In December 2023, 25 tons of pellets were lost by the Toconao ship in the Northwest Atlantic Ocean in front of the Portuguese coast. In this work, a coastal stretch of 633 km in Asturias and Galicia (Spain) and Northern Portugal was investigated to assess pellets’ concentration on 31 beaches. Field surveys were carried out in March 2024 and focused on sampling plastic pellets deposited along the shoreline. All the 7263 sampled pellets were characterized by size, degradation level, and color, while one subset was characterized by weight (40 % of the total) and another subset by polymer type (15 % of the total) using FT-IR spectroscopy. The results reveal that 94 % of the surveyed sites containing pellets, whereas the concentration values vary significantly among beaches, ranging from 0 to 40.3 pellets/kgdw. By combining the accounted variables, it emerges that 48.0 % of the collected pellets can be linked to the Toconao spill.

After marine oil spills, natural processes like photooxidation and biodegradation can remove the oil from the environment. However, these processes are strongly influenced by environmental conditions. To achieve a greater understanding of how seasonal variations in temperature, light exposure and the bacterial community affect oil depletion in the marine environment, we performed two field experiments during the spring and autumn. Field systems equipped with a thin oil film of Statfjord, Grane or ULSFO were deployed in northern Norway. Depletion of the total extractable matter was faster during the spring than during the autumn. Statfjord showed faster depletion of n-alkanes during spring, while depletion of polycyclic aromatic hydrocarbons varied between the seasons based on the degree of alkyl-substitutions. ULSFO displayed the overall slowest depletion. Biodegradation of the oils was associated with high abundances of unassigned bacteria during the spring but was governed by Alcanivorax, Cycloclasticus, Oleibacter and Oleispira during the autumn. | publishedVersion

peer reviewed | Metconazole (MEZ), a chiral triazole fungicide, produces enantioselective adverse effects in non-target organisms. Among MEZ's isomers, cis-MEZ displays robust antimicrobial properties. Evaluating MEZ and cis-MEZ's toxicity may mitigate fungicide usage and safeguard non-target organisms. Our study evaluated the toxicity of MEZ and its cis-isomers at concentrations of 0.02, 0.2, 2, and 4 mg L−1. We report stereoselectivity and severe cardiovascular defects in zebrafish, including pericardial oedema, decreased heart rate, increased sinus venous and bulbous arteries distances, intersegmental vessel defects, and altered cardiovascular development genes (hand2, gata4, nkx2.5, tbx5, vmhc, amhc, dll4, vegfaa, and vegfc). Further, MEZ significantly increased oxidative stress and apoptosis in zebrafish, primarily in the cardiac region. Isoquercetin, an antioxidant found in plants, partially mitigates MEZ-induced cardiac defects. Furthermore, MEZ upregulated the Wnt/β-catenin pathway genes (wnt3, β-catenin, axin2, and gsk-3β) and β-catenin protein expression. Inhibitor of Wnt Response-1 (IWR-1) rescued MEZ-induced cardiotoxicity. Our findings highlight oxidative stress, altered cardiovascular development genes, and upregulated Wnt/β-catenin signaling as contributors to cardiovascular toxicity in response to MEZ and cis-MEZ treatments. Importantly, 1R,5S-MEZ exhibited greater cardiotoxicity than 1S,5R-MEZ. Thus, our study provides a comprehensive understanding of cis-MEZ's cardiovascular toxicity in aquatic life. © 2024 Elsevier Ltd

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Microplastic pollution was studied in surface waters of Isfjorden, Svalbard in July 2021 as a part of an international regional harmonisation exercise. Surface microplastics (0.5–5 mm) were sampled with a neuston net in triplicate per study site in several branches of Isfjorden, covering populated and unpopulated fjords. High spatial variability of microplastic abundance (0–32,700 items/km2) was observed within a single fjord resulting from the hydrodynamic pattern formed through the interaction of surface currents, freshwater runoff, and wind conditions. Maximum microplastic abundance was not correlated with the distance from the local source and was instead defined by local small-scale hydrodynamics. Future recommendations for correct assessment of surface microplastics concentration in estuarine environments are presented. | publishedVersion

This paper presents a novel method to select the optimal combination of grid resolution and number of Lagrangian elements (LEs) required in numerical modelling of oil concentrations at sea. A sensitivity analysis in terms of grid resolution and the number of LEs, was carried out to understand the uncertainty that these userdependent parameters introduce in the numerical results. A dataset of 211,200 simulations performed under 400 metocean patterns, 6 initial volumes, 11 grid resolutions, and different numbers of LEs (100 to 500,000), was used to analyze the sensitivity of the model along different Thresholds of Concern. Results show the importance of a correct selection of the number of LEs and the grid resolution in Lagrangian modelling of surface oil concentrations. The method proposed will allow selecting the optimal combination of these parameters to find an optimal balance between the accuracy and the computational cost of the simulation. | This work was partly carried out in the framework of the project PID2020-117267RB-I00 (COIL) funded by MCIN/AEI/10.13039/501100011033// and the project PDC2021-120892-I00 (BLOWHAZARD) funded by MCIN/AEI/10.13039/501100011033 and by European Union Next GenerationEU/PRTR.

Microplastics (MPs) were investigated at 19 sandy beaches along the eastern Mediterranean Moroccan coast. Sediment samples (5 mm–63 μ m) were analyzed to identify MPs abundance, size, shape, color and nature. MPs concentration ranged from 40 ± 7.4 to 230 ± 48.6 MPs kg 1 ; fibrous MPs were the most abundant (74.72 %), followed by fragments (20.26 %), films (3.27 %), pellets (1.42 %) and foams (0.33 %). Large MPs (1–5 mm) accounted for 58 %, while small (< 1 mm) for 42 %. The 1–2 mm fraction of sediments presented the greatest amounts (30.67 %) of MPs. Transparent (50 %) and blue (17 %) were most common colors and most of particles were angular and irregularly shaped. Fourier Transform Infrared Spectroscopy (FTIR) analysis showed that PE (Polyethylene), PS (Polystyrene) and PP (Polypropylene) and PVC (Polyvinyl chloride) were the most common polymers. These findings revealed a moderate level of microplastic pollution along the beaches of the eastern Moroccan Mediterranean coast.