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.

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.