Journal of Rescue and Relief (امداد و نجات)

Letter to the Editor

INTRODUCTION: Urban air pollution, especially in densely populated metropolises such as Tehran, has become one of the most serious environmental and health challenges. Nitrogen dioxide (NO₂), as one of the most important pollutants resulting from human activities, accumulates persistently in the surface layers of the atmosphere, especially in the cold seasons and under temperature inversion conditions. This study conducts an integrated hazard analysis of the spatiotemporal distribution of NO₂ in Tehran County over a seven-year period (2018–2024), with a focus on the interplay between anthropogenic and natural factors in shaping this hazard. METHODS: In this descriptive-analytical study with an integrated approach, daily NO2 concentration data were extracted from Sentinel-5P satellite images (TROPOMI sensor) during the winter seasons from 2018 to 2024 in Tehran. Using spatial extraction tools in the GIS environment, data were processed, seasonal averages were calculated, and raster-to-vector conversion was performed, and spatiotemporal distribution patterns of NO2 were plotted and analyzed. FINDINGS: The findings showed that the distribution pattern of NO2 has undergone significant spatial and temporal changes over the seven years. In 2018 and 2019, the highest concentrations were observed in central Tehran and the northeastern districts. A marked decline occurred in 2020 and 2021, coinciding with mobility restrictions imposed during the COVID-19 pandemic. However, from 2022 onward, NO₂ levels began to rise again, with a notable increase in southern and eastern areas by 2024. These shifts reflect a transition in pollution hotspots—from the city center toward peripheral zones—followed by a partial return to a more centralized pattern, indicative of extensive urban expansion and intensified human activities in suburban areas. CONCLUSION: According to the research results, NO₂ pollution in Tehran constitutes a “compound hazard,” resulting from the complex interaction of anthropogenic drivers (e.g., traffic, industry, and urban development) and natural conditions (e.g., temperature inversions and topography). The observed changes in NO₂ spatial patterns mirror transformations in urban morphology, land use, and environmental management policies.

INTRODUCTION: The safety of aid workers in relief and rescue operations is of particular importance due to the exposure to life-threatening, physical, and psychological risks. The International Federation of Red Cross and Red Crescent Societies (IFRC) has provided frameworks for reducing risks and saving the lives of aid workers by providing standards such as the Sphere Project and the Pinheiro Principles. This study aimed to examine these standards and their application in relief operations in Iran in order to provide solutions to improve and promote the level of aid worker safety. METHODS: This descriptive-analytical study was conducted with purposive sampling of 20 authentic documents and the data were analyzed using thematic content analysis. Key sources included the Sphere handbook (2018), the Pinheiro principles (2005), ISO 45001 and ISO 14001 standards, and IFRC reports (2023-2024). The validity was confirmed by triangulation of sources and reliability by recoding (85% agreement). FINDINGS: According to the findings, four main components of supporting standards were identified, namely participation (Sphere), competence and training (ISO 45001), communication and participation, and operational control. Sphere standards reduce operational risks by 30%, ongoing incident training by 25%, and equipment inspections reduce technical risks by 40%. Challenges of the current situation in Iran include the lack of specialized training and digital reporting systems. CONCLUSION: The results show that integrating IFRC standards with local practices can improve the safety of aid workers, along with strengthening specialized training, creating digital reporting platforms, and continuously reviewing guidelines.

INTRODUCTION: Road traffic accidents represent a significant public health challenge globally, and Iran continues to experience disproportionately high rates of traffic-related morbidity and mortality. This study presents a provincial-level analysis of road traffic accidents in Iran during the 2022–2023 period. METHODS: A retrospective analysis was conducted using data from the Statistical Center of Iran, encompassing all recorded road traffic accidents across 31 provinces during the March 2022 to March 2023. Descriptive statistics, accident and fatality rate calculations, independent-samples t-tests, and Pearson correlation analyses were performed to compare intra-city and extra-city patterns. Statistical significance was defined as p<0.05. Data processing and statistical analyses were carried out using Excel 2019 and SPSS 26, while geographic visualizations were developed using Datawrapper. FINDINGS: In 2022, a total of 2,119,406 road traffic accidents occurred in Iran, leading to 18,799 deaths and 379,020 injuries. Although intra-city areas accounted for the majority of accidents (81.8%; n = 1,733,200), extra-city crashes were markedly more severe. The fatality rate in extra-city areas was 37.48 per 1,000 accidents, compared with 2.5 per 1,000 in intra-city areas (p < 0.001), indicating that extra-city crashes are approximately 15 times more likely to result in death. Significant regional heterogeneity was also observed: Tehran Province reported the highest number of accidents (514,498), whereas Sistan and Baluchistan exhibited the highest fatality rate (167.76 per 1,000 accidents). A negative correlation was identified between total accidents and fatality rates across provinces (r =–0.42, p =0.018), suggesting that provinces with fewer accidents often experience more severe outcomes when crashes occur. CONCLUSION: The results reveal a pronounced intra-city/extra-city divide, where extra-city crashes are 15 times more fatal, alongside severe provincial disparities and highlight the need for differentiated prevention strategies, emphasizing improvements in extra-city infrastructure, enforcement, and emergency response capacity—particularly in high-risk regions. Keywords: Road Traffic Accidents; Injury Prevention; Mortality; Public Health; Intra-city/Extra-city Disparities; Geographical Analysis; Epidemiology.

