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REVIEW article
Front. Microbiol.
Sec. Aquatic Microbiology
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1504254
AI-DRIVEN OPTIMIZATION OF BIOREMEDIATION STRATEGIES FOR RIVER POLLUTION: A COMPREHENSIVE REVIEW AND FUTURE DIRECTIONS
Provisionally accepted
Blessing Allen-Adebayo1*
Kehinde Olateru2- 1Igbinedion University,Okada, Okada, Nigeria
- 2Zero Complex AI, Nigeria, Lagos, Nigeria
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This narrative review explores the transformative potential of artificial intelligence (AI) in optimizing bioremediation systems for river pollution control while addressing the challenges and limitations associated with its implementation. The review begins by examining traditional and emerging bioremediation methods, highlighting their limitations and the pressing need for innovative solutions. It then delves into the application of AI technologies in pollution monitoring and bioremediation optimization, providing examples and success stories from existing studies.The challenges of AI-driven bioremediation, including ethical concerns, technological constraints, and the need for responsible deployment, are critically analyzed. Emphasis is placed on fostering interdisciplinary collaboration to overcome these barriers. The review also presents future directions and actionable recommendations, including integrating AI with traditional approaches, addressing technological and policy gaps, and ensuring sustainable management of river ecosystems.Ultimately, this review stresses the revolutionary potential of AI in enhancing bioremediation systems and advocates for urgent action to address the challenges involved, paving the way for sustainable and effective river pollution control strategies.
Keywords: AI-driven optimisation, bioremediation, River pollution, artificial intelligence, machine learning
Received: 30 Sep 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Allen-Adebayo and Olateru. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Blessing Allen-Adebayo, Igbinedion University,Okada, Okada, Nigeria
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.