The climate effect and environmental pollution caused by dust discharged into the atmosphere have attracted much attention. However, the driving factors of dust emissions have not been studied thoroughly. Here, spatiotemporal variations in dust emissions and the relationship between dust emissions and large-scale atmospheric circulation in East Asia from 2000 to 2021 were investigated using Modern-Era Retrospective Analysis for Research and Applications version 2, Cloud-Aerosol Lidar Pathfinder Satellite Observations, ERA5 reanalysis data, and climate indices. Results showed that the Taklimakan Desert in the Tarim Basin, the Gurbantonggut Desert in the Junggar Basin, the Turpan Basin, and the Gobi Desert in western Inner Mongolia and southern Mongolia are the main sources of dust emissions in East Asia. The period of strong dust emissions is from March to May, and emissions to the atmosphere were mainly distributed at 0–4 km in the troposphere. In the eastern and southwestern Tarim Basin, northern Junggar Basin, and parts of the Gobi Desert in southern Mongolia, dust emissions have significantly increased over the past 22 years, whereas in the southwestern Tibetan Plateau, southwestern Inner Mongolia, and a small part of the northern Mongolian Gobi Desert there was a significant decreasing trend. The winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with East Asian dust emissions the following spring. The various phases of the AO/NAO coupling have clear different effects on East Asian dust emissions in the spring. When the AO/NAO coupling was negative (positive), the East Asian trough and Siberian High were strengthened (weakened), the frequency of cold air activity increased (weakened), 800 hPa wind speed strengthened (weakened), and East Asian emissions increased (decreased). In AO−/NAO+ years, the Asian polar vortex was stronger to the south and the East Asian trough was stronger to the west. The Lake Baikal trough was in the deepening phase, which caused more polar cold air to move into East Asia, aggravating the intensity of dust activity. In the AO+/NAO− years, the Siberian High and East Asian trough weakened, which was unfavorable to the southward movement of cold air from Siberia. Therefore, the frequency of windy weather in East Asia decreased, partly weakening dust emissions. However, a positive geopotential anomaly in northeast China and a negative geopotential anomaly in South Asia triggered an anomalous enhancement in easterly wind in the tropospheric area over northwest China. Strengthening of the Balkhash trough provides favorable conditions for gale weather in northwest China. The frequency of gale weather increased, and dust emissions were enhanced in northwest China.

The Tibetan Plateau ozone valley is a prominent summertime ozone minimum in the Northern Hemisphere upper troposphere–lower stratosphere (UTLS), with implications for regional radiative balance and climate. Although previous studies emphasized large-scale circulation during the Asian summer monsoon, the contributions of local topographic and thermodynamic processes remain less well quantified, in part because most analyses rely on coarse-resolution data. Here we develop a refined multi-source remote sensing framework integrating high-resolution TROPOMI observations (total column ozone, NO₂, and HCHO), Microwave Limb Sounder (MLS) ozone profiles, ERA5 reanalysis, and SRTM topography. Within a physically informed scheme, local factors are treated as pixel-level predictors and large-scale circulation as regional-mean background fields, complemented by interpretable machine-learning attribution (XGBoost-SHAP). The ozone valley exhibits a pronounced horizontal and vertical structure, with its core anomaly confined to the UTLS and spatially anchored to plateau topography. The covariability between the South Asian High (SAH) and ozone mainly reflects a shared response to thermal forcing, while circulation modulates its amplitude. Attribution shows that topography (~41%) and stratosphere–troposphere exchange (~16%) together dominate the spatial organization of the ozone minimum (~58% of the explained variance; anomaly–elevation slope −0.0035 DU m⁻¹, R² = 0.89), whereas large-scale circulation (~13%) mainly drives its seasonal amplification. Mechanistically, the elevated plateau and intense summer heating lift the tropopause, injecting ozone-poor tropospheric air into the stratospheric reservoir and thereby diluting its ozone content. Consequently, while topographic and thermodynamic factors dictate the spatial pattern of ozone, large-scale circulation primarily modulates its seasonal amplitude over complex terrain. The ozone valley thus represents a quasi-stationary climatic imprint, and our framework advances refined satellite-based atmospheric observation over complex terrain.

Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO₂/HCHO, and surface observations within machine learning models, achieving cross-validated R² of 0.94 and RMSE of 12.05 μg/m³ for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.91, RMSE = 10.83 μg/m³) and reproduce seasonal gradients, with summer concentrations (131 μg/m³) more than double winter levels (61 μg/m³). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is potentially applicable to other regions and sensors under similar conditions, providing a case study for integrating national satellite products into multi-source surface ozone estimation.

Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China's Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO2/HCHO, and surface observations within machine learning models, achieving cross-validated R2 of 0.94 and RMSE of 12.29 μg/m3 for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.97, RMSE = 10.05 μg/m3) and reproduce seasonal gradients, with summer concentrations (130 μg/m3) more than double winter levels (59 μg/m3). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is transferable to other regions and sensors, supporting broader applications of national satellite assets in air pollution monitoring.