Hazard Zones Research Articles

The Northern Giona Fault Zone, a Major Active Structure Through Central Greece

The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates a significant elevation difference of approximately 1500 m between the northern Giona footwall and the southern Iti hanging wall. The footwall comprises imbricated Mesozoic carbonates of the Parnassos unit, which exhibit large-scale drag folding near and parallel to the fault. The hanging wall comprises deformed sedimentary rocks of the Beotian unit and tectonic klippen of the Eastern Greece unit, forming a southward-tilted neotectonic block with subsidence near the Northern Giona Fault and uplift near the Ypati fault to the north. These two E-W faults represent younger structures disrupting the older NNW-trending tectonic framework. Fault scarps are observed all along the 14 km length of the Northern Giona fault accompanied by cataclastic zones, separating the carbonate formations of the Parnassos Unit from thick scree, slide blocks, boulders and olistholites. Inversion of fault-slip data has shown a mean slip vector of 45°, N004°E, which aligns with the current regional extensional deformation of the area, as confirmed by focal mechanism solutions. Based on the general asymmetry of the alpine units in the hanging wall, we interpret a listric fault geometry at depth using slip-line analysis and we forward modelled a disrupted fault-propagation fold using kinematic trishear algorithms, estimating a total displacement of 6500 m and a throw of approximately 2000 m. Seismic activity in the area of the Northern Giona Fault includes a magnitude 6.1 earthquake in 1852, which caused casualties, rockfalls and extensive damage, as well as a magnitude 5.1 event in 1983. The expected seismic magnitude is deterministically estimated between 6.2 and 6.7, depending on the potential westward continuation of the Northern Giona Fault beyond the Mornos River to the Northern Vardoussia saddle. The seismic hazard zone includes several villages located near the fault, particularly on the hanging wall, where intense landslide activity during seismic events could result in severe damage to regional infrastructure. The neotectonic development of the Northern Giona Fault highlights the importance of extending seismotectonic research into the mountainous regions of central Greece within the alpine formations, beyond the post-orogenic sedimentary basins.

Ground Robots Bring Potential for Efficiency, Safety in FPSO Operations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32872, “Evaluation of the Potential Impact of Ground Robots on FPSO Operations,” by Gustavo L.L. da Silva, Maurício M. Cordeiro Jr., and Maurício Galassi, Petrobras, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ The objective of this paper is to present the operator’s learnings in evaluating the processes by which the asset crew of a floating production, storage, and offloading (FPSO) vessel identifies scenarios for the application of robotics in day-to-day offshore activities. The studies reveal a glimpse of the addressable market size of ground robots for routine offshore verification tasks. An initial analysis suggests that 30–50% of the theoretical equivalent of hours per year could be freed. Introduction In the complete paper, the hypothetical use of ground mobile robots to carry out operational activities is considered. For that goal, the capability of robotic platforms to move along an offshore facility (an FPSO, for the purposes of this paper) has been explored. This includes the ability to overcome obstacles, move up and down stairs or ramps, and walk on uneven floors or gratings while being piloted remotely or even doing so autonomously. Some of the considered equipment may have coupled robotic manipulators that also enable them to interact with the environment, allowing them to open or close doors, turn handles, and activate commands. These robotic platforms, in general, include actuator mechanisms and sensors that enable visual inspections, thermography, dimensional analysis, area mapping, gas measurements, noise and vibration analysis, and other operations requiring specific sensing. Operational Requirements A paramount requirement for a robot to be widely used in an FPSO is its ability to operate in hazardous areas. Robotic platforms for hazardous zones are not widely available. However, some companies are quickly advancing in the development and commercialization of these platforms. A Swiss company has already made available a quadruped robot with high mobilization capacity, including the ability to navigate stairs and carry out autonomous missions that have been preprogrammed. An Austrian company has marketed a caterpillar robot that can carry out inspections autonomously in classified areas. Every such activity will demand thorough analysis to properly define technical and process requirements. On FPSO platforms, the procedure for planning any service takes place at least a few days before its execution, a task already demanding many hours. Subsequently, a permit to work (PTW) is issued to perform the service. Sensors and Actuators for a Robotic System Some of the main data-acquisition or processing sensors used in robotic systems are detailed in the complete paper. In this context, “data” means everything that can be processed online or is capable of producing useful information in the future. Cameras for Acquisition of Images and Videos. These can be very useful in the visual inspection of systems or equipment, the visualization of the workspace, or the generation of input for image processing with the purpose of identifying patterns of interest, segmentation, classification, or other aspects related to computer vision.

