Smart Ventilation Systems For Real-Time Pollution Control: A Review Of Ai-Driven Technologies In Air Quality Management

Abstract

This review paper examines the implementation and effectiveness of AI-powered smart ventilation systems for pollution control, focusing on industrial and urban environments. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic literature search was conducted across databases such as IEEE Xplore, ScienceDirect, and PubMed. Studies were screened and selected based on relevance to AI-driven ventilation systems, real-time air quality monitoring, and pollution management, resulting in a final set of 40 articles for in-depth analysis. The review synthesizes information on various technologies, including machine learning algorithms, Internet of Things (IoT) sensors, and pollutant scrubbers, that are integrated into smart ventilation systems to detect and respond to fluctuations in air quality autonomously. Key findings indicate that AI-powered systems can dynamically adjust airflow, filtration, and pollutant removal processes, leading to significant improvements in air quality in both industrial facilities and densely populated urban areas. This study highlights both the potential benefits and challenges associated with implementing AI-driven ventilation systems, including the need for high-quality data, real-time processing capabilities, and cost-efficiency. The review concludes with recommendations for future research directions, such as enhancing system interoperability and addressing ethical considerations in air quality monitoring and data privacy.