Enhancing Emergency Response Research Articles

Numerical simulation and risk assessment of toluene tank leakage in petrochemical industries, China.

Large-capacity toluene storage tank leakage accidents can lead to severe casualties and environmental damage. In this study, numerical simulation of the toluene leakage accidents is performed using PHAST and ALOHA softwares across 16 scenarios. The present research focuses on the dispersion of toluene lethal and toxicity clouds, as well as the associated risks from vapor cloud explosions and pool fires resulting from the large-capacity toluene storage tank leakage. Meanwhile, the effects of influential factors such as leakage height, wind speed, ambient temperature, and atmospheric stability on toluene leakage accidents are also discussed, and then the worst-case scenario of the toluene leakage accident is obtained. The effects of wind speed, ambient temperature, leakage height, and atmospheric stability on outcomes such as toxic clouds, vapor dispersion, explosions, and pool fires are also analyzed. The result reveals that toxic cloud dispersion increases with ambient temperature, while the impact of vapor cloud explosions and pool fires decreases with the increase of leak heights. Wind speed significantly affects pool fire spread. The influential factors related to maximizing hazard range including low leak heights, low wind speeds, high temperatures, and stable conditions are obtained. Risk analysis indicates that vapor cloud explosions significantly impact outdoor potential loss of life (PLLoutdoor) compared with other incidents. Scenario 4 within a directional range from 33.8 to 56.3° shows the highest incident frequencies, highlighting its importance for risk monitoring. These findings are crucial for enhancing emergency response strategies and safety protocols in industrial safety management.

Emergency Capability Evaluation of Port-Adjacent Oil Storage and Transportation Bases: An Improved Analytic Hierarchy Process Approach

The large-scale storage and stable supply of oil products are essential for national energy security and economic development. As the economy expands and energy demands rise, centralized storage and supply systems become increasingly vital for ensuring the efficiency and reliability of oil product distribution. However, large oil storage depots present substantial safety risks. In the event of fires, explosions, or other accidents, emergency response efforts face stringent demands and challenges. To enhance the emergency response capabilities of oil storage and transportation bases (OSTBs), this paper proposes an innovative approach that integrates the improved analytic hierarchy process (IAHP) with the Entropy Weight Method (EMW) to determine the combined weights of various indices. This approach reduces the subjective bias associated with the traditional analytic hierarchy process (AHP). The emergency response capabilities of OSTBs are subsequently evaluated through fuzzy comprehensive analysis. An empirical study conducted on an OSTB in the Zhoushan archipelago quantitatively assesses its emergency preparedness. The results show that the base excels in pre-incident prevention, demonstrates robust preparedness and response capabilities, and exhibits moderate recovery abilities after incidents. These findings provide a theoretical foundation for reducing the likelihood of accidents, enhancing emergency response efficiency, and mitigating the severity of consequences. Practical recommendations are also offered based on the results.

Exploring the effects of time pressure and distracting elements in an augmented reality game for emergency preparedness

The development of emergency preparedness and response skills is crucial for ensuring individual safety and well-being during natural disasters. Realistic and engaging games can effectively promote these skills. This paper explores the impact of introducing additional game objects as distractors and time pressure on user experience and willingness to prepare for emergencies in an Augmented Reality (AR) game. Additional, distracting game elements and time pressure are included in the game scenario to simulate the cognitive demands and stress experienced in real emergency situations. To investigate the impact of distracting game elements and time pressure, a within-subjects study was conducted with 52 participants who played two different versions of an AR game designed to help locate recommended items for an emergency grab bag. In the first version, participants focused solely on finding the recommended items, while in the second version, distractors and a timer were introduced to emphasize the urgency of packing the bag. The results showed no significant difference in user experience between the two versions. However, the version with time pressure and distractors demonstrated a significant improvement in attitudes and perceived behavioral control regarding immediate actions in response to evacuation warnings. Qualitative feedback from participants supported these effects. These findings suggest new directions for further research and highlight the potential of AR games to enhance emergency response strategies.

