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Second Edition of Occupational Accidents and Risk Prevention
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Second Edition of Occupational Accidents and Risk Prevention

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Occupational Safety and Health".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 6687

Special Issue Editors

Dr. Antonio López Arquillos

E-Mail Website
Guest Editor
Department of Economics and Business Administration, School of Industrial Engineers, University of Malaga, 29016 Málaga, Spain
Interests: occupational health and safety management; risk assessment methods; wearables for worker monitoring; construction safety; prevention through design
Special Issues, Collections and Topics in MDPI journals
Dr. María del Carmen Rey Merchán

E-Mail Website
Guest Editor
Consejería de Educacion y Deporte, Junta de Andalucía, 18071 Granada, Spain
Interests: occupational health and safety; accidents; carpooling; injuries; IoT

Special Issue Information

Dear Colleagues,

A second edition of the Special Issue on “Occupational Accidents and Risk Prevention” is being organized in the International Journal of Environmental Research and Public Health. For detailed information on the journal, I refer you to https://0-www-mdpi-com.brum.beds.ac.uk/journal/ijerph.

Occupational accidents are a cause of concern due to their negative consequences to the workers, companies, governments, organizations, and society in general. A better understanding of such accidents (risks, causes, influence factors, personal variables, etc.) will help us to come up with more effective preventive measures and to create safer workplaces. In order to improve our understanding of the problem, this Special Issue on “Occupational accidents and risk prevention” aims to provide an overview of the most recent research related to occupational accidents. Original research articles are welcomed on but not limited to the following topics:

  • Occupational risk assessment (hazard prevention and management);
  • Accident analysis (causation, characterization of workers, accidents models, official records, etc.); New technology for the prevention of accidents (sensors, IoT, big data, machine learning);
  • Economic aspects of accidents (organizations, workers, insurance companies, etc.);
  • Promotion of safety at the workplace (training for safety, procedures, signals, communication); Analysis of dangerous sectors (Construction, Mining, Manufacturing, etc.) 

Dr. Antonio López Arquillos
Dr. María del Carmen Rey Merchán
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • occupational accidents
  • risk assessment
  • prevention
  • wearables
  • workplace
  • safety
  • accident
  • injury
  • causation
  • IoT
  • fall from height
  • construction safety

Related Special Issue

Published Papers (4 papers)

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Research

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12 pages, 2765 KiB  
Article
Numerical Investigation of Overtopping Prevention for Optimal Safety Dike Design
by Namjeong Son, Yoojin Kim, Mimi Min, Seungho Jung and Chankyu Kang
Int. J. Environ. Res. Public Health 2022, 19(24), 16429; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192416429 - 7 Dec 2022
Cited by 2 | Viewed by 1043
Abstract
Leakage accidents at chemical facilities have a negative impact on both the environment and human life, and the government has established and implemented regulations on dikes in order to minimize such accidents. However, the overtopping phenomenon in which chemicals overflow the dike due [...] Read more.
Leakage accidents at chemical facilities have a negative impact on both the environment and human life, and the government has established and implemented regulations on dikes in order to minimize such accidents. However, the overtopping phenomenon in which chemicals overflow the dike due to catastrophic leakage requires additional safeguards. In this study, the mitigation effect was confirmed by simulating tanks and dikes using various deflector plates to minimize the effect of spilled chemicals. ANSYS Fluent 19.1, a computational fluid dynamics program, was used, and the overtopping effect was compared with a dike design that satisfies the safety regulations using a volume of fluid (VOF) model that analyzes multiphase flow through a surface tracking technique. Nitric acid and sulfuric acid were used in the study; they were selected because they are frequently involved in leakage accidents. In the event of a leak in a liquid tank, a dike with a deflector plate was very effective in reducing overtopping, and a deflector at a 45° angle was more effective than a 30° deflector. However, it is necessary to install additional safeguards at the joint between the dike and the deflection plate to withstand the force of the liquid. Full article
(This article belongs to the Special Issue Second Edition of Occupational Accidents and Risk Prevention)
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22 pages, 3158 KiB  
Article
Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM
by Junqi Zhu, Li Yang, Xue Wang, Haotian Zheng, Mengdi Gu, Shanshan Li and Xin Fang
Int. J. Environ. Res. Public Health 2022, 19(19), 12869; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912869 - 8 Oct 2022
Cited by 7 | Viewed by 1339
Abstract
Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a [...] Read more.
Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability. Full article
(This article belongs to the Special Issue Second Edition of Occupational Accidents and Risk Prevention)
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29 pages, 6776 KiB  
Article
Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD
by Li Yang, Xue Wang, Junqi Zhu, Liyan Sun and Zhiyuan Qin
Int. J. Environ. Res. Public Health 2022, 19(17), 10762; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191710762 - 29 Aug 2022
Cited by 7 | Viewed by 1987
Abstract
The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep [...] Read more.
The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines. Full article
(This article belongs to the Special Issue Second Edition of Occupational Accidents and Risk Prevention)
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Review

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22 pages, 1670 KiB  
Review
Research Paradigm of Network Approaches in Construction Safety and Occupational Health
by Mei Liu, Boning Li, Hongjun Cui, Pin-Chao Liao and Yuecheng Huang
Int. J. Environ. Res. Public Health 2022, 19(19), 12241; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912241 - 27 Sep 2022
Cited by 2 | Viewed by 1697
Abstract
Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational [...] Read more.
Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational health (CSOH) and summarize the research paradigm of recent decades. We solicited 119 peer-reviewed journal articles and performed a bibliometric analysis as the foundation of the future directions, application bottlenecks, and research paradigm. (1) Based on the keyword cluster, future directions are divided into four layers: key directions, core themes, key problems, and important methods. (2) The network approaches are not independently applied in the CSOH research. It needs to rely on different theories or be combined with other methods and models. However, in terms of approach applications, there are still some common limitations that restrict its application and development. (3) The research paradigm of network analysis process can be divided into four stages: description, explanation, prediction, and control. When the same network method encounters different research objects, it focuses on different analysis processes and plays different roles. Full article
(This article belongs to the Special Issue Second Edition of Occupational Accidents and Risk Prevention)
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