The objective of this study is to investigate the levels, inter-species-specific, locational differences and seasonal variations of vanadium in sea cucumbers and to validate further several potential factors controlling the distribution of metals in sea cucumbers. Vanadium levels were evaluated in samples of edible sea cucumbers and were demonstrated exhibit differences in different seasons, species and sampling sites. High vanadium concentrations were measured in the sea cucumbers, and all of the vanadium detected was in an organic form. Mean vanadium concentrations were considerably higher in the blood (sea cucumber) than in the other studied tissues. The highest concentration of vanadium (2.56 μg g−1), as well as a higher degree of organic vanadium (85.5 %), was observed in the Holothuria scabra samples compared with all other samples. Vanadium levels in Apostichopus japonicus from Bohai Bay and Yellow Sea have marked seasonal variations. Average values of 1.09 μg g−1 of total vanadium and 0.79 μg g−1 of organic vanadium were obtained in various species of sea cucumbers. Significant positive correlations between vanadium in the seawater and Vorg in the sea cucumber (r = 81.67 %, p = 0.00), as well as between vanadium in the sediment and Vorg in the sea cucumber (r = 77.98 %, p = 0.00), were observed. Vanadium concentrations depend on the seasons (salinity, temperature), species, sampling sites and seawater environment (seawater, sediment). Given the adverse toxicological effects of inorganic vanadium and positive roles in controlling the development of diabetes in humans, a regular monitoring programme of vanadium content in edible sea cucumbers can be recommended.
The primary objective of this study was to investigate the effect of biochar, produced from wheat residue at different temperatures, on the adsorption of diesel oil by loess soil. Kinetic and equilibrium data were processed to understand the adsorption mechanism of diesel by biochar-affected loess soil; dynamic and thermodynamic adsorption experiments were conducted to characterize this adsorption. The surface features and chemical structure of biochar, modified at varying pyrolytic temperatures, were investigated using surface scanning electron microscopy and Fourier transform infrared analysis. The kinetic data showed that the adsorption of diesel oil onto loess soil could be described by a pseudo-second-order kinetic model, with the rate-controlling step being intraparticle diffusion. However, in the presence of biochar, boundary layer control and intraparticle diffusion were both involved in the adsorption. Besides, the adsorption equilibrium data were well described by the Freundlich isothermal model. The saturated adsorption capacity weakened as temperature increased, suggesting a spontaneous exothermic process. Thermodynamic parameter analysis showed that adsorption was mainly a physical process and was enhanced by chemical adsorption. The adsorption capacity of loess soil for diesel oil was weakened with increasing pH. The biochar produced by pyrolytic wheat residue increased the adsorption behavior of petroleum pollutants in loess soil.
This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008-2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters (tempera- ture, pH and E.coli) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient (Spear- man R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater. 相似文献