Abstract
Organic pollution of water bodies caused by human activities poses a significant environmental challenge. A promising and sustainable solution to this issue lies in harnessing microorganisms for the bioremediation of contaminated aquatic ecosystems. The present work explores potential of biofilm forming microorganisms as a sustainable solution for pollution control, thereby offering a cost-effective and ecofriendly method to mitigate the harmful effects of organic pollutants in water bodies. In this study, wastewater samples along with sludge, was collected from kitchen sink outlet, to foster biofilm development over a 10-day period. After treatment with pond water and measurement of Biological Oxygen Demand (BOD) reduction, the most efficient biofilm was selected based on BOD values. The selected biofilm exhibited 90 % reduction in BOD. Subsequent experiments revealed substantial BOD reducing capability of one specific bacterial strain (50 % reduction in BOD) which was isolated from the most efficient biofilm. Through biochemical and molecular characterization, the organism was identified as Enterobacter cloacae, specifically designated as E. cloacae honeykp. Qualitative and quantitative assays confirmed biofilm-forming capability of the new isolate. The application of E. cloacae HoneyKP in biofilm-based reactors, biofilters, activated sludge systems, membrane bioreactors etc. holds significant potential in terms if bioremediation of polluted water bodies. By utilizing its biofilm-forming capabilities, this isolate could potentially enhance the degradation of organic pollutants and other toxic compounds, while supporting microbial community resilience. These approaches offer innovative strategies for bioremediation, enabling the efficient treatment of wastewater, restoration of polluted water bodies, and reduction of the environmental impact of anthropogenic contaminants.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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