A Comprehensive Analysis of Air Pollution in Dhaka City, Bangladesh, and the Application of Artificial Intelligence and Machine Learning for Enhanced Management and Forecasting

Abstract

This research meticulously examines the escalating air pollution crisis in Dhaka, Bangladesh, advocating for sophisticated monitoring and mitigation strategies. It comprehensively analyzes the spatiotemporal dynamics of key pollutants, including PM_2.5, PM₁₀, NO₂, SO₂, O₃, and CO, attributing their prevalence to diverse anthropogenic sources such as vehicular emissions, industrial activities (particularly brick production), construction aerosols, agricultural outgassing, and inefficient waste management. The paper elucidates the intricate seasonal fluctuations in pollutant concentrations and their profound health implications, ranging from acute cardiorespiratory morbidities to potential long-term neurological sequelae. A significant focus is placed on leveraging the transformative potential of cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) paradigms to transcend the limitations of conventional air quality control. The study evaluates the efficacy of advanced deep learning architectures, notably spatiotemporal Recurrent Neural Networks (RNNs) with attention mechanisms and Convolutional Neural Networks (CNNs), for achieving highly accurate air quality monitoring and predictive forecasting. Furthermore, it investigates state-of-the-art Explainable AI (XAI) frameworks, such as SHAP, to provide critical insights into pollutant source attribution, enhancing interpretability. The integration of real-time, high-fidelity data streams from remote sensing platforms and cost-effective sensor networks into AI-driven analytical pipelines is emphasized. The research candidly addresses the inherent scientific and technical challenges associated with deploying advanced AI/ML models, including the development of physically informed neural networks (PINNs) for superior interpretability, the effective management of data heterogeneity and biases, and robust uncertainty quantification through Bayesian techniques. Ultimately, this paper proposes a rigorous, data-driven, and scientifically grounded roadmap for developing next-generation, adaptive air quality management systems for Dhaka City. By seamlessly combining AI/ML capabilities with established environmental science principles, the objective is to forge innovative solutions that facilitate precise predictions, enable proactive responses, and significantly advance public health outcomes in the face of this pervasive environmental threat.