Assessing flood risk and vegetation dynamics: Implications for sustainable land management in Fars province

The design of effective flood risk mitigation strategies and their subsequent implementation is crucial for sustainable development in mountain areas. The assessment of the dynamic evolution of flood risk is the pillar of any subsequent planning process that is targeted at a reduction of the expected adverse consequences of the hazard impact. This study focuses on riverbed cities, aiming to analyze flood occurrences and their influencing factors. Through an extensive literature review, five key criteria commonly associated with flood events were identified: slope height, distance from rivers, topographic index, and runoff height. Utilizing the network analysis process within Super Decision software, these factors were weighted, and a final flood risk map was generated using the simple weighted sum method. 75% of the data was used for training, and 25% of it was used for testing. Additionally, vegetation changes were assessed using Landsat imagery from 2000 and 2022 and the normalized difference vegetation index (NDVI). The focus of this research is Qirokarzin city as a case study of riverbed cities, situated in Fars province, with Qir city serving as its central hub. Key rivers in Qirokarzin city include the Qara Aghaj River, traversing the plain from north to south; the primary Mubarak Abad River, originating from the east; and the Dutulghaz River, which enters the eastern part of the plain from the southwest of Qir, contributing to plain nourishment during flood events. The innovation of this paper is that along with the objective to produce a reliable delineation of hazard zones, a functional distinction between the loading and the response system (LS and RS, respectively) is made. Results indicate the topographic index as the most influential criterion, delineating Qirokarzin city into five flood risk zones: very low, low, moderate, high, and very high. Notably, a substantial portion of Qirokarzin city (1849.8 square kilometers, 8.54% of the area) falls within high- to very-high flood risk zones. Weighting analysis reveals that the topographic humidity index and runoff height are the most influential criteria, with weights of 0.27 and 0.229, respectively. Conversely, the height criterion carries the least weight at 0.122. Notably, 46.7% of the study area exhibits high flood intensity, potentially attributed to variations in elevation and runoff height. Flood potential findings show that the middle class covers 32.3%, indicating moderate flood risk due to changes in elevation and runoff height. The low-level risk is observed sporadically from the east to the west of the study area, comprising 12.4%. Analysis of vegetation changes revealed a significant decline in forest and pasture cover despite agricultural and horticultural development, exacerbating flood susceptibility.

The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China

Abstract. In mountainous areas, damage caused by debris flows is often aggravated by subsequent dam-burst floods within the main river confluence zone. On 30 August 2020, a catastrophic disaster chain occurred at the confluence of the Heixiluo Gully and Niri River in Ganluo County, southwest China, consisting of a debris flow, the formation of a barrier lake, and subsequent dam break that flooded the community. This study presents a comprehensive analysis of the characteristics of the two hazards and the resulting damage to buildings from the cascading hazards. The peak discharge of the debris flow in the gully mouth reached 1871 m3 s−1. Following the dam break, the flood with a peak discharge of 2737 m3 s−1 significantly altered the main river channel, causing a 4-fold increase in flood inundation compared to an ordinary flood. Three hazard zones were established based on the building damage patterns: (I) primary debris flow burial, (II) secondary dam-burst flood inundation, and (III) sequential debris flow burial and dam-burst inundation. Vulnerability curves were developed for Zone (II) and Zone (III) using impact pressures and inundation depths, and a vulnerability assessment chart is presented that contains the three damage categories. This research addresses a gap in the vulnerability assessments of debris flow hazard cascades and can support future disaster mitigation within confluence areas.

Application Of Remote Sensing Technology And Geographic Information Systems For Tsunami Vulnerability Analysis In Mamuju District

This research aims to analyze the vulnerability to tsunami disasters in Mamuju District, West Sulawesi, using remote sensing technology and Geographic Information Systems (GIS). Mamuju District is at high risk of tsunamis due to its coastal location and its position between active tectonic plates. The analysis utilizes five main parameters: elevation, slope gradient, distance from rivers, distance from the coastline, and land cover. The GIS overlay analysis method is applied to map tsunami hazard zones, combining weighting and scoring for each parameter. The results indicate that 3% of the area has very high vulnerability, 7% of the area has high vulnerability, while 82% of the area has low to very low vulnerability. Areas with very high vulnerability include the coastal zone directly adjacent to the shoreline and residential areas. This study is expected to serve as a reference for local governments and disaster management agencies in efforts to mitigate and plan safer spatial arrangements against tsunami threats. Mitigation strategy recommendations include the construction of evacuation routes, safe shelters, and the regular enhancement of disaster education and simulations for the community. Furthermore, this research encourages the use of mountainous areas as primary evacuation zones and collaboration with research institutions for the development of more innovative mitigation technologies.

Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed

At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors on landslide occurrences in the upper Blue Nile basin of Ethiopia remain largely unexplored. This study aims to identify landslide triggers, quantify landslide-susceptible zones, and validate the landslide models. Topographic parameters, geology, hydrology, and land use-land cover inventories were utilized to generate a landslide susceptibility map. The factors were analyzed using a combination of logistic regression (LR) and frequency ratio (FR) models. The area under the curve (AUC) under the receiver operating characteristic (ROC) was used to compare the performance of the models. The result indicates that about 185 sq. km (40.2%) of the total falls under high to very-high susceptible landslide zones, and 92 sq. km (20%) falls under moderate susceptibility. Yet, 183.1 sq. km (40.2%) of the total is classified in the low-to-no landslide hazard zones. The LR and FR model validation demonstrated an average predictive performance of 75 and 81.45%, indicating good precision. The landslide evaluation can help policymakers identify LSH zones for early warning systems and mitigation purposes.

Study on the influence of advancing speed on the dynamic distribution laws of spontaneous combustion hazard zones and high-temperature points in goaf areas

Spontaneous combustion of coal occurs when arising from a combination of factors, including oxidation and heating of residual coal in goaf and favorable heat retention conditions, and the spontaneous combustion hazard zones and high-temperature points formed exhibit dynamic, concealed, and three-dimensional characteristics, further complicating the on-site prevention and control of coal spontaneous combustion. Therefore, it is particularly important to study the dynamic evolution of coal spontaneous combustion (CSC) hazard areas in the goaf during the working face advancement process. With 010803 working face of Wang Wa Coal Mine for reference, the paper develops a three-dimensional Multiphysics coupling model of the goaf through field tests, laboratory experiments, and numerical simulations. For the first time, three-dimensional dynamic mesh technology is employed to investigate the spatial dynamic variation laws of spontaneous combustion hazard zones in goaf areas under advancing speeds ranging from 2 to 8 m/day. The results indicate that the relative error between simulated leakage airflow and field measurements is 1.4 %. As the advancing speed of the working face increases, the oxidation zone in the goaf shifts deeper, and its area increases linearly, though the rate of increase gradually decreases. With higher advancing speeds, the temperature of the high-temperature points in the goaf decreases, and the distances of these points from the lower corner of the working face grow exponentially in both strike and dip directions. However, the high-temperature points remain within the oxidation zone, with their strike migration ranging from 86.3 to 149 m and dip migration from 44 to 51 m. Based on this analysis, a spatial distribution function for high-temperature points in goaf areas is proposed. This study provides theoretical support for preventing and controlling coal spontaneous combustion in goaf areas.

Spatial Modeling and Mapping of Riverbank Erosion Through the Integration of Machine Learning Application and In-situ Data

Assessing riverbank erosion is crucial for identifying hazard zones and implementing protective measures from potential disasters. Traditional in-situ measurements, though effective, are often costly and time-consuming for large-scale evaluations. This study proposes a methodology integrating machine learning (ML) applications to assess riverbank erosion across an entire area using existing submerged Jet erosion test (JET) measurements. In-situ JETs were used to measure the bank erosion rate at each site, identify influencing factors, and randomly split datasets for training and testing. Four ML techniques, random forest(RF), decision trees(DT), multiple linear regression(MLR), and gradient boosting regressor(GBR), are applied to establish the correlation between riverbank erosion and its influencing factors. The RF model demonstrated the highest accuracy (R2 = 0.94, RMSE = 3.04, and NSE = 0.93) among all algorithms, and the optimal model was applied to predict and map annual erosion rates, which were validated with additional submerged JET data. The proposed methodology can effectively model and map riverbank erosion in similar settings.

