Microplastics are a global threat, with production projected to reach 1.1 billion tons by 2050. As the world confronts this mounting environmental crisis, there is a dire need in paradigm shift in monitoring and mitigation strategies.

Anwar Faizaan Reza, a young and emerging researcher from the Department of Computer Science and Engineering, has developed a strategic roadmap for smart water management and sustainable environmental protection. His publication in International Journal of Water Conservation Science and Engineering by Springer Nature, highlights innovative approaches to tackling microplastic pollution.

Collaborating with researchers from South Korea and Taiwan, Anwar Faizaan authored his research titled “IoT Driven Smart Innovation for Microplastic Monitoring and Detection: A Multidimensional Review”, which explores advanced IoT-based frameworks for real-time monitoring and detection of microplastics, offering scalable and technology-driven solutions for environmental sustainability.

The paper advocates for a shift from traditional, energy-intensive, and laboratory-based analytical methods to precision-driven IoT interventions for real-time water quality management, exploring how IoT and AI (such as YOLOv8 and CNNs) can provide real-time, low-cost monitoring of these contaminants.

His research notes that while Raman spectroscopy offers high accuracy for small particles, it has high power demands compared to more energy-efficient acoustic sensors. He emphasises that Machine learning (ML) algorithms, such as Convolutional Neural Networks (CNN) and Random Forests, are essential for processing sensor data to accurately recognise the size, shape, color, and chemical composition of microplastics.

Moreover, by combining IoT data with machine learning, researchers can analyse ocean currents and wind patterns to forecast microplastic movement and identify high-accumulation “hotspots”.

Anwar Faizaan successfully bridges the gap between environmental science (the biological impact of plastics) and computer engineering (IoT architecture and ML models) highlighting the multidisciplinary nature of his research.

Given the relevance of this work to Global Sustainable Development Goals (SDG 6) and the innovative use of IoT and Machine Learning, this research publication serves as a testament to the research excellence at SRM University-AP.

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