Industrial Accidents Research Articles

Risk assessment of chemical release accident triggered by landslide using Bayesian network

This study evaluated the risk of 461,260,800 scenarios of chemical release accidents triggered by landslides. Several industrial accidents triggered by landslides have recently occurred in Japan; however, only a few studies have analyzed the impact of landslide-triggered chemical release accidents on the surrounding areas. Bayesian networks (BNs) have recently been used in the risk assessment of natural hazardtriggered technological accidents (Natech) to quantify uncertainties and develop methods applicable to multiple scenarios. However, the scope of BN-based quantitative risk assessment is limited to the risk assessment of explosions triggered by earthquakes and lightning. We aimed to extend the BN-based risk analysis methodology and evaluate the risk and the effectiveness of the countermeasures for specific facility. A methodology was developed to assess human health risk in the surrounding areas when n-hexane was released and dispersed into the atmosphere due to a landslide. Risk assessment results showed that the societal risk (representing the relationship between frequency and number of people suffering from a particular harm) of the storage tank closest to the slope exceeded the Netherlands' criteria, which are the safest among the criteria in the United Kingdom, Hong Kong, Denmark, and the Netherlands. Limiting the storage rate reduced the probability of one or more fatalities by up to about 40% compared with the no countermeasure case and was a more effective countermeasure than using oil fences and absorbents. Diagnostic analyses quantitatively showed that the distance between the tank and slope was the main contributing factor. The catch basin parameter contributed to the reduction in the variance of the results compared to the storage rate. This finding indicated that physical measures, such as strengthening or deepening the catch basin, are essential for risk reduction. Our methods can be applied to other natural disasters for multiple scenarios by combining it with other models.

Predicting the severity of occupational accidents in the construction industry using standard and regularized logistic regression models

Occupational accidents in the construction industry occur frequently when compared with other industries. Construction accidents still cannot be prevented at the desired level. These accidents have been explored for prediction purposes using traditional statistical and machine-learning techniques. However, some challenges regarding imbalanced and multicollinearity problems present in the dataset are not considered while analyzing large and categorical variable-rich data. For this reason, this study aims to predict the severity of non-fatal construction accidents considering these challenges to obtain accurate results. In this study, standard binary logistic regression with Firth, Ridge, Lasso, and Elastic Net regularized models were utilized for the prediction of lost workdays in the construction industry. The data used were classified into five groups: victim, workplace, accident time, accident and sequence of events, and post-accident state-related variables. The results showed that Firth’s logistic model is the best-performing model and age, education, vocational education, size of the workplace, project type, working environment, accident month and year, general and specific activities, material agent of deviation, type of injury, and part of body injured are the most significant variables. This study by providing interpretable machine learning tools is the first attempt to use proposed models in the construction safety field.

Introduction to Human Factors in Accident Investigation

This is a book about human factors and accident investigation. The author, Dr R. S. Bridger, is a consultant and educationalist in human factors and ergonomics. Dr Bridger is past president of the Chartered Institute of Ergonomics and Human Factors (UK). He is experienced in accident investigation in the maritime and healthcare sectors. His book is aimed at safety professionals, engineers, accident investigators and psychologists who apply the principles of human factors. Occupational Health Practitioners with both a health and a safety remit, seeking to understand how human behaviour contributes to accidents (unplanned events causing personal injury or property damage), will find this book interesting. Human Factors refer to working conditions that affect people’s behaviour. High-risk industries and some safety professionals prefer ‘Human Factors’ to ‘Ergonomics’, but the two are often used interchangeably. The book has 15 chapters. It contains an abundance of thought-provoking examples of accidents in healthcare and other industries illustrating the contribution of human factors (an example is the Ferry Herald disaster near Zeebrugge in 1987). The first chapter explains how people in organizations and systems operate where cognitive, physical and organizational factors intersect. Accidents happen when the body, the mind, the senses and skills do not fit with the workspace. Another chapter on human–machine interaction and accidents is most relevant to the current Digital Revolution of how we work.

