Although a remarkable reduction in the frequency of sand and dust storms (SDSs) in the past several decades has been reported over northern China (NC), two unexpected mega SDSs occurred on 15–20 and 27–29 March 2021 (abbreviated as the “3.15” and “3.27” SDS events), which has reawakened widespread concern. This study characterizes the optical, microphysical, and radiative properties of aerosols and their meteorological drivers during these two SDS events using the Sun photometer observations in Beijing and a comprehensive set of multiple satellite (including MODIS, VIIRS, CALIOP, and Himawari-8) and ground-based observations (including the CMA visibility network and AD-Net) combined with atmospheric reanalysis data. Moreover, a long-term (2000–2021) dust optical depth (DOD) dataset retrieved from MODIS measurements was also utilized to evaluate the historical ranking of the dust loading in NC during dust events. During the 3.15 and 3.27 events, the invasion of dust plumes greatly degraded the visibility over large areas of NC, with extreme low visibility of 50 and 500 m recorded at most sites on 15 and 28 March, respectively. Despite the shorter duration of the 3.27 event relative to the 3.15 event, sun photometer and satellite observations in Beijing recorded a larger peak AOD (∼2.5) in the former than in the latter (∼2.0), which was mainly attributed to the short-term intrusion of coarse-mode dust particles with larger effective radii (∼1.9 µm) and volume concentrations (∼2.0 µm³ µm⁻²) during the 3.27 event. The shortwave direct aerosol radiative forcing induced by dust was estimated to be −92.1 and −111.4 W m⁻² at the top of the atmosphere, −184.7 and −296.2 W m⁻² at the surface, and +92.6 and +184.8 W m⁻² in the atmosphere in Beijing during the 3.15 and 3.27 events, respectively. CALIOP observations show that during the 3.15 event the dust plume was lifted to an altitude of 4–8 km, and its range of impact extended from the dust source to the eastern coast of China. In contrast, the lifting height of the dust plume during the 3.27 event was lower than that during the 3.15 event, which was also confirmed by ground-based lidar observations. The MODIS-retrieved DOD data registered these two massive SDS events as the most intense episode in the same period in history over the past 2 decades. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favored enhanced dust emissions in the Gobi Desert (GD) across southern Mongolia and NC. Meteorological analysis revealed that both SDS events were triggered by an exceptionally strong Mongolian cyclone generated at nearly the same location (along the central and eastern plateau of Inner Mongolia) in conjunction with a surface-level cold high-pressure system at the rear, albeit with differences in magnitude and spatial extent of impact. In the GD, the early melting of spring snow caused by near-surface temperature anomalies over dust source regions, together with negative soil moisture anomalies induced by decreased precipitation, formed drier and barer soil surfaces, which allowed for increased emissions of dust into the atmosphere by strongly enhanced surface winds generated by the Mongolian cyclone.

An extreme haze event occurred in the Beijing area from 17 to 23 December 2015. Ground-based measurements and satellite observations during this event were used to further our understanding of the formation process of haze pollution and aerosol optical properties. The results suggest that high relative humidity, poor diffusion conditions (low wind speed and stable stratification) and favorable secondary transformation conditions under the hygroscopic growth of aerosol and high emissions led to this serious haze episode. During the haze period, the daily average value was 1.15 and 0.42 for aerosol optical depth (AOD_500nm) and columnar water-vapor (CWV, in cm), respectively. On 19 December, the correlation coefficient between CWV and AOD_500nm was 0.91, indicating the effect of hygroscopic growth of fine-mode articles. The daily average values for Ångström exponent, fine-mode fraction, aerosol absorption optical depth, and Ångström absorption exponent were 1.19, 0.81, 0.11 and 1.47, respectively, which suggests that fine aerosol particles were dominant in the atmosphere and fine-mode particles were the dominant contributor to atmospheric extinction during the haze period. Moreover, it also reflects that there were more absorbing aerosol particles during the haze period. Compared with other polluted periods with a bimodal distribution, there was an obvious trimodal distribution on 19 December. There were three peaks at radii of about 0.1 μm, 0.5–0.8 μm and 4 μm. Satellite observations show that there was an obvious aerosol layer in the Beijing area during the haze period, concentrated at ground level to within 2 km in the upper layers. The types of aerosol were mainly composed of mixed pollution aerosols.

A special period in Beijing from 6 August to 17 September 2015, during which the World Athletics Championships and Victory Day military parade took place, and which involved measures to restrict traffic and reduce factory emissions, was selected to analyze the aerosol optical properties and the impact of meteorological conditions on pollution levels. The study was based on AERONET observational and retrieval data, particulate matter measurements (TEOM 1405), meteorological data, and then the HYSPLIT model was used to analyze the pollution sources. The study period was divided into three sub-periods according to the different stages of implementation of the control measures, and the main conclusions can be summarized as follows. During the period in which the restrictive measures were applied, the air quality improved significantly, with the average value of the AOD being 0.34 ± 0.20, about 69% less than before. Meanwhile, the average Ångström exponent was about 9.5% higher than before, with an average value of 1.38 ± 0.25, indicating that the main pollutants were fine particles. Single scattering albedo decreased as wavelength increased, being higher than in the other two stages (mean value of 0.944 ± 0.045). This showed that the strong scattering capacity and absorption aerosol optical depth was at its lowest, at about 0.008 ± 0.009. The peaks of aerosol volume concentration in the fine and coarse mode were significantly reduced. Meteorological conditions also had a certain effect on the aerosol optical properties, with the blowing of clean and dry wind and the occurrence of precipitation contributing to the overall improvement in air quality.

