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In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Urban agglomerations (UAs) are the main battlefield of urbanization and the most concentrated areas of carbon emissions (CEs). Nevertheless, limited studies have examined the impact of urbanization level (UL) on CEs in UAs in China. This study aimed to identify the spatial relationship between UL and CEs in Chinese UAs and to conduct a comprehensive analysis of the differences in CEs caused by urbanization. The findings would provide scientific support for the China's dual-carbon goals and the achievement of green and low-carbon urban development. Spatial variations in UL and CEs in 19 Chinese UAs were assessed in 2000, 2010, and 2020 using distribution dynamics and spatial regression models. The results indicated that the UL of UAs in China evidently increased over time, and UAs contributed approximately 80% of the national CEs. Significant spatial dependence was identified between urbanization factors and CEs. The regression results indicated that an increase in UL promoted the growth of CEs, and the form of the urban land had a significant and highly variable impact on CEs. Our findings provide a valuable case study for exploring relationships between UL and CEs in other UAs worldwide. [ABSTRACT FROM AUTHOR]

The scaling of agricultural land is a trend in land use transformation and is important for modernizing agriculture. Therefore, the reasons for large-scale agricultural land formation should be explored. The spatial distribution of large-scale agricultural land and the factors for its formation vary between different regions. Currently, the exploration of the formation mechanism of large-scale agricultural land from the perspective of heterogeneity is not yet sufficient. Therefore, the main objectives of this article are as follows: first, analyze the spatial pattern characteristics of large-scale agricultural land; second, explore the spatial heterogeneity characteristics of influencing factors from both global and local perspectives; third, explore the mechanism of the formation of large-scale agricultural land from the perspective of heterogeneity. The results indicate the following: (1) The large-scale agricultural land distribution pattern in the Wuling Mountains area was high in the east and low in the west. (2) Natural conditions, production factors, and location conditions all significantly impacted large-scale agricultural land, but with differences in their degree of influence. From a local perspective, the influences of various factors in different regions also exhibited spatial heterogeneity. These two types of heterogeneity can be attributed to the differences in regional development stages. (3) Natural conditions, location conditions, and production factors had negative, positive, and positive effects on the agricultural land scale, respectively, but the influence of the first two decreased with the improvement in the regional development stages. The influence of different factors on production factors was related to the regional development stage, and production factors that were suitable for the regional development stage had a greater impact. The conclusion can provide differentiated policy support for regional land use in practice. [ABSTRACT FROM AUTHOR]