Biodegradation of direct blue 129 diazo dye by Spirodela polyrrhiza: An artificial neural networks modeling

摘要

Phytoremediation potential of the aquatic plant Spirodela polyrrhiza was examined for direct blue 129 (DB129) azo dye. The dye removal efficiency was optimized under the variable conditions of the operational parameters including removal time, initial dye concentration, pH, temperature and amount of plant. The study reflected the significantly enhanced dye removal efficiency of S. polyrrhiza by increasing the temperature, initial dye concentration and amount of plant. Intriguingly, artificial neural network (ANN) predicted the removal time as the most dominant parameter on DB129 removal efficiency. Furthermore, the effect of dye treatment on some physiologic indices of S. polyrrhiza including growth rate, photosynthetic pigments content, lipid peroxidation and antioxidant enzymes were studied. The results revealed a reduction in photosynthetic pigments content and in multiplication of fronds after exposure to dye solution. In contrast, malondialdehyde content as well as catalase (CAT) and peroxidase (POD) activities significantly increased that was probably due to the ability of plant to overcome oxidative stress. As a result of DB129 biodegradation, a number of intermediate compounds were identified by gas chromatography-mass spectroscopy (GC-MS) analysis. Accordingly, the probable degradation pathway of DB129 in S. polyrrhiza was postulated.