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An Internet-of-Things (IoT) Sustainable Water Filtering and Monitoring System using Big Data Analysis and Clean Energy
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Researchers developed MyRiiver, a solar-powered IoT-based water filtration and monitoring system designed to remove microplastics from freshwater ecosystems, integrating big data analytics for real-time environmental monitoring. The system addresses limitations of current microplastic filtration methods and demonstrates the potential of smart technologies for freshwater pollution management.
Microplastic contamination in freshwater ecosystems is a growing environmental concern. This paper introduces MyRiiver, a solar-powered microplastic filtration system, designed to overcome limitations in current methods. The background underscores the urgency of addressing microplastic pollution, emphasizing the need for an efficient, adaptable, and economical solution. MyRiiver employs a sophisticated multi-layered filtration system without requiring pre-treatment, offering advantages over existing methodologies. Challenges identified in previous approaches, such as electrode wear and biofilter maintenance, are addressed through the simplicity of MyRiiver's design. Experimental trials showcase its adaptability and superior efficiency in filtering microplastics as small as 1 µm. Results demonstrate a significant removal rate, positioning MyRiiver as a practical, scalable, and eco-friendly solution. The study concludes by asserting MyRiiver's potential as a transformative tool for combating the escalating global issue of microplastic contamination in freshwater environments.
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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.
IoT-Based Microplastic Detection and Water Quality Monitoring System
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Researchers built a low-cost IoT device combining turbidity and TDS sensors to estimate microplastic contamination in real time, using a threshold algorithm to trigger automatic water recirculation and purification — providing a household-scale monitoring approach where conventional laboratory analysis is impractical.
Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health
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Researchers developed a low-cost, IoT-enabled system called TEMPT for real-time microplastic detection in water using turbidity sensors. The accompanying algorithm achieved 91.47 percent accuracy in identifying microplastic contamination, outperforming conventional methods. The study demonstrates how affordable sensor technology could enable large-scale monitoring of microplastic pollution in diverse water bodies.
Microplastics Detection in Soil and Water: Leveraging IoT Technologies for Environmental Sustainability
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Researchers explored the integration of IoT sensor technologies for detecting and monitoring microplastics in soil and water environments, proposing a connected sensing framework for real-time environmental surveillance. The system enables automated data collection and remote monitoring of microplastic contamination.
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.
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.