One-Step Hydrothermal Strategy for Preparation of a Self-Cleaning TiO2/SiO2 Fiber Membrane toward Oil-Water Separation in a Complex Environment

Oil pollution caused by a large number of industrial activities and oil spill accidents has posed serious harm to the environment and human health. However, some challenges remain with the existing separation materials, such as poor stability and fouling resistance. Herein, a TiO2/SiO2 fiber membrane (TSFM) was prepared by a one-step hydrothermal method for oil-water separation in acid, alkali, and salt environments. The TiO2 nanoparticles were successfully grown on the fiber surface, endowing the membrane with superhydrophilicity/underwater superoleophobicity. The as-prepared TSFM exhibits high separation efficiency (above 98%) and separation fluxes (3016.38-3263.45 L·m-2·h-1) for various oil-water mixtures. Importantly, the membrane shows good corrosion resistance in acid, alkaline, and salt solutions and still maintains underwater superoleophobicity and high separation performance. The TSFM displays good performance after repeated separation, demonstrating its excellent antifouling ability. Importantly, the pollutants on the membrane surface can be effectively degraded under light radiation to restore its underwater superoleophobicity, showing the unique self-cleaning ability of the membrane. In view of its good self-cleaning ability and environmental stability, the membrane can be used for wastewater treatment and oil spill recovery and has a broad application prospect in water treatment in complex environments.

Transformational Leadership Influences Employee Performance: A Review and Directions for Future Research

The world is currently in an era of great turmoil, from the increase in sudden changes such as industrial accidents and terrorist threats to the global spread of the COVID-19, thus showing that the volatility, uncertainty, complexity and ambiguity characteristic of the market environment has become a norm. The personal traits and abilities of the entrepreneur determine the survival and success or failure of the company, and it is only by choosing a more appropriate leadership style according to the time and the right changes that one can seek opportunities in the crisis. Leadership style affects the work atmosphere and morale of the entire organization, and plays an important role in work performance, and how to use leadership theory to improve employee performance is attracting more and more attention from scholars at home and abroad. In this paper, based on a review of related studies at home and abroad, and from the perspectives of social exchange theory, upper echelons theory and leadership-member exchange theory, the mechanisms and relationships between transformational leadership and employee performance are organized based on a review of the conceptual structure, influencing factors and mechanisms of action of related concepts. At the same time, the more complex influence relationship between the two is also sorted out to reveal the impact path of transformational leadership on employee performance. In addition, we summarize the shortcomings of the existing research results and propose the future outlook, so as to provide some reference value for future scholars who study transformational leadership and employee performance.

Investigation of ambient temperature effects on the characteristics of turbulent diffusion flames: An experimental approach

Pool fires, commonly associated with industrial accidents, have posed a significant threat to the safety of industrial processes. This is particularly relevant in environments where storage tanks, engine cabinets, and boiler rooms are typically maintained. The effects of ambient temperature on the flame characteristics of gaseous pool fires were explored through a series of experiments conducted in a temperature-controlled chamber, with temperatures ranging from 40 ℃ to − 20 ℃. A modified flame height correlation accounting for the temperature effect was proposed. These findings have potential implications for enhancing the reliability of industrial design and fire risk assessment in environments with varying temperature conditions.

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.

Assessing the Preparedness for a Cyanide Poisoning Mass Casualty Incident in Brooklyn

