Abstract
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.