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PLASTRACE: A Distributed IoT Architecture for Active Microplastic Detection and Intervention
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Researchers designed a system called PLASTRACE to detect tiny plastic particles in rivers and waterways in real time, using sensors, a small onboard computer and AI, instead of manual water sampling. The system aims to trigger capture devices when pollution levels are high and use computer models to forecast pollution patterns, with deployment planned over 12 months.
Microplastic pollution (< 5mm) has breached plan-etary boundaries, infiltrating global freshwater systems and posing severe bio-accumulation risks. While the Indo-Pacific region contributes nearly 80% of ocean plastic leakage via riverine transport, current monitoring methodologies remain fundamentally archaic—relying on manual sampling and ret-rospective laboratory analysis. This temporal latency creates a critical “blind spot” where pollution spikes go undetected and unmanaged. This study aims to develop and deploy PLAS-TRACE, a Cyber-Physical System functioning as an environmen-tal “nervous system,” capable of detecting and quantifying mi-croplastic flux in real-time across aquatic environments within 12 months. We integrate an affordable sensor stack with an micro-controller and Edge AI to activate targeted capture mechanisms when pollution exceeds predefined thresholds. A Data Analytics framework using Time-Series Forecasting (ARIMA and LSTM) predicts pollution plumes, enabling proactive river management instead of reactive cleanup.
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Towards an IOT Based System for Detection and Monitoring of Microplastics in Aquatic Environments
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This paper proposes using Internet of Things (IoT) sensors to build a real-time monitoring network for microplastics in aquatic environments. Automated, continuous monitoring systems could provide much better spatial and temporal coverage than current sampling-based approaches.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
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Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
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.
Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
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Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
Development of an Iot-Integrated AI System for Microplastic Detection in Water Samples
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Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
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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.