Phytoremediation potential of Arundo donax in arsenic-contaminated synthetic wastewater.

The present study reports the potential of Arundo donax for phytoextraction of arsenic from synthetic wastewater. A. donax plants were grown under greenhouse conditions in pots containing a nutrient solution amended with increasing doses of As (0, 50, 100, 300, 600 and 1000 microg L(-1)) for 21 days in a completely randomized design. Shoot and roots dry matter production, growth parameters, arsenic and nutrient tissue concentrations were measured at the end of the experiment. Increasing As concentration in nutrient solution caused an increase in shoot and root biomass without toxicity symptoms in A. donax growing under a range of As concentration from 50 to 600 microg L(-1). Elevated oxidative stress was observed at As supplied level of 1000 microg L(-1). The As doses up to 600 microg L(-1) did not affect the growth of A. donax. It is suggested that A. donax plants may be employed to treat contaminated waters containing arsenic concentrations up to 600 microg L(-1).

Isolation and characterization of Pseudomonas stutzeri QZ1 from an anoxic sulfide-oxidizing bioreactor.

Bacterial strain QZ1 was isolated from sludge of anoxic sulfide-oxidizing (ASO) reactor. Based on 16S rDNA sequence analysis and morphological characteristics, the isolate was identified as Pseudomonas stutzeri. The isolate was found to be a facultative chemolithotroph, using sulfide as electron donor and nitrite as electron acceptor. The strain QZ1 produced sulfate as the major product of sulfide oxidation, depending on the initial sulfide and nitrite concentrations. The isolate was capable of growth under strictly autotrophic conditions. The growth and substrate removal of Pseudomonas stutzeri QZ1 were optimal at an initial pH of 7.5-8.0 at 30 degrees C. The specific growth rate (mu) was found as 0.035 h(-1) with a doubling time of 21.5 h. For isolate QZ1, the EC(50) values both for sulfide and nitrite were found to be 335.95 mg S L(-1) and 512.38 mg N L(-1), respectively, showing that the sulfide oxidation into sulfate by Pseudomonas stutzeri QZ1 was badly affected beyond these substrate concentrations.

Isolation of Ochrobactrum sp.QZ2 from sulfide and nitrite treatment system.

A bacterial strain QZ2 was isolated from sludge of anoxic sulfide-oxidizing (ASO) reactor. Based on 16S rDNA sequence analysis and morphology, the isolate was identified as Ochrobactrum sp. QZ2. The strain was facultative chemolithotroph, able of using sulfide to reduce nitrite anaerobically. It produced either elemental sulfur or sulfate as the product of sulfide oxidation, depending on the initial sulfide and nitrite concentrations. The optimum growth pH and temperature for Ochrobactrum sp. QZ2 were found as 6.5-7.0 and 30 degrees C, respectively. The specific growth rate (micro) was found as 0.06 h(-1) with a doubling time of 19.75h; the growth seemed more sensitive to highly alkaline pH. Ochrobactrum sp. QZ2 catalyzed sulfide oxidation to sulfate was more sensitive to sulfide compared with nitrite as indicated by IC(50) values for sulfide and nitrite utilization implying that isolate was relatively more tolerant to nitrite. The comparison of physiology of Ochrobactrum sp. QZ2 with those of other known sulfide-oxidizing bacteria suggested that the present isolate resembled to Ochrobactrum anthropi in its denitrification ability.

Prediction of anoxic sulfide biooxidation under various HRTs using artificial neural networks.

OBJECTIVE: During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance. METHODS: Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network. RESULTS: Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner. CONCLUSION: Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.