Microplastics in aquatic environments: Bridging occurrence and mitigation through machine learning detection and bioremediation strategies

Microplastics (MPs) are pervasive environmental contaminants that pose risks to aquatic ecosystems and human health. This review examines the sources, transport mechanisms, and ecological impacts of MPs in aquatic environments, and critically evaluates the effectiveness of current mitigation strategies including bioremediation innovations. Alarmingly high concentrations of MPs have been recorded, with estimates reaching the millions of MPs per liter in water bodies. Several studies reveal that certain microbial consortia, particularly those involving fungi and specific algae, show removal efficiencies exceeding 90%, though scalability and efficacy in natural settings are limited by environmental variability. Additionally, machine learning models have demonstrated high accuracy in detecting and classifying MPs, especially when leveraging neural networks. These technologies hold promises for real-time monitoring and management of MP pollution but require extensive datasets and robust training to achieve operational reliability. The review also highlights the potential of engineered bioremediation technologies to effectively address MP pollution.