## 5 Conclusions  
Firstly, we used meteorological and environmental ground-based observation data to explore the fitted hygroscopic correction at five different background sites located in Chengdu Linjianglu, Leshan Jiajiangxian, Yibin Xingwenerzhong, Bazhong Tongjiang Zhongxue, and Chongqing Guanyinqiao. Additionally, the f (90%) was calculated. The results showed that in Sichuan and Chongqing regions with special climatic conditions, the hygroscopic correction factor varies greatly by station and month. Therefore, a hygroscopic correction factor grid with a spatial resolution of 1 km from January to December was constructed by fitting the data from 2017 to 2018 on a site-by-site basis.  
Secondly, we verified that the correlation of dry extinction coefficient (\(\sigma_{\text{dry}}\)) against PM\({}_{2.5}\) is improved compared with AOD against PM\({}_{2.5}\). The hourly near-ground PM\({}_{2.5}\) concentrations in the study area from 09:00 to 16:00 were estimated by using vertical-humidity method, and the accuracy is verified, with r between 0.69-0.85.  
Thirdly, considering that the satellite cannot detect the atmospheric aerosols under the clouds, especially in the Sichuan and Chongqing regions, which have more continuous cloud coverage and gaps in spatial distribution, the advantage of atmospheric model data was fully utilized to obtain seamless coverage results for the region by using the IVW method to fuse PM\({}_{2.5}\) estimated by satellite and simulated by the atmospheric model. The PM\({}_{2.5}\) fusion results were used to analyze the processes of aggregation, migration, and dissipation of pollutants.  
Finally, we utilized the LiDAR data to demonstrate the reasonableness of this study in discussion. So, the results can effectively provide important supporting data for air pollution control and other works.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12147065/s1](https://www.mdpi.com/article/10.3390/app12147065/s1)]([https://www.mdpi.com/article/10.3390/app12147065/s1](https://www.mdpi.com/article/10.3390/app12147065/s1)), Figure S1. E\({}_{\text{ext}}\)(RH) fitting Leshan Jiajiangxian station. The red solid lines are fitting lines of the scattered dots. Figure S2. E\({}_{\text{ext}}\)(RH) fitting Yibin Xingwenerzhong station. The red solid lines are fitting lines of the scattered dots. Figure S3. E\({}_{\text{ext}}\)(RH) fitting Bazhong Tongjiangzhongxue station. The red solid lines are fitting lines of the scattered dots. Figure S4. E\({}_{\text{ext}}\)(RH) fitting Chongqing Guanyinqiao station. The red solid lines are fitting lines of the scattered dots.  
[PERSON] proposed the method and wrote this paper; [PERSON] collected data; [PERSON] revised the manuscript. [PERSON] translated the manuscript. [PERSON] constructed the model and analyzed data. [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 42001315, the National Natural Science Foundation of China under Grant 41830109, the National Science and Technology Support Program of China under Grant 2014 BAC16B06.  
**Data Availability Statement:** The datasets presented in this study can be found here: [[http://111.20](http://111.20)]([http://111.20](http://111.20)) 2.113.138:8081/h8/dataquery_h8.jsp; [[http://106.37.208.233:20035/](http://106.37.208.233:20035/)]([http://106.37.208.233:20035/](http://106.37.208.233:20035/)); [[https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/)]([https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/)) (accessed on 12 June 2022).  
All authors would sincerely thank the reviewers and editors for their beneficial, careful, and detailed comments and suggestions for improving the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.