An Integrated IoT-Based Framework for Real-Time Urban Air Quality Monitoring and Predictive Analytics
DOI:
https://doi.org/10.64229/atgbs834Keywords:
IoT, Air Quality Monitoring, Urban Pollution, Smart Cities, Wireless Sensor Networks, Machine Learning, Environmental Monitoring, SDGAbstract
This paper presents a novel IoT-based framework for real-time urban air quality monitoring aimed at addressing the limitations of traditional reference-grade station networks. The proposed system deploys dense networks of low-cost multi-pollutant sensor nodes equipped with microcontrollers and wireless communication modules (Wi-Fi, LoRaWAN). Embedded edge computing enables local data preprocessing and calibration using machine learning methods to enhance sensor accuracy and reduce transmission overhead. Cloud analytics leverage time-series databases and advanced neural network models to provide real-time pollution mapping, forecasting, and anomaly detection. A user-friendly web dashboard and mobile applications offer personalized exposure tracking, health advisories, and public engagement functionalities. Extensive laboratory and field evaluations demonstrate strong correlations between sensor outputs and regulatory measurements for particulate matter (PM2.5, PM10) and gases (NO, CO), with data availability above 96% and latency near 2.3 seconds. Cost analysis reveals the system delivers 125 times greater spatial coverage at a fraction of the cost compared to traditional stations. The architecture’s scalability, security features, and calibrated sensor reliability establish it as a practical solution for smart city environmental monitoring. The integration of edge intelligence and cloud-based machine learning advances actionable urban air pollution management, promoting healthier communities through accessible, continuous, and high-resolution air quality data.
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Copyright (c) 2025 Sumit Kushwaha, Kovvuri P C Durga Reddy (Author)
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