East Asia is one of the most important sources of aerosols in the world. The distribution of aerosols varies across time and space. Accurate aerosol data is crucial to identify its spatiotemporal dynamics; thus, it is of great significance to obtain and verify new aerosol data for this region. Based on the Aerosol Optical Depth (AOD) data of the Aerosol Robotic Network (AERONET) program for 17 stations from 2011 to 2020, this study comprehensively verified the accuracy and applicability of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD 1 km products among different seasons, elevations, and climate zones over entire East Asia. The results showed that: (1) The overall accuracy of MAIAC AOD was high in East Asia, and the accuracy of Terra was slightly better than that of Aqua. MAIAC AOD showed significant heterogeneity among sites. MAIAC AOD performed well in areas with high vegetation cover and flat terrain, while the inversion accuracy was relatively low in areas with low vegetation cover and high terrain. (2) In general, MAIAC AOD and AERONET AOD showed good agreement in different seasons, presenting as winter > spring > autumn > summer. Yet the accuracy and consistency of Terra AOD product were better than Aqua product. (3) MAIAC AOD showed different accuracy at different elevations and climate zones. It had a high correlation and best inversion accuracy with AERONET AOD at low and medium elevations. MAIAC AOD had better inversion accuracy in the arid and warm temperate zones than that in the equatorial and cold temperate zones. (4) AOD distribution and its trend showed significant spatial differences in East Asia. The high AOD values were dominant in the Sichuan basin and the eastern plains of China, as well as in India and Bangladesh, while the relatively low AOD values were distributed in southwestern China and the areas north of 40°N. AOD in most parts of East Asia showed a negative trend, indicating a great improvement in air quality in these regions.

In recent years, atmospheric aerosol pollution has seriously affected the ecological environment and human health. Understanding the spatial and temporal variation of AOD is essential to revealing the impact of aerosols on the environment. Based on the MAIAC AOD 1 km product from 2011 to 2020, we analyzed AOD’s distribution patterns and trends in different time series across East Asia. The results showed that: (1) The annual average AOD in East Asia varied between 0.203 and 0.246, with a decrease of 14.029%. The areas with high AOD values were mainly located in the North China Plain area, the Sichuan Basin area, and the Ganges Delta area, with 0.497, 0.514, and 0.527, respectively. Low AOD values were mainly found in the Tibetan Plateau and in mountainous areas north of 40° N, with 0.061 in the Tibetan Plateau area. (2) The distribution of AOD showed a logarithmic decreasing trend with increasing altitude. Meanwhile, the lower the altitude, the faster the rate of AOD changes with altitude. (3) The AOD of East Asia showed different variations in characteristics in different seasons. The maximum, minimum, and mean values of AOD in spring and summer were much higher than those in autumn and winter. The monthly average AOD reached a maximum of 0.326 in March and a minimum of 0.190 in November. The AOD showed a continuous downward trend from March to September. The highest quarterly AOD values in the North China Plain occurred in summer, while the highest quarterly AOD values in the Sichuan Basin, the Ganges Delta, and the Tibetan Plateau all occurred in spring, similar to the overall seasonal variation in East Asia.

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere.

In order to reveal the correlation between aerosols and pollution indicators, the MODIS aerosol optical depth (AOD) was used to investigate the distribution of AOD in 16 prefecture-level cities in Shandong Province from 2015 to 2020. This study quantitatively analyzed the coupling degree and the coupling coordination degree between AOD and pollution indicators based on the coupling coordination model. The results showed that: (1) The annual average AOD in Shandong Province showed a rapid downward trend with a mean value of 0.615. The seasonal AOD of Shandong Province and prefecture-level cities was characterized by spring and summer > autumn and winter. The distribution of AOD in Shandong Province showed a spatial pattern of high in the west and low in the east, and high in the surrounding area and low in the middle. The decreasing rate of AOD was high in the west and low in the east. (2) The annual average AOD and Air Quality Index (AQI) were in a highly coupled and coordinated state. Their spatial distribution pattern decreased from west to east. There were certain fluctuations with seasonal changes, with the largest fluctuation in winter. (3) Except for O₃, the overall coupling and coordination level between AOD and each pollutant was relatively high. The coupling coordination effect was as follows: C (PM_2.5, AOD) and C (PM₁₀, AOD) > C (NO₂, AOD) > C (SO₂, AOD), and C (CO, AOD) > C (O₃, AOD). Except for the O₃, its distribution was characterized by highs in the west and lows in the east. The degree of coupling between each pollution indicator and the seasonal average AOD was high. The study showed that there was a high degree of coupling and coordination between pollutant concentration indicators and AOD, and remote sensing AOD data can be used as an effective supplement to regional pollutant monitoring indicators.