A bayesian approach for the continuous monitoring of the prediction of the physiological evolution of a crisis victim: A decision support system

Catastrophic events like earthquakes demand innovative tools for crisis management. Mathematical modeling and decision support systems (DSSs) have proved crucial for understanding, predicting and mitigating disaster impact. The quantification of complex phenomena through probabilistic models, to estimate the likelihood of events, provides actionable insights that are essential for disaster risk reduction (DRR).The present work stems from research conducted within the framework of the Search & Rescue (S&R) project (H2020-SU-SEC-2019), in particular from the development of the PHYSIO DSS module, the medical component of the S&R Decision Support System (DSS). The PHYSIO DSS focuses on predicting the physiological evolution of crisis victims: using a Bayesian approach, it incorporates real-time field observations to forecast patient conditions. This enables the prediction of the evolution of physiological compensation, allowing efficient resource allocation and timely interventions. By providing real-time insights into victim severity, PHYSIO DSS empowers medical personnel to prioritize treatment, potentially saving lives. Its adaptability allows integration into different platforms, from crisis management systems to apps to personal health devices.This tool has the potential to substantially enhance emergency response capability and overall disaster resilience by offering real-time, data-driven decision support.

Enhancing Emergency Response: The Role of Collective Competence in Epidemiological Teams for Effective Laboratory Data Collection and Analysis

Background: Health systems globally face ncreasing challenges from health crises and epidemic threats, necessitating the pivotal role of epidemiological teams in emergency response. This study investigates the significance of collective competence within these teams, focusing on their ability to collect, analyze, and manage laboratory data effectively. Methods: A cross-sectional descriptive study design was used to survey epidemiological teams in Saudi Arabia. A structured questionnaire was developed based on existing literature and distributed electronically to participants. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS), using descriptive and inferential statistics to summarize participant characteristics and explore associations between variables. Results: A total of 208 epidemiological teams member completed the questionnaire, yielding insights into their The Role of Collective Competence in Epidemiological Teams. The findings reveal high levels of agreement regarding the effectiveness of management approaches, knowledge sharing, and team collaboration, with the average score exceeding 4.0 on a 5-point scale. Notably, team efficacy was found to significantly enhance the efficiency of laboratory data collection and analysis, accounting for approximately 25.9% of the variance in efficacy. Discussion: These findings underscore the need to invest in developing team competencies within epidemiological teams to improve emergency response capabilities. The study provides practical insights into enhancing team collaboration and coordination, ultimately contributing to more effective public health interventions in future health crises.

Category Mapping of Emergency Supplies Classification Standard Based on BERT-TextCNN

In recent years, the escalation in emergency occurrences has underscored the pressing need for expedient responses in delivering essential supplies. Efficient integration and precise allocation of emergency resources under joint government–enterprise stockpiling models are pivotal for enhancing emergency response effectiveness and minimizing economic repercussions. However, current research predominantly focuses on contract coordination and cost-sharing within these joint reserve modes, overlooking significant discrepancies in emergency supply classification standards between government and enterprise sectors, as well as the asymmetry in cross-sectoral and cross-regional supply information. This oversight critically impedes the timeliness and accuracy of emergency supply responses. In practice, manual judgment has been used to match the same materials under differing classification standards between government and enterprise reserves. Still, this approach is inefficient and prone to high error rates. To mitigate these challenges, this study proposes a methodology leveraging the BERT pre-trained language model and TextCNN neural network to establish a robust mapping relationship between these classification criteria. The approach involves abstracting textual representations of both taxonomical classes, generating comparable sentence vectors via average pooling, and calculating cosine similarity scores to facilitate precise classification mapping. Illustrated with China’s Classification and Coding of Emergency Supplies standards and Global Product Classification standards, empirical validation on annotated data demonstrates the BERT-TextCNN model’s exceptional accuracy of 98.22%, surpassing other neural network methodologies such as BERT-CNN, BERT-RNN, BERT-BiLSTM, etc. This underscores the potential of advanced neural network techniques in enhancing emergency supply management across diverse sectors and regions.

