Estimation of PM2.5 Vertical Profiles from MAX-DOAS Observations Based on Machine Learning Algorithms

The vertical profile of PM2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes: it also provides important data support for studying the causes and sources of PM2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction&mdash:as well as the vertical profiles of precursors such as NO2 and SO2&mdash:to evaluate the vertical distribution of PM2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM2.5 can diffuse to high altitudes between 500 and 1000 m: in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM2.5 mass concentration in Beijing, a high-altitude PM2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM2.5 every hour.