Real-Time Air Pollution Monitoring and AQI Prediction System: Environmental Intelligence with IOT-Based Approach and Machine Learning

Air pollution is one of the most significant health concerns on earth, and the World Health Organization believes that 7 million premature deaths happen annually due to air quality. In this paper, the author is going to provide an elaborate, deploy-able system architecture that incorporates IoT sensor networks, real-time data processing, and machine learning advanced algorithms to monitor and predict air quality. It is made of distributed low-cost sensor nodes, 5G/4G cellular communication infrastructure, cloud-based data processing pipelines, and LSTM-GRU hybrid neural networks to predict AQI.

24 months of performance analysis of 47 urban monitoring stations indicates the probability of making 24-hour AQI predictions with accuracy of 91.3 percent with RMSE of 12.8µg/m³ for PM2.5 concentration. Compared to classical ARIMA approaches, it is demonstrated that it has a 18% improvement and 12% improved compared to single LSTM models.  Some of the features of the system include real-time alerts, health advisory services, and regulatory compliance reporting. Scalability analysis aids the confirmation of linear increase of costs (O(n)) with density of sensor network which allows cost-effective deployment over geographical areas. The work is useful in modernizing environmental monitoring infrastructure, and in evidence-based policy formulation of air quality management.

Air Quality Index, IoT Sensors, time series prediction, real-time monitoring, machine learning, environmental monitoring, sensor, networks, time-series forecasting.

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