Scanning vertical distributions of typical aerosols along the Yangtze River using elastic lidar

Improved algorithm for retrieving aerosol optical properties based on multi-wavelength Raman lidar

Multi-wavelength Raman lidar has been widely used in profiling aerosol optical properties. The accuracy of measured aerosol optical properties largely depends on sophisticated lidar data retrieval algorithms. Commonly to retrieve aerosol optical properties of Raman lidar, the extinction-related Ångström exponent (EAE) is assumed (to be 1). This value usually generally differs from the true value (called EAE deviation) and adds uncertainty to the retrieved aerosol optical properties. Lidar-signal noise and EAE-deviation are two important error sources for retrieving aerosol optical properties. As the measurement accuracy of Raman lidar has been greatly improved in recent years, the influence of signal noise on retrieval results becomes relatively small, and the uncertainty of retrieved aerosol optical properties caused by an EAE-deviation becomes nonnegligible, especially in scenes that EAE deviation is large. In this study, an iteration retrieval algorithm is proposed to obtain more reliable EAE based on multi-wavelength Raman lidar. Results from this iteration are more precise values of aerosol optical properties. Three atmospheric scenarios where aerosol distribution and the values of EAE vary widely were simulated with a Monte Carlo method to analyze the characteristics and robustness of the iterative algorithm. The results show that the proposed iterative algorithm can eliminate the systematic errors of aerosol optical properties retrieved by traditional retrieval method. The EAEs after iteration does converge to the true value, and the accuracy of aerosol optical properties can be greatly improved, especially for the particle backscatter coefficient and lidar ratio, which has been improved by more than 10% in most cases, and even more than 30%. In addition, field observations data of a three-wavelength Raman lidar are analyzed to illustrate the necessity and reliability of the proposed iterative retrieval algorithm.

Comprehensive study of regional haze in the North China Plain with synergistic measurement from multiple mobile vehicle-based lidars and a lidar network

Recently, haze pollution has emerged as a regional characteristic that needs to be monitored and mitigated sensibly in China, particularly in the North China Plain (NCP). Clarifying the distribution and source characteristics of haze is necessary to better understand its formation mechanism on a regional scale. In this study, a comprehensive study of regional haze using synergistic measurement from multiple mobile vehicle-based lidars, a ground-based lidar network, and in suit instruments is presented. To investigate the distribution and source characteristics of regional haze in the NCP during the winter of 2017, simultaneous measurements of aerosol under different wind conditions are conducted. The regional distribution characteristics of the aerosol were observed using three sets of mobile vehicle-based lidars, and the source characteristics were achieved using an analysis of transport flux (with the ground-based lidar network and the WRF-Chem model). High aerosol extinction was observed on the southwest pathway under a southern wind. Backward trajectories also indicated that the air masses at 500 m were primarily from the southwest. The transport flux at the boundary of Beijing (BJ) and Baoding (BD) on the southwest pathway was calculated. Below 500 m, the transport flux from BD to BJ was positive under a southern wind and negative under a northern wind. In addition to the transport layer below 500 m, an upper transport layer was observed both on November 6, 2017 and January 15, 2018. The upper transport layer from 500 m to 1500 m on November 6, 2017 was obviously noticeable, which decreased dramatically with a maximum transport flux of 539.53 μg m² s. The significant transport layer at 1250 m with a maximum flux of 614.93 μg m² s was observed on January 15, 2018, while it had no impact on the ground because it had not yet fallen.

Comparison of mixing layer height inversion algorithms using lidar and a pollution case study in Baoding, China

Beijing-Tianjin-Hebei area is suffering from atmospheric pollution from a long time. The understanding of the air pollution mechanism is of great importance for officials to design strategies for the environmental governance. Mixing layer height (MLH) is a key factor influencing the diffusion of air pollutants. It plays an important role on the evolution of heavy pollution events. Light detection and ranging (lidar), is an effective remote-sensing tool, which can retrieve high spatial and temporal evolution process within mixing layer (ML), especially the variation of MLH. There are many methods to retrieve MLH, but each method has its own applicable limitations. The Mie-lidar data in Beijing was firstly used to compare three different algorithms which are widely used under different pollution levels. We find that the multi-layer structure near surface may cause errors in the detection of mixing layer. The MLH retrieved based on image edge detection was better than another two methods especially under heavy polluted episode. Then we applied this method to investigate the evolution of the mixing layer height during a pollution episode in December 2016. MLH at Gucheng county showed the positive correlation with the concentration of particulate matters during the start of this pollution episode. The elevated pollution level in Gucheng was not associated with MLH's decrease, and the significantly increased particulate matters raised the boundary layer, which trapped the pollutants near the surface.