A Constantly Updated Flood Hazard Assessment Tool Using Satellite-Based High-Resolution Land Cover Dataset Within Google Earth Engine

This work aims to develop a constantly updated flood hazard assessment tool that utilizes readily available datasets derived by remote sensing techniques. It is based on the recently released global land use/land cover (LULC) dataset Dynamic World, which is readily available, covering the period from 2015 until now, as an open data source within the Google Earth Engine (GEE) platform. The tool is updated constantly following the release rate of Sentinel-2 images, i.e., every 2 to 5 days depending on the location, and provides a near-real-time detection of flooded areas. Specifically, it identifies how many times each 10 m pixel is characterized as flooded for a selected time period. To investigate the fruitfulness of the proposed tool, we provide two different applications; the first one in the Thrace region, where the flood hazard map computed with the presented herein approach was compared against the flood hazard maps developed in the frames of the EU Directive 2007/60, and we found several inconsistencies between the two approaches. The second application focuses on the Thessaly region, aiming to assess the impacts of a specific, unprecedented storm event that affected the study area in September 2023. Moreover, a new economic metric is proposed, named maximum potential economic loss, to assess the socioeconomic implications of the flooding. The innovative character of the presented methodology consists of the use of remotely sensed-based datasets, becoming available at increasing rates, for developing an operational instrument that defines and updates the flood hazard zones in real-time as required.

Trench investigation to quantify debris flow activity for landslide hazard mapping in populated areas: Lessons learned from Gol, southern Norway

AbstractWe here describe the results of stratigraphic and sedimentological examinations of debris flow deposits at Breidokk, Gol, southern Norway. The deposits are situated at the valley floor, below a steep slope with three large and several smaller debris flow channels incised into the thick till cover. The study area is populated and with abundant infrastructure such as roads, public and private buildings and other types of infrastructure, including underground water pipes and cables. Six, 10–15 m long and 1–3 m deep trenches were dug out with an excavator and examined. The sediments in the trenches consist of moraine‐, glaciofluvial/fluvial‐ and debris flow deposits. The latter consist of matrix supported, unsorted, massive beds from 1 cm to more than 1 m in thickness, with clasts up to 80 cm in diameter. A total of 16 post glacial debris flow beds are identified in five of the six trenches, representing a minimum of eight individual debris flow events. This is probably an underestimation of the debris flow activity through postglacial times as the location of the trenches was in large determined by infrastructure and were not optimally placed for mapping all debris flow deposits in the area. Also, correlation between trenches proved difficult. A total of 37 radiocarbon ages of buried soil and other organic material situated above and below debris flow deposits, together with the sedimentological and stratigraphical interpretation, show that debris flow activity has prevailed throughout the Holocene, also within the last 1000 years. A possible increase in activity within the last 3–4000 years BP has been noted. This is important knowledge to aid in the interpretation of the Quaternary history of the area but also to determine the hazard zones.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Identification and Categorization of Technical Errors and Hazard-zones of Robotic versus Laparoscopic total Gastrectomy for Gastric Cancer: A Single Center Prospective Randomized Controlled Study.

The current research aimed to conduct a detailed analysis of intraoperative surgical performance, short-term outcomes, identify and categorize technical errors, and hazard-zones enacted during total gastrectomy performed robotically and laparoscopically by surgeons. Prospective research is needed to determine whether the technical advantages of robotic surgery translate to patient outcomes. Identify and process risk areas in robot-assisted total gastrectomy (RTG) and laparoscopic total gastrectomy (LTG) to get the best patient results. Patients undergoing RTG and LTG were recruited and randomized into the study. Six consultant/attending surgeons participated in this study and all surgical procedures were recorded. The unedited surgical video-recordings were handed over to third-party experts for granular analysis of the procedures using objective clinical human reliability analysis (OC-HRA) for the quality of intraoperative performance, technical errors, intraoperative complications. This study is a single center prospective randomized controlled trial. 82 patients were recruited and participated in this study with 40 cases undergoing RTG and 42 cases for LTG. RTG vs LTG. Determine whether RTG or LTG can provide the better intraoperative technical performance and identify the most hazardous zone (area) during total gastrectomy (TG). The technical errors enacted and identified in the RTG and the LTG were (46.11±5.63 VS 58.79±8.45, P＜0.001) respectively. The highest number of technical errors was identified during the dissection of the supra-pancreatic lymph nodes (Task Zones3, TZ3), including No.5, 7, 8a, 9, 11p, and 12a to complete the nodal clearance around the celiac artery and its trifurcation (7.29±1.88 VS 9.43±2.24, P <0.001) in both RTG and LTG. The number of lymph nodes harvested with RTG was higher than LTG (35.36±7.51 VS 30.54±6.95, P=0.016), especially in the upper margin of the pancreas (13.32±4.17 VS 9.36±3.81, P＜0.001). The total cost of hospitalization in the RTG group cost 3% more than LTG group ($15953.41±3533.91 VS $12198.26±2761.27, P＜0.001). This study offers compelling OC-HRA evidence demonstrating that RTG facilitates significantly superior technical performance compared to LTG. Whether examining short-term clinical outcomes or intraoperative operations, the robotic surgery system consistently outperforms laparoscopic surgery. Lymph node dissection in the supra-pancreatic region emerged as a major hazard zone in both procedures. chictr.org.cn: ChiCTR2000039193.

