Concentrations of cadmium, copper, and lead were determined in surficial sediments and the soft tissues (foot and gills) of swan mussel Anodonta cygnea from two sampling sites in Anzali wetland, which is an internationally important wetland registered in the Ramsar Convention. The metal contents in the mussel species from the studied region were comparable to other world areas. In most cases, the levels of the metals either fell within the range for other areas or were lower. There were significant differences between the tissues for the accumulation of Cd and Pb. Only in the case of Pb accumulation in gills significant differences between the specimens from the selected sampling sites could be observed. Age-related correlations were found in the case of Cu accumulation in foot and Cd levels in gills. No weight-dependent trend could be observed for the accumulation of the three elements. There was significant negative width-dependent relationship in the case of Cu. A significant negative correlation was also found between the maximum shell height and Cu accumulation in the gills. The only association among the elements in the selected soft tissues was found between Cd and Pb. Highly significant differences could be found between the sampling sites from the concentration of the elements in sediments point of view. The pattern of metal occurrence in the selected tissues and sediments exhibited the following descending order: Pb, Cu>Cd for gills, Cu>Pb, Cd for foot, and Cu>Pb>Cd for sediments. The mean concentrations of Cd and Pb in the sediments from the study area were higher than the global baseline values and world average shale. In the case of Cu, our results were somewhat higher than the baseline values but well below the world average shale.

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Refrigerated storage is the simplest method of preserving for short time handling and storage of fish. The objective of this study was to investigate the effects of refrigerated storage on the microstructure, physicochemical (proximate composition, pH, TVB-N, LHC), microbial (total mesophilic count, TMC and total psychrotrophic count, TPC) and sensory changes of grouper (Epinephelus coioides) fillets during a 14-day period at +4°C. The estimated TVB-N value was significantly different in all days of storage. TPC value reached to a limiting level for human consumption (10 7 microorganism/g) after the 10th day of storage while TMC remained under the limited acceptability until 14 days. Cellular tissue damage was observed after 7 days of storage. The shelf life of grouper was approximately~8-9 days according to the results of sensory, chemical and microbial analysis.

Most models used in land surface hydrology, vadose zone hydrology, and hydro-climatology require an accurate representation of soil thermal properties (soil thermal conductivity and volumetric heat capacity). Various empirical relations have been suggested to estimate soil thermal properties. However, they require many input parameters such as soil texture, mineralogical composition, porosity and water content, which are not always available from laboratory experiments and field measurements. In this paper, to overcome the above challenge, a hybrid numerical method, Genetic Algorithm-Finite Difference (GA-FD), is proposed to estimate soil thermal properties using land surface temperature (LST) as the only input. The genetic algorithm (GA) optimization method coupled with the finite difference (FD) modeling technique is a viable hybrid approach for estimating soil thermal properties. The finite difference method is employed to solve the heat diffusion equation and simulate LST, while a robust optimization technique (GA) is used to retrieve soil thermal properties by minimizing the difference between observed and simulated LST. Furthermore, a generalization of the hybrid model is developed for inhomogeneous soil, in which soil thermal properties are not constant throughout the soil slab. The proposed model is applied to the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The results show that the proposed hybrid numerical method is able to estimate soil thermal properties accurately, and therefore effectively eliminate the need for the unavailable soil parameters which are required by empirical methods for determining the soil thermal conductivity and volumetric heat capacity. Remarkably, the temporal variation of the retrieved soil thermal conductivity is consistent with the volumetric water content, even though no water content information is used in the model.

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