Review of Intelligent Detection, Identification and Warning Methods for Airborne Particulate Matter Based on Machine Perception and Learning

As air pollution problems continue to grow, accurate detection and timely warning of particulate matter (PM) are becoming increasingly important. Traditional methods rely on specialized equipment and are not suitable for real-time monitoring. In comparison, machine perception and learning-based methods have technological advantages such as real-time monitoring and high accuracy. Therefore, a detailed review of intelligent PM detection, identification and early warning methods based on machine perception and learning in recent years was presented. First, an introduction to the standards and sources of PM was presented. Then, various methods from the detection, identification, and warning fields were comprehensively summarized, highlighting the characteristics and performance differences among them. Methods based on machine learning and deep learning have made significant progress in each of these research areas. Finally, the paper concluded by summarizing the main content and suggesting the current challenge in the field and future research direction. Future research effort should continue to prioritize technological innovation and data quality to achieve improved air quality monitoring and management.