## 1 Introduction  
In our increasingly interconnected world, grasping disasters' geospatial and temporal patterns has become vital for effective disaster management and resilience planning [1]. Disasters, whether natural or human-induced, can have profound impacts on communities, economies, and the environment [2; 3]. Environmental hazards, particularly air pollution, are of critical importance due to their role in increasing the susceptibility of populations to both natural and technological disasters. Specifically, air pollution poses direct threats to health and also compounds the effects of other environmental stressors, thereby elevating the overall risks faced by communities [4]. Advances in big data analytics, alongside the integration of various data sources, have greatly enhanced our ability to track and assess these risks [5]. For instance, the utilization of big data has enabled the merging of a wide range of environmental information, resulting in more refined risk evaluations and the formulation of more precise mitigation strategies [6]. These advancements are becoming indispensable for addressing the complex challenges posed by the convergence of air pollution with other disaster-related risks. The risk of a disaster occurring is always a combination of vulnerability and exposure due to inadequate preparation or coping capacity [7]. This underscores the necessity of delving into the complex dynamics that influence their occurrence and distribution. Environmental events like earthquakes, floods, hurricanes, and wildfires are driven by natural and climatic forces [8; 9; 10; 11; 12; 13; 14; 15]. The patterns of these disasters, including their frequency, intensity, and locations, have evolved over time due to shifts in climate, land usage, and population growth [16; 17; 18; 19; 20; 21]. Also, trends can be uncovered and high-risk areas identified through the analysis of past data, thus improving the ability to forecast and respond to future events [22]. In contrast, man-made (technological) disasters arise from human activities such as industrial mishaps, nuclear accidents, and oil spills. These incidents often occur due to equipment failures, human and organizational mistakes [23], or weak regulatory measures. Failures can occur during the design, construction, operation or maintenance phases [24; 25]. Insights into industrial operations and regulatory practices can be gained by examining when and where these disasters happen, leading to improved safety protocols and emergency response strategies [26]. Recent research has shed light on significant advancements and methodologies for understanding the geospatial and temporal patterns of natural disasters [27; 28; 29]. [PERSON] et al. [30], for instance, use textual records to uncover the spatial-temporal patterns of natural disasters in China. Their findings highlight critical intervals and interactions between events, offering valuable guidance for disaster prevention efforts [30]. [PERSON] [31] explores the integration of various geospatial technologies and data sources in disaster management. Also, he emphasizes how AI and big data can enhance emergency responses and decision-making processes. On the other hand, [PERSON] [32] delves into the historical patterns of marine disasters in China, providing a multiscalar analysis of their temporal changes and spatial evolution. This research helps in understanding long-term trends and impacts ([PERSON], 2020). Additionally, [PERSON] et al. [33] conduct a comprehensive study on the spatial-temporal evolution and prediction of flood disasters in China over the past 500 years, highlighting critical trends and future risks. Together, these studies deepen our understanding of disaster patterns and offer valuable insights for effective disaster management and mitigation strategies. The wealth of existing research on this subject provides crucial insights into the factors shaping these disaster patterns [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42].