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Microplastics in aquatic environments: Bridging occurrence and mitigation through machine learning detection and bioremediation strategies
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Tiny plastic particles called microplastics are showing up in shockingly high amounts in our water, sometimes millions of particles per liter, and they can end up affecting both aquatic life and, potentially, the food and water we consume. This review paper (summarizing existing research) highlights two promising solutions: certain fungi and algae that can remove over 90% of microplastics from water in lab settings, and AI tools that can quickly detect and track plastic pollution. The catch is that these cleanup methods work great in controlled settings but haven't yet been proven to work reliably in messy, real-world water systems.
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.
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A Machine-Learning Model for Investigating Microplastics Source–Receptor Relationships in Aquatic Environments
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Researchers developed a machine-learning model to trace where microplastics in aquatic environments come from and where they end up, potentially giving scientists and regulators a more powerful tool to identify pollution sources and prioritize cleanup efforts.
Recent Advances on Impact, Hazard, and Microbial Bioremediation of Microplastics in Marine Ecosystems: Challenges and Artificial Intelligence Way Forward
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This comprehensive review of marine microplastic research spanning 1966-2025 analyzed weathering mechanisms, ecological impacts, pollutant transport roles, and emerging AI-assisted detection and bioremediation strategies. Understanding how microplastics persist and move through marine food webs is essential for estimating cumulative human exposure via seafood consumption and guiding policy interventions.
A Review on Aquatic Impacts of Microplastics and Its Bioremediation Aspects
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Microplastics enter aquatic ecosystems through improper disposal, causing reduced feeding, lower fecundity, inflammation, and gene-level changes in aquatic organisms. Bacterial biodegradation shows promise as an eco-friendly remediation approach, as microbes can enzymatically break down plastic particles into carbon dioxide, methane, and water.
Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning
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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.