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The Role of Artificial Intelligence in Marine Pollution: A Review
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This review pulls together current research on how artificial intelligence is being used to track ocean pollution, including oil spills, trash, and microplastics — tiny plastic particles that end up in seafood and drinking water. Since these pollutants can make their way into the food we eat, faster and more accurate AI-powered detection could help catch problems earlier and inform cleanup decisions, though the technology is still being tested and isn't yet solving the problem on its own.
Marine pollution represents a significant threat to the sustainable use of ocean resources and is increasing globally. Consequently, multidimensional and sustainable ocean governance frameworks have placed growing emphasis on the prevention, reduction, and elimination of marine pollution. In recent years, artificial intelligence (AI) has been increasingly adopted as an effective tool for monitoring, assessing, and managing marine pollution. This review aimed to highlight the role of artificial intelligence in addressing marine pollution. The efforts of scientists in employing and leveraging the advantages of AI to tackle the issue of marine pollution were examined. It was found that AI, supported by machine learning, has been applied to predict, locate, manage, and control marine environmental pollution. Recent research has clearly demonstrated the significant role of AI in advancing marine pollution studies, enhancing analytical accuracy, and fostering sustainable coastal management. These applications can be summarized in several fields, including water quality assessment; monitoring and detecting pollutants such as oil, marine litter and microbial contaminants; predictive modeling; study of emerging pollutants like microplastics (MP); and decision support to assist policymakers with risk evaluations and interventions based on real-time detection or projected scenarios. In conclusion, managing marine pollution with AI faces complexities due to natural factors influencing marine ecosystems. While AI is still in the experimental phase in controlling marine pollution, it holds great promise for addressing this issue despite challenges such as limited expertise and data-related constraints. Advances in algorithms, data collection, and computing power are expected to enhance monitoring and mitigation efforts.
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Implementing AI in Ocean Waste Tracking and Management
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This review paper looks at how artificial intelligence, like satellite image analysis and smart tracking systems, can help spot and clean up ocean pollution, including plastic waste and oil spills, faster and more accurately than older methods. This matters because ocean plastic breaks down into microplastics that end up in seafood and drinking water, so better cleanup technology could mean less of that pollution making its way into our bodies. That said, this is a summary of existing studies rather than new experiments, and the authors note that AI tools still need more data, cross-team collaboration, and attention to their own environmental costs before they can fully deliver on this promise.
Implementing AI in Ocean Waste Tracking and Management
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This review paper looks at how artificial intelligence, like satellite image analysis, can help spot and clean up ocean pollution such as plastic waste and oil spills more efficiently than traditional methods. This matters because ocean plastic breaks down into microplastics that enter the seafood we eat and even our drinking water, so better detection and cleanup could mean less of this pollution ending up in our bodies. The authors note that AI isn't a perfect fix yet, it requires huge amounts of data and has its own environmental costs, like energy use, so more research and teamwork across science, government, and industry are still needed.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
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This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
Artificial Intelligence in Ocean Clean-Up: Deep Learning Approaches to Marine Litter Detection
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This review paper explores how AI and deep learning can help spot and track ocean litter, including plastic waste, using drone and underwater camera footage. Cleaner oceans matter for us too: plastic debris breaks down into microplastics that enter seafood and drinking water, so better detection tools could support faster cleanup and less human exposure down the line.
The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review
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This systematic literature review analyzed AI applications for marine pollution management from Web of Science and Scopus databases, finding that 57% target monitoring, 24% management, and 19% prediction. The review identifies oil spill detection, water quality monitoring, and plastic identification as the three major application areas where AI offers the greatest capabilities.
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