Unmanned aerial vehicle (UAV) experiments, multiple datasets from ground-based stations and satellite remote sensing platforms, and backward trajectory models were combined to investigate the characteristics and influential mechanisms of the air pollution episode that occurred in Nanjing during 3–4 December 2017. Before the experiments, the position of the detector mounted on a UAV that was minimally disturbed by the rotation of the rotors was analyzed based on computational fluid dynamics (CFD) simulations. The combined analysis indicated that the surface meteorological conditions—high relative humidity, low wind speed, and low temperature—were conducive to the accumulation of PM_2.5. Strongly intense temperature inversion layers and the low thickness of the atmospheric mixed layer could have resulted in elevated PM_2.5 mass concentrations. In the early stage, air pollution was affected by the synoptic circulation of the homogenous pressure field and low wind speeds, and the pollutants mainly originated from emissions from surrounding areas. The aggravated pollution was mainly attributed to the cold front and strong northwesterly winds above 850 hPa, and the pollutants mostly originated from the long-distance transport of emissions with northwesterly winds, mainly from the Beijing‒Tianjin‒Hebei (BTH) region and its surrounding areas. This long-distance transport predominated during this event. The air pollution level and aerosol optical depth (AOD) were positively correlated with respect to their spatial distributions; they could reflect shifts in areas of serious pollution. Pollution was concentrated in Anhui Province when it was alleviated in Nanjing. Polluted dust, polluted continental and smoke aerosols were primarily observed during this process. In particular, polluted dust aerosols accounted for a major part of the transport stage, and existed between the surface and 4 km. Moreover, the average extinction coefficient at lower altitudes (<1 km) was higher for aerosol deposition.

In order to simulate the light-current (LI) characteristics of the Vertical Cavity Surface Emitting Laser (VCSEL), we adopted the simplex method to determine the optimal solution of the model parameters on the basis of experimental data. Besides, improvement was made to fit the current-voltage (IV) data using the relationship which accounted for a resistance in series with a diode, and it was more appropriate to fit LI curves according to the model evaluation index. Furthermore, the improved model can be applied to fit the LI curves at different ambient temperatures, and it illustrates that maximum value of the output power decreases as the ambient temperature increases.