Structural equation model of the influence of noise annoyance on miners’ unsafe behavior in coal mines

Miners' unsafe behavior (UB) is the main cause of coal mine accidents. Previous research has suggested that excessive noise in tunnels impacts miners' temperaments and safe production behavior. To explore the influence of noise on UB, four different dimensions of noise annoyance were identified: cognitive, emotional, communication, and physical mechanism annoyance. The coal mine noise annoyance scale, miners' unsafe behavior intention (UBI) scale, and miners’ UB scale were developed according to different dimensions. In the first survey, a total of 200 questionnaires were distributed in the two coal mines, and 193 valid questionnaires were recovered. Exploratory factor analysis was conducted, and invalid items were removed according to the analysis results to form the final scale. Then, 500 final scales were distributed in two Chinese coal mines, and 482 valid questionnaires were collected. Confirmatory factor analysis was conducted by collating the data of the second questionnaire. Based upon the structural equation model (SEM), the SEM of the influence of noise annoyance degree on UB and the path analysis with latent variable path analysis with the latent variables (PA-LVs) mediating model of the effect of UBI on UB were established, and the path coefficients with fitting data of the model were analyzed. The results indicated that the four dimensions of coal mine noise annoyance had a strong positive impact on UB, which was reduced by less noise annoyance. In addition, the Bootstrap method was used to verify the mediating method among the four dimensions of noise annoyance, cognitive annoyance, emotional annoyance, with communication annoyance indirectly affecting UB through UBI. The research results provide theoretical support for reducing the UB of miners and improving coal mine safety levels.     

A disaster scene simulation system in 3D for oil transmission stations: Design and implementation

The disaster scene in three-dimensional (3D) plays a crucial role in disaster emergency management and risk communication of oil transmission stations. However, existing research for the disaster scene mainly focuses on reproducing the disaster environment and rarely predicts the damage state of the disaster-affected object. This paper proposes an object-oriented modeling method that utilizes a multilevel decomposition pattern for disaster scenes. This method combines earthquake vulnerability assessment with 3D visualization technology to predict and characterize the damage state of critical infrastructure in oil transmission stations. To enhance earthquake risk perception, a simulation system is designed and developed, which allows for the construction of virtual scenes and quick simulation of disaster scenes in 3D. The case application shows that the system improves the 3D modeling efficiency of disaster scenes and enhances public awareness of earthquake risks. The simulation system can provide technical support for seismic mitigation planning and emergency management decision-making at oil transmission stations and has good application prospects.     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

A data-driven narratives skeleton pattern recognition from accident reports dataset for human-and-organizational-factors analysis

Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

Simulation of scenario characteristics of LPG tank explosion accident and its contribution to emergency planning: A case study

Leakage and explosion of hazardous chemicals during road transportation can cause serious building damage and casualties, and adoption of highly-efficient emergency rescue measures plays a critical role in reducing accidental hazards. Considering a liquefied petroleum gas (LPG) transport tanker explosion accident that occurred in Wenling, Zhejiang Province, China on June 13, 2020 as example, this study proposes a risk assessment framework. This framework recreates the leakage and explosion of the accident process using FLACS v10.9, suggests plans for evacuation, describes the rescue areas of different levels, and explores the influence of environmental factors on the evacuation and rescue areas. The results show that simulated and predicted distributions of fuel vapour cloud concentration and explosion overpressure can provide a reference basis for rapid rescue activities; the characterization of the dynamic effects of wind speed, wind direction, and temperature with respect to the evacuation and rescue areas can be used as theoretical support for on-site adjustment of rescue forces. The role of obstacles can prevent the expansion of the evacuation areas under low wind-speed conditions, and the presence of highly congested obstacles determines the level of the rescue area. The results obtained are important for the risk analysis and the development of emergency rescue measures in case of explosion accidents associated with transportation of hazardous chemicals on high-hazard and high-sensitive road sections.     

When apologizing hurts: Felt transgression and restoration efforts

Apologizing is important for conflict resolution and relationship reconciliation, yet apologies often fail to restore the damaged relationship. While much research has been devoted to investigating the victims' reactions upon receiving an apology, in this paper, we adopt an apologizer-centric approach and explore the phenomenon of apologizing with no felt transgression (vis-à-vis when there is felt transgression) along with its affective and reconciliation outcomes for the apologizer. Based on Appraisal Tendency Framework, we predict that apologizing with no felt transgression will lead to reduced guilt and increased anger in the apologizer, which will result in a decreased level of their restoration efforts towards the victim. In addition, we further hypothesize about the role of organizational conflict cultures in influencing the relationship between apologizing and restoration efforts via guilt and anger. Study 1 uses a micro-narrative procedure and an inductive data analysis approach to demonstrate the varied situations and motivations of employees apologizing with (no) felt transgression, Study 2 utilizes an experimental design to examine the mediation effect, and Study 3 employs the critical incident technique to test our whole research model. Our hypotheses were largely supported across our studies. Theoretical and practical implications are discussed.     

珠江三角洲机动车排放控制措施协同效应分析

预测了2015年珠江三角洲机动车空气污染物和温室气体排放量,设计了6类单一控制、技术控制、结构控制以及综合控制措施的情景,并运用基准线年排放清单编制和协同效应坐标系法分析了污染物与温室气体减排的协同效应.结果表明,按目前机动车保有量增长趋势,2015年污染物和温室气体将以18%~120%的幅度增加;各控制措施下污染物和温室气体排放量均有下降,且均具有正向的协同效应,但减排的贡献差异较大.6类单一控制措施中淘汰黄标车和结构性控制措施分别对各污染物和温室气体的削减效果最明显,减排幅度均在40%以上,且正向协同效应突出,但相比其他措施,结构性控制措施实施难度大.     

城市可持续发展环境经济评价及案例

基于城市发展测度演变的分析、明确真实储蓄作为城市可持续发展综合评价指标的经济学含义和政策意义：并针对世界银行在地区真实储蓄测算方法上的不足,重点探讨在真实储蓄测算中如何考虑城市开放性、资源折旧、环境污染损失评估计算的界定问题。文中还以烟台市为试点城市,利用真实储蓄及相关指标评价了该市不同发展时期的可持续发展水平。     

植物多酚物质原位钝化污染土壤重金属的研究Ⅱ.对土壤Cu环境行为和生物活性的影响动态

通过室内好气培养和生物盆栽试验研究了缩合类植物多酚L和Y以及 8 羟基喹啉在污染红壤和潮土中的降解动态及其对土壤Cu环境行为的影响 .结果表明植物多酚在两周内降解相对较快 ,而 8 羟基喹啉则难以降解 .与空白相比 ,Cu污染土壤中施用植物多酚后土壤水溶性Cu显著下降 ,钝化Cu的效果次序为 :8 羟基喹啉 >多酚Y >多酚L .随着植物多酚的降解 ,土壤Cu被释出 ,其水溶性Cu浓度与残留在土壤中多酚浓度 (以DOC浓度表示 )呈负相关关系 ,尤其在潮土上更为明显 ,而pH与水溶性Cu没有相关性 .试验还发现 ,钝化剂施用后 ,小麦体内的Cu含量降低 ,因此 ,植物多酚是钝化重金属污染土壤的良好选择