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Integrating AI with Advanced Sensor Technologies for Real‐Time Monitoring
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Researchers explored how combining AI-driven machine learning with advanced sensor platforms enables real-time, large-scale microplastic monitoring across aquatic, soil, and urban environments, demonstrating that satellite-derived models paired with laboratory data can estimate microplastic concentrations accurately and identify pollution hotspots to support targeted mitigation policy.
This chapter addresses the increasing presence of microplastics in ecosystems worldwide, highlighting the pressing need for operative detection and mitigation strategies. It further explores the use of artificial intelligence (AI) in combination with precocious sensor technologies to monitor microplastics. AI-driven approaches, particularly machine learning (ML), are transforming sensor platforms into their analytical capacity and enabling the prediction of future microplastic pollution trends. These developments respond to the intrinsic challenges of monitoring and managing complex environmental systems. By integrating spatial and temporal data, researchers can create comprehensive toolkits for mapping, tracking, and analyzing microplastics across different environments such as aquatic systems, soils, and urban areas. AI integration enhances the identification of pollution hotspots and supports the design of targeted mitigation policies. Laboratory-based microplastic analyses, when combined with satellite-derived models, demonstrate the feasibility of large-scale, real-time monitoring with favorable accuracy in estimating microplastic concentrations. The synergy between AI and advanced sensor technologies bridges technological innovation with applicable applications, generating actionable insights to address microplastic pollution, strengthen environmental protection, and safeguard open health.
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Researchers reviewed how AI methods — including machine learning, deep learning, and IoT-integrated real-time monitoring — are transforming microplastic detection, classification, and pollution mapping, while identifying persistent challenges around data availability, computational cost, and the need for explainable models.
Microplastic Monitoring Using Artificial Intelligence
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Researchers outlined how deep learning and explainable AI are replacing slow, labor-intensive manual methods for microplastic identification, enabling real-time, large-scale detection while addressing key concerns about model transparency, regulatory implications, and the scalability of AI-driven environmental monitoring.
AI‐Driven Technologies in Mitigation of Microplastic Pollution
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Researchers reviewed how AI-assisted detection methods address the accuracy, efficiency, and cost limitations of traditional microplastic identification, while also surveying how artificial intelligence is being applied to model and mitigate microplastic impacts on marine ecosystems and human health including metabolic and respiratory effects.
Real-time detection of microplastics in aquatic environments using emerging technologies
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Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
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This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.