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<p>Microplastics transport toxins, disrupt microbial and nutrient cycles, bioaccumulate to cause oxidative stress and endocrine disruption, jeopardizing ecosystems and human health. Despite understanding microplastic origins, distribution, and microbial degradation biotechnological remediation efforts remain fragmented and largely at the proof-of-concept stage. Recent high-throughput meta-omics has uncovered diverse plastisphere associated enzymes, while metabolic engineering platforms have demonstrated programmable biofilm trap-and-release mechanism and enzymatic upcycling of PET monomers; however, the translation of these technologies to diverse polymer classes and field applications is limited. Machine learning is emerging as a powerful tool to uncover efficient microplastic degradation strategies, a domain previously underexplored. This review critically synthesizes these interdisciplinary advances spanning microbial and enzymatic remediation evolution, metabolic-engineering architectures for capture and valorization, and AI-driven monitoring to identify persistent bottlenecks and propose a unified roadmap for deploying sustainable, biotechnology-driven solutions that can be scaled to address the global microplastic crisis. By bridging these domains, we aim to inform future research priorities and accelerate the translation of laboratory findings into industrial scale mitigation strategies.</p>

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Advances in microplastic mitigation: current progress and future directions

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Vivek Kumar Gaur,
  • Yashika Raheja,
  • Prachi Gaur,
  • Nitish Kumar,
  • Poonam Sharma,
  • Ajay Kumar,
  • Janmejai Kumar Srivastava

摘要

Microplastics transport toxins, disrupt microbial and nutrient cycles, bioaccumulate to cause oxidative stress and endocrine disruption, jeopardizing ecosystems and human health. Despite understanding microplastic origins, distribution, and microbial degradation biotechnological remediation efforts remain fragmented and largely at the proof-of-concept stage. Recent high-throughput meta-omics has uncovered diverse plastisphere associated enzymes, while metabolic engineering platforms have demonstrated programmable biofilm trap-and-release mechanism and enzymatic upcycling of PET monomers; however, the translation of these technologies to diverse polymer classes and field applications is limited. Machine learning is emerging as a powerful tool to uncover efficient microplastic degradation strategies, a domain previously underexplored. This review critically synthesizes these interdisciplinary advances spanning microbial and enzymatic remediation evolution, metabolic-engineering architectures for capture and valorization, and AI-driven monitoring to identify persistent bottlenecks and propose a unified roadmap for deploying sustainable, biotechnology-driven solutions that can be scaled to address the global microplastic crisis. By bridging these domains, we aim to inform future research priorities and accelerate the translation of laboratory findings into industrial scale mitigation strategies.