A Review on: TinyML for Air Quality Monitoring: Bridging the Gap Between Low-Cost Sensors and Predictive Modeling

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Authors : Priya Ramesh Gupta , Dr. Ishwar Jadhav, Maheshkumar N. Patil, Dr. Vijay D. Chaudhari

DOI :  10.46335/IJIES.2025.10.7.6

Abstract – Air quality monitoring and purification have emerged as critical areas of research due to the escalating levels of air pollution and its detrimental effects on human health and the environment. This review paper synthesizes findings from 20 studies on smart air purifiers, air quality monitoring systems, IoT- based solutions, and machine learning applications, with a particular focus on the integration of TinyML models and linear regression techniques for predictive analysis. The reviewed papers span a range of technologies, including IoT, low-cost sensors, machine learning, and e-commerce integrated systems, highlighting advancements in real-time data collection, predictive modeling, and scalable solutions. TinyML, with its ability to run machine learning models on low- power devices, has shown significant potential in reducing costs and enabling real-time air quality monitoring. However, challenges such as data quality, scalability, and integration with larger IoT networks remain. Linear regression techniques, when combined with TinyML, offer a promising approach for accurate air quality prediction, particularly in resource- constrained environments. This paper identifies gaps in current research, such as the need for improved sensor calibration, real-time processing, and user-friendly interfaces, and proposes future directions, including the integration of TinyML with linear regression for enhanced predictive capabilities. By addressing these challenges, the integration of TinyML and linear regression can pave the way for more efficient, scalable, and accessible air quality monitoring and purification systems, ultimately contributing to better air quality management and public health outcomes.