Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the ${\delta }_p$-$G_f$ method based on the particle depolarization ratio at 355 nm (${\delta }_{p355}$) and the fluorescence capacity ($G_f$), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10⁻⁴ to 0.63 × 10⁻⁴. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10⁻⁴ to 1.10 × 10⁻⁴ and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10⁻⁴ to 1.23 × 10⁻⁴, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the ${\delta }_{p355}$-$G_f$ method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the ${\delta }_{p355}$-$G_f$ classification method and the ${\delta }_{p355}$-$G_f$ distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing.

The pure rotational Raman (PRR) lidar technique relies on calibration functions (CFs) to extract temperature information from raw detection data. The choice of CF significantly impacts the accuracy of the retrieved temperature. In this study, we propose a method that combines multiple Monte Carlo simulation experiments with a statistical analysis, and we first conduct simulated comparisons of the calibration effects of different CFs while considering the impact of noise. We categorized ten common CFs into four groups based on their functional form and the number of calibration coefficients. Based on functional form, specifically, we defined 1/T = f(lnQ) as a forward calibration function (FCF) and lnQ = g(1/T) as a backward calibration function (BCF). Here, T denotes temperature, and Q denotes the signal intensity ratio. Their performance within and outside the calibration interval is compared across different integration times, smoothing methods, and reference temperature ranges. The results indicate that CFs of the same category exhibit similar calibration effects, while those of different categories exhibit notable differences. Within the calibration interval, the FCF performs better, especially with more coefficients. However, outside the calibration interval, the linear calibration function (which can be considered a two-coefficient FCF) has an obvious advantage. Conclusions based on the simulation results are validated with actual data, and the factors influencing calibration errors are discussed. Utilizing these findings to guide CF selection can enhance the accuracy and stability of PRR lidar detection.

To address the issue of photon counting rate saturation distortion caused by the photon pulse stacking effect in the photomultiplier tube (PMT) output of atmospheric LiDAR systems, a photon counting method for LiDAR echo signals based on waveform decomposition is proposed. This method simulates the single-photon pulse response waveform of the PMT and uses it as the basis function for waveform decomposition. The Levenberg–Marquardt fitting optimization algorithm is then applied to separate the photon components from the full waveform signal. Additionally, an algorithm is proposed to locate missing photons based on a local evaluation function and insert photons into these positions, solving the problem of initial parameter determination for the stacked components, which enables the counting of photons whose peaks are obscured due to the stacking effect. The experimental results indicate that, for echo signals sampled at 1 GHz, this method increases the upper limit of the photon counting rate from 200 to 500 MHz compared to traditional peak detection counting methods, which expands the effective measurement range of photon counting methods.

Rotational Raman (RR) lidar is widely employed for atmospheric temperature detection, but its detection capability is constrained in cloudy or hazy conditions due to the interference of elastic scattering leakage to the pure RR channels. To address this limitation, a vibrational-rotational Raman (VR) temperature measurement technique has been proposed and validated. This study compares the performance of VR and RR techniques through simulations and observational experiments. The simulations account for both the theoretical (intrinsic) errors of the methods and the impact of random errors. Additionally, this study evaluates the performance of ten calibration functions (CFs) in the VR method, including nine applied to VR for the first time. The results indicate that when the signal-to-noise ratio of the low quantum number channel is low, the VR technique can effectively improve the detection performance of the system. For the VR technique, all tested CFs can successfully retrieve atmospheric temperature. Among them, the four-coefficient forward CFs achieve the best calibration performance within the calibration interval, while the linear CF performs best in extrapolation scenarios.

