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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62197

PLASTRACE: A Distributed IoT Architecture for Active Microplastic Detection and Intervention

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62197

Volume:

10 |

4 |

April 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62197

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Karan Yadav

Karan Yadav

Computer Science Engineering (Data Science)

D.Y. Patil College of Engineering & Technology, Kasaba Bawada

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SP

Sarthi Patil

Computer Science Engineering

D.Y. Patil College of Engineering & Technology, Kasaba Bawada

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PN

Pooja Nashte

Computer Science Engineering

D.Y. Patil College of Engineering & Technology, Kasaba Bawada

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KC

Kalaivani Chellappan

Computer Engineering

Universiti Kebangsaan Malaysia (UKM)

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Abstract

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.

KEYWORDS

Index Terms—Microplastics Internet of Things (IoT) Edge Computing Time-Series Analysis Computer Vision.

Cite This Article

Karan Yadav, Sarthi Patil, Pooja Nashte, Kalaivani Chellappan, "PLASTRACE: A Distributed IoT Architecture for Active Microplastic Detection and Intervention," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-4, April 2026. https://doi.org/10.55041/IJSREM62197

Full Article/Conclusion

The invisible nature of microplastic pollution has long been its greatest defense. By hiding in plain sight, it has evaded political will and technological intervention. PLASTRACE destroys this invisibility. By combining the optical precision of computer vision with the connectivity of IoT and the predictive power of Data Science, we propose a scalable “Sentinel Network” capable of monitoring the vast river systems of the Indo-Pacific. This system does not claim to clean the ocean overnight. Instead, it offers something more immediate and practical: the ability to see the enemy. It turns the passive river into an intelligent entity that can signal for help, locate the source of the injury, and, for the first time, defend itself.

References

[1]     F. Murphy, C. Ewins, F. Carbonnier, and B. Quinn, “Wastewater Treatment Works (WWTWs) as a Source of Microplastics in the Aquatic Environment,” Environ. Sci. Technol., vol. 50, no. 11, pp. 5800–5808, 2016. [2]     A. Ragusa et al., “Plasticenta: First evidence of microplastics in human placenta,” Environment International, vol. 146, p. 106274, 2021. [3]     H. A. Leslie et al., “Discovery and quantification of plastic particle pollution in human blood,” Environment International, vol. 163, p. 107199, 2022. [4]     J. R. Jambeck et al., “Plastic waste inputs from land into the ocean,” Science, vol. 347, no. 6223, pp. 768–771, 2015. [5]     A. A. Koelmans et al., “Microplastics in freshwaters and drinking water: Critical review and assessment of data quality,” Water Research, vol. 155, pp. 410–422, 2019. [6]     I. E. Napper et al., “The abundance and characteristics of microplastics in surface water in the transboundary Ganges River,” Environmental Pollution, vol. 274, p. 116348, 2021. [7]     P. K. Singh et al., “Microplastic pollution in the Ganga River: A state-of-the-art review of pathways, mechanisms, and mitigation,” Water Supply, vol. 25, no. 2, pp. 249–267, 2025. [8]     R. K. Singh, “Microplastic pollution in the Ganga River at Kanpur: Abundance, morphology, and polymer characterization,” Scholarly Re-search Journal for Interdisciplinary Studies, vol. 12, pp. 324–331, 2025. [9]     S. Gupta et al., “Microplastic pollution in the Ganga River: A state-of-the-art review of pathways, mechanisms, and mitigation,” Water Supply, vol. 25, no. 2, pp. 249–267, 2025. [10]    L. Lebreton et al., “River plastic emissions to the world’s oceans,” Nature Communications, vol. 8, no. 15611, 2017. [11]    Y. B. Ibrahim et al., “Microplastic abundance and distribution in surface water and sediment of the Terengganu River Estuary, Malaysia,” Marine Pollution Bulletin, vol. 168, p. 112444, 2021. [12]    T. V. Nguyen et al., “Microplastic pollution in the Mekong River: Spatiotemporal distributions and polymer types,” Science of The Total Environment, vol. 719, p. 137457, 2020. [13]    P. Ta and K. Babel, “Microplastics in the surface water of the Chao Phraya River, Thailand,” Environmental Pollution, vol. 261, p. 114135, 2020.

Editorial & Review Transparency

Publication Date

5/1/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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