Research on Traceability of Atmospheric Particulate Pollutants Based on Particle Size Data

Particle pollution is one of the important sources of air pollution. With the maturity and portability of domestic particle size monitoring equipment, a large amount of air particle size monitoring data has been generated. How to use these data to analyze pollution sources and propose corresponding prevention measures is currently an urgent problem to be solved. Firstly, this study analyzed particle size data and determined the distribution characteristics of particle size in different pollution sources; Secondly, a traceability model based on random forest and factor analysis was constructed to achieve the problem of analyzing air pollution sources using particle size spectrum data; Finally, through experimental comparison, case analysis, and expert experience, the model was verified its effectiveness in the actual situation. This study is the first to use pollutant particle size monitoring data, which achieves high-resolution pollutant traceability compared to traditional chemical composition methods. This study introduces machine learning methods into traditional factor analysis method to improve processing efficiency and accuracy, providing a reference basis for air pollution control.