Abstract: Internet of Things (IoT) is transforming environmental monitoring by enabling real-time, remote, and automated data collection across diverse settings. It introduces an IoT-based air quality monitoring system designed for early detection and proactive management of air pollution. The system integrates low-cost gas sensors (MQ2, MQ7, MQ135) with a NodeMCU ESP8266 microcontroller to measure harmful gases such as CO, CO₂, and smoke. Sensor data is transmitted wirelessly to the ThingSpeak cloud for real-time visualization and analysis. Automated air purification is triggered when pollutant levels exceed safety thresholds, reducing the need for manual intervention. The system also incorporates machine learning models to predict pollution trends, enabling smarter, data-driven responses to environmental changes. Developed using Python, ThingSpeak, and TensorFlow, the solution achieves high accuracy in pollutant detection and fast response times. Designed for deployment in urban areas, hospitals, and schools, the project highlights the role of IoT in building smarter, healthier environments. Future enhancements include AI-based forecasting, multi-sensor expansion, and solar-powered deployment for improved scalability and sustainability. The proposed system addresses limitations of traditional air monitoring by offering a cost-effective, scalable alternative. Its modular design allows easy integration with smart city infrastructure. This project exemplifies how IoT can support environmental sustainability and public health initiatives
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DOI:
10.17148/IJARCCE.2025.14528