The vertical distribution of PM_2.5 during urban haze events is jointly affected by aerosol composition, boundary-layer structure, and regional transport. Empirical PM_2.5 retrievals based on lidar-derived extinction coefficients can be biased by the optical contribution of coarse-mode mineral particles. This study used fluorescence–Raman–Mie polarization lidar observations in Beijing to identify anthropogenic pollution aerosols (APA), desert dust (DD), and mineral dust (MD) using the particle depolarization ratio (PDR) and fluorescence capacity (G_f). The classification results were incorporated into the construction and application of an empirical extinction–PM_2.5 relationship. For mixed-aerosol samples, the APA component fraction and its associated extinction contribution were estimated before retrieving the vertical PM_2.5 distribution. To quantify the effect of classification constraints, three linear models were developed: a model without aerosol-type constraints, a PDR-only screening model, and a model constrained by combined PDR–G_f classification. Near-surface, date-separated validation using 411 hourly samples from 69 observation dates showed that the combined-classification model achieved an RMSE of 17.32 µg m⁻³, an MAE of 13.54 µg m⁻³, and a Bias of 3.30 µg m⁻³. Compared with the model without aerosol-type constraints, the RMSE, MAE, and Bias decreased by 38.2 %, 45.0 %, and approximately 83.0 %, respectively. Uncertainty analysis showed that classification end-member and aerosol-type-dependent lidar-ratio perturbations affected the quantitative PM_2.5 estimates under mixed-aerosol conditions, whereas the residual scatter and parameter stability of the empirical extinction–PM_2.5 relationship were the dominant sources of prediction uncertainty. For a mixed-to-pollution aerosol evolution episode in Beijing in November 2024, the classification-constrained PM_2.5 vertical structure was physically consistent with temperature stratification, wind fields, and backward trajectories. Seasonal testing showed no stable and consistent improvement from season-specific fitting. The proposed method provides classification-constrained estimates of the PM_2.5 vertical structure under APA-dominated conditions in Beijing. Model parameters should be recalibrated using local observations when applied to regions, seasons, or aerosol conditions with substantially different composition.

The vertical distribution of PM_2.5 during urban haze events is jointly affected by aerosol composition, boundary-layer structure, and regional transport. Empirical PM_2.5 retrievals based on lidar-derived extinction coefficients can be biased by the optical contribution of coarse-mode mineral particles. This study used fluorescence–Raman–Mie polarization lidar observations in Beijing to identify anthropogenic pollution aerosols (APA), desert dust (DD), and mineral dust (MD) using the particle depolarization ratio (PDR) and fluorescence capacity (G_f). The classification results were incorporated into the construction and application of an empirical extinction–PM_2.5 relationship. For mixed-aerosol samples, the APA component fraction and its associated extinction contribution were estimated before retrieving the vertical PM_2.5 distribution. To quantify the effect of classification constraints, three linear models were developed: a model without aerosol-type constraints, a PDR-only screening model, and a model constrained by combined PDR–G_f classification. Near-surface, date-separated validation using 411 hourly samples from 69 observation dates showed that the combined-classification model achieved an RMSE of 17.32 µg m⁻³, an MAE of 13.54 µg m⁻³, and a Bias of 3.30 µg m⁻³. Compared with the model without aerosol-type constraints, the RMSE, MAE, and Bias decreased by 38.2 %, 45.0 %, and approximately 83.0 %, respectively. Uncertainty analysis showed that classification end-member and aerosol-type-dependent lidar-ratio perturbations affected the quantitative PM_2.5 estimates under mixed-aerosol conditions, whereas the residual scatter and parameter stability of the empirical extinction–PM_2.5 relationship were the dominant sources of prediction uncertainty. For a mixed-to-pollution aerosol evolution episode in Beijing in November 2024, the classification-constrained PM_2.5 vertical structure was physically consistent with temperature stratification, wind fields, and backward trajectories. Seasonal testing showed no stable and consistent improvement from season-specific fitting. The proposed method provides classification-constrained estimates of the PM_2.5 vertical structure under APA-dominated conditions in Beijing. Model parameters should be recalibrated using local observations when applied to regions, seasons, or aerosol conditions with substantially different composition.