This paper constitutes the second installment in a series dedicated to exploring the uncertainties associated with pure rotational Raman-Rayleigh temperature measurement lidars in the mid-to-upper atmosphere (20-90 km). It presents uncertainty evaluation methods aimed at addressing the challenge of uncertainty assessment. These methods leverage both the Guide to the Expression of Uncertainty in Measurement (GUM) method and the Monte Carlo Method (MCM) to ascertain the uncertainty of air density and temperature derived from lidar signals. Within the framework of these evaluation methods, various uncertainty sources are considered, encompassing saturation correction, photon noise, background noise, reference temperature, and two special uncertainty sources: atmospheric transmittance correction in Rayleigh lidar and the calibration process in Raman lidar. An uncertainty evaluation example in accordance with the proposed evaluation method is provided by employing raw signals from full-chain simulation system introduced in another series paper. In this example, we compare the evaluation results of the GUM method with those derived from the MCM. Our findings reveal consistent trends in different uncertainty components between the two methods. Additionally, the uncertainty caused by atmospheric transmittance correction is involved with detailed considerations of correlation and iterative methods. The uncertainty resulting from the calibration process in Raman lidar, attributed to its nonlinear characteristics, can only be evaluated using the MCM. These findings affirm the applicability of the proposed uncertainty evaluation methods of pure rotational Raman-Rayleigh lidar. This research enhances our understanding of the sources of uncertainty in lidar systems and provides a convenient approach for evaluating the data quality of temperature lidar.

Although it is quite challenging to image and analyze the spatial distribution of bioaerosols in a confined space, a three-dimensional (3D) modeling system based on the planar laser-induced fluorescence (PLIF) technique is proposed in this paper, which is designed to analyze the temporal and spatial variations of bioaerosol particles in a confined chamber. The system employs a continuous planar laser source to excite the fluoresce, and a scientific complementary metal oxide semiconductor (sCMOS) camera to capture images of 2048 × 2048 pixels at a frame rate of 12 Hz. While a sliding platform is moving back and forth on the track, a set of images are captured at different positions for 3D reconstruction. In this system, the 3D reconstruction is limited to a maximum measurement volume of about 50 cm × 29.7 cm × 42 cm, with a spatial resolution of about 0.58 mm × 0.82 mm × 8.33 mm, and a temporal resolution of 5 s. Experiments were carried out to detect the PLIF signals from fluorescein aerosols in the chamber, and then 3D reconstruction was used to visualize and analyze the diffusion of aerosol particles. The results prove that the system can be applied to clearly reconstruct the 3D distribution and record the diffusion process of aerosol particles in a confined space.

The laser-induced fluorescence (LIF) technique, which has been widely used for food testing, can be combined with various algorithms to classify and recognize different kinds of honey. This paper proposes the Kolmogorov–Smirnov test-Gaussian mixture model (KS-GMM) algorithm, which is coupled with the LIF technique to realize accurate classification and recognition of different types of pure honey. The experiments are designed and carried out to obtain a set of LIF spectrum data from various honey and syrup samples. The proposed KS-GMM algorithm is applied for classification and recognition, with GMM, k-nearest neighbor (kNN), and decision tree algorithms as cross-validation methods. By comparing recognition results of training sets containing different amounts of data, it is found that the KS-GMM algorithm exhibits a maximum recognition accuracy of 96.52%. The research results prove that the KS-GMM algorithm outperforms, to the best of our knowledge, the other three algorithms in classifying and recognizing the honey types.