Short communicationMetal and dye removal using fungal consortium from mixed waste stream: Optimization and validation
Introduction
In the recent years, extensive research has occurred on biological remediation of heavy metals and dyes (Gupta et al., 2010, Mittal et al., 2010, Mittal et al., 2013). However, metal bioaccumulation has been largely studied under single metal or dye exposures (Kaushik and Malik, 2009, Bhatia et al., 2011, Gupta et al., 2011, Singh et al., 2012, Asgher and Bhatti, 2012, Akar et al., 2013, Mishra and Malik, 2013). Such studies do not exactly reflect the real situation in wastewaters containing metal ions and dyes together. Some investigations with metal-dye mixtures indicate that double/multiple stress may influence the bioaccumulation performance. Cu(II) uptake and reactive dye decolorization ability of growing Candida tropicalis was tested as a function of initial Cu(II) and dye ion concentrations, both singly and in mixture by Gonen and Aksu (2009). Poorer performance under the dual stress as compared to single exposure in metal–dye mixtures was noticed. A suitable combination of efficient strains can handle both metal and dye simultaneously without compromising the removal efficiency (Jadhav et al., 2010) as mixed cultures are better able to withstand pollutant stress due to close interactions and protections offered by the partners. Therefore, it is desirable to investigate the potential of microbial consortiums in handling mixed metal–dye waste streams. However, the nutrient demands, substrate uptake rates and pollutant affinities of the consortium members may be different from each other.
Process optimization for biological removal of hazardous contaminants has emerged as an important initiative towards reducing the process cost (Gonen and Aksu, 2009). Previous studies from our group on the nutrient optimization for Cr(VI) removal (Sharma et al., 2009) and Acid Navy Blue (ANB) removal (Kaushik and Malik, 2011) using RSM, presented interesting observations. It was revealed that the nutrient requirement is pollutant specific with yeast extract being crucial for Cr(VI) removal while urea being important for ANB removal. Hence, process optimization for multiple pollutant removal through a consortium shall be a challenging task. This would mimic the actual process performance under field conditions. However, no such study is reported in the literature. Hence, the present study attempts to optimize the process conditions (media components) for simultaneous dye and metal removal employing a fungal consortium developed earlier (Mishra, 2013). So far, this is the first reports utilizing fungal consortium for the simultaneous removal of metal and dye from synthetic medium.
Section snippets
Experimental
Two metals (copper and chromium) as Potassium dichromate [K2Cr2O7] and copper sulphate [CuSO4·5H2O] salts and two industrial quality metal complex azo dyes (Acid Blue 161 and Pigment Orange 34) were used (Sup. Table 1). The stock solutions of 10 g L−1 were prepared for each metal and dye in double distilled water.
Effect of nutrient sources on biomass production, metals and dyes removal
Preliminary studies revealed that yeast extract (YE) cannot be totally replaced from growth media during the metal bioremediation (Sharma et al., 2009, Mishra and Malik, 2012, Sharma et al., 2011). However, among the low cost alternates, urea (UR) and ammonium nitrate (AN) were found to be suitable sources for partial replacement of yeast extract (Kaushik and Malik, 2011, Mishra, 2013). Therefore, a combination of YE, UR and AN was optimized through full factorial RSM and influence on biomass
Conclusion
Concentration of yeast extract is an important parameter regulating the biomass production, Cu(II) and dye removal by fungal consortium in a metals–dye mixture. Nevertheless, the input of YE can be reduced by supplementing low cost nitrogen source and optimizing the interaction among the same using RSM. Moreover, in the complex matrix, nutrient input can be designed for optimum removal of each pollutant. In the present study, the data followed quadratic model and following concentrations of
Acknowledgements
Authors gratefully acknowledge NFBSFARA, Indian Council of Agricultural Research (grant no. NFBSAFARA/WQ-2023/2012-13) and Ministry of Environment and Forests, Govt. of India (grant no. 19/6/2008-RE) for funds and Dr. Rahul Bhambure, IIT Delhi, for help in design of experiment.
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