Journal of Health and Safety at Work

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Introduction: Determining methods for assessing heat stress in different work environments is one of the major challenges for researchers in this field. The purpose of this study was to validate WBGT index and heat pressure assessment (HPA) by some physiological responses in Iranian South Oil Company.
Material and Methods: This descriptive-analytical study was carried out on 154 employees of Kharg, Asalouyeh and Mahshahr oil terminals recruited from three different working conditions including indoor, outdoor and rest environments in the summer. The amount of heat stress in the workplace was evaluated by WBGT index HPA method. To meet this purpose, the environmental parameters i.e., temperature, wet temperature, radiation temperature, relative humidity, water vapor pressure and air flow rate were determined. In addition to the direct reading method by the WBGT meter, ISO 7243 was used to calculate the WBGT index. In order to validate the heat stress indicators, physiological parameters of oral temperature, tympanic temperature and work metabolism were measured.
Results: The results of paired sample t-test showed a significant difference between WBGT index and HPA in indoor and outdoor environment (P<0.05). Moreover, there was a significant difference between the physiological indices of oral and tympanic temperatures and work metabolism in the indoor and outdoor environment (P<0.05). Furthermore, the correlation test was significant between WBGT index as an independent variable and HPA as a dependent variable (P<0.05)  and showed a high correlation (R2=0.914) between WBGT index with oral temperature and tympanic temperature . Also, the rate of work metabolism was significantly related with the aforementioned parameters (P<0.05) and was equal to R2=0.423, R2=0.335, R2=0.552, respectively. The correlations were also significant between HPA with Oral temperature, tympanic temperature and work metabolism (P<0.05)and were equal to R2=0.632, R2=0.605, R2=0.557, respectively. The results showed also that the correlation rate between the HPA with physiological parameters is stronger than that between physiological parameters with the WBGT index.
Conclusion: This study showed that WBGT and HPA methods are useful for assessing the heat stress of Iranian South Oil Company. Also, the HPA method is more reliable for assessing heat stress in these regions.

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Introduction: Many industries have multiple factors harmful to health, leading to simultaneous exposure of these factors to each other. Noise is one of the most common physical parameters in the work environment. On the other hand, heat is also increasing due to various energy processes in industries. Therefore, this study was conducted with the aim of determining changes in physiological parameters and visual-auditory attention in acute exposure to heat and noise.
Material and Methods: In this experimental study, 72 individuals (36 men and 36 women) aged between 23 and 33 years participated according to the inclusion criteria. In total, 12 different test modes were performed (3 temperature levels and 4 noise pressure levels). The test was conducted for each person in 4 steps during a specific day. The duration of exposure to each condition was half an hour, and a half-hour rest was given between each test condition. Saliva samples of each subject were collected before and immediately after exposure to noise and heat. Also, The Integrated Visual and Auditory (IVA) was recorded by the participants simultaneously.
Results: The results of combined exposure to noise and heat on visual and auditory attention showed that only two modes of combined exposure, SPL95+WBGT34 and SPL95+WBGT29, caused a significant increase (P < 0.05) in mental workload and a significant decrease (P < 0.05) in visual and auditory attention. In addition, the results showed that independent exposure to noise at the levels of 85 and 95 dB and exposure to heat at higher temperature levels of 34 and 29 degrees Celsius cause a significant increase (P < 0.05) in salivary cortisol after exposure. The results of the effect of combined exposure to noise and heat on salivary cortisol showed that three experimental modes of combined exposure (SPL85+WBGT34, SPL95+WBGT29, and SPL95+WBGT34) caused a noticeable and significant (P<0.001) increase in salivary cortisol.
Conclusion: Indeed, salivary cortisol can be recommended as a physiological index to evaluate noise and heat exposure. It’s also noteworthy that salivary cortisol is more affected by noise and heat exposure than cognitive performance.

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Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network.
Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders.
Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results.
Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.

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Introduction: Nowadays, the statistics prove that the underground construction projects in the country are increasing, as well as the number of accidents arising from the unsafe condition of these projects. The purpose of this study was to create a framework of safety and health risk management in the construction phase of the Tehran Metro Line 7 tunnel, using IoT technology.
Material and Methods: In the first stage, the national safety and health standards, laws and requirements related to the mentioned hazards were collected. In the second stage, the criteria and permissible limits of exposure to occupational hazards were determined. Next, sensors with optical, auditory, gas detection, and visual capabilities connected to the network were examined, and computer programming and comparing sensor information with the specified standards were carried out. Finally, intelligent warning and control systems related to the determined hazards were proposed.
Results: In this study, a combined model of risk management utilizing IoT for controlling and monitoring safety and health hazards such as sound, light, explosive and toxic gases was proposed. According to the model, sensors for detecting the mentioned hazards were determined and coded based on the permissible limit of each of the harmful factors.
Conclusion: This study has shown that by employing specialized IT and safety knowledge and utilizing relevant software and hardware, the concept of the Internet of Things can be utilized in precise monitoring of the concentration levels of flammable and toxic gases, as well as monitoring of physical agents such as noise and light in various workplace, such as metro tunnel construction sites.