## 4 Discussion  
In this study, the near-ground extinction coefficient was calculated using the visibility in the vertical correction to calculate the H\({}_{\text{a}}\) of the corresponding pixel. According to the principle of ground-based observation visibility, the near-ground extinction coefficient calculated by using Equation (2) represents the integration of the particle extinction ability in the horizontal direction, while the AOD represents the integration of the particle extinction ability in the vertical direction. To verify its reasonableness, we conducted a comparative analysis by obtaining the observation data from the LiDAR located at Southwest Jiao Tong University (SWJTU, 104.5 E, 30.73 N). Considering the distance between the LiDAR and meteorological observation stations, Jinquanhe Station (104.3 E, 30.72 N) and Xinduquidishui (104.2 E, 30.78 N) station in Chengdu were used for comparative analysis, and the results are shown in Figure 9. The scatter plots show that the correlation coefficients between the near-ground extinction coefficients calculated by the boundary layer height observed by LiDAR (EXT_vertical (PBL)) and ground-based PM\({}_{2.5}\) are 0.19 and 0.27, respectively, which are lower than the correlation coefficients between the results calculated based on visibility (EXT_vertical (VIS)) and PM\({}_{2.5}\). Therefore, the near-ground extinction coefficient calculated by visibility in this study has a higher correlation with PM\({}_{2.5}\) and is feasible for calculating H\({}_{\text{a}}\). The study area is located in southwest China, which is less affected by sand and dust so the pollutants in the region are mainly industrial and anthropogenic emissions. Therefore, the defined average mass extinction efficiency is reasonable assuming PM\({}_{2.5}\) as the main extinction particle in the humidity correction process. The E\({}_{\text{ext}}(\text{RH})\) model was studied experimentally by [PERSON] [52] for Brazilian aerosols and [PERSON] [55] for South African aerosols. The model is effective for the scattering hygroscopic growth of the carbonaceous aerosol population and can better characterize its variation with humidity. According to the results of the hygroscopic correction fitting (Figure 7), there are some observations that deviate from the fitted curve, but the overall fitting results for the regional stations are good and the fitted curves are relatively smooth, indicating that the adopted E\({}_{\text{ext}}(\text{RH})\) model is feasible. In addition, when calculating the corrected factor f(90%) (Table 1), no values of relative humidity less than 40% were observed, so their corresponding f(90%) could not be calculated, such as at the Leshan Jiajiangxian station and the Chongqing Guanyinqiao station; this may be related to the humidity environment around the stations. The calculation  
Figure 9: Scatter plots between LiDAR\(-\)calculated and visibility\(-\)calculated near\(-\)ground extinction coefficient and PM\({}_{2.5}\). The black and red line are LiDAR-calculated and visibility-calculated, respectively. results indicate that the aerosol hygroscopic growth capacity is different at different stations in the same month, and it also varies greatly at same stations at different times, so it is necessary to construct a hygroscopic correction factor grid to estimate PM\({}_{2.5}\) by fitting the hygroscopic growth factor month by month and station by station in the humidity correction, especially for regions with complex topographic conditions. In this study, the IVW method was used to fuse PM\({}_{2.5}\) estimated by satellite and simulated by NAQPMS, which bridges the gap area of satellite retrieval and obtains a seamless regional coverage of hour-by-hour PM\({}_{2.5}\) concentrations (Figures 7 and 8). Compared with other polar-orbiting satellite inversions, the PM\({}_{2.5}\) concentrations we obtained have a higher temporal-spatial resolution and can help to analyze the variation of pollutants in detail throughout the day.