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Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health
Summary
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 are a growing environmental threat due to their pervasive presence in aquatic ecosystems and their risks to both ecology and public health. Conventional monitoring methods, such as microscope-based analysis, are costly, labor-intensive, and impractical for large-scale deployment. To overcome these limitations, the study has proposed a cost-effective, IoT-enabled system for real-time detection and an algorithm to extract turbidity-based features for detection. The study introducess the Turbidity Enhanced Microplastic Tracker (TEMPT)-a cost-effective, IoT-enabled system for real-time detection. TEMPT integrates a turbidity sensor with a microcontroller, enabling scalable monitoring with ultra-low power consumption for long-term use in diverse water bodies. Complementing the hardware, the Turbo-Enhanced Tracking Microplastic for Water Sanity (TETM-Water) algorithm extracts turbidity-based features to ensure robust detection even under noisy conditions. Unlike standard techniques that typically yield below 85 % accuracy and high error rates, TETM-Water achieves 91.47 % accuracy with a 5.40 % error rate, demonstrating superior reliability. Key Highlights of the study are - IoT-enabled turbidity sensing and real-time data processing, Low-power hardware optimized for long-term field deployment, TETM-Water algorithm for accurate and noise-resilient detection.TEMPT provides actionable insights for policymakers and supports UN SDGs 3 and 6, advancing cleaner water and better health worldwide.