Introduction: Emotional intelligence represents an advanced and evolved perspective on human resource management in organizations. It serves as a modern and effective tool that enables managers to manage crises and guide employees toward the achievement of organizational goals. Method: In this applied, descriptive–survey research, the statistical population consisted of 478 senior staff members and experts of the Red Crescent Society in Yazd province. A total of 215 participants were selected using Cochran’s formula. Findings: In the present study, four components of emotional intelligence—self-regulation, motivation, empathy, and social skills (each consisting of specialized subcomponents)—were first coded. Subsequently, pairwise comparisons of the emotional intelligence components were conducted, and the weights of these comparisons were calculated using the geometric mean. Finally, the collected components were ranked using the fuzzy Analytic Hierarchy Process (AHP) method. Conclusion: The results showed that among the evaluated indicators, self-awareness(A), having the highest weight and social skills (E), having the lowest weight, is identified as the most and the least important component of emotional intelligence.

INTRODUCTION: Prehospital emergency personnel face numerous challenges when time is poorly managed, which can adversely affect both patient outcomes and the overall healthcare system. Identifying factors that contribute to effective time management is essential for enhancing performance in emergency care settings. This study aimed to examine the relationship between social intelligence and time management among prehospital emergency staff in Markazi province, Iran. METHODS: This cross-sectional study was conducted in 2024 among 200 emergency medical technicians working in urban and road EMS bases in Markazi province using convenience sampling. Data were collected using a demographic questionnaire, the Time Management questionnaire, and the Social Intelligence questionnaire. Data analysis was performed in SPSS-16 using descriptive statistics and Pearson correlation tests, with the level of statistical significance set at p<0.05. FINDINGS: The results indicated that the participants had a mean age of 33.64 ± 7.39 years, with males comprising 95% of the sample. The majority of participants (89.6%) exhibited strong time management skills. The mean social intelligence score was 93.15 ± 14.45, reflecting a high level of social intelligence. Furthermore, social intelligence and all of its subscales showed positive and statistically significant correlations with all dimensions of time management (p<0.001). CONCLUSION: Social intelligence, as a critical component of human capital, significantly influences the time management abilities of prehospital emergency personnel. Interventions aimed at enhancing social intelligence may therefore improve time management skills and overall efficiency in prehospital emergency care.

INTRODUCTION: The Red Crescent Society, as one of the largest relief and humanitarian organizations, needs to implement modern management techniques in order to enhance the effectiveness and adaptability in complex and dynamic crisis. The aim of this study is to examine the relationship between knowledge management and organizational culture and innovation capacity in the Red Crescent Society in Alborz province. METHODS: In this study, a standard questionnaire was used to measure knowledge management, organizational culture, and innovation capacity. Data were analyzed using descriptive and inferential statistics with SPSS and SmartPLS software. FINDINGS: According to the findings, there are a positive and significant relationship between knowledge management and organizational culture with innovation capacity and all proposed hypotheses were confirmed. CONCLUSION: The results suggest that organizational culture has the strongest impact on innovation capacity, while knowledge creation had the least impact.

INTRODUCTION: The unpredictable magnitude and scope of disasters make it particularly challenging to respond effectively and provide timely assistance to affected populations. In many situations, geographical location, regional topography, and adverse weather conditions, especially in the early stages, hinder rapid access to disaster-affected areas. In recent years, Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as an innovative technology that offers rapid data collection, real-time surveillance, and access to remote areas, thereby enhancing situational awareness and decision-making during disasters. METHODS: This study employed a narrative review methodology to synthesize existing research on the application of UAVs across the pre-disaster, during and post-disaster phases of disaster management. A comprehensive search of relevant databases yielded a total of 1,986 articles. After removing duplicate records and screening titles, abstracts, and full texts based on predefined inclusion criteria, nine articles were selected for final analysis and review. FINDINGS: The findings were categorized into four main phases of disaster management: prevention and mitigation, preparedness, response, and recovery. The reviewed studies demonstrated that UAVs play a significant role in improving situational awareness, damage assessment, Search and Rescue (SAR) operations, infrastructure monitoring, and recovery planning. Despite certain technical, regulatory, and operational challenges, the overall evidence highlights the substantial potential of UAVs to enhance disaster management effectiveness across all phases. CONCLUSION: According to the results of this review, systematic planning for the integration of UAV technology across various stages of disaster management is essential. Although challenges remain, these can be addressed through the adoption of advanced technologies such as deep learning algorithms, as well as improved equipment, software, and analytical tools for data collection and processing. Such advancements can significantly enhance the cost-effectiveness and operational value of UAVs, supporting more efficient disaster response, mitigation, and recovery strategies.