Emergency Response Strategies Research Articles

Understanding community resilience during the drinking water contamination event on Oahu, Hawaii, 2021-2022: a mixed mode approach.

A petroleum leak into the Joint Base Pearl Harbor-Hickam water system on Oahu, Hawaii in November 2021 contaminated the drinking water of approximately 93,000 users, causing many to relocate for months. Perceptions of health and wellbeing were captured using the Centers for Disease Control/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Assessment of Chemical Exposures (ACE) cross-sectional survey in collaboration with the Hawaii Department of Health (HDOH). Responses from the ACE online survey of community members, businesses, schools, health care and veterinary care organizations during the contamination event, containing quantitative questions and qualitative information from an open text field, were analyzed. Separately, a qualitative key informant questionnaire was administered to community establishments. Thematic content analysis was used to analyze and identify prominent themes from the ACE open text field and the key informant responses that were triangulated by the quantitative data when the themes aligned. Six major themes of disruption, communication, trust, stress, support, and ongoing needs were identified. Burdensome logistics from obtaining alternate water, negative financial impacts from relocation or losing business, distrust of information, perceived lack of support from response entities and uncertainty of long-term health impact caused significant disruption, stress and mental health. Individuals reported needing water, shelter, and mental health care while establishments wanted financial reimbursement and a resolution. The findings show that environmental disasters have significant disruptive and mental health impacts from stress. Identified themes can inform and improve emergency response and communication strategies and increase trust with community members during and after large chemical exposure events.

Robust Human Activity Recognition for Intelligent Transportation Systems Using Smartphone Sensors: A Position-Independent Approach

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies.

Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques

Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies.

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Knowledge Mapping of the Auxiliary Police: A Basis for Crafting a Capability Training Program

The auxiliary police force, particularly barangay tanods in the Philippines, faces significant challenges in effectively performing their roles due to limited formally training and capacity-building opportunities. This study was conducted to assess the level of knowledge among barangay tanods regarding their duties, responsibilities, roles as first responders, and patrolling and hazard identification and to develop a Capability Training Program tailored to address identified deficiencies. The need for this research arises from the observed gaps in advanced patrolling strategies and emergency response protocols, which hinder their effectiveness as community enforcers. Using a descriptive-quantitative research design, data were analyzed through regression analysis and descriptive statistics, including mean and standard deviation, using SPSS. The study employed adapted questionnaires targeting knowledge in key areas, such as duties and responsibilities, first responder actions, patrolling techniques, and hazard identification. Simple random sampling was applied, resulting in 140 barangay tanods participating as respondents. Findings revealed that although the overall knowledge of the respondents was high, specific gaps were evident in advanced emergency response and proactive patrolling strategies. These insights were instrumental in the development of a comprehensive Capability Training Program aimed at enhancing the effectiveness of police auxiliary services in critical operational areas.

3.X.2. Integrating trust as a key component of pandemic preparedness and resilience

Abstract Trust is a key component of pandemic preparedness, which impacts the success of response measures during the public health emergency. Trust is built during preparedness, however the drivers and dimensions of trust are complex and context-specific. What is required is a collaborative, inclusive approach that provides a platform for communities to express their questions and concerns. The proposed workshop will begin with a presentation from a global expert on trust in the content of epidemics and pandemics and the current knowledge about how trust can be built during preparedness. This will be followed by interactive group-work, designed to encourage collaborative discussions and decision-making. Finally, introducing the WHO Hive digital platform that provides a safe space for public health communities to share information and ask questions. Attendees will then reconvene in plenary to discuss these approaches and interventions that can be implemented to build and maintain trust prior to and during an epidemic or pandemic. This workshop has the following objectives: 1. To demonstrate how trust is an important determinant of successful pandemic response; 2. To enable attendees to explore how the implementation of public health and social measures, infodemic management and digital community partnerships and engagement can build trust during pandemic preparedness for both online and offline communities; 3. To provide opportunity for attendees to workshop how the different features of the Hive digital space can enhance trust building, and to develop actionable strategies for health emergency preparedness, response and resilience. The workshop will deliver these objectives through an interactive simulation activity. Attendees will be placed into small groups to work through a scenario, having to decide on the actions that need to be taken, considering budget constraints, other health priorities, and the current local context. The teams will co-create actionable strategies for building and maintaining trust. While there is not a one size fits all approach to trust building, this workshop will aim to increase understanding of the drivers and determinants of trust to encourage participants to consider how they can be applied in their local context. It will be interactive and hands-on requiring active participation from participants. Key messages • Trust as a construct is an important component of pandemic preparedness and readiness that impacts on response. • A multidimensional approach to building and sustaining trust needs to considered across a range of drivers and determinants, where actionable strategies are vital.

