# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 1 Introduction  
Effective management of hazardous substance releases is a cornerstone of industrial safety. In recent years, crisis management tools, such as ALOHA (Areal Locations of Hazardous Atmospheres), have become indispensable for predicting and modelling the spread of chemical hazards, providing critical data for emergency responses. However, despite the widespread adoption of these tools, several challenges persist in ensuring they fully address the complexities of real-world scenarios [1]. One of the primary challenges lies in the precision of the models used. Real-world scenarios are influenced by a myriad of factors, including fluctuating weather conditions, diverse terrains, and unpredictable human activity, all of which can significantly alter the spread of hazardous substances [2]. While ALOHA is highly effective for modelling short-term incidents, it often encounters limitations when addressing long-term health risks from exposure, as well as potential environmental impacts that may arise following a release [3, 4]. For example, long-term exposure to low concentrations of ammonia in the environment, especially in enclosed or poorly ventilated areas, can lead to chronic respiratory issues and other health complications. From an environmental perspective, the accumulation of ammonia in soil and water sources can lead to the degradation of water quality and damage to local ecosystems. These gaps create a critical shortfall in comprehensive risk management, particularly in industrial settings where both immediate and long-term risks must be considered. Another significant challenge in crisis management is the integration of various tools and technologies. Tools such as Geographic Information Systems (GIS) and real-time environmental monitoring provide enormous potential to enhance the capabilities of current models [5; 6]. However, the current level of integration remains insufficient, slowing down decision-making during time-critical situations [7]. Moreover, many software systems, including ALOHA, rely heavily on accurate input data, which may not always be available in real time, further complicating crisis management efforts [8]. ALOHA has proven particularly valuable for modelling chemical releases, such as ammonia, providing reliable predictions of danger zones and supporting evacuation planning. Its adaptability in incorporating meteorological and chemical data makes it a preferred tool in industrial safety [9; 10]. Nevertheless, its strength in modelling short-term, localised incidents raises questions about its efficacy in long-duration or large-scale accidents [11]. For example, long-term contamination from a persistent ammonia release could result in delayed toxic effects on the environment, including contamination of water sources and soil, with negative impacts on ecosystems and public health. This study seeks to address these concerns by expanding ALOHA's application, integrating not only immediate hazard assessments but also long-term risk management. One of ALOHA's key features is its dual-layered approach: the offline component allows for detailed pre-incident planning, providing a framework for simulating potential emergency scenarios [12]. This offers emergency personnel a foundation for preparedness, enabling them to model various outcomes and develop evacuation strategies before an incident occurs. On the other hand, ALOHA's real-time online component allows dynamic adjustment of predictions as new data emerges during a crisis. By combining pre-event simulations with real-time updates, ALOHA helps refine decision-making during emergencies [13; 14]. In this study, we explore the role of ALOHA not only in immediate crisis management but also in long-term risk mitigation. We address both acute risks associated with chemical releases and the long-term environmental and health consequences of such incidents. The research emphasises the importance of real-time data integration, improving user accessibility, and enhancing the accuracy of long-term hazard predictions.