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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62197
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.

Computer Science Engineering (Data Science)
D.Y. Patil College of Engineering & Technology, Kasaba Bawada
Computer Science Engineering
D.Y. Patil College of Engineering & Technology, Kasaba Bawada
Computer Science Engineering
D.Y. Patil College of Engineering & Technology, Kasaba Bawada
Computer Engineering
Universiti Kebangsaan Malaysia (UKM)
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
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.
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Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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