An investigation and future prospects of artificial intelligence applications in natural disaster prediction

The monitoring of natural hazards, such as those affecting the oceans, forests and geological formations, is important for safeguarding human life, protecting property, preserving the ecological balance and promoting technological progress. Research on the prediction and prevention of natural disasters has been conducted for decades in a wide range of areas, and this paper summarise recent research on the use of emerging computer technologies for such prediction, with the expectation that it will serve as a recommendation for future developments in the field. Effective prediction of such disasters depends on the ability to detect anomalous signals in a timely manner. Current methods that rely heavily on manual observation and data analysis are inefficient and prone to human error. Rapid advances in computer science and artificial intelligence technologies offer a promising solution, such as the use of deep learning algorithms or the Spark framework to improve model prediction accuracy and response timeliness. In recent years, computer technology has played a key role in improving these capabilities, contributing to better management of the nations natural resources, and enhancing emergency response strategies. By leveraging the capabilities of deep learning, machine learning and others in data processing and analysis, the field of natural disaster monitoring will continue to evolve towards more efficient and reliable methods.

Responsive Assistance at your Fingertips: The Quick Assist System

This research study addresses the immediate actions and challenges faced by Barangay San Manuel City of San Jose del Monte, Bulacan, in responding to emergency concerns using traditional methods. The specific aim of this study was to develop and evaluate the effectiveness of the Barangay San Manuel Responsive Assistance at your Fingertips: The Quick Assist System. The study employed a descriptive quantitative approach, utilizing tests, survey questionnaires, interviews, and document analysis as primary instruments for data collection and analysis. To gather data from the locale of the study, a research tool comprising tests, survey questionnaires, and interviews was specifically designed. The tests assessed the proficiency of the response team in emergency response procedures, while survey questionnaires gathered feedback from community members on the existing emergency response methods and their experiences with the Quick Assist System. Interviews were conducted with key stakeholders to obtain in-depth insights into the challenges and potential improvements. The key findings of the study revealed several factors that influenced immediate action in emergency response. It was identified that a lack of knowledge in navigating the mobile application posed a challenge for respondents in utilizing the Quick Assist System. To address this, seminars and hands-on training were recommended to enhance system proficiency among response teams. Furthermore, providing free internet load was suggested to ensure uninterrupted access to the system. Despite the valuable insights gained from this study, it is important to acknowledge its limitations. The research was limited to a specific geographic location and may not be fully generalizable to other barangays or regions. Additionally, the study focused on the effectiveness of the Quick Assist System and did not explore other potential technological solutions. The practical implications of this study are twofold. First, it highlights the need for comprehensive training programs to enhance the proficiency of response teams in utilizing technology-driven emergency response systems. Second, it emphasizes the importance of providing accessible and uninterrupted internet connectivity to ensure the effectiveness of such systems. This research contributes to the development of technological methods in the fields of Criminal Justice, Public Safety, and Security. By highlighting the potential of the Barangay San Manuel Responsive Assistance at your Fingertips: The Quick Assist System, it underscores the significance of embracing technology to improve emergency response capabilities. The study’s findings provide valuable insights for policymakers, emergency response agencies, and community stakeholders in their efforts to enhance emergency response systems at the barangay level.

