ArticleMethodsX2025
Tracking microplastic pathways: Real-time IoT monitoring for water quality and public health.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.