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Highlights • A fluorescence-Mie lidar for observing fluorescent aerosols was developed in Beijing. • The spatio-temporal variability of fluorescent aerosols in the lower atmosphere was investigated. • The LIF results was validated by backward trajectories and local pollutants emission data. • The mean ratio of LIF-to-Mie is good correlated with PM2.5 concentration. Abstract An integrated fluorescence-Mie lidar was built to observe fluorescent aerosol at the altitude below 1.5 km. It was constructed with the third harmonic of Nd:YAG laser (355 nm), a Newtonian telescope and two single-channel photomultiplier tubes. Based on the Mie scattering theory and laser-induced fluorescence (LIF) theory, the vertical distribution of fluorescent aerosol was retrieved by the LIF-to-Mie signal ratio. As a common factor, the geometric form factor of the system was cancelled out when dividing LIF signal by Mie signal, therefore the high-precision of the LIF-to-Mie signal ratio was obtained in the lower atmosphere. After continuous observations under different air quality conditions, the time-height indications of fluorescent aerosol were obtained. The fluorescence efficiency was preliminary estimated. By combining the backward trajectories analysis and the local emission data of SO 2 and NO 2 , the time-height indications were analyzed for investigating the sources of fluorescent aerosol. The correlation between PM2.5 concentration and the mean value of LIF-to-Mie signal ratio was also studied. The good agreement between the results indicate that the above mentioned method can be employed to analyze the spatio-temporal distribution of fluorescent aerosol and has a great potential in meteorological application. [ABSTRACT FROM AUTHOR]

The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85), weakest root-mean-square error (203 m), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 d experiments using other algorithms, reasonable parameter selection can help in distinguishing between layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically address the uncertainty with multiple structures and obtain a stable NBLH with a high temporal resolution, which is expected to contribute to air pollution monitoring and climatology, as well as model verification. [ABSTRACT FROM AUTHOR]

The types of aerosols are essential for estimating radiative forcing effects, improving the lidar retrieval algorithms, and identifying aerosol sources. However, it is difficult to categorize the aerosols with similar optical properties based on lidar techniques. In this study, an approach was proposed to distinguish desert dust (DD), mineral dust (MD), air pollution aerosols (APA), and their mixtures (DD&MD, DD&APA, MD&APA, DD&MD&APA). We established the fluorescence–Mie​ polarization lidar (FMPL) system to categorize three types of main aerosols using the customized fluorescence-to-Mie ratio (FMR) and volume depolarization ratio (VDR) in the experiments from September 2017 to December 2018. Then, the mixture of the aerosols was classified by combining backward trajectory analysis. The feasibility of this method was verified through five typical cases in the paper, and it is proved that the method could be employed to study air pollution issues based on FMPL, which could provide references for the meteorological investigation. • We established a fluorescence–Mie polarization lidar system. • We defined Fluorescence-to-Mie ratio (FMR). • Aerosols were divided into seven types according to their FMR and VDR. [ABSTRACT FROM AUTHOR]