Measurement report: Dust and anthropogenic aerosols' vertical distributions over northern China dense aerosols gathered at the top of the mixing layer

Abstract

Abstract. Over the past decades, northern China has been suffering from persistent air pollution caused by both fine and coarse atmospheric particles. Although there are plenty of theoretical and observational studies on aerosols in northern China, most of them only consider total aerosol concentrations and focus on heavy pollution episodes; the long-term vertical distributions of dust (coarse) and anthropogenic aerosols (fine) and their relationships with the mixing layer height (MLH) have not been revealed. In this study, the dust and anthropogenic aerosols' mass concentration and the MLH were retrieved by polarization Raman lidar over Beijing from May 2019 to February 2022. We found that large amounts of anthropogenic aerosols accumulate at the top of the mixing layer, which is most noticeable in summer, with monthly mean mass concentration up to 57 µg m−3. It is mainly influenced by the southward transport in the upper air, where the atmosphere is relatively stable and moist, favoring hygroscopic growth of particles. Dust mass concentration is discontinuous in the vertical direction, not only on the ground but also in lofted layers that reach up to several kilometers. The heights of these lofted dust layers exhibited apparent seasonal dependence, with the height of the main dust layer gradually ascending from 1.1 km to about 2.5 km from April to June and below 3 km from October to December. In addition, there is a significant negative correlation between bottom anthropogenic aerosols' mass concentration and the MLH, and an inverse function fit is more suitable to characterize this relationship, while the relationship between bottom dust mass concentration and the MLH is insignificant. These results will enhance our understanding of the sophisticated interactions between dust and anthropogenic aerosols, the MLH, and regional transport in northern China. It will also help to refine atmospheric chemistry models and improve surface prediction capabilities.