Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016-2018 using the correlation coefficient (R), the PM2.5/AOD ratio (eta), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R > 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R > 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (eta > 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q > 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R > 0.5, q > 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.

In order to quantify air pollution effects on climate change, we investigated the climate response associated with anthropogenic particulate matters (PMs) by dividing fine PM (PM2.5, particle size 2.5 mu m) in great detail in this work, with an aerosol-climate coupled model. We find that the changes in PM2.5 and CPM are very different and thus result in different, even opposite effects on climate, especially on a regional scale. The column burden of PM2.5 increases globally from 1850 to the present, especially over Asia's southern and eastern parts, whereas the column concentration of CPM increases over high-latitude regions and decreases over South Asia. The resulted global annual mean effective radiative forcing (ERF) values due to PM2.5 and CPM changes are -1.21 W center dot m(-2) and -0.24 W center dot m(-2), respectively. Increases in PM2.5 result in significant cooling effects on the climate, whereas changes in CPM produce small and even opposite effects. The global annual mean surface air temperature (SAT) decreases by 0.94 K due to PM2.5 increase. Coolings caused by increased PM2.5 are more apparent over Northern Hemisphere (NH) terrain and ocean at mid- and high latitudes. Increases in SATs caused by increased CPM are identified over high latitudes in the NH, whereas decreases are identified over mid-latitude regions. Strong cooling due to increased PM2.5 causes a southward shift of the Intertropical Convergence Zone (ITCZ), whereas the Hadley circulation associated with CPM is enhanced slightly over both hemispheres, along with the weak movement of corresponding ITCZ. The global annual mean precipitation decreases by approximately 0.11 mm day(-1) due to the increased PM2.5. Generally, PM2.5 concentration changes contribute more than 80% of the variation caused by all anthropogenic aerosols in ERF, SAT, cloud fraction, and precipitation.