Authors - Prathilothamai M., R. Rinitha, Priyanshu Raj, Jishnu Hari, Lucky Goyal Abstract - The rapid growth of industrialization and urbanization has intensified the release of emerging air and water pollutants, posing significant threats to environmental sustainability and public health. This paper presents an Internet of Things (IoT) driven monitoring and forecasting framework designed for the early detection of emerging contaminants in air and water systems. The proposed system integrates distributed sensor nodes for real-time measurement of key environmental parameters, including particulate matter, volatile organic compounds (VOCs), heavy metals, pH, turbidity, and dissolved oxygen. Data collected from heterogeneous IoT sensors are transmitted through secure communication proto-cols to a cloud-based analytics platform. Advanced data processing and machine learning models are employed to identify pollution patterns, predict contamination trends, and generate early warning alerts. The framework emphasizes scalability, low power consumption, and cost-effectiveness to support deployment in urban, industrial, and remote environments. Experimental evaluation demonstrates improved detection accuracy and forecasting reliability compared to conventional monitoring approaches. The proposed solution enables proactive environmental management, supports regulatory compliance, and contributes to sustainable development by facilitating timely intervention and mitigation strategies for emerging air and water pollutants.
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