the MEDSLIK-II model (FORTRAN 90) into scalable environments. This approach ensures portability, efficiency, and reliability for deployment in platforms like iMagine and EDITO.On the iMagine platform (https://www.imagine-ai.eu/case-study/oil-spill-detection-oil-spill-detection-from-satellite-images), WITOIL-for-iMagine by using Bayesian optimization, the MEDSLIK-II model achieves enhanced accuracy for spill behavior predictions. Users can launch simulations and access results quickly, combining remote sensing past data with modeling in a streamlined workflow for pollution analysis. The system represents one of the first approaches on merging traditional oil spill simulations and machine learning techniques that were applied in the optimisation process.The EDITO Model Lab (https://edito-modellab.eu/) presents WITOIL-Cloud as a decision support system for oil spill emergency management. It integrates the MEDSLIK-II model, operational meteo-oceanographic services (Copernicus Marine Services and Climate Data Store). With a user-friendly interface, stakeholders can simulate oil spill trajectories and impacts in historical scenarios, enabling informed decision-making. The system’s accessibility democratizes advanced modeling tools, expanding their reach to diverse users.WITOIL-Cloud's integration of real-time environmental data ensures accuracy and relevance in emergency responses. A real-world case study via the EDITO Model Lab and iMagine platform exemplifies this capability. It was chosen the Syria 2021 oil spill case as an example of usage on both DT to demonstrate the platform capabilities, showing how the platforms could support on future events by exemplifying on a real past scenarioBy connecting traditional models with digital twin platforms, WITOIL enhances oil spill modeling's accessibility, and efficiency. These innovations empower stakeholders to address maritime pollution challenges with easier access, quicker response of digital twins in shaping the future of environmental management and emergency response in the case of oil spill accidents.

A working group from the Global Library of Underwater Biological Sounds effort collaborated with the World Register of Marine Species (WoRMS) to create an inventory of species confirmed or expected to produce sound underwater. We used several existing inventories and additional literature searches to compile a dataset categorizing scientific knowledge of sonifery for 33,462 species and subspecies across marine mammals, other tetrapods, fishes, and invertebrates. We found 729 species documented as producing active and/or passive sounds under natural conditions, with another 21,911 species deemed likely to produce sounds based on evaluated taxonomic relationships. The dataset is available on both figshare and WoRMS where it can be regularly updated as new information becomes available. The data can also be integrated with other databases (e.g., SeaLifeBase, Global Biodiversity Information Facility) to advance future research on the distribution, evolution, ecology, management, and conservation of underwater soniferous species worldwide.

Despite the importance of acoustic signaling in fishes, the prevalence of the behavioral contexts associated with their active (i.e., intentional) sound production remains unclear. A systematized review was conducted to explore documented acoustic behaviors in marine, subtropical fishes and potential influences affecting their relative pervasiveness. Data were collected on 186 actively soniferous fish species studied across 194 publications, identified based on existing FishSounds and FishBase datasets. Disturbance was the most common behavioral context associated with active sound production—reported for 140 species or 75% of the species studied—and then aggression (n = 46 species, 25%) and reproduction (n = 34 species, 18%). This trend, however, somewhat differed when examined by research effort, study environment, and fish family, such as reproductive sounds being more commonly reported by studies conducted in the wild. The synthesis of fish sound production behaviors was in some ways stymied by the fact that many species' sound production did not have discernible associated behavioral contexts and that some investigations did not clearly identify the study environments in which active sound production was observed. These findings emphasize the importance of context—behavioral or otherwise—when studying acoustic behaviors in fishes.