Long-term (2000–2019) assessment of aerosol loads and dominant aerosol types at spatiotemporal scales using multi-source datasets can provide a strong impetus to the investigation of aerosol loads and to the targeted prevention control of atmospheric pollution in densely populated regions with frequent anthropogenic activities and heavy aerosol emissions. This study uses multi-source aerosol datasets, including Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET), to conduct a long-term variation assessment of aerosol load, high aerosol load frequency, and dominant aerosol types over Asia. The results indicate that regional aerosol type information with adequate spatial resolution can be combined with aerosol optical depth (AOD) values and heavy aerosol load frequency characterization results to explore the key contributors to air pollution. During the study period, the aerosol load over the North China Plain, Central China, Yangtze River Delta, Red River Delta, Sichuan Basin, and Pearl River Delta exhibited an increasing trend from 2000–2009 due to a sharp rise in aerosol emissions with economic development and a declining trend from 2010–2019 under stricter energy conservation controls and emissions reductions. The growth of urban/industrial (UI) type and biomass burning (BB) type aerosol emissions hindered the improvement of the atmospheric environment. Therefore, in future pollution mitigation efforts, focus should be on the control of UI-type and BB-type aerosol emissions. The Indus–Ganges River Plain, Deccan Plateau, and Eastern Ghats show a continuously increasing trend; however, the aerosol load growth rate of the last decade was lower than that of the first decade, which was mainly due to the decrease in the proportion of the mixed type aerosols.

Downward shortwave radiation (DSR) to the Earth’s surface is an essential renewable energy component. Accurate knowledge of solar radiation, i.e., solar energy resource assessment, is a prior requirement for the development of the solar energy industry. In the framework of solar resource assessment, site adaptation refers to leveraging short-term, high-quality ground-based observations as unbiased references to correct long-term, site-specific gridded model datasets, which has been playing an important role in this research area. This study evaluates 12 probabilistic site adaptation (PSA) methods for the correction of the hourly DSR data from multiple gridded DSR products in the Western Sichuan Plateau (WSP). Surface pyranometer observations are used as the reference to adapt predictions from two satellite products and two reanalysis products, collectively. Systematic quantification reveals inherent errors with root mean square errors (RMSEs) > 200 W/m² across all datasets. Through a comparative evaluation of three methodological categories (benchmarking, parametric/non-parametric, and quantile combination approaches), it is demonstrated that quantile-based ensemble methods achieve superior performance. The median ensemble (MED) method delivers optimal error reduction (RMSE: 163.97 W/m², nRMSE: 34.43%). The resulting optimal dataset, with a temporal resolution of 1 h and a spatial resolution of 0.05° × 0.05°, identifies the WSP as a region of exceptional energy potential, characterized by substantial annual total solar radiation (1593.10 kWh/m²/yr) and a stable temporal distribution (negative correlation between the total solar radiation and the coefficient of variation). This methodological framework provides actionable insights for solar resource optimization in complex terrains.

Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.