Determination and climatology of the diurnal cycle of the atmospheric mixing layer height over Beijing 2013–2018: lidar measurements and implications for air pollution

摘要

The atmospheric mixing layer height (MLH) determines the space in which pollutants diffuse and is thus conducive to the estimation of the pollutant concentration near the surface. The study evaluates the capability of lidar to describe the evolution of the atmospheric mixing layer and then presents a long-term observed climatology of the MLH diurnal cycle. Detection of the mixing layer heights (MLHL and MLHL′) using the wavelet method based on lidar observations was conducted from January?2013 to December?2018 in the Beijing urban area. The two dataset results are compared with radiosonde as case studies and statistical forms. MLHL shows good performance in calculating the convective layer height in the daytime and the residual layer height at night. While MLHL′ has the potential to describe the stable layer height at night, the performance is limited due to the high range gate of lidar. A nearly 6-year climatology for the diurnal cycle of the MLH is calculated for convective and stable conditions using the dataset of MLHL from lidar. The daily maximum MLHL characteristics of seasonal change in Beijing indicate that it is low in winter (1.404±0.751 km) and autumn (1.445±0.837 km) and high in spring (1.647±0.754 km) and summer (1.526±0.581 km). A significant phenomenon is found from 2014 to 2018: the magnitude of the diurnal cycle of MLHL increases year by year, with peak values of 1.291±0.646 km, 1.435±0.755 km, 1.577±0.739 km, 1.597±0.701 km and 1.629±0.751 km, respectively. It may partly benefit from the improvement of air quality. As to converting the column optical depth to surface pollution, the calculated PM2.5 using MLHL data from lidar shows better accuracy than that from radiosonde compared with observational PM2.5. Additionally, the accuracy of calculated PM2.5 using MLHL shows a diurnal cycle in the daytime, with the peak at 14:00 LST. The study provides a significant dataset of MLHL based on measurements and could be an effective reference for atmospheric models of surface air pollution calculation and analysis.