4.7 Review

Recent advances of algae-bacteria consortia in aquatic remediation

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TAYLOR & FRANCIS INC
DOI: 10.1080/10643389.2022.2052704

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Algae; algae-bacteria consortia (ABC); biogeochemical cycling; machine learning (ML); quorum sensing (QS); wastewater treatment; Amit Bhatnagar and Lena Ma

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Algae-bacteria consortia treatment is a promising method for aqueous remediation. This review aims to summarize recent literature on the mechanism of algal selection and enrichment, interactions between algae and bacteria, chemical signaling, aqueous remediation using algae-bacteria consortia, and advanced treatment techniques. The review also highlights current challenges and future perspectives in algae-bacteria consortia wastewater treatment, including establishing interaction networks, understanding chemical exchange, using machine learning to track interactions, and integrating advanced treatment techniques.
Algae-bacteria consortia treatment has been found to be a promising method for the remediation of aqueous systems. Given the scope of previous reviews on algae-bacteria interactions, the sections on chemical signaling between algae and bacteria don't cover the current knowledge gap, and recent advances of algae-bacteria consortia in aqueous remediation don't explore the full depth. Accordingly, the specific aim of this review was to thoroughly screen and summarize recent peer-reviewed literature on (1) the mechanism of algal selection and enrichment in wastewater treatment; (2) interactions between algae and bacteria in ecological niche environments; (3) chemical signaling between algae and bacteria; (4) aqueous remediation using the algae-bacteria consortia; and (5) advanced treatment techniques combined with algae-bacteria systems for improved aqueous remediation. The main current challenges and future perspectives in algae-bacteria consortia wastewater treatment are proposed, including: (i) comprehensively establishing the network of interactions between algae and bacteria, especially quorum sensing and phycospheric interactions; (ii) developing a detailed understanding of the chemical exchange between microbial species based on molecular diffusion processes; (iii) tracking complex algae-bacteria interactions in aquatic environments using machine learning (ML), providing a potential tool for the design of beneficial and customizable synthetic microbial communities for wastewater treatment; (iv) integrating advanced treatment techniques (e.g., MBRs, UV photolysis and biological activated carbon) with algae-bacteria consortia systems, increasing the sustainability and applicability of treatment processes. Therefore, this review provides guidance and insights on the future development of algae-bacteria consortia treatment systems and their potential application for aqueous remediation.

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