## 1 Introduction
As the relationship between AOD and PM\({}_{2.5}\) varies greatly from region to region, moment to moment, and season to season [30; 31], most studies that used a single machine learning method to estimate PM\({}_{2.5}\) concentrations in a large area have some shortcomings in spatial distribution. Meanwhile, the accuracy of the estimation using machine learning methods is related to the training samples. Meanwhile, there is still a huge challenge in estimating regional PM\({}_{2.5}\) concentrations using AOD, which is missing AOD retrievals caused by cloud cover and bright surfaces (desert or snow) [32; 33]. Different methods are proposed for addressing missing AOD retrievals, such as kriging interpolation [34], machine learning methods to combine chemical transport models such as the Community Multi-scale Air Quality Model (CMAQ), Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) to simulate AOD or PM\({}_{2.5}\) with complete spatial and temporal coverage. For example, [PERSON] et al. combined Multi-angle Implementation of Atmospheric Correction (MAIAC) AOD, AHI AOD, and meteorological data to obtain 24 h full-coverage PM\({}_{2.5}\) concentrations in Beijing [35]. [PERSON] et al. used residual networks for the downscaling fusion of MERRA-2 and MAIAC AOD to obtain full-coverage of PM\({}_{2.5}\) concentrations in the California [36]. [PERSON] et al. used AQUA and TERRA AOD to perform linear regression firstly, and then combined with Copernicus Atmosphere Monitoring Service (CAMS) AOD, using a self-adaptive deep neural network (SADNN) to estimate regional full-coverage PM\({}_{2.5}\) in China [37]. [PERSON] et al. performed ten different fusion strategies for comparison to obtaining full-coverage PM\({}_{2.5}\) concentrations using the classical RF model [38]. Due to the unique climatic conditions in the Sichuan and Chongqing regions, where the air pollution is heavier but with more cloud coverage in autumn and winter, and less cloud coverage but relatively better air quality in spring and summer. As well know, machine learning method is relation with training samples, the training samples in the special area of the study may not truly represent the distribution of PM\({}_{2.5}\) long-time series. Thus, we proposed the elaborate Vertical-Humidity correction method to estimate PM\({}_{2.5}\) concentrations in areas of Sichuan and Chongqing (the scope of the study mainly includes the Sichuan area except Ganzi, Aba, Liangshan, and Panzhihua, and the main urban area of Chongqing). Meanwhile, to make up for the missing data of PM\({}_{2.5}\) retrieved by satellite, we combine satellite and NAQPMS data to obtain full-coverage PM\({}_{2.5}\) concentration over Sichuan and Chongqing. The main research of this study is as follows:  
1. Considering the complex topography and meteorological conditions in Sichuan and Chongqing, the scale height of aerosols (H\({}_{a}\)) was introduced for the vertical correction of regional AOD. For humidity correction, five different stations were selected for the analysis of the fitted curves and f(90%), and the results showed that the hygroscopic correction factors varied greatly in different months and stations, so the data in the study area were fitted site by site and month by month to construct a grid of hygroscopic correction factors. 2. The hourly PM\({}_{2.5}\) concentrations in the study area from 09:00 to 16:00 were estimated using the AHI AOD data based on the constructed hygroscopic correction factors grid, and the accuracy was verified. 3. Considering the frequent cloud coverage in Sichuan and Chongqing regions and the existence of gaps in the satellite retrieval PM\({}_{2.5}\), we adopt inverse variance weighting (IVW) for effective fusion with the help of NAQPMS data to finally obtain PM\({}_{2.5}\) concentrations with seamless coverage and effectively analyze the processes of pollutant aggregation, migration, and dissipation in this place.