International Journal For Multidisciplinary Research

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IoT-Based Air Quality Monitoring and Prediction System

Author(s) Kadagala Venkatesh, Konijeti Venkata Siva Jaswanth, Inavoli Preethi, Jujjavarapu Sri Sai Ganesh, Mandapati Lakshmi Thirupathamma
Country India
Abstract Air pollution is a major environmental issue affecting public health and urban sustainability. Traditional air quality monitoring systems are costly, have limited coverage, and lack predictive capabilities. This paper proposes an IoT-enabled Air Quality Monitoring and Predicting System consisting of MQ-series gas sensors, Raspberry Pi, Random Forest machine learning algorithm, and cloud computing for real-time monitoring and AQI prediction.The system uses MQ-2, MQ-4, MQ-7, MQ-9, and MQ-135 sensors in conjunction with a DHT-11 temperature and humidity sensor, connected to a Raspberry Pi via an MCP3208 ADC. The sensor data is processed using the Random Forest algorithm to provide predictions for AQI. The data is streamed over the ThingSpeak IoT cloud, graphed, and made accessible via a mobile application. Users receive real-time alerts when air quality is poor.Outcomes show that the system gives a sensor accuracy of 92-95 percent and AQI forecast accuracy of 85-90 percent. The cloud-to-mobile latency is approximately three seconds, which means almost instantaneous updates. Compared to conventional monitoring mediums, the system is cost-effective, scalable, efficient, and suitable for use in smart cities and industries.The study concludes that the convergence of IoT, machine learning, and cloud computing makes real-time air quality monitoring and forecasting possible. Some future enhancements include deep learning, edge AI, and increased sensor coverage.
Keywords Air Quality Monitoring, IoT, Machine Learning, Random Forest, Raspberry Pi, AQI Prediction, Cloud Computing.
Field Engineering
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-01
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40370
Short DOI https://doi.org/g9dg2b
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