Aerosol forecasting is important for air-quality management, health risk assessment and climate change mitigation^1,2. However, it is more complex than weather forecasting, owing to the interactions between aerosol physicochemical processes and atmospheric dynamics, resulting in high uncertainty and computational costs^3,4. Here we develop a machine-learning-driven Global Aerosol–Meteorology Forecasting System (AI-GAMFS), which provides reliable 5-day, 3-hourly forecasts of aerosol optical components and surface concentrations. AI-GAMFS combines a vision transformer and U-Net in a backbone network, robustly capturing the complex aerosol–meteorology interactions via global attention and spatiotemporal encoding. Trained on 42 years of aerosol reanalysis data and initialized with Global Earth Observing System Forward Processing (GEOS-FP) analyses, AI-GAMFS delivers operational 5-day forecasts in 1 minute. Evaluation with independent ground-based observations suggests improved performance compared with the Copernicus Atmosphere Monitoring Service⁵ and regional dust models^6,7,8,9 in forecasting aerosol optical depth and dust components. Compared with GEOS-FP¹⁰, it has a lower root-mean-square error for global aerosol optical depth, with comparable dust forecasting skill and improved surface aerosol component forecasts over the USA and China. Our results provide a step forward in leveraging machine learning to refine aerosol forecasting and may help warn against aerosol pollution events such as dust storms and wildfires.

Northern China experienced a severe sand and dust storm (SDS) on 14/15 March 2021. It was difficult to simulate this severe SDS event accurately. This study compared the performances of three dust-emission schemes on simulating PM₁₀ concentration during this SDS event by implementing three vertical dust flux parameterizations in the Comprehensive Air-Quality Model with Extensions (CAMx) model. Additionally, a statistical gusty-wind model was implemented in the dust-emission scheme, and it was used to quantify the gusty-wind contribution to dust emissions and peak PM₁₀ concentration. As a result, the LS scheme (Lu and Shao 1999) produced the minimum errors for peak PM₁₀ concentrations, the MB scheme (Marticorena and Bergametti 1995) underestimated the PM₁₀ concentrations by 70–90%, and the KOK scheme (Kok et al. 2014) overestimated PM₁₀ concentrations by 10–50% in most areas. The gusty-wind model could reasonably reproduce the probability density function of 2-min wind speeds. There were 5–40% more dust-emission flux and 5–40% more peak PM₁₀ concentrations generated by the gusty wind than the hourly wind in the dust-source regions. The increase of peak PM₁₀ concentration caused by gusty wind in the non-dust-source regions was higher than in the dust-source regions, with 10–50%. Implementing the gusty-wind model could help improve the LS scheme’s performance in simulating PM₁₀ concentrations of this severe SDS event. More work is still needed to investigate the reliability of the gusty-wind model and LS scheme on various SDS events.

The difference in meteorological factors (such as weather phenomena, wind speed, and visibility) of sand–dust weather between China and Mongolia from 2011 to 2021 was analyzed using meteorological observational data and international exchange of meteorological observation data. Additionally, consistency analysis was performed by integrating satellite retrieval products with meteorological observation data. The results showed that the average annual frequency of sand–dust weather in Mongolia was significantly higher than that in China. In China, the sand–dust weather was mainly characterized by floating dust or blowing dust, while in Mongolia, it was primarily characterized by blowing dust or a sand and dust storm. The average annual wind speed and visibility during sand–dust weather in Mongolia were relatively higher than those in China. Based on the dust grade standard of China, when the floating dust occurred in Mongolia, there were cases with wind speed > level 3 and visibility > 10 km; when the blowing dust or sand and dust storm occurred in Mongolia, there were cases with wind speed ≤ level 3 and visibility > 10 km. In China, the sand–dust weather mainly occurred in the spring, while the sand-dust weather occurred frequently throughout the year in Mongolia. The number of days with dust lasting for 2 days or more in Mongolia exceeded that of China, and Mongolia had a significant impact on the sand–dust weather in China. According to the ground observation data and satellite retrieve products during the dust events, all dust events that significantly affected China and Mongolia during the same period from 2021 to 2022 were classified into three categories; among them, the proportion of types of large-scale sand–dust weather phenomena observed by both satellite and ground observation stations was significantly higher (6 times). By integrating ground observation data and satellite retrieval products and following the dust grade standard of China, the consistent correction of sand–dust weather phenomena was carried out. This laid the foundation for the future development of international dust grade standards and provided technological support for improved dust forecasting services in the Asian region.