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.

Geo-Fenced Aerial Fire Response System

Purpose: We aim to create a prototype of a fire extinguisher drone equipped with advanced geo-fencing technology explicitly designed for use in high-rise buildings. This drone will be capable of both ground movement and flight, allowing it to quickly navigate narrow spaces and difficult-to-reach areas. Methodology: The study involves an in-depth analysis of various drones, features, and geo-fencing to develop a prototype for precise and targeted fire suppression. The aim is to minimize the damage caused by fires and improve overall safety by enabling swift intervention, particularly in confined or elevated locations where traditional firefighting methods may face challenges. Findings: The prototype drone, equipped with geo-fencing capabilities, has demonstrated its ability to be efficiently deployed within specific, pre-defined areas, significantly improving its precision and effectiveness in targeted fire suppression. This feature ensures that the drone operates only within designated zones, reducing the risk of interference or wandering into unsafe areas. The drone is controlled remotely, allowing it to fly freely and access hard-to-reach locations quickly. A small surveillance camera mounted on the drone enhances its effectiveness by providing real-time visuals of the fire's location and intensity. This capability allows operators to assess the situation more accurately and deploy the necessary response measures, making the drone a powerful tool for modern firefighting operations. Unique Contribution to Theory, Practice and Policy: The findings from this prototype provide valuable insights that can shape the future of fire response mechanisms and drone designs. The drones can target fires while minimizing risks to firefighting personnel, and they can potentially revolutionize emergency response strategies. The ability to remotely control drones, equipped with geo-fencing and surveillance capabilities, reduces the need for human intervention in dangerous situations and improves the speed and accuracy of fire suppression efforts. Furthermore, this technology can be applied to detect and combat wildfires, helping to minimize their destructive impact by enabling quicker detection and targeted responses in remote or difficult-to-access areas. These advancements pave the way for safer and more efficient firefighting methods in the future.

Study of Flooding Behavior and Discharge from Karot Dam in the Event of a Possible Breach by Using the Hydrodynamic Model

This study utilizes the MIKE 11 hydrodynamic model developed by the Danish Hydraulic Institute to simulate flood behavior downstream of Karot Dam under multi-year in-flow conditions. The key parameters analyzed include breach characteristics, flood duration, water depth, flow velocity, discharge rate, and downstream distance. After dam failure, the peak discharge reaches 33,171 m3/s, exceeding the 10,000-year recurrence peak flow of 32,300 m3/s, with a breach duration of 2 h. The estimated peak discharge after simulation using empirical equations and comparative analyses showed maximum flood discharges of 28,187 m3/s, 28,922 m3/s, and 29,769 m3/s, with breach widths of 181 m, 256 m, and 331 m, respectively. The peak discharge predicted to reach the outlet with travel time ranging from 4 h 25 min to 4 h 40 min. Under multi-year average inflow conditions, Mangla Dam faces no risk of failure, with a maximum outflow of 12,097 m3/s and a spillway capacity of 30,147 m3/s. The model accurately predicted discharge values, with a strong correlation coefficient of R2 = 0.9653, indicating strong agreement between the actual water level data and predicted discharge. These insights are essential for developing effective emergency response strategies to mitigate the risks associated with dam failure.

Emergency evacuation risk assessment for toxic gas attacks in airport terminals: Model, algorithm, and application

Transportation infrastructure has often been the target of terrorist attacks, and mitigation of the risk of toxic gas attacks is a challenging task in the design of indoor emergency evacuation systems. Considering multiple emergency response modes, we propose an agent-based risk assessment model and its algorithm to integrate gas diffusion and pedestrian movement data for emergency response, quickly assessing average individual exposure risk. We assessed the exposure status of individuals with respect to their emergency response actions following a toxic gas attack in an airport terminal. The results indicate that in the event of a general gas attack on an airport terminal, ventilation must be immediately ceased along with early evacuation. In areas with a shelter-in-place environment, the ventilation mode and shelter-in-place time should be determined based on the concentration of indoor and outdoor gases. In areas with nerve gas exposure and high population density, a new exit must be established at evacuation bottlenecks, and pedestrians must be guided to evacuate while promptly closing ventilation. These results offer suggestions and strategies for emergency response and decision-making in airport terminals during such incidents.

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.

Harnessing the power of mobile and messaging apps for risk communication and intervention during the COVID-19 pandemic: lessons from the Western Pacific.

