Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.

Land surface temperature (LST) is an important parameter that affects the water cycle, environmental changes, and energy balance at global and regional scales. Herein, a time series analysis was conducted to estimate the monthly, seasonal, and interannual variations in LST during 2001–2019 in the Tarim Basin, China. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, air temperature, air pressure, relative humidity, wind speed, precipitation, elevation, and land-cover type data, we analyzed the spatio-temporal change characteristics of LST and the influencing factors. High LSTs occurred in the desert and plains and low LSTs occurred in surrounding mountain regions. The highest LST was recorded in July (25.1 °C) and the lowest was in January (−9.5 °C). On a seasonal scale, LST decreased in the order: summer > spring > autumn > winter. Annual LST showed an increasing trend of 0.2 °C/10 a in the desert and mountain areas, while the plains indicated a decreasing trend. In spring and autumn, western regions were dominated by a downward trend, whereas in winter a downward trend occurred in eastern regions. In summer, areas covered by vegetation were dominated by a downward trend, and desert and bare lands were dominated by an upward trend. Random forest (RF) model analysis showed that elevation was the most significant influencing factor (22.1%), followed by mean air temperature (20.1%). Correlation analysis showed that the main climatic factors air temperature, relative humidity, and elevation have a good correlation with the LST. Land-cover type also affected LST; during February–December the lowest LST was observed for permanent glacier snow and the highest was observed in the desert. El Nino and La Nina greatly influenced the LST variations. The North Atlantic Oscillation and Pacific Decadal Oscillation indices were consistent with the mean LST anomaly, indicating their considerable influence on LST variations.

Cluster analyses, potential source contribution function (PSCF) and concentration-weight trajectory (CWT) were used to identify the main transport pathways and potential source regions with hourly PM_2.5 and PM₁₀ concentrations in different seasons from January 2017 to December 2019 at Akedala Station, located in northwest China (Central Asia). The annual mean concentrations of PM_2.5 and PM₁₀ were 11.63 ± 9.31 and 19.99 ± 14.39 µg/m³, respectively. The air pollution was most polluted in winter, and the dominant part of PM₁₀ (between 54 to 76%) constituted PM_2.5 aerosols in Akedala. Particulate pollution in Akedala can be traced back to eastern Kazakhstan, northern Xinjiang, and western Mongolia. The cluster analyses showed that the Akedala atmosphere was mainly affected by air masses transported from the northwest. The PM_2.5 and PM₁₀ mainly came with air masses from the central and eastern regions of Kazakhstan, which are characterized by highly industrialized and semi-arid desert areas. In addition, the analyses of the pressure profile of back-trajectories showed that air mass distribution were mainly distributed above 840 hPa. This indicates that PM_2.5 and PM₁₀ concentrations were strongly affected by high altitude air masses. According to the results of the PSCF and CWT methods, the main potential source areas of PM_2.5 were very similar to those of PM₁₀. In winter and autumn, the main potential source areas with high weighted PSCF values were located in the eastern regions of Kazakhstan, northern Xinjiang, and western Mongolia. These areas contributed the highest PM_2.5 concentrations from 25 to 40 µg/m³ and PM₁₀ concentrations from 30 to 60 µg/m³ in these seasons. In spring and summer, the potential source areas with the high weighted PSCF values were distributed in eastern Kazakhstan, northern Xinjiang, the border between northeast Kazakhstan, and southern Russia. These areas contributed the highest PM_2.5 concentrations from 10 to 20 µg/m³ and PM₁₀ concentrations from 20 to 60 µg/m³ in these seasons.

Near-surface wind measurements obtained with five 100-m meteorology towers, 39 regional automatic stations, and simulations by the Weather Research and Forecasting (WRF) model were used to investigate the spatial structure of topography-driven flows in the complex urban terrain of Urumqi, China. The results showed that the wind directions were mainly northerly and southerly within the reach of 100 m above ground in the southern suburbs, urban area, and northern suburbs, which were consistent with the form of the Urumqi gorge. Strong winds were observed in southern suburbs, whereas the winds in the urban, northern suburbs, and northern rural areas were weak. Static wind occurred more frequently in the urban and northern rural areas than in the southern suburbs. In the southern suburbs, wind speed was relatively high throughout the year and did not show significant seasonal variations. The average annual wind speed in this region varied among 1.9–5.5, 1.1–3.6, 1.2–4.3, 1.2–4.3, and 1.1–3.5 m s ⁻¹ within the reach of 100 m above ground at Yannanlijiao, Shuitashan, Liyushan, Hongguangshan, and Midong, respectively. The flow characteristics comprised more airflows around the mountain, where the convergence and divergence were dominated by the terrain in eastern and southwestern Urumqi. Further analysis showed that there was a significant mountain–valley wind in spring, summer, and autumn, which occurred more frequently in spring and summer for 10–11 h in urban and northern suburbs. During daytime, there was a northerly valley wind, whereas at night there was a southerly mountain wind. The conversion time from the mountain wind to the valley wind was during 0800–1000 LST (Local Standard Time), while the conversion from the valley wind to the mountain wind was during 1900–2100 LST. The influence of the mountain–valley wind in Urumqi City was most obvious at 850 hPa, according to the WRF model.

Based on observations from the Unmanned Aerial Vehicle (UAV) together with an environmental particulate matter analyzer (Grimm-180) and Global Positioning System (GPS) sounding balloons, the vertical structure of dust with different particle sizes was explored over the Taklimakan Desert (TD) during an intensive observation from 1 July 2021 to 31 July 2021. The power functions were fitted between the particle counts and particle sizes, indicating negative correlations with an R² higher than 0.99 under different dust pollution conditions in Tazhong (TZ). The dust concentrations show a sharp vertical increase over the TD during dust pollution; however, more particles with larger sizes are entrained into the air in TZ compared with Minfeng (MF). The total solar radiation during dust pollution days is significantly weakened, accompanied by major modifications in the temperature stratification, which were characterized by low-level cooling (with −2.71 K mean intensity) and high-level heating (with +0.70 K mean intensity). On clear days, the average convective boundary layer (CBL) heights at the TZ and MF are approximately 3.94 and 2.84 km, respectively, and the average stable boundary layer (SBL) height at the TZ and MF are approximately 0.19 and 0.14 km, respectively. With the increasing dust pollution level, the CBL height decreases rapidly while the SBL height shows the opposite trend. The unique ultra-high atmospheric boundary layer structure in daytime provides beneficial conditions for the suspension and vertical transportation of dust over TD. Moreover, a negative correlation between the CBL height and near-surface PM₁₀/PM_2.5/PM_1.0 concentration in TD is revealed by power function fittings.