In April 2023, a major dust storm event in Lanzhou attracted widespread attention. This study provides a comprehensive analysis of the causes, progression, and dust sources of this event using multiple data sources and methods. Backward trajectory analysis using the HYSPLIT model was employed to trace the origins of the dust, while FY-2H satellite data provided high-resolution dust distribution patterns. Additionally, the MAIAC AOD product was used to analyze Aerosol Optical Depth, and concentration-weighted trajectory (CWT) analysis was used to identify key dust source regions. The study found that PM₁₀ played a dominant role in the storm, and the AOD values during the storm in Lanzhou were significantly higher than the annual average, highlighting the severe impact on regional air quality. Key meteorological conditions influencing the storm’s occurrence were analyzed, including the formation and eastward movement of a high-potential ridge, convection driven by diurnal temperature variations, and surface temperature increases coupled with decreased relative humidity, which together promoted the generation and development of dust. Backward trajectory and dust distribution analyses revealed that the dust primarily originated from Central Asia, western Mongolia, Xinjiang, and Gansu. From the 19th to the 21st, the dust distribution showed similarities between day and night, with a noticeable increase in dust concentration from night to day due to strong vertical atmospheric mixing. To mitigate the impacts of future dust storms, this study highlights both short-term and long-term strategies, including enhanced monitoring systems, public health advisories, and vegetation restoration in key source regions. Strengthening regional and international cooperation for transboundary dust management is also emphasized as critical for sustainable mitigation efforts. These findings are significant for understanding and predicting the causes, characteristics, and environmental impacts of dust storms in Lanzhou and the Northwestern region.

Economic development，national strategic science and technology strength，modern industrial system

Emergency management requires structured representations that can integrate heterogeneous event reports and support cross-event analysis. This study develops an emergency event knowledge graph (EE-KG) by combining fine-grained event classification, lifecycle-oriented ontology modeling, and ontology-guided knowledge extraction. We collected 6,418 Chinese emergency-related reports from public government portals, news websites, and safety information platforms published between 2000 and 2024, and aggregated them into 1,971 event records. A manually annotated multi-label dataset was constructed according to four primary categories and 36 subcategories. Four classification strategies were evaluated: fine-tuned ERNIE 3.0, prompt-based GLM-4-9B-Chat, agent-based GLM-4-9B-Chat with reflective reasoning, and fine-tuned GLM-4-9B-Chat. The fine-tuned GLM-4-9B-Chat achieved the best performance, with an accuracy of 0.80 and a micro-F1 score of 0.85. Classification outputs were used to assign standardized event types to event nodes, while a five-dimensional ontology guided the extraction of event description, impact assessment, emergency response, recovery and reconstruction, and evaluation information. The resulting EE-KG contains 16,057 nodes and 18,428 relationships. Expert review indicates that the proposed framework provides a reusable structure for emergency event knowledge organization and analysis.