Introduction:Urban communities are under constant threat of numerous potential disasters, including cyanide exposure events. Exposure can occur in settings such as structure fires, industrial accidents, or even intentional acts of terrorism. The typical treatment modality for cyanide toxicity employs the antidote, hydroxocobalamin. While studies regarding antidote availability have been conducted in Korea and Hong Kong, a literature search did not reveal any such studies in any part of New York City.The borough of Brooklyn has a population of 2.57 million people. In the setting of a mass casualty incident (MCI) involving cyanide toxicity, such as a large structural fire or a chemical attack, it is uncertain of the region's capability to provide hydroxocobalamin. The objective of this study is to assess the stockpile of hydroxocobalamin across acute care hospitals in Brooklyn.The amount of hydroxocobalamin required to treat a cyanide-related MCI was based on recommendations from the 2018 US Expert Consensus Guidelines for Stockpiling Antidotes. Ten grams of hydroxocobalamin are needed for each 100-kg patient. Theoretically, a minimum of 50 grams of hydroxocobalamin would be required for a mass casualty incident (5 patients).Method:Fifteen acute care hospitals within Brooklyn were identified as potential treatment sites for cyanide exposure. Each site’s emergency manager was sent a survey identifying hydroxocobalamin availability in both their pharmacy and their emergency department.Results:All 15 hospitals responded to the survey. Two of the 15 hospitals had at least 50g of hydroxocobalamin in their inventory, however, no hospital had 50g stored in their emergency department. The median amount of hydroxocobalamin stored was 20g or two doses.Conclusion:Should a mass casualty incident involving cyanide exposure occur, only two hospitals in the borough of Brooklyn would be prepared to treat five or more patients presenting to their hospital.

Differences in Workers’ Safety Behavior by Project Size and Risk Level of Work in South Korea

Unsafe behavior is the main cause of accidents in the construction industry. Considering the fact that accidents occur more frequently in small projects and on more hazardous works, this study tests the hypotheses that workers’ safety behavior varies (1) with project size, and (2) with the risk level of work. Survey data were collected from a total of 313 construction workers at 21 sites using questionnaires, and the data were analyzed using a series of factor analyses and structural equation modeling. Multigroup factor analysis revealed that the mean safety behavior value for the workers in large projects was greater than that for the workers in small projects, with a statistically significant difference. This indicates that large-project workers tend to behave more safely than small-project workers. However, no statistically significant difference was found for the workers’ safety behavior with regards to the risk level of work. In addition, the differences in the impacts of three factors (management commitment, supervisor’s role, and workers’ perception of safety) on safety behavior depending on the project size and risk level of work were investigated using multigroup structural equation modeling. Management commitment had the greatest impact on workers’ safety behavior in large projects, whereas workers’ perception of safety had the greatest impact in small projects. Regarding the risk level, management commitment had the greatest impact on safety behavior with low-risk work, and workers’ perception of safety had the greatest impact with high-risk work. These results suggest that management commitment is more important to control worker’s safety behavior to prevent accidents for large projects and low-risk works, whereas the workers’ safety perception is more important to prevent accidents for small projects and high-risk works. This study provides a new theoretical basis for explaining the variations in workers’ safety behavior group to group, and facilitates the development of future safety policies and systems. This study also contributes to the establishment of strategic planning and the justification for spending relevant resources to improve workers’ safety behavior.

A Descriptive Analysis of the Health Care Aspects of Industrial Disasters Around the World

Introduction:Industrial disasters can have a myriad of repercussions ranging from acute deaths, injuries, and long-term adverse health impacts on nearby populations to political fallout and environmental damage. This is a descriptive epidemiological analysis of industrial disasters occurring between 1995 and 2021 which may provide useful insight for health care systems and disaster medicine specialists to better prevent and mitigate the effects of future industrial disasters.Method:Data was collected using a retrospective database search of the Emergency Events Database (EM-DATS) for all industrial disasters occurring between January 1, 1995, and December 31, 2021.Results:1,054 industrial disasters were recorded from 1995 to 2021. The majority of these disasters occurred in Asia (720, 68.3%), with 131 (12.4%) in Africa, 107 (10.2%) in Europe, 94 (8.9%) in the Americas, and 2 (0.2%) in Oceania. Half of these disasters were explosions (533, 50.6%), 147 (13.9%) were collapses, 143 (13.6%) were fires, 46 (4.4%) were chemical spills, 41 (3.9%) were gas leaks, and 34 (3.2%) were poisonings. There were 6 (0.6%) oil spills and 3 (0.3%) radiation events.Conclusion:A total of 29,708 deaths and 57,605 injuries were recorded as a result of industrial disasters and they remain a significant contributor to the healthcare risks of both workers and regional communities. The need for specialized emergency response training, the potential devastation of an industrial accident, and the vulnerability of critical infrastructure as terror targets highlight the need to better understand the potential immediate and long-term consequences of such events and to improve healthcare responses in the future.

