Using Artificial Intelligence to Predict Air Quality for Smart Cities

Air quality prediction in urban environments is a critical challenge with profound implications for public health, urban planning, and environmental sustainability. This study introduces a predictive air quality monitoring system designed for smart cities, leveraging Spatio-Temporal Graph Neural Networks (ST-GNNs) to address the complexities of spatial and temporal dynamics in pollutant data. By integrating temporal variations in pollutant concentrations with spatial relationships among monitoring stations, the proposed ST-GNN model achieves a remarkable prediction accuracy of 96%. The system utilizes large-scale data from diverse sources, including meteorological services, traffic density reports, and industrial emissions, to provide actionable insights. These insights enable proactive measures for environmental management and public health interventions. Compared to conventional models such as ARIMA and CNNs, the ST-GNN model demonstrates significant improvements in accuracy, robustness, and scalability, making it highly adaptable to varied urban conditions. Comprehensive testing across different smart city scenarios validated the system’s effectiveness, highlighting its potential for real-time monitoring, health risk forecasting, and policy formulation. The proposed system lays a robust foundation for advancing predictive analytics in smart cities, ensuring better environmental quality and enhanced urban resilience.