Deep Learning and Optimization Approaches in Enhancing Air Pollution Detection Accuracy and Quality Monitoring Using Pyramidal Convolution Split-Attention Networks and IoT: A Review

Rapid urbanization and industrialization have escalated global air pollution, presenting severe risks to public health and environmental stability. Traditional monitoring systems often fail to provide high spatial resolution or real-time predictive capabilities due to localized sensor limitations and delayed data processing. To address these challenges, this research review examines the integration of Internet of Things (IoT) sensor networks with advanced deep learning architectures and optimization algorithms. Specifically, the methodology highlights the application of Pyramidal Convolution Split-Attention Networks (PCSAN) alongside hybrid models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture complex spatiotemporal dependencies in air quality data. By synthesizing literature from 2020 to 2023, the study

evaluates how these deep learning frameworks process multi-modal environmental inputs—such as PM2.5, CO2, NO2, and SO2 concentrations—to enhance prediction accuracy. Furthermore, the role of metaheuristic optimization techniques in hyperparameter tuning and sensor node deployment is analyzed. This review provides structured guidance for developing robust, scalable, and highly accurate air quality forecasting systems, establishing a methodological foundation for future simulation and implementation in smart city frameworks.

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