Real-Time Air Quality Detection and Prediction System
DOI:
https://doi.org/10.67308/irjist.098Keywords:
Air Quality Monitoring, Air Quality Prediction, Machine Learning, MQ135, Arduino Uno, Data VisualizationAbstract
Air pollution is a growing environmental concern that affects human health and the environment. This paper presents a low-cost Real-Time Air Quality Detection and Prediction System using an Arduino Uno, MQ135 gas sensor, and DHT11 temperature sensor. The system collects and processes sensor readings to generate indicative PPM and AQI-style values. A multiple linear regression model is used to predict air-quality trends for the next ten time steps. A Flask-based web dashboard displays real-time readings, temperature, air-quality categories, graphs, and predicted values. Tests conducted in indoor, outdoor, and smoke-exposed environments demonstrated the system's response to changing environmental conditions. Approximately 70–75% of predicted readings were reported within the study's accuracy tolerance. The proposed system provides a low-cost approach to local air-quality awareness and short-term prediction.
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Copyright (c) 2026 Shivam Sonawane, Sumit Suryawanshi, Aishwarya Sonawane, Supesh Sonar, Prof. Bhausaheb P. Dane (Author)
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Articles published in the International Research Journal of Innovation in Science and Technology (IRJIST) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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