Abstract:

During a large-scale heavy pollution weather process in Lianyungang area from December 11 to 13, 2020, this paper used the aerosol sampling device and laser particle counter carried by the Unmanned Aerial Vehicle (UAV) to vertically detect the near-ground atmospheric particulate matter, combined with wind profile radar, ground-based microwave radiometer data, ground meteorological observation data, and reanalysis data, and analyzed the atmospheric particulate matter concentration profile and the vertical distribution of meteorological elements. The source and diffusion of pollutants were analyzed through the backward trajectory tracking mode, and the causes of the polluted weather were explored. The results show that the high-altitude wind field observed by the wind profile radar and the temperature and humidity characteristics measured by microwave radiometer have a good correspondence with the vertical characteristics of particulate matter. From the vertical distribution of pollutants, during the serious pollution period, the pollutants are mainly concentrated at the height below 200 m, the pollution layer is shallow, showing an obvious bimodal structure. In the pollution diffusion stage, the pollutants spread upward, the concentration of pollutants on the ground decreases significantly, and the pollution layer is relatively deep with the weakening and rising of the inversion layer. The exogenous transport of pollutants and the hygroscopic growth of pollutants under favorable meteorological conditions in Lianyungang area are the main reasons for this polluted weather process. The main sources of upstream pollutant transport are Xuzhou and the central region of Shandong Peninsula, and after leaving Lianyungang, pollutants mainly spread along southeast to east to sea. The change of wind direction probably a key meteorological condition for predicting the beginning of pollutant dissipation, while wind speed is the key to the pollutant dissipation speed.

Key words: Fog and haze, Particulate matter, Vertical detection, Unmanned Aerial Vehicle(UAV), Mass concentration