Potential of Hydrocotyle vulgaris for phytoremediation of a textile dye: Inducing antioxidant response in roots and leaves.

The potential of Hydrocotyle vulgaris as an aquatic plant species was evaluated for phytoremediation of C.I. Basic Red 46 (BR46) from nutrient solution. Under the optimized experimental conditions, BR46 was removed up to 95% from incubation medium by H. vulgaris. The ability of the plant in consecutive removal under long term repetitive experiments confirmed the biodegradation process. Accordingly, a number of produced intermediate compounds were identified. An artificial neural network (ANN) model was developed to predict the biodegradation efficiency. A predictive performance (R(2)=0.974) was obtained based on the network results. Interestingly, dye stress enhanced the activity of antioxidant enzymes including superoxide dismutase, peroxidase and catalase in H. vulgaris roots and leaves. Enzymatic responses found to be highly depended on the plant organ and dye concentration in the liquid medium. Overall, the increase in the activity of antioxidant enzymes was much higher in the roots than in the leaves. Nevertheless, no significant increase in the malondialdehyde (MDA) content was detected in both roots and leaves which reflects the high efficiency of antioxidant system in the elimination of reactive oxygen species.

Phytoremediation potential of duckweed (Lemna minor L.) in degradation of C.I. Acid Blue 92: artificial neural network modeling.

In present study, the potential of duckweed (Lemna minor L.) for degradation of an azo dye C.I. Acid Blue 92 (AB92) has been investigated. The effect of operational parameters such as initial dye concentration, pH, temperature and amount of plant on the efficiency of biological decolorization process was determined. The reusability of Lemna minor L. in long term repetitive operations was also examined. Growth and some biochemical parameters (photosynthetic pigments content, superoxide dismutase, catalase and peroxidase activity) were used to detect the toxic effects of AB92 on duckweed plant. The biological degradation compounds formed in the present process were analyzed by GC-MS technique. In addition, an artificial neural network (ANN) model was expanded to predict the biological decolorization efficiency. The obtained data indicated that ANN provide realistic predictive performance (R(2)=0.954).