Seismic rumors can mislead the public and trigger unnecessary actions, underscoring the importance of their control in disaster management. This study examined the impact of two different intervention tools—rule-based intervention and knowledge-based intervention—on the trust and sharing of seismic rumors. We designed a survey experiment to explore this issue, and 500 respondents participated in the experiment. The results indicate that the rule-based intervention significantly reduced the public’s trust in and intention to share seismic rumors, but the knowledge-based intervention failed. Possible mechanisms are that the rule-based intervention raises awareness of the unreliability of disaster information sources and costs associated with sharing rumors. It is suggested that communicating the existing rules and policies regarding disaster information release might be an effective approach to rendering disaster rumors uncreditable and then reducing people’s intention to share. These findings enrich our understanding of the effectiveness of different intervention tools regarding rumor behavior in disaster scenarios and offer insights for practical seismic rumor management.
This study explored how the Swedish Fire and Rescue Service identifies problems and implements necessary actions during complex problem-solving in emergencies, aiming to increase our understanding of this process. Primary and secondary data from large-scale fire events were analyzed, including semistructured interviews and incident reports. The concept of “possibility space” is applied to identify factors affecting complex problem-solving. This refers to the scope of action available at a specific point in time for a particular operation. The study identified eight factors, including problem identification, incident development, capability, collaboration, management, legal framework, logistics, and time available, that could either decrease or increase the scope of action. The findings contribute to an increased understanding of complex problem-solving in real-world environments and suggest that the possibility space could be a valuable tool for practitioners in enhancing problem-solving during emergency response.
This article uses postcolonial theory to examine optimal risk communication practices of new risks and scientific information to non-indigenous communities. The article calls on risk communication scholars and health practitioners to embrace postcolonial theory as it provides a critical and reflective framework to examine ontological beliefs and methodological and structural aspects in the communication of public health messages. The article draws on insight from three studies on COVID-19 vaccine hesitancy within Scotland’s African, Caribbean, and Black communities between March 2021 and April 2022. The article offers new insight into why some communities hesitate to respond to public health messages such as vaccine uptake advice. Therefore, risk communication scholars should use the postcolonial lens to examine their assumptions, thinking, and perspectives on communicating new science and risk information in emergencies. Postcolonial theory enables risk communication scholars to address power imbalances, representation, and inclusion challenges in public health communication and trust-building efforts.
This study investigated the economic impact of tornadoes and damaging winds on county-level income growth in the United States from 1969 to 2023. Using a fixed effect regression methodology, the analysis showed that an average damaging storm and an average tornado reduced income growth in the affected counties by 0.16–0.23% and 0.21–0.23%, respectively, translating into a one-time growth loss of approximately USD 5.6 million and USD 6.3 million. The findings also indicate that tornadoes have a 20% greater economic impact than damaging winds. This study underscored the unique challenges posed by tornadoes and damaging winds, which are characterized by their unpredictable nature and concentrated but extreme damage.
Subway fires often cause significant casualties and property losses. There are some special bidirectional coupling scenarios during subway fires, such as firefighters moving against the evacuation flow to extinguish fires, emergency managers going to the fire scene to respond to emergencies, or other similar scenarios. How to evacuate passengers quickly and enable responders to enter the fire scene has become a big challenge for subway fire evacuation and response. The core goal is to reduce the degree of mutual interaction between these people moving in opposite directions. In this study, the impact of counterflow individuals and proactive avoidance behavior on evacuation processes was investigated through experiments and simulations. The Fire Dynamic Simulator was used to simulate the development of a fire scenario to determine the available safe egress time. Micro-evacuation experiments were conducted to obtain actual evacuation parameters, such as the speeds of different objects. With these parameters as input, a macro subway fire scenario was built to simulate the bidirectional evacuation process. Consistent conclusions were obtained from the experiments and evacuation simulations. The results indicate that the overall evacuation time increases with the number of retrograders. Proactive avoidance behavior can effectively reduce the travel time of counterflow individuals, but it causes slight delays for forward-moving evacuees. An optimization strategy was implemented through conductor guidance. All passengers can successfully evacuate under the optimization strategy, with a 25.3% improvement in overall evacuation time. This research provides some insights into the coordinated evacuation and emergency response during subway fires or similar scenarios.
