Artificial intelligence for ambient air quality control

Abstract

Air quality, integral to public health and environmental stability, necessitates innovative solutions for effective monitoring and control. Existing methodologies are often limited in their predictive accuracy, scalability, and cost-effectiveness. This paper explores the potential of Artificial Intelligence (AI) in transforming ambient air quality control. We conduct an in-depth review of current AI applications, examining various models’ strengths and weaknesses in predicting and controlling air quality. These include machine learning, deep learning, and other AI methodologies. Real-world case studies are analyzed to assess the practicality and effectiveness of AI applications. While AI presents promising capabilities, its implementation is not without challenges such as data requirements, interpretability, and scalability. We discuss these issues, propose possible solutions, and explore future prospects for AI in air quality control. The aim is to provide a comprehensive understanding of the role AI can play in environmental management and a pathway towards its enhanced application. This paper invites further research in harnessing AI’s potential to create sustainable and effective air quality control systems.