Fine particulate matter (PM_2.5) pollution’s passive effects on public health have attracted a great deal of attention. Extensive studies have tried to uncover the impacts of external drivers on PM_2.5 pollution variation; however, the topography’s effects on PM_2.5 pollution remain poorly understood. Using annual high-resolution concentration estimates for PM_2.5, this paper quantitatively disentangled the geographical impacts of topography on the PM_2.5 pollution in China and quantified the mountain blocking effects on the PM_2.5 pollution dispersion on a macro scale. The results show that, in China, the plains and surrounding platforms and hills tend to suffer from long-term severe PM_2.5 pollution. The most polluted topography type is the plains. In comparison, regions such as high-altitude mountains and plateaus are less affected by PM_2.5 pollution. Mountains have significant and evident blocking effects on the cross-regional spread of PM_2.5 pollution. Generally, Level I mountains (high elevation, density and coverage mountains) provide better blocking effects than Level II (middle elevation, density and coverage mountains) mountains and Level III mountains (low elevation, density and coverage mountains). The mountains’ blocking effects begin to play an efficient role when their altitudes reach a certain value; however, the exact altitude values vary by different mountains with a value of 163 m for all typical mountains with absolute PM_2.5 concentration differences between their two sides greater than 10 μg/m³. In heavily polluted areas, PM_2.5 pollution may overflow where the surrounding mountains are not high enough or the mountains’ stretch breaks. This study can provide key theoretical support for air pollution modelling and regional air pollution joint prevention and control.

Vegetation indexes have been widely used to qualitatively and quantitatively evaluate vegetation cover and its growth vigor. To further extend the study of vegetation indexes, this paper proposes to study the spatial and temporal distribution characteristics and specific driving mechanisms of vegetation indexes based on the example of Yunnan Province, China, and also adds the study of spatial and temporal prediction methods of vegetation indexes. This paper used data on this region’s normalized vegetation index (NDVI), three meteorological factors, and eight social factors from 1998 to 2019. The dynamic change in and driving mechanism of the NDVI were studied using mean value analysis, univariate linear trend regression analysis, and partial correlation analysis. In addition, the Fourier function model and the CA–Markov model were also used to predict the NDVI of Yunnan Province from 2020 to 2030 in time and space. The results show that: (1) The NDVI value in Yunnan Province is high, showing a significant growth trend. The increased vegetation coverage area has increased in the past 22 years without substantial vegetation degradation. (2) The positive promotion of meteorological factors is greater than the negative inhibition. The partial correlation of relative humidity among meteorological factors is the highest, which is the main driving factor. (3) The NDVI value is significantly positively correlated with population and economy and negatively correlated with pasture land and agricultural area. (4) The NDVI values are predicted well in time (R = 0.64) and space (Kappa = 0.8086 and 0.806), satisfying the accuracy requirements. This paper aims to enrich the theoretical and technical system of ecological environment research by studying the dynamic change, driving mechanism, and spatiotemporal prediction of the normalized vegetation index. Its results can provide the necessary theoretical basis for the simulation and prediction of vegetation indexes.

The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km², air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR.

Methods based on the absorption Ångström exponent (AAE) are widely used to estimate the absorption by brown carbon (BrC), and the estimated absorption by BrC can be significantly different from 0, even for pure black carbon (BC). However, few studies have systematically quantified the effects of BC microphysical properties. Moreover, the conditions under which AAE-based methods are applicable are still unclear. In this work, we used BC models partially coated with non-absorbing materials to calculate the total absorption. Since the total absorption is entirely due to BC, the estimated BrC absorption should be 0 if the retrieval methods are accurate. Thus, the ratio of the estimated BrC absorption to BC absorption (ABS_BrC) should be the proportion of the BC absorption that is incorrectly attributed to BrC. The results show that a BC AAE of 1 can generally provide reasonable estimates for freshly emitted BC, since ABS_BrC is generally in the range of −4.8 % to 2.7 % during that period. However, when BC aerosols are aged, ABS_BrC can sometimes reach about 38.7 %. The wavelength dependence of the AAE (WDA) method does not necessarily improve the estimates; sometimes a negative ABS_BrC of about −40.8 % is found for partially coated BC. By combining simulations of a global chemical transport model, this work also quantified the effects of BC microphysical properties on BrC global aerosol absorption optical depth (AAOD) estimates. The AAE = 1 method sometimes leads to a misassigned global mean AAOD of about −0.43–$\mathrm{0.46}\times {\mathrm{10}}^{-\mathrm{3}}$ if the BC aerosols have a complex morphology. The WDA method does not necessarily improve the estimates. In our cases, the WDA methods based on spherical models could lead to a global-mean misassigned AAOD range of about −0.87–$\mathrm{0.04}\times {\mathrm{10}}^{-\mathrm{3}}$ . At the regional scale, the AAE = 1 method sometimes leads to a distributed AAOD of about −7.3 to $\mathrm{5.7}\times {\mathrm{10}}^{-\mathrm{3}}$ in some specific regions. Mie-theory-based WDA methods lead to an estimated AAOD error of about $-\mathrm{22}\times {\mathrm{10}}^{-\mathrm{3}}$ in some regions (e.g., East Asia). This work also showed that the misattributed BrC absorption would lead to substantial uncertainties in the estimation of the global direct radiative forcing (DRF) of absorbing aerosols from different sources.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols’ impact and trends.