Utilizing Data Mining Techniques in Geophysical and Biological Analysis for Assessing Environmental Risks

This study aimed to assess environmental risks using data extraction techniques. It focused on geophysical and biological factors and addressed the urgent need for effective risk management strategies to reduce soil erosion, water pollution, and air quality deterioration. A comprehensive dataset was created through the systematic collection of geophysical and biological data including temperature, soil composition, and biological abundance index. It used equipment such as satellite sensors and mountain transmitting stations. Various statistical tools used include decision trees and random forest algorithms. They were used to analyze data and identify important environmental risk factors. The results showed some interesting insights, revealing that the Neural Network has an accuracy of 95.5%, and the Random Forest algorithm predicts risk with an accuracy of 92.0%. It analyzed the classification of environmental hazard zones and identified high-risk zones, such as Zone A, which contains 10,000 people affected by erosion and Zone D, 20,000 people who were at risk from soil contamination. The study concludes that social media can significantly improve understanding and management of environmental risks. Additionally, it provides a useful framework for decision-makers and stakeholders to promote sustainable environmental practices.

Expected First Occurrence Time of Uncertain Future Events in One-Dimensional Linear Systems

The rapid advancement of machine learning algorithms has significantly enhanced tools for monitoring system health, making data-driven approaches predominant in Prognostics and Health Management (PHM). In contrast, model-based approaches have seen modest progress, as they are often constrained by the need for prior knowledge of specific governing equations, limiting their applicability to a wide range of problems. Recently, rigorous theoretical foundations have been established to extend dynamical systems theory by incorporating prognosis of uncertain events. This article leverages this formal framework to introduce and demonstrate a fundamental mathematical result for one-dimensional linear dynamical systems. The presented theorem offers an exact expression for calculating the expected time at which an event will first occur in the future. Unlike typical thresholds, this event is triggered by a hazard zone, defined as an uncertain event likelihood function over the system's state space. Applications of this theorem can be found in implementing real-time prognostic frameworks, where it is crucial to quickly estimate the magnitude of impending failures. Emphasis is placed on minimizing computational burden to facilitate prognostic decision-making.

The influence of land use planning and exposure to flooding on urban growth in San José, Costa Rica

We estimate areas of potential flooding exposure within the San José Metropolitan Region with morphometric methods. Based on this potential hazard zoning, we use a spatial-statistical model to explore the difference introduced by land use regulations (municipal and regional) on urban growth, controlling for potential exposure to hazards and other sources of spatial heterogeneity. The potential flood zoning derived coincided with upstream rivers and flat areas between them (likely susceptible to urban drainage capacity problems). We estimated regional land use regulations significantly constrain urban development (with urban growth reductions of about 6 %), but the constraint does not increase in areas exposed to potential flooding; municipal environmental protection zoning was estimated to decrease urban development by 8 %, with an additional but very small 0.1–0.9 % reduction in potential flooding areas. Regional instruments fundamentally applied to the steeper (and hence less exposed to flooding) periphery whereas municipal land use instruments did constrain land from development (especially along rivers) within urban areas.

