We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Emerging advanced technologies for monitoring and managing plastic pollution in aquatic ecosystems
AI summary Read the abstract
Plastic pollution in our oceans and rivers—including tiny microplastic particles—is a growing threat to wildlife and, ultimately, human health, since these pollutants can work their way into the food chain. This review paper looks at how new technology like drones, satellite imaging, and AI can help scientists track plastic waste more efficiently and predict where it's spreading, making cleanup efforts smarter and faster. While these tools show real promise, the researchers note that even better AI systems are still needed to fully understand how plastic moves through our environment.
Plastic debris in marine and freshwater ecosystems has become a growing global concern, negatively impacting aquatic life and human health. The worldwide challenge of persistent macro-litter, which originates from various origins and is conveyed by rivers to the oceans, has significant biological, chemical, and ecological impacts on oceanic environments. Plastic contamination is a critical ecological issue that can cause a variety of adverse effects on ecosystems. Its presence affects organisms in both water and soil, significantly threatening the stability of these natural systems. This work aims to advance unmanned aerial vehicles (UAVs), remote sensing (RS), and machine learning (ML) methods for litter tracking and cleanup support, promoting ecological protection and waste reduction. AI-powered tools, such as camera- and sensor-equipped UAVs (drones), enable real-time monitoring of microplastics (MPs) contamination across ecosystems. AI approaches (e.g., ML) have greatly improved the performance and accuracy of observing MP sources and forecasting contamination levels. These solutions reduce the requirement for human engagement and simplify the decision-making procedure. Significantly, more advanced algorithms (e.g., YOLOv12) are needed to assess the migration trends of plastics into the ecosystem. This advancement indicates promoting AI's ability to control current pollution and mitigate additional environmental degradation. The protocol facilitates large-scale comparative studies, promoting integrated coastal management efforts at both international and national levels. • Marine plastic contamination is a critical environmental issue that harms aquatic ecosystems. • Monitoring plastic presence is necessary to develop strategies for controlling contamination. • Powered tools like UAVs, RS, and ML efficiently track plastic sources and predict pollution. • Integrating advanced monitoring technologies, such as UAVs, RS, and ML, is crucial. • Future developments should focus on advanced technologies to enhance remote and autonomous monitoring with high accuracy.
More Papers Like This
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
AI summary Read the abstract
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Advancing microplastic pollution management in aquatic environments through artificial intelligence
AI summary Read the abstract
This review examines how artificial intelligence and robotics are being applied to tackle microplastic pollution in aquatic environments, covering waste collection, particle identification, and degradation monitoring. Researchers highlight several successful AI-driven projects deployed by countries and organizations around the world. The study suggests that integrating AI with traditional environmental methods holds significant promise for improving both the speed and accuracy of microplastic management.
AI-Powered Solutions for Combating Plastic Pollution
AI summary Read the abstract
This review paper looks at how AI and robotics could help tackle the growing plastic pollution crisis — from smart sorting systems that improve recycling to drones and underwater robots that track plastic waste in oceans and rivers before it breaks down into microplastics. Since plastic pollution and the microplastics it creates are increasingly found in our food, water, and even our bodies, these tech tools could help cut off pollution at the source, though the authors note real-world challenges remain before AI solutions can be widely used.
Artificial intelligence for microplastic pollution monitoring, predictive modeling, and risk assessment: advances, challenges, and future perspectives
AI summary Read the abstract
This review paper explains how artificial intelligence is helping scientists detect, track, and analyze microplastics faster and more accurately than older lab methods allow. This matters because better detection tools could speed up efforts to understand how these tiny plastic particles spread through our environment, food, and bodies, ultimately supporting stronger health protections and cleanup strategies.
Advancing microplastic analysis in the era of artificial intelligence: From current applications to the promise of generative AI
AI summary Read the abstract
This review summarizes existing research on how artificial intelligence can assist microplastic research, including collection using drones, identification using computer vision, and predicting pollution levels using machine learning. It also discusses current gaps, such as limited data and model transparency issues, noting these need to be addressed for AI to improve microplastic pollution research.
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.