ОПЫТ ИСПОЛЬЗОВАНИЯ ЭКСТРАКОРПОРАЛЬНОЙ МЕМБРАННОЙ ОКСИГЕНАЦИИ У ПАЦИЕНТА С ИНГАЛЯЦИОННЫМ ОТРАВЛЕНИЕМ СОЕДИНЕНИЯМИ ФТОРА

Throughout the history of mankind, technological progress has not stood still, but, unfortunately, the production and discoveries that the age of progress brings to us always go hand in hand with an industrial injury. Despite the fact that over the past decades, the requirements and control of compliance with safety regulations have significantly increased, yet sometimes accidents occur that can take more than one human life. Work involving the risk of inhalation poisoning with particularly dangerous chemical compounds, which can lead to severe systemic and respiratory damage, is particularly dangerous for humans. According to the Federation of Independent Trade Unions of Russia, chemical poisoning accounts for 21.93% of all industrial injuries. Depending on the damaging agent, duration and area of exposure, all victims need a certain amount and specifics of assistance, which often requires prosthetics of several affected body systems, including using extracorporeal technologies. We present a clinical case of the successful application of extracorporeal membrane oxygenation (ECMO) methods in the complex therapy of inhalation poisoning with hydrofluoric acid vapors received by a patient as a result of an industrial accident. Based on the lack of clear clinical recommendations for the treatment of inhalation poisoning of this etiology, the method of extracorporeal membrane oxygenation and respiratory protection for volumetric lung tissue damage in complex therapy has shown its effectiveness and prospects that require further study.

EFFECT OF EXOSOMES DERIVED FROM BONE MARROW MESENCHYMAL STEM CELLS ON PROGRAMMED CELL DEATH IN BLAST-INDUCED LUNG INJURY IN RATS.

Blast lung injuries (BLIs) are frequent because of industrial accidents and terrorist groups. Bone marrow mesenchymal stem cells (BMSCs) and exosomes derived from BMSCs (BMSCs-Exo) have become a hot topic in modern biology because of their significance in damage healing, immune regulation, and gene therapy. The aim of this study is to investigate the effect of BMSCs and BMSCs-Exo on BLI in rats caused by gas explosion. Here, BMSCs and BMSCs-Exo were transplanted into BLI rats via tail vein and then evaluated pathological alterations, oxidative stress, apoptosis, autophagy, and pyroptosis in the lung tissue. Through histopathology and changes in malondialdehyde (MDA) and superoxide dismutase (SOD) contents, we discovered that oxidative stress and inflammatory infiltration in the lungs were significantly reduced by BMSCs and BMSCs-Exo. After treatment with BMSCs and BMSCs-Exo, apoptosis-related proteins, such as cleaved caspase-3 and Bax, were significantly decreased, and the ratio of Bcl-2/Bax was significantly increased; the level of pyroptosis-associated proteins, including NLRP3, GSDMD-N, cleaved caspase-1, IL-1β, and IL-18, were decreased; autophagy-related proteins, beclin-1 and LC3, were downregulated while P62 was upregulated; the number of autophagosomes was decreased. In summary, BMSCs and BMSCs-Exo attenuate BLI caused by gas explosion, which may be associated with apoptosis, aberrant autophagy, and pyroptosis.

