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The concentrations of manganese (Mn), copper (Cu), zinc (Zn), nickel (Ni), cobalt (Co), lead (Pb) and cadmium (Cd) in the muscles of grey mullet (Mugil cephalus) that were collected from Gaza fishing harbor and the surrounding areas were investigated in this study. Eight sampling locations were selected to conduct the study along the coast of Gaza. The samples were taken in September, November 2013 and March 2014. Heavy metals were identified and analyzed by Flame Atomic Absorption Spectrophotometer. The mean concentrations of heavy metals in soft tissues of fish were as follows: Mn:0.90 µg/g; Cu:13.15 µg/g; Zn:25.87 µg/g; Ni:1.10 µg/g; Co:0.68 µg/g; Pb:1.82 µg/g and Cd:0.27 µg/g, respectively. The highest concentrations of metals in fish tissues were found to be detected for zinc (13.56-40.43 µg/g) and the lowest were for cobalt (nd-2.93 µg/g) and cadmium (0.02-0.51 µg/g). The heavy metal concentrations in most fish samples were found to be below the acceptable limits proposed for fish by various international standards such as European Union (EU), World Health Organization (WHO), and Turkish guidelines (TFC). Therefore, it can be concluded that no risk on human health would be elevated at present from the consumption of fish collected from Gaza fishing harbor.

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Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.