Lagrangian relaxation-based approaches for cooperative location and assignment of emergency responders

Efficient fire response and the coordination of various fire vehicles are paramount for delivering optimal services and conducting effective rescue operations. This study explores the challenges of cooperative station location and vehicle assignment, focusing on the synergistic dispatch of fire vehicles from multiple stations. It highlights that the response time of the last arriving vehicle, crucial for forming a complete and effective response unit, is a critical determinant in the overall success of fire rescue operations. The primary objective of the model is to minimize the total system cost, encompassing both the capital investment in fire stations and the potential losses from fire incidents. By employing a Lagrangian relaxation technique, the model effectively simplifies the optimization challenge into more manageable sub-problems, thereby enhancing solution accuracy. The robustness and flexibility of the model were tested through simulations in a hypothetical network and a real-world scenario, using data from urban fire services. The results demonstrate the model’s scalability and adaptability to different network configurations and operational constraints, showcasing its potential to substantially enhance emergency response efficiency and decision-making in urban fire management. This research makes a significant contribution to emergency response management by providing a validated framework that improves the coordination and effectiveness of fire rescue operations, thereby reducing fire damage and enhancing public safety.

Uniform or demand-driven allocation? Optimal management of social donations distribution in response to sudden outbreaks

PurposeThis study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.Design/methodology/approachFirstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.FindingsNumerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.Practical implicationsThe findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.Social implicationsThe equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.Originality/valueThis research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

An IoMT-based Federated Learning Survey in Smart Transportation

Abstract: Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL

Culturally-informed for designing motorcycle fire rescue: Empirical study in developing country

Fire must be responded to quickly and effectively. Difficulties in reaching the fire location may arise due to high traffic. Motorcycle fire rescue can be an alternative to first aid in challenging areas. However, these areas, characterized by diverse cultural backgrounds, require careful consideration. Culture significantly impacts how such rescue systems are perceived, accepted, and utilized. This study delves into the efficiency and implementation of motorcycle fire rescue systems across different contexts, focusing on their alignment with regional cultural norms. Objectives include assessing operational challenges, identifying cultural implications, proposing culturally sensitive design recommendations, evaluating acceptance and effectiveness, and suggesting strategies and policy enhancements to strengthen emergency response capabilities. A preliminary trial was conducted to validate the survey instrument, followed by statistical analysis such as structural equation modeling, confirmatory factor analysis, and satisfaction level assessments. Findings highlight how cultural attitudes shape design preferences, the need for adaptable approaches to overcome operational hurdles, and the importance of adhering to culturally relevant design principles for system efficacy. Field tests also suggest potential improvements in emergency response effectiveness. Recognizing cultural considerations is essential in designing motorcycle fire rescue systems to enhance emergency response in developing nations. Furthermore, aligned with sustainable urban development goals, this research provides policymakers with valuable insights and practical approaches for engaging stakeholders. Equipping designers with information to address motorcycle fire incidents using environmentally friendly solutions underscores the importance of crafting emergency response plans sensitive to cultural differences across diverse landscapes.

Determining the possibility of passage through narrow alleys using ultrasonic sensors

The complex road topography of South Korea presents significant challenges to the timely arrival of emergency vehicles. Compounding the issue, obstacles such as legally or illicitly constructed structures, and improperly parked or stationary vehicles, frequently obstruct the path of emergency vehicles. To address these challenges, this study introduces a novel system aimed at enhancing emergency response times. The system employs ultrasonic sensors that can be integrated into personal devices to measure the width of the numerous narrow alleys prevalent in Korea's densely populated regions. Experiments demonstrate that within a 1-meter range in front of a narrow alley with widths varying between 270 cm and 450 cm where vehicle maneuvering is possible, it's possible to accurately gauge the width using two ultrasonic sensors, achieving a precision within a 5 cm margin of error. This level of accuracy enables the practical assessment of whether emergency vehicles can access the area in real-time by identifying the alley's narrowest point. The proposed system is a cost-effective method using easy-to-buy devices for augmenting emergency preparedness and enhancing emergency response times by ensuring that emergency vehicles can navigate through alleys, thereby fostering a safer living environment.

