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