Due to an increase in demand of petroleum products which are transported by vessels or exported by pipelines, oil spill management becomes a controversial issue in coastal environment safety as well as making serious financial problems. After spilling oil in the water body, oil spreads as a thin layer on the water surface. Currents, waves and wind are the main causes of oil slick transport. These phenomena depend on the overall interaction among gravity, viscosity, surface tension and interfacial tension of oil in water bodies. In the current study, Artificial Neural Network (ANN) models have been designed and trained for the prediction of oil spreading and advection under different hydrodynamic conditions. In this regard, results obtained from a multiphase Lagrangian numerical model are deployed to train ANN model. The mentioned numerical model which is based on the moving particle semi-implicit (MPS) method is developed in the earlier stage of the study. In this research study, the MPS numerical model is first validated and verified against the analytical formulas which are based on experimental data cited in the literature. Then, various hydrodynamic conditions and oil spill scenarios were chosen to obtain different numerical model results. Finally, numerical model results are then deployed for training ANN model to provide a useful tool for urgent prediction of oil slick trajectory in order to manage the oil slick transport in the coastal environments.

Ships transport about 80 percent of world trade and transfer approximately three to five billion tons of ballast water internationally every year. Due to the likely presence of pollutants, the ballast water discharged by ships can have negative effects on aquatic ecosystems. This study was conducted on 10 ships that entered the Bushehr port to determine the effectiveness of the ballast water exchange method and also to specify the contents of heavy metals (Ni, Cd, Pb and Cu) in the water and sediment of the ships’ ballast tanks. The samples were collected from January 2017 to July 2018 during a cold and a hot season. The results indicate the values of heavy metals in the samples in this order: Ni> Cu > Pb > Cd. The heavy metals concentrations in the sediment samples did not exceed the standard of the National Oceanic and Atmospheric Administration (NOAA). Whereas, Cu and Ni in all water samples and Cd in samples 2 and 7 exceeded the NOAA quality standard value. A correlation analysis of the metals showed that the sources of heavy metals vary in water and sediment samples, except for Pb and Cu in sediment samples which a positively significant relationship were observed. The results also revealed that the ballast water exchange method cannot by itself be effective and an efficient management together with continuous monitoring seems to be essential to prevent pollution of the ballast tanks of the ships entering the Bushehr port.

peer reviewed | Plastic debris are accumulating in the marine environment and aggregate microorganisms that form a new ecosystem called the plastisphere. Better understanding the plastisphere is crucial as it has self-sufficient orga- nization and carries pathogens or organisms that may be involved in the pollutant adsorption and/or plastic degradation. To date, the plastisphere is mainly described at the taxonomic level and the functioning of its microbial communities still remains poorly documented. In this work, metagenomic and metaproteomic analyzes were performed on the plastisphere of polypropylene and polyethylene plastic debris sampled on a pebble beach from the Mediterranean Sea. Our results confirmed that the plastisphere was organized as self-sufficient eco- systems containing highly active primary producers, heterotrophs and predators such as nematode. Interestingly, the chemical composition of the polymer did not impact the structure of the microbial communities but rather influenced the functions expressed. Despite the fact that the presence of hydrocarbon-degrading bacteria was observed in the metagenomes, polymer degradation metabolisms were not detected at the protein level. Finally, hydrocarbon degrader (i.e., Alcanivorax) and pathogenic bacteria (i.e., Vibrionaceae) were observed in the plas- tispheres but were not very active as no proteins involved in polymer degradation or pathogeny were detected. This work brings new insights into the functioning of the microbial plastisphere developed on plastic marine debris.

Staphylococcus is a significant food safety hazard. The marine environment serves as a source of food for humans and is subject to various human-induced discharges, which may contain Staphylococcus strains associated with antimicrobial resistance (AMR). The aim of this study was to assess the occurrence and geographical distribution of AMR Staphylococcus isolates in seawater and whiting (Merlangius merlangus) samples collected from the English Channel and the North Sea. We isolated and identified 238 Staphylococcus strains, including 12 coagulase-positive (CoPs) and 226 coagulase-negative (CoNs) strains. All CoPs isolates exhibited resistance to at least one of the 16 antibiotics tested. Among the CoNs strains, 52% demonstrated resistance to at least one antibiotic, and 7 isolates were classified as multi-drug resistant (MDR). In these MDR strains, we identified AMR genes that confirmed the resistance phenotype, as well as other AMR genes, such as quaternary ammonium resistance. One CoNS strain carried 9 AMR genes, including both antibiotic and biocide resistance genes. By mapping the AMR phenotypes, we demonstrated that rivers had a local influence, particularly near the English coast, on the occurrence of AMR Staphylococcus. The analysis of marine environmental parameters revealed that turbidity and phosphate concentration were implicated in the occurrence of AMR Staphylococcus. Our findings underscore the crucial role of wild whiting and seawater in the dissemination of AMR Staphylococcus within the marine environment, thereby posing a risk to human health.