A Survey on Emergency Vehicle Route Optimization and Traffic Management Application

Abstract: Ambulance delays in India result in approximately 10,000 lives lost annually, highlighting the critical need for efficient emergency response systems. To address this issue, we present the "Emergency Vehicle Priority with Route Optimization and Traffic Management Application." Leveraging advanced technology, including an Android application, Firebase, and IoT integration with traffic signals, our system dynamically calculates and optimizes ambulance routes using Google API, minimizing delays and improving overall efficiency. The application facilitates seamless coordination between ambulance drivers and traffic police officers, triggering real-time route updates and traffic signal adjustments to expedite ambulance passage. By streamlining traffic flow and enhancing emergency response coordination, our solution aims to significantly reduce ambulance delays and save lives in urban areas.

Novel Approach for Vehicle Accident Tracking using IoT

Abstract: In recent years, there has been significant interestin integrating Internet of Things (IoT) technology into vehicle safety systems to enhance emergency response capabilities and road safety. This paper introduces a comprehensive Vehicle Accident Tracking System (VATS) that utilizes IoT principles. The proposed system incorporates various sensors and communication devices installed within vehicles to monitor theirstatus and detect potential accidents in real-time. When anaccident is detected, the system automatically triggers an alert mechanism, notifying relevant stakeholders such as emergency services and fleet managers. Key components of the VATSinclude accelerometers, GPS modules, and onboard communication units, which work together to provide precise accident geospatial data and vehicle condition data. The systemutilizes cloud-based platforms for data storage and processing, ensuring seamless integration with existing infrastructure and scalability. By continuously monitoring and analyzing vehicle telemetry data, the VATS facilitates proactive accident prevention measures and post-accident analysis. Additionally, it offers benefits such as reduced emergency response times, improved accident documentation for insurance claims, and enhanced overall road safety. Privacy and security concerns areaddressed through encryption protocols and access control mechanisms to protect sensitive information. Overall, the Vehicle Accident Tracking System presented in this paper represents a significant advancement in leveraging IoT technology to mitigate the impact of accidents on roadways, thereby contributing to a safer and more efficient transportation ecosystem.

Enhancing emergency response planning for natech accidents in process operations using functional resonance analysis method (FRAM): A case of fuel storage tank farm

Recent increases in climate-induced natural disasters have amplified the risk of Natech (natural hazard-triggered technological) accidents, particularly in the chemical industry. These emergencies, characterized by their urgency and resource constraints, pose significant challenges for emergency planning. The Functional Resonance Analysis Method (FRAM) offers a systematic approach to enhance emergency response strategies. This study introduces a FRAM-based methodology specifically designed for fuel storage tank farms, structured into four critical stages: understanding, designing, analyzing, and enhancing the response process. It promotes a cycle of continuous improvement. A case study on a seismic Natech incident at a fuel storage facility demonstrates the methodology's effectiveness and its potential to boost the resilience of emergency response systems against Natech challenges.

Advanced Traffic Clearance System for Emergency Vehicles

This paper elaborates development of Traffic Clearance System using camera and mobile Net. In urban areas, navigating through traffic congestion poses a significant challenge for emergency vehicles, often leading to delays that can impact response times and potentially endanger lives. This paper presents an innovative traffic control system leveraging camera technology to prioritize the passage of emergency vehicles. The proposed system utilizes a network of cameras strategically placed at intersections to detect emergency vehicle sirens and flashing lights. Upon detection, the system dynamically adjusts traffic signal timing to create a green corridor, facilitating the swift and safe passage of emergency vehicles. Additionally, the system incorporates machine learning algorithms to optimize traffic flow, minimize congestion, and ensure efficient utilization of road space. Through simulation studies and real-world testing, the effectiveness and reliability of the proposed system are evaluated, demonstrating its potential to enhance emergency response capabilities and improve overall traffic management in urban environments.

