Using Lidar technology to assess regional air pollution and improve estimates of PM_2.5 transport in the North China Plain

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.

Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM<sub>2.5</sub> concentrations over the North China Plain

“…9). Here the PM 2.5 transport flux is defined as the product of PM 2.5 mass concentration (µg m −3 ), wind speed (m s −1 ), and wind direction projection in the current pathway (Xiang et al, 2020). The southwest, southeast, and east pathways in Fig.…”

“…On the other hand, although the distribution of pollutants can be simulated by air quality models (M. Zhang et al, 2008), large uncertainties remain, mainly from the influence of emission inventory, meteorological fields, and some hypothetical conditions (Xu et al, 2016;Zhang et al, 2017;Huang et al, 2016). Fortunately, the above observed data and the results of the model can be fused using data assimilation techniques, which can correct the model simulation results via the observed data (Wang et al, 2013;Ma et al, 2019).…”

Comment on acp-2020-1273

“…9). Here the PM2.5 transport flux is defined as the product of PM2.5 mass concentration (µg m -3 ), wind speed (m s -1 ), and wind direction projection in the current pathway (Xiang et al, 2020). The southwest, southeast, and east pathways in Fig.…”

Characteristics and Source Apportionment of the Vertical Distribution of Ozone at a Site of the Pearl River Delta Region of China

“…Sounding balloons are usually used for ground-to-tropospheric observations, but they are expensive and weather sensitive and cannot be launched in windy weather (Chen et al, 2020;Zhang et al, 2019b). In comparison, as an active remote sensing tool, lidar has the advantages of high precision and high spatial and temporal resolution in monitoring the spatial and temporal distribution characteristics of atmospheric pollutants, lidar can also be continuously monitored online (Xiang et al, 2020;Xiang et al, 2019). Klein et al (2017) verified the accuracy of lidar data by using Eiffel Tower data and four years of data are used to study the seasonal variability of the vertical structure of ozone in the urban boundary layer.…”