Biosorption characteristics of unicellular green alga Chlorella sorokiniana immobilized in loofa sponge for removal of Cr(Ⅲ)

Loofa sponge （LS） immobilized biomass of Chlorella sorokiniana （LSIBCS）, isolated from industrial wastewater, was investigated as a new biosorbent for the removal of Cr（Ⅲ） from aqueous solution. A comparison of the biosorption of Cr（Ⅲ） by LSIBCS and free biomass of C. sorokiniana （FBCS） from 10-300 mg Cr（Ⅲ）/L aqueous solutions showed an increase in uptake of 17.79% when the microalgal biomass was immobilized onto loofa sponge. Maximum biosorption capacity for LSIBCS and FBCS was found to be 69.26 and 58.80 mg Cr（Ⅲ）/g biosorbent, respectively, whereas the amount of Cr（Ⅲ） ions adsorbed onto naked LS was 4.97 mg/g. The kinetics of Cr（Ⅲ） biosorption was extremely rapid and equilibrium was established in about 15 and 20 min by LSIBCS and FBCS, respectively. The biosorption equilibrium was well defined by Langmuir adsorption isotherm model. The biosorption kinetics followed the pseudo-second order kinetic model. The biosorption was found to be pH dependent and the maximum sorption occurred at the solution pH 4.0. Desorption studies showed that 98% of the adsorbed Cr（Ⅲ） could be desorbed with 0.1 mol/L HNO3, while other desorbing agents were less effective in the order： EDTA 〉 H2SO4 〉 CH3COOH 〉 HCl. The regenerated LSIBCS retained 92.68% of the initial Cr（Ⅲ） binding capacity up to five cycles of reuse in continuous flow-fixed bed columns. The study revealed that LSIBCS could be used as an effective biosorbent for the removal of Cr（Ⅲ） from wastewater.

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Mechanisms involved in triggering debris flows,within a cohesive gravel soil mass on a slope:a case in SW China

The triggering mechanisms of debris flows were explored in the field using artificial rainfall experiments in two gullies, Dawazi Gully and Aizi Gully, in Yunnan and Sichuan Provinces, China,respectively. The soils at both sites are bare, loose and cohesive gravel-dominated. The results of a direct shear test, rheological test and back-analysis using soil mass stability calculations indicate that the mechanisms responsible for triggering debris flows involved the decreases in static and dynamic resistance of the soil. The triggering processes can be divided into 7 stages: rainfall infiltration, generation of excess runoff, high pore water pressure, surface erosion, soil creep, soil slipping, debris flow triggering and debris flow increment. In addition, two critical steps are evident:(i) During the process of the soil mass changing from a static to a mobile state, its cohesion decreased sharply(e.g., the cohesion of the soil mass in Dawazi Gully decreased from 0.520 to0.090 k Pa, a decrease of 83%). This would have reduced the soil strength and the kinetic energy during slipping, eventually triggered the debris flow.(ii) When the soil mass began to slip, the velocity and the volume increment of the debris flow fluctuated as a result of the interaction of soil resistance and the sliding force. The displaced soil mass from the source area of the slope resulted in the deposition of a volume of soil more than 7-8 times greater than that in the source area.

Probability Prediction Model for Landslide Occurrences in Xi'an, Shaanxi Province, China

Landslides are increasing since the 1980s in Xi'an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall(IR) and antecedent effective rainfall(AER). Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; "A" region is safe, "B" region is on watch alert, "C" region is on warning alert and "D" region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi'an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi'an region.