Integrating geoenvironmental and socioenvironmental analyses for flood vulnerability assessment in the Kullu Valley, Himachal Pradesh, India

The escalating impacts of climate change necessitate a focused analysis of flood vulnerability in ecologically fragile and densely inhabited mountain areas, particularly in the Himalayan region. This study concentrates on the Kullu Valley within the Beas Basin, Himachal Pradesh, India, a locale characterized by its intricate terrain and dynamic climatic conditions. Employing an integrated approach of using satellite imagery, Geographic Information System (GIS), and field surveys, the research evaluates the region's topographical and hydrological attributes alongside land use patterns to discern flood vulnerabilities. Key findings from this study include, the identification of zones with elevated runoff potential, high-risk settlements, and vulnerable agricultural lands. The complex interplay of steep slopes and intricate stream networks exacerbates the susceptibility to flash floods in the region. The study's socio-environmental analysis resulted in the delineation of five zones, each exhibiting distinct adaptive capacities, exposures, and sensitivities. Particularly, Zones 5 and 4 emerged as highly vulnerable due to dense settlements and economic dependence on agriculture and tourism. This study suggests implementing a 100-m buffer zone along the river and monitoring the use of Common Property Resource (CPR) lands to mitigate flood risks. Our recommendations extend to enhancing emergency response protocols, improving disaster management team preparedness, improving communication and transport infrastructures, and ensuring medical facility accessibility. Overall, this research offers a comprehensive perspective on flood risk factors in mountainous landscapes, serving as a guide for policymakers and disaster management authorities in formulating resilient and sustainable flood mitigation strategies.

Instinctive Detection of Accident Occurrence using Numerous Machine Learning Techniques with Comparative Study

Objectives: The research work aims to develop an automated detection system that uses video captures to identify roadside accidents or significant events. By alerting nearby hospitals and emergency services, the system reduces response times and potentially saves lives. The system’s integration with existing emergency response systems ensures prompt assistance to those in need. Methods: The video processing pipeline begins by converting video files into frames for analysis and feature extraction. These features serve as inputs for classification algorithms such as Random Forest, SVM, and KNN. The model’s performance is evaluated using a training set and unseen test data, with the predicted classifications compared against the ground truth labels. Findings: Among the tested classification algorithms, the Random Forest algorithm achieved the highest accuracy. Using 128 frames for analysis provided more comprehensive information, yielding a 96% accuracy rate. This combination proves to be a powerful tool in classification tasks, providing reliable and accurate outputs. Novelty: Machine learning algorithms are instrumental in automating accident detection from video captures. They analyse video footage to identify accidents and promptly alert relevant authorities. This technology can also dispatch emergency messages to nearby hospitals, ensuring quick assistance. The consideration of different frame counts in classification improves accuracy by capturing critical moments and patterns. Machine learning algorithms applied in this work significantly enhance emergency response, reduce response times, and potentially save lives. Keywords: ­ Emergency Services, Accidents, Feature Extraction, Random Forest, SVM, KNN

Estimating specific acute exposure guideline levels for emergency response at the scene of chemical accidents: A case study of acrylic acid

Acute Exposure Guideline Levels (AEGLs) proposed by the United States Environmental Protection Agency (USEPA) are reference values for disaster management during scenarios involving chemical accidents. We attempted to specify the present AEGLs of acrylic acid that are provided with only five intervals (10, 30 min, 1, 4, and 8 h) as a brief case study. The AEGL-1, 2, 3 values for acrylic acid were estimated at 96 intervals (5 min–8 h) in detail using the revised Haber's formula (k = Cn × t) and applying a regression model. The AEGL-1 value was constant for the entire exposure duration of 1.5 ppm since it caused only slight irritation without irreversible health effects. The AEGL-2 value was obtained with fixed values (n = 1.8, uncertainty factor of 3, and each 5 min interval), with that for the initial 5 min being derived as 183 ppm, which decreased to 43 ppm after 8 h of exposure. The AEGL-3 values fluctuated because of the risk to human health. The AEGL-3 for the initial 5 min exposure was derived as 696 ppm, which decreased to 58 ppm after 8 h exposure applied to the regression model. Specific AEGL values for acrylic acid were derived using toxicity data and the revised Haber's formula. By applying the methodology described in this study, the reference values for chemical substances that have no AEGL values can be calculated to potentially enhance emergency response in the event of chemical accidents.