Seismic simulation of urban buildings and roads is significant for regional pre-disaster mitigation and post-disaster recovery. To consider the interrelated influences of buildings and roads, an integrated seismic assessment method for urban buildings and roads is proposed. The seismic damages of buildings were assessed using various methods based on structural characteristics and different degrees of available building information. Both physical and topological characteristics of the road network are considered in the proposed method to determine post-earthquake road network traffic capacity. To quantitatively evaluate post-earthquake road network traffic capacity, we comprehensively considered the seismic damage to roads, blockages caused by earthquake-induced debris, and the potential risk of falling debris from damaged buildings. The proposed integrated seismic assessment method was applied to a real earthquake event to demonstrate its feasibility and effectiveness, and also applied to a real city, of which information on buildings and roads was based on open-source data and statistical data, to demonstrate its applicability. The proposed method provides a solid prediction on the seismic performance of urban buildings and road networks, serving as a reference for urban earthquake disaster rescue and relief.
In this study, a multi-source data fusion method was proposed for the development of a Hybrid seismic hazard model (HSHM) in China by using publicly available data of the 5th Seismic Ground Motion Parameter Zoning Map (NSGM) and historical seismic catalogues and integrating with modern ground motion prediction equations (GMPEs). This model incorporates the characteristics of smoothed seismicity and areal sources for regional seismic hazard assessment. The probabilistic seismic hazard for the North China Plain earthquake belt was investigated through sensitivity analysis related to the seismicity model and GMPEs. The analysis results indicate that the Hybrid model can produce a consistent result with the NSGM model in many cases. However, the NSGM model tends to overestimate hazard values in locations where no major events have occurred and underestimate hazard values in locations where major events have occurred. The Hybrid model can mitigate the degree of such biases. Compared to the modern GMPEs, the GMPE with epicentral distance measures significantly underestimate the seismic hazard under near-field and large-magnitude scenarios. In addition, a comparison of the uniform hazard spectra (UHS) obtained by the models, with China’s design spectrum, shows that the current design spectrum is more conservative than the calculated UHS.
Current simulation models considerably underestimate local-scale, short-duration extreme precipitation induced by tropical cyclones (TCs). This problem needs to be addressed to establish active response policies for TC-induced disasters. Taking Shanghai, a coastal megacity, as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018, this study optimized the parameterized Tropical Cyclone Precipitation Model (TCPM) initially designed for TCs at the national scale (China) to the local or regional scales by using machine learning (ML) methods, including the random forest (RF), extreme gradient boosting (XGBoost), and ensemble learning (EL) algorithms. The TCPM-ML was applied for multiple temporal scale hazard assessment. The results show that: (1) The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations, but also preserved the physical meaning of the results, contrary to ML methods; (2) Machine learning algorithms enhanced the TCPM ability to reproduce observations, although the hourly extreme precipitations remained slightly underestimated; (3) Best performance was obtained with the XGBoost or EL algorithms. Combining the strengths of both XGBoost and RF, the EL algorithm yielded the best overall performance. This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.
As sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai’s automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies.
Intensive human activity in global coastal areas has led to increasing exposure to hazards. Cartagena Bay in Chile, an area with a long history of tsunami disasters, has undergone significant urbanization and experiences heavy tourist activity during the summer. While some studies have examined risk in Cartagena by focusing on hazard and vulnerability characteristics, challenges remain in delivering more spatially accurate studies and incorporating the population’s coping capacities. We undertook a tsunami risk assessment of Cartagena Bay that disaggregates social vulnerability to the census block level and assesses the inhabitants’ pedestrian evacuation potential through an agent-based model. Our findings indicate that urban coastal areas in Cartagena Bay might face substantial tsunami risk in a worst-case scenario, with 31.0% to 54.1% of its territory—depending on the scale of analysis—classified as high-risk areas. Of the examined urban blocks, 31.4% have average evacuation times exceeding 17 min (the critical time required by the tsunami to reach its run-up), and the most disadvantaged census block is 1,971.9 m away from its nearest shelter. We also demonstrated that a more spatially accurate vulnerability analysis is more conservative too. For instance, zones with high-risk levels decreased by 42.8% when the study scale moved from the block to the zone level of analysis. Similarly, areas with low risk increased by 80%. In comparison to previous studies, our findings show that tsunami risk in Cartagena Bay is significantly lower if coping capacities such as evacuation potential are included in the analysis.