The technology for retrieving aerosol extinction coefficients from LiDAR is mature. However, further progress is required to retrieve the vertical distribution of aerosol mass concentration. In addition, accuracy evaluation of aerosol mass concentration from LiDAR is challenging owing to the lack of standard vertical PM2.5 mass concentration. Therefore, in this study, a PM2.5 mass concentration retrieval algorithm was developed by integrating real-time temperature, relative humidity, and extinction coefficient profiles. The PM2.5 mass concentration at four heights of the Shenzhen Meteorological Gradient Observation Tower was used as the standard value to evaluate the accuracy of the model under different weather conditions and seasons.
The influence of meteorological factors on the vertical distribution of aerosol mass concentration is extremely complex, particularly under precipitation conditions where the LiDAR signal attenuation is severe. Therefore, in this study, only the effects of temperature and relative humidity on the vertical distribution of aerosols under non-precipitation weather conditions were investigated. In practical applications, sample data are initially preprocessed, including the outlier handling (triple standard deviation removal), rainy day data, and missing value removal. The extinction coefficient at the lowest height of the LiDAR, ground temperature, relative humidity from the microwave radiometer, and PM2.5 mass concentration near the ground were substituted into an exponential model. The data from 2500 h were subsequently used for model fitting. The model parameters were automatically determined based on the minimum mean square error. Thus, the extinction coefficient, temperature, and relative humidity profiles at a specific height could be selected to calculate the PM2.5 mass concentration at the corresponding height. To investigate the accuracy of the PM2.5 mass concentration inversion, comparisons were conducted between PM2.5 mass concentrations at four heights (70, 120, 220, and 335 m) on the Shenzhen Meteorological Gradient Observation Tower.
By comparing different weather conditions, the correlation coefficients between the simulated and measured values at the four heights are over 0.68 (Figs.3 and 4). The maximum mean absolute error (MAE) and root mean square error (RMSE) are 6.88 μg/m3 and 18.56 μg/m3, respectively, appearing at a height of 335 m on sunny days. In different seasons, the correlation coefficients at the four heights range from 0.78?0.93, 0.71?0.81, 0.73?0.80, and 0.63?0.75, respectively (Table 4). The PM2.5 mass concentration spatiotemporal distribution and transport process on July 29, 2022, was selected as a case study for analysis (Fig.6
This study established a multivariate PM2.5 mass concentration fitting model based on an exponential model combining temperature, relative humidity, and extinction profiles. The optimal parameters were selected based on the minimum mean-square deviation index, and the output was validated. Compared to the linear and exponential basic models, the accuracy of the multivariate fitting model has been improved, with correlation coefficients at all four heights above 0.80. The minimum MAE and RMSE are approximately 4 μg/m3 and 7 μg/m3, respectively. Under clear and cloudy weather conditions, the correlation coefficients at four altitudes exceed 0.68, and the MAE and RMSE are below 7 μg/m3 and 19 μg/m3,respectively. The simulation results spanning different seasons demonstrate that the average mass concentration of PM2.5 in Shenzhen is below 35 μg/m3. The simulated PM2.5 mass concentration exhibited seasonal variation patterns. In addition, the simulation results for spring, summer, and autumn are better than those for winter. This may be due to the uncertainty caused by the relatively high aerosol mass concentrations in winter. Considering the uncertainty caused by the LiDAR and microwave radiometer measurement processes, the validation results of the proposed multivariate model performed well within an acceptable range.
