Multimetal bioremediation from aqueous solution using dead biomass of Mucor sp. NRCC6 derived from detergent manufacturing effluent

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Abstract

Among ten metal-tolerant fungal isolates obtained from the microbiomes of detergent industry effluent, Mucor sp. NRCC6 showed the highest tolerance and an adaptive behavior toward the heavy metals Ni2+, Pb2+, Mn2+, and Zn2+. It gave the highest growth rates 0.790 ± 0.59, 0.832 ± 0.32, 0.774 ± 0.40, and 0.741 ± 1.06 mm/h along with the lowest growth inhibition 9.19, 4.37, 11.04, and 14.83% in the presence of Pb2+, Zn2+, Ni2+, and Mn2+, respectively, at a concentration of 5.0 g/L. Then, Mucor sp. NRCC6 was selected as a biotrap for the removal of these heavy metals. The optimized operating conditions were detected to be pH 6.0 for Pb2+, Zn2+, and Mn2+ and pH 5.5 for Ni2+ at 30 °C; agitation speed 150 rpm; contact time 30 min for Mn2+ and Ni2+, 30–60 min for Pb2+, and 90–180 min for Zn2+; NRCC6 biomass dosage 5.0 g/L for Ni2+ and Pb2+ and 10.0 g/L for Mn2+ and Zn2+; and initial concentration 12 mg/L of each ion in the multimetal aqueous solutions. Under these optimized conditions, the adsorption capacity for Pb2+, Ni2+, Mn2+, and Zn2+ reached 98.75, 59.25, 58.33, and 50.83%. The Langmuir isotherm was the best for describing the adsorption of Zn2+ (0.970) and Mn2+ (0.977). The Freundlich isotherm significantly giving a good fit to the adsorption of Pb2+ (0.998) while the adsorption of Ni2+ onto NRCC6 biomass can follow DKR (0.998). Furthermore, the current study revealed that Mucor sp. NRCC6 fungus is a new efficient and eco-friendly method that revealed a maximum removal of 100% for Pb2+ and Zn2+ as well as 97.39, 88.70, 78.95, 74.0, 70.22, 68.57, and 60.0% for Ni2+, Mn2+, Cd2+, Cu2+, Fe3+, As2+, and Cr6+ from the industrial wastewater, respectively.

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El-Gendy, M. M. A. A., Abdel-Moniem, S. M., Ammar, N. S., & El-Bondkly, A. M. A. (2023). Multimetal bioremediation from aqueous solution using dead biomass of Mucor sp. NRCC6 derived from detergent manufacturing effluent. Journal of Applied Genetics, 64(3), 569–590. https://doi.org/10.1007/s13353-023-00765-9

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