The spread of mis- and disinformation on mobile and messaging apps during the COVID-19 pandemic not only fuelled anxieties and mistrust in health authorities but also undermined the effectiveness of the overall public health response. Mobile and messaging apps help users stay informed and connected to their families, friends, colleagues and communities. However, during the COVID-19 pandemic, these apps were also one of the primary channels where mis- and disinformation were circulated. Recognizing the importance of including mobile and messaging apps in risk communication and emergency response strategies, the World Health Organization (WHO) and some countries in the WHO Western Pacific Region independently piloted initiatives to reach messaging app users, meet their evolving information needs, and streamline health ministry communication. The enhanced use of mobile and messaging apps enabled consistent and timely communication and improved coordination during the COVID-19 pandemic. Leveraging their features also helped identify and potentially fill crucial information gaps, mitigating the harms of mis- and disinformation and fostering stronger trust in health authorities. The findings from the work carried out by WHO and countries in the Western Pacific Region identified some promising innovative communication interventions using mobile and messaging apps. While these interventions should be further explored and evaluated, they have demonstrated that interventions need to be proactive, flexible, and able to adapt to changes in mis- and disinformation content being shared through messaging apps.

Integrating radiological technology in environmental health surveillance to enhance public safety

The integration of radiological technology into environmental health surveillance represents a significant advancement in enhancing public safety. Radiological technologies, including advanced imaging and monitoring systems, offer powerful tools for detecting and assessing environmental hazards that impact public health. This review explores the potential benefits, methodologies, and challenges associated with integrating radiological technology into environmental health surveillance. Recent advancements in radiological technology, such as high-resolution imaging systems and portable radiation detectors, have greatly improved the ability to monitor and analyze environmental radiation levels with precision. These technologies enable real-time detection of radiation exposure, providing critical data for assessing risks associated with radiation sources, such as industrial activities, nuclear power plants, and natural radioactive materials. By integrating these tools into environmental health surveillance frameworks, public health agencies can enhance their capacity to identify and address radiation-related hazards more effectively. The application of radiological technology in environmental health surveillance facilitates comprehensive monitoring of radiation levels across various environments, including urban, rural, and industrial areas. This data is crucial for identifying contamination hotspots, tracking changes in radiation levels over time, and evaluating the effectiveness of radiation control measures. Additionally, the integration of radiological technology supports the development of predictive models for assessing potential health impacts and guiding emergency response strategies in the event of radiation incidents. Despite its advantages, integrating radiological technology into environmental health surveillance presents several challenges. These include the high costs of advanced equipment, the need for specialized training for personnel, and the management of large volumes of data generated by monitoring systems. Addressing these challenges requires a coordinated approach involving government agencies, research institutions, and industry stakeholders to ensure effective implementation and sustainability. In conclusion, integrating radiological technology into environmental health surveillance has the potential to significantly enhance public safety by providing accurate and timely data on radiation exposure. Continued innovation and collaboration in this field will be essential for optimizing the benefits of radiological technology and addressing the challenges associated with its integration into environmental health monitoring systems. Keywords: Integrating; Radiological Technology; Environmental Health Surveillance; Enhance; Public Safety

Requirements for the Development and Operation of a Freeze-Up Ice-Jam Flood Forecasting System

This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling techniques that can better capture the complex interactions between ice cover, water levels, and channel geometry. (iii) Combining data from multiple sources such as satellite imagery, ground-based sensors, numerical models, and machine learning algorithms can significantly improve the accuracy and reliability of ice-jam flood forecasts. (iv) Effective decision-support tools are crucial for integrating ice-jam flood forecasts into emergency response and mitigation strategies.

Assessing feasibility and sex-related inequity in the cardiac rehabilitation quality indicators in Manitoba.

The cardiac rehabilitation quality indicators (CRQIs) developed by the Canadian Cardiovascular Society provide a means to standardize program assessment and identify sex-related inequities. No formal evaluation of the CRQIs has been conducted in Manitoba. An environmental scan for the CRQIs was performed using data in the electronic medical record at two cardiac rehabilitation (CR) sites in Winnipeg for 2016-2019 referrals. Of the 8116 referrals, 7758 (5491 males and 2267 females) had geographical access and were eligible for CR. The Manitoba Centre for Health Policy Data Quality Framework informed the data quality assessment. Thirteen CRQIs were available; four were considered high quality; nine demonstrated moderate to significant missing data. In addition to missing values, potential misclassification of risk (CR-4) and physiologically implausible and invalid dates were assessed and identified (CR-13 and CR-17). Each site had a physician medical director (CR-31) and a documented emergency response strategy (CR-32). Only high-quality data were evaluated for sex-related differences using chi-square and median tests. Women had lower enrollment (CR-3), and more women enrolled after the median of 41 days (CR-2b). Engagement with CR partners, including frontline staff, and utilizing strategies to assess and limit physiologically implausible values and dates will enhance data capture and quality.

Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes

Backward erosion piping (BEP) is an increasingly critical failure mechanism in dike systems, often triggered by floods resulting from extreme rainfall events, which are exacerbated by the ongoing shifts in climate patterns. This study embarks on a nuanced examination of BEP, a phenomenon that substantially undermines flood control and emergency management endeavors. Addressing the complexities of BEP incidents and the consequential variable impact on dike infrastructure, we have incorporated Monte Carlo dropout techniques within a multi-fidelity deep learning framework to enhance predictive accuracy and provide an uncertainty-aware assessment of BEP risk. Drawing on 16 sets of physical model experiments emulating dike structures from the lower Yangtze River basin in China, we conducted a detailed study of the BEP evolution mechanism under various hydrological scenarios intensified by climate change. These experiments allowed us to observe the initiation and progression of BEP in different conditions. The development of a structural equation model quantifies the effects of several critical factors—including those exacerbated by climatic variability—on BEP dynamics. Seven key factors were identified as influential to BEP risk levels, integrating distinct BEP traits, hydrological attributes, and dike engineering conditions. A Monte Carlo dropout-enhanced multi-fidelity deep neural network (MFDNN) was crafted, synthesizing low-fidelity experimental data with high-fidelity field case studies, to construct an advanced model for BEP risk level identification. Compared against four sophisticated machine learning models, our MFDNN demonstrated superior performance, effectively blending experimental insights with real-world occurrences. The proposed model emerges as a scientifically robust and pragmatic tool for delineating BEP risk levels in dike systems, providing vital guidance for categorizing BEP incidents and forging targeted, climate-informed emergency response strategies.

Scenario Planning for Water System Emergency Response Strategy

Scenario Planning for Water System Emergency Response Strategy

Radiological impact assessment of a dirty bomb using Hotspot Code and AI: Insights from spent nuclear fuel isotopes

This study evaluates the potential radiological effects resulting from the use of a Radiological Dispersal Device (RDD), commonly known as a “dirty bomb,” in Dhamar City, Yemen, focusing on its impact on human health. Seasonal atmospheric variations were analyzed using the HotSpot Health Physics Code to model the Total Effective Dose Equivalent (TEDE) distribution following an explosion. The TEDE’s effect on different human organs was assessed, considering both external and internal radiation exposures. The study demonstrated that winter nights posed the highest radiation risk, with dose distribution areas of 0.018 km2, 0.14 km2, and 3.3 km2 for inner, middle, and outer zones, respectively, surpassing other seasonal and daily values. Conversely, summer recorded the lowest radiation spread. A detailed temporal analysis during winter nights showed an initial TEDE of 9.5 Sv at 0.1 min post-explosion, which decreased exponentially to 1.3 Sv after 2000 min (33.3 h) and further to 0.0038 Sv after 6500 min (108.3 h) with the aid of AI-enhanced “Long Short Term Memory Networks (LSTM)”. The highest radiation doses to human organs within a 100 m2 area during the initial 10 min post-detonation were significantly above permissible limits, with critical organs like the surface bone, liver, and red marrow receiving doses of 68 Sv, 12 Sv, and 5.5 Sv, respectively. These findings underline the need for robust emergency response strategies and long-term health monitoring to mitigate the adverse health impacts of RDDs. The comprehensive assessment presented in this study, enhanced by artificial intelligence, provides essential data for improving public health safety and emergency preparedness in regions susceptible to radiological threats.

Risks and Safety of CO2 Pipeline Transport: A Case Study of the Analysis and Modeling of the Risk of Accidental Release of CO2 into the Atmosphere

In the field of CO2 capture and sequestration, ensuring the safety of pipeline infrastructure is paramount to successful climate change mitigation efforts. This study investigates the dynamics of CO2 dispersion from pipeline systems, assessing not only the transport process but also the physical properties and associated hazards. Advanced simulation techniques are used to model how different states of CO2 (gas, liquid, and supercritical) and varying pipeline characteristics—such as perforation sizes, flow rates, and orientations—affect the dispersion patterns in the event of a leak. Simulations cover a range of atmospheric conditions, emphasizing the role of atmospheric stability and wind speed in shaping dispersion and defining potential impact zones. An analysis of historical pipeline accidents is included to inform risk management strategies. The results show that the orientation of the pipeline has a significant effect on dispersion, with downward leaks causing the largest impact zones, particularly under supercritical conditions. The results highlight the need for adaptive safety strategies that take into account real-time CO2 transport conditions and localized environmental data. By integrating these factors, the study recommends refining safety protocols and emergency response strategies to improve pipeline resilience and public safety against potential leaks. Key findings include the quantification of the relationship between leak parameters and dispersion areas, providing a valuable framework for future safety improvements.

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.