Development of an IOT Based Industrial Fault Detection and Diagnosis System

This paper has proposed an IoT based industrial fault detection and diagnosis system. The IoT (Internet of things) based industry protection system employing Arduino is intended to safeguard industries from losses brought on by accidents and monitoring the faults. Industrial accidents such as gas leakage causes fire resulting in significant industrial setbacks. Due to furnace explosions, electrical short circuits, or other circumstances, quick fire detection is also required. The proposed system uses flame sensor along with gas sensor to detect fire as well as gas leakage to avoid any industrial mishaps and prevent economic damage. The system consists of temperature detector to detect the high/low temperature, the phase fault detector (either single or three phases), voltage controller (high/low), and frequency monitoring. Flame and Gas sensors are also interfaced with arduino and LCD screen. The sensor data is constantly scanned to record values and check for fire, gas leakage and then this data is transmitted to online. The wifi module is used to achieve internet functionality. GSM module is sent message to specific numbers in case human operators aren’t present in online always. IP camera monitor the whole system from anywhere in the world.

산업재해 감지 스마트 디바이스 설계 방안 및 성능평가를 위한 지표 도출에 관한 연구

There are various ICT technologies continuously being developed to reduce damage by industrial accidents. And research is being conducted to minimize damage in case of industrial accidents by utilizing sensors, IoT, big data, machine learning and artificial intelligence. In this paper, we propose a design method for a smart device capable of multilateral communication between devices and smart repeater in the communication shaded Areas such as closed areas of industrial sites, mountains, oceans, and coal mines. The proposed device collects worker’s information such as worker location and movement speed, and environmental information such as terrain, wind direction, temperature, and humidity, and secures a safe distance between workers to warn in case of a dangerous situation and is designed to be attached to a helmet. For this, we proposed functional requirements for smart devices and design methods for implementing each requirement using sensors and modules in smart device. And we derived evaluation items for performance evaluation of the smart device and proposed an evaluation environment for performance evaluation in mountainous area.

설명가능한 인공지능(XAI) 방법론의 산업별 적용가능성에 관한 연구

The learning performance of artificial intelligence (AI) technologies such as machine learning and deep learning is approaching or surpassing that of humans, and humans are gaining new insights through hidden patterns and rules discovered by AI, but their delivery and explanatory power is in short supply. As the use of AI technology expands by industry, values such as transparency, fairness, and accountability are continuously required in addition to accuracy. Accordingly, the demand and necessity for eXplainable Artificial Intelligence (XAI) has recently been emphasized. XAI is an analysis model, process, and service for determining the reliability of AI results by explaining what the reason is if there is an error in the output produced by the AI model. This study reviewed SHAP, LIME, and LRP, which are XAI methodologies, and explored the applicability of these methodologies to each industry. In the financial field, algorithms such as SHAP, LIME, and LRP can be applied to support credit rating prediction, loan decision-making, and investment decision-making, and to ensure their explainability and interpretability. This can increase fairness and reliability of AI results between financial consumers and financial institutions. In the defense and military fields, AI staff is being used as an auxiliary tool for key decision makers. If the explainability and interpretability of the results are guaranteed by applying XAI, the role of AI staff will change to active utilization. In the field of industrial accidents, SHAP can be used in a model that predicts industrial accidents to determine the degree of influence of variables that affect the occurrence of industrial accidents. As such, the XAI methodology can contribute to increasing the transparency and interpretability of models in various AI-based prediction models.

Optimization the reliability of emergency safety barriers based on the subjective safety analysis and evidential reasoning theory. Case study

PurposeThis study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.Design/methodology/approachThe emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.FindingsA real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.Research limitations/implicationsThis study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.Originality/valueOur contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

ASSESSMENT OF SAFETY MANAGEMENT ATTITUDE PRACTICES TOWARD THE SAFETY CULTURE OF THE CONSTRUCTION SECTOR

A safety management attitude is essential to create a safety culture at construction sites. The construction sector was known to have a high risk of site accidents. Then, preventing the risk of accidents requires the implementation of safety culture measures. A safety culture acts as a guide to decrease the risk. This study aims to assess the safety management attitudes toward safety culture at construction sites. A sample of 376 respondents was recruited to complete the questionnaires regarding management commitment, safety procedures, and compliance with safety culture. The SPSS 24.0 and AMOS 24.0 software were used to conduct the data analysis. The findings demonstrated that management commitment, safety procedure, and safety compliance significantly and positively affected safety culture (p-value< 0.05). Thus, adequate implementation of a safety culture was ensured to decrease the accidents at the construction site and achieve zero industrial accidents.

