Two severe dust storm (DS) events (15–17 March and 28–29 March) hit northern China in 2021 consecutively. The lower atmospheric vertical dynamic and thermal structures during the two cases were compared using the ground-based sensor data from the microwave radiometer and radar wind profiler, combined with the environmental and meteorological observations data in Jinan, China. It was found that both cases occurred under the background of cold vortexes over northeastern China. The dust was transported through the cold air on the northwest route. During the dust period, 2–3 km was the west or northwest airflow, and below 2 km was the northeast wind. The variation in the dynamic structure determined the duration of the DS. During the DS maintenance phase, the vertical wind shear (VWS) below 3 km measured approximately 10 m∙(s∙km)⁻¹. The increased VWS during the dust intrusion period facilitated the transportation of dust. In contrast, the more significant VWS was not conducive to the maintenance of DS, and the shift to south wind control in the upper middle layer indicated the weakening of DS. In both cases, we observed a cliff-like decrease in relative humidity as a prominent indicator of dust outbreaks, occurring approximately 2–5 h beforehand. The diurnal difference between the vertical temperature and relative humidity during the dust maintenance period was found to be insignificant.

The Taklimakan Desert (TD), located in the Tarim Basin, and the Gobi Desert (GD), spanning northern China and southern Mongolia, are the two major dust source regions in East Asia, with substantial influence on China’s atmospheric environment and ecosystem. Using dust storm day (DSD) observations from national meteorological stations between 2000 and 2024, along with meteorological variables and the Normalized Difference Vegetation Index (NDVI), this study examines the spatiotemporal trends of dust storms and their key driving factors in both source regions. The TD and GD regions exhibit high levels of dust storm activity in China, with a distinct decreasing gradient from the source areas to downstream regions observed across all seasons. Trend analysis of DSD reveals distinct temporal patterns: the TD region experienced a moderate decline (−0.11 d/a) followed by a fluctuating increase (0.04 d/a), while the GD region exhibited a sharp drop (−0.32 d/a) and a subsequent marked resurgence (0.09 d/a). Seasonally, dust storm events in the TD region were frequent in both spring and summer, whereas in the GD region they were concentrated almost entirely in spring. In both source regions, spring dust storm events were closely linked to wind speed and the frequency of strong wind days. However, in summer, precipitation played a more suppressive role in the GD region, while wind remained the primary driver in the TD region. Between 2001 and 2013, both regions experienced a reduction in barren land area, aligning with a decline in dust storm events. In contrast, barren land in the GD region expanded from 2013 to 2023, accompanied by a significant increase in dust storm events. Notably, in the GD region, spring and summer NDVI values were negatively correlated with DSD, indicating stronger vegetation control over dust emission. In contrast, this relationship was weaker in the TD region, underscoring distinct ecological–meteorological interactions between the two desert systems.

Accurate identification of dust emission sources is crucial for simulating dust aerosols in atmospheric chemical models. Therefore, a dynamically updated dust source function (DSF) was developed within the dust emission scheme of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an East Asian dust storm event from 13 to 16 March 2021. Utilizing satellite-derived input of vegetation cover, snow cover, soil texture, and land use, the DSF was updated to better identify dust source areas over bare soils and sparsely vegetated regions in western China and central-western Mongolia. With the updated DSF, simulated dust emissions increase significantly over western China and Mongolia. The dust aerosol simulations demonstrate substantial improvements in near-surface PM₁₀ concentrations, a better agreement with remotely sensed dust aerosol optical depth (DOD), and a more accurate representation of the vertical distribution of dust extinction coefficients compared to observations. This study highlights the importance of integrating real-time data to accurately characterize dust emission sources, thereby improving atmospheric environment simulations.

To evaluate the applicability of online monitoring for water-soluble ions and inorganic elements in atmospheric PM_2.5, this study conducted a comparative analysis using both online and manual monitoring data collected in Taizhou during autumn and winter (18 September 2023–6 January 2024). The Positive Matrix Factorization (PMF) model was employed for source apportionment. For water-soluble ions, online and manual results for Cl⁻, NO₃⁻, SO₄²⁻, NH₄⁺, and K⁺ showed good agreement (R² > 0.8), while Na⁺ exhibited moderate correlation and Ca²⁺/Mg²⁺ showed poor correlation. For inorganic elements, eight elements (K, Ca, Cr, Mn, Fe, Zn, Pb, Si) agreed well between methods, though online values were consistently lower than manual ones. Cu and Ni showed poor correlation. The PMF source apportionment results from online and manual monitoring data indicate that PM_2.5 in Taizhou during autumn and winter mainly originated from six sources: traffic (25.8%, 28.9%), secondary nitrate (18.6%, 20.9%), dust (16.1%, 14.2%), sea salt (15.8%, 11.5%), secondary (12.1%, 13.8%) and biomass burning (11.6%, 10.7%). Sensitivity analysis confirmed the robustness of PMF results to uncertainty assumptions. Overall, the results suggest that online monitoring data are generally applicable for PM_2.5 chemical characterization and source apportionment in Taizhou.