Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain

Abstract

Abstract Air pollutants seriously impact climate change and human health. In this study, the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation system was extended from ground data to vertical profile data, which reduced the simulation error of the model in the vertical layer. The coupled GSI-Lidar-WRF-Chem system was used to improve the accuracy of fine particulate matter (PM2.5) simulation during a wintertime heavy pollution event in the North China Plain in late November 2017. In this experiment, two vehicle-mounted Lidar instruments were utilized to make synchronous observations around the 6th Ring Road of Beijing, and five ground-based Lidars were used for long-term network observations on the North China Plain. Data assimilation was then performed using the PM2.5 vertical profile retrieved from the seven Lidars. Compared with the model results, the correlation of assimilation increased from 0.74–0.86, and the root-mean-square error decreased by 36.6%. Meanwhile, the transport flux and transport flux intensity of the PM2.5 were analyzed, which revealed that the PM2.5 around the 6th Ring Road of Beijing was mainly concentrated below 1.8 km, and there were obvious double layers of particles. Particulates in the southwest were mainly input, while those in the northeast were mainly output. Both the input and output heights were around 1 km, although the input intensity was higher than the output intensity. The GSI-Lidar-WRF-Chem system has great potential for air quality simulation and forecasting.