Study on the preparation of novel NiB/Hβ-Al2O3 micro-mesoporous suppressant and its inhibition mechanism of coal dust explosion flame

Coal dust explosion is one of the serious accidents in the coal industry. It is of great significance to study the flame suppression of coal dust explosions. In this paper, a novel active component NiB with amorphous structure for explosion suppression was synthesized by the chemical reduction method. Furthermore, the novel explosion suppressant NiB/Hβ-Al2O3 was prepared through the kneading method by loading novel amorphous NiB nanoparticles on Hβ-Al2O3 with the micro-mesoporous structure as the carrier. The morphology and structure of NiB/Hβ-Al2O3 were characterized by XRD, BET, SEM, and FTIR, which showed that the NiB/Hβ-Al2O3 has proper pore structure and NiB nanoparticles are uniformly distributed as active components for explosion suppression in suppressant. Hartmann tube was used to evaluate the inhibition of coal dust deflagration. The results showed that the flame propagation distance and velocity decreased with the increase of the explosion suppressant. When the addition of explosion suppressant was 30 wt%, the explosion of coal dust was suppressed effectively. Furthermore, combing with the analysis results of the products after coal dust deflagration, the physical and chemical inhibition mechanism of the novel NiB/Hβ-Al2O3 explosion suppressant on coal dust deflagration was put forward.

Near-real-time detection of unexpected atmospheric events using principal component analysis on the Infrared Atmospheric Sounding Interferometer (IASI) radiances

Abstract. The three Infrared Atmospheric Sounding Interferometer (IASI) instruments on board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect the inhabitants and the environment in the impacted areas. With its near-real-time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events in order to support operational decisions. In this paper, we describe a new approach to the near-real-time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analyzing both the IASI raw and compressed spectra, we applied a PCA-granule-based method on various past, well-documented extreme events such as volcanic eruptions, fires, anthropogenic pollution, and industrial accidents. We demonstrate that the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data. It is planned to be used as an online tool for the early and automatic detection of extreme events, which was not done before.

Scenario deduction of Natech accident based on dynamic Bayesian network: A case study of landslide accident in a liquor storage tank area in Guizhou Province, China

Industrial technical accidents caused by natural disasters are defined as Natech accidents, such as earthquakes and landslides, which can cause tremendous damage to industrial storage tanks, and lead to accidental leakage and even serious fire and explosion accidents. In this study, a landslide-induced storage tank accident model under earthquake disasters was proposed, and the relationship between landslide mass impact and target impact resistance was taken into account. Also, tank failure and the formation of the pool fire were considered to be the consequences of the Natech accident. Through scenario deduction, the dynamic process of landslide Natech was transformed qualitatively into a disaster chain network diagram composed of a scenario state, a disaster-causing factor and emergency management. The Bayesian network was used to learn and deduce the parameters of the network diagram, and in this process, the prior probability and conditional probability of nodes were obtained primarily by Monte Carlo simulation, and by an improved expert scoring method based on the fuzzy set theory. Through visualization software, the sensitivity analysis of landslide Natech was achieved. Finally, a case study of a liquor storage tank area in Guizhou Province, China was carried out, and the results show that a large amount of hazardous material leakage caused by buckling is key to the formation of pool fire accidents, and several prevention measures for earthquake-induced landslide Natech was proposed according to the sensitivity analysis.