China has made great efforts to monitor and control air pollution in the past decade. Comprehensive characterization and understanding of pollutants in three-dimensions are, however, still lacking. Here, we used data from an observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations combined with a data assimilation technique to conduct a comprehensive analysis of extreme heavy aerosol pollution (HAP) over the North China Plain (NCP) from November–December 2017. During the studied period, the maximum hourly mass concentration of surface PM_2.5 reached ∼390 µg m⁻³. After assimilation, the correlation between model results and the independent observation sub-dataset was ∼50 % higher than that without the assimilation, and the root mean square error was reduced by ∼40 %. From pollution development to dissipation, we divided the HAP in the NCP (especially in Beijing) into four phases: an early phase (EP), a transport phase (TP), an accumulation phase (AP), and a removal phase (RP). We then analyzed the evolutionary characteristics of PM_2.5 concentration during different phases on the surface and in 3-D space. We found that the particles were mainly transported from south to north at a height of 1–2 km (during EP and RP) and near the surface (during TP and AP). The amounts of PM_2.5 advected into Beijing with the maximum transport flux intensity (TFI) were through the pathways in the relative order of the southwest > southeast > east pathways. The dissipation of PM_2.5 in the RP stage (with negative TFI) was mainly from north to south with an average transport height of ∼1 km above the surface. Our results quantified the multi-dimensional distribution and evolution of PM_2.5 concentration over the NCP, which may help policymakers develop efficient air pollution control strategies.

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 (PM_2.5) 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 PM_2.5 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 PM_2.5 were analyzed, which revealed that the PM_2.5 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.

A homemade portable no-blind zone laser detection and ranging (lidar) system was designed to map the three-dimensional (3D) distribution of aerosols based on a dual-field-of-view (FOV) receiver system. This innovative lidar prototype has a space resolution of 7.5 m and a time resolution of 30 s. A blind zone of zero meters, and a transition zone of approximately 60 m were realized with careful optical alignments, and were rather meaningful to the lower atmosphere observation. With a scanning platform, the lidar system was used to locate the industrial pollution sources at ground level. The primary parameters of the transmitter, receivers, and detectors are described in this paper. Acquiring a whole return signal of this lidar system represents the key step to the retrieval of aerosol distribution with applying a linear joining method to the two FOV signals. The vertical profiles of aerosols were retrieved by the traditional Fernald method and verified by real-time observations. To effectively and reliably retrieve the horizontal distributions of aerosols, a composition of the Fernald method and the slope method were applied. In this way, a priori assumptions of even atmospheric conditions and the already-known reference point in the lidar equation were avoided. No-blind-zone vertical in-situ observation of aerosol illustrated a detailed evolution from almost 0 m to higher altitudes. No-blind-zone detection provided tiny structures of pollution distribution in lower atmosphere, which is closely related to human health. Horizontal field scanning experiments were also conducted in the Shandong Province. The results showed a high accuracy of aerosol mass movement by this lidar system. An effective quantitative way to locate pollution sources distribution was paved with the portable lidar system after validation by the mass concentration of suspended particulate matter from a ground air quality station.