The Impact of the Configuration of a Hydrogen Refueling Station on Risk Level

The paper discusses potential hazards at hydrogen refueling stations for transportation vehicles: cars and trucks. The main hazard analyzed here is an uncontrolled gas release due to a failure in one of the structures in the station: storage tanks of different pressure levels or a dispenser. This may lead to a hydrogen cloud occurring near the source of the release or at a given distance. The range of the cloud was analyzed in connection to the amount of the released gas and the wind velocity. The results of the calculations were compared for chosen structures in the station. Then potential fires and explosions were investigated. The hazard zones were calculated with respect to heat fluxes generated in the fires and the overpressure generated in explosions. The maximum ranges of these zones vary from about 14 to 30 m and from about 9 to 14 m for a fires and an explosions of hydrogen, respectively. Finally, human death probabilities are presented as functions of the distance from the sources of the uncontrolled hydrogen releases. These are shown for different amounts and pressures of the released gas. In addition, the risk of human death is determined along with the area, where it reaches the highest value in the whole station. The risk of human death in this area is 1.63 × 10−5 [1/year]. The area is approximately 8 square meters.

Assessing Crisis Management Tools for Sustainability of Industrial Safety

This study presents a comprehensive risk assessment of ammonia leaks, focusing on the quantitative modelling of hazardous area ranges, concentration dynamics, and thermal radiation effects under varying leakage scenarios using the ALOHA 5.4.7 software. The analysis involves two key scenarios: an ammonia gas leak and a pool fire, each modelled under distinct atmospheric conditions. For the gas leak scenario, ammonia concentrations were mapped across ERPG-defined hazard zones, ranging from low-level irritation zones (ERPG-1) to life-threatening exposure levels (ERPG-3), with maximum concentrations reaching 1500 ppm within a 110 m radius. The second scenario examined the impact of thermal radiation from a pool fire, identifying critical radiation zones where exposure to heat fluxes exceeding 10 kW.m−2 could cause fatal outcomes within 12 m. Despite ALOHA’s strengths in modelling acute exposure risks and providing valuable input for emergency response planning, the study identifies several limitations, particularly regarding the long-term environmental and health impacts of chemical releases and the effects of varying meteorological conditions. These findings suggest that integrating ALOHA with advanced real-time monitoring and AI-based prediction systems could significantly improve its capacity to manage dynamic, rapidly evolving industrial hazards.

A Study on Risk Factors and Analysis Methods for Identifying Hazardous Zones of Safety Accidents on Community Roads

A Study on Risk Factors and Analysis Methods for Identifying Hazardous Zones of Safety Accidents on Community Roads

Dynamic Analysis and Hazard Assessment of Debris Flows in Donghaolitaogao

Utilizing the FLO-2D software, the motion processes of debris flows in Donghaolitaogao under 20-year, 50-year, and 100-year return periods of rainfall are analyzed, revealing that as the rainfall return period increases, the debris flow velocity, accumulation depth, and accumulation area all significantly expand. Specifically, under the 20-year, 50-year, and 100-year rainfall conditions, the maximum debris flow velocities are 1.85 m/s, 1.95 m/s, and 1.99 m/s, respectively, while the maximum accumulation depths are 1.31 m, 1.33 m, and 1.35 m, and the total accumulation areas are 27,267.8 m2, 31,500.6 m2, and 33,901.3 m2, respectively. Using the depth of debris flow and the product of debris flow depth and velocity as evaluation indicators, the debris flow hazard assessment indicates that the Donghaolitaogao debris flow is primarily characterized by low- and medium-risk areas, with high-risk areas scattered sporadically along the central part of the channel. Moreover, as the rainfall return period increases, the area of high-risk zones continues to expand, gradually accounting for a larger proportion of the total hazard zone.

Gas Leakage Risk Management Of Biogas Powerplant Using Aloha Gas Dispersion Modelling And Bowtie Analysis

Biogas, a fuel derived from organic waste, has gained popularity as a sustainable energy source. The adaptability of biogas allows its utilization for electricity and heating. This study aims to evaluate the risk of biogas leakage using ALOHA gas dispersion modeling and Bowtie analysis to identify hazard zones and evaluate the effectiveness of preventive and mitigation measures. The research method using Gas dispersion modeling in ALOHA software can simulate the spread of leaked biogas, identify potential hazard zones, and inform emergency response planning. The results show that gas dispersion reaches up to 296 meters from the leak source in a worst-case scenario without ignition, with significant implications for safety. The ignition scenario produces high thermal radiation, further exacerbating the risk. In conclusion, these findings highlight the intolerable risk of biogas leakage, requiring comprehensive risk mitigation measures. This study recommends the implementation of a robust control system, routine maintenance, and emergency response plan to ensure the safety and sustainability of biogas facility operations