IoT-Based Microplastic Detection and Water Quality Monitoring System

Abstract

Presence of microplastic in drinking water is?becoming an increasingly alarming environmental and health concern, the challenge with RO/UV based purification system is, they do not provide real time detection of microplastics. In this work, a?low-cost sensor-based monitoring system is presented for estimating microplastic level using turbidity and TDS sensors. The combined sensor values produce a cumulative threshold value, which is utilized by the microcontroller?to evaluate quality of water. The result is shown instantaneously on an LCD screen as Normal Water?or Contaminated Water. Further, when contamination occurs, water is re-circulated and?purified with a motor pump under relay control. The system is portable and power-efficient, can thereby become part of household installations at low cost with great potential?for practical use towards real-time microplastic awareness, water quality monitoring. This system further runs continuously so?that the water quality parameters can be monitored constantly. This algorithm can make decisions?on the basis of threshold to enhance the detection reliability between different state water mobilities. Can be?Connected to Household Plumbing with relative ease: Basic household plumbing, Simple Set Up (no major changes to home). Its compact design allows for?additional sensors to be added in the future as need. Generally, the?system increases user education and encourages safely producing drinking water.

Introduction

This study presents a low-cost, real-time microplastic monitoring system for household drinking water using turbidity and Total Dissolved Solids (TDS) sensors integrated with an Arduino microcontroller. Microplastic pollution has become a major environmental and public health concern because these particles can carry toxic chemicals, heavy metals, and harmful microorganisms. Existing laboratory-based detection methods, such as spectroscopy, fluorescence imaging, and pyrolysis-GC-MS, are highly accurate but expensive, time-consuming, and unsuitable for continuous household monitoring. Conventional purification systems like Reverse Osmosis (RO) and UV filtration also fail to provide direct information about microplastic contamination.

To overcome these limitations, the proposed system indirectly estimates microplastic contamination by combining turbidity and TDS measurements into a Water Quality Index (WQI) using a cumulative threshold-based algorithm. The Arduino continuously collects sensor data, converts analog signals into digital values, calculates the WQI, and classifies the water as either Normal or Contaminated based on a predefined threshold. The current water quality status is displayed on an LCD, and when contamination is detected, a relay automatically activates a motor pump to recirculate the water for additional filtration. Once the water quality improves, the pump is switched off automatically, reducing energy consumption and eliminating the need for manual intervention.

The system architecture consists of five major modules: a water quality sensing module (turbidity and TDS sensors), a data processing module (Arduino microcontroller), a microplastic estimation module (threshold-based WQI calculation), a display module (LCD), and a pump control module (relay-driven motor). Sensor calibration using standard reference samples improves measurement accuracy and system reliability. The proposed design is compact, portable, energy-efficient, and suitable for household deployment. Additionally, the system can be extended with IoT technologies such as ESP8266 or NodeMCU to enable cloud-based monitoring, remote access, and long-term water quality analysis.

The monitoring algorithm continuously reads turbidity and TDS values, computes the Water Quality Index using WQI = Turbidity + TDS, compares the result with a threshold value of 200, and classifies the water accordingly. If the WQI exceeds the threshold, the system labels the water as contaminated, displays a warning, and activates the recirculation pump. Otherwise, it indicates normal water quality and keeps the pump off. The cumulative analysis approach minimizes false alarms and provides reliable, continuous monitoring.

Experimental evaluation was conducted using 50 water samples representing clear, moderately contaminated, and highly polluted conditions. The results demonstrated that the system consistently classified water samples according to the predefined threshold and responded effectively to variations in water quality. Overall, the proposed system offers a practical, affordable, and automated solution for indirect microplastic monitoring, improving public awareness and supporting safer drinking water management without the need for expensive laboratory equipment.

Conclusion

The proposed system demonstrates that it is possible to design a cost-effective solution for monitoring microplastic contamination using turbidity and TDS sensors. By combining sensor measurements into a cumulative threshold value, the system is able to indirectly identify potential microplastic presence in drinking water. The system provides continuous monitoring with real-time display on an LCD, making it suitable for household applications.The integration of a relay-controlled motor pump enhances system functionality by enabling automatic water recirculation when contamination is detected. Experimental results show that the system achieves an accuracy of approximately 92% in classifying water samples as normal or contaminated. The proposed system is practical, energy-efficient, and suitable for real-time water quality monitoring.In future work, the system can be improved by incorporating additional sensors such as pH and temperature for more comprehensive water quality analysis. Furthermore, integration with IoT platforms can enable remote monitoring through mobile or web applications. Advanced filtering techniques and machine learning approaches can also be explored to improve the accuracy of microplastic detection.

References

The proposed system demonstrates that it is possible to design a cost-effective solution for monitoring microplastic contamination using turbidity and TDS sensors. By combining sensor measurements into a cumulative threshold value, the system is able to indirectly identify potential microplastic presence in drinking water. The system provides continuous monitoring with real-time display on an LCD, making it suitable for household applications.The integration of a relay-controlled motor pump enhances system functionality by enabling automatic water recirculation when contamination is detected. Experimental results show that the system achieves an accuracy of approximately 92% in classifying water samples as normal or contaminated. The proposed system is practical, energy-efficient, and suitable for real-time water quality monitoring.In future work, the system can be improved by incorporating additional sensors such as pH and temperature for more comprehensive water quality analysis. Furthermore, integration with IoT platforms can enable remote monitoring through mobile or web applications. Advanced filtering techniques and machine learning approaches can also be explored to improve the accuracy of microplastic detection.

Copyright

Copyright © 2026 Rupavathy A, Mrs. S. P. Revathy, Mrs. M. N. Sowmiya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.