Assessing the impact of COVID-19 pandemic on the performance indicators of safety management using the analytic hierarchy process (AHP) in an electricity industry.

Introduction: The importance of evaluating the performance of different management systems in industries necessitates examining the performance of the Health, Safety, and Environment (HSE) management system along with other management systems. Particularly during the COVID-19 pandemic, there is a greater need than ever to assess the impact of the COVID-19 spread on the performance of the HSE management system compared to before this pandemic. This research aims to investigate safety performance indicators (SPIs) on the performance of the HSE management system and the impact of the COVID-19 spread on these indicators. Methods: This cross-sectional, descriptive-analytical study was conducted to evaluate the influence of the COVID-19 outbreak on the safety performance to revise the industry safety index in an electricity distribution company using the multi-criteria decision-making method before and after the disease epidemic in three stages. In the first part, the safety indicators were identified according to the comprehensive safety indicators available in the industries and experts' opinions. In the second part, safety indicators were ranked, weighted, and prioritized using the Analytic Hierarchy Process (AHP). In the third part, these indicators were calculated and compared in the periods before and after the outbreak of COVID-19. Results: Two main criteria, namely the "effectiveness criterion" and the "applicability/calculation criterion", were identified for the evaluation and pairwise comparisons of performance indicators. Among these two criteria, the applicability/calculation criterion had higher priority and importance for the evaluation of indicators. Pairwise comparisons of the indicators indicated that the "accident frequency rate" and "safe T-factor" indicators (with weights of 0.238 and 0.023 respectively) had the highest and lowest priorities, respectively, for the assessment of organizational safety performance among the SPIs. Conclusion: Based on the calculations of the indicators and their analyses before and after the outbreak of COVID-19, the current status of the safety performance of the HSE unit was not significantly affected by this pandemic. However, the investigations carried out while collecting the data needed to calculate the indicators and evaluate the performance of this unit demonstrated that some indicators were not considered sufficiently in the studied electricity industry. Since conducting regular performance evaluations greatly impacts the achievement of continuous improvement, more attention should be paid to compiled indicators, which should be periodically assessed in the organization to achieve continuous improvement.

Measuring and assessing the effects of extremely low-frequency electromagnetic fields (ELF-EMF) on blood parameters and liver enzymes of personnel working in high voltage power stations in a petrochemical industry.

Introduction: Exposure to electric and magnetic fields (EMF) is a phenomenon that has always been present. In the last two decades, there have been numerous worries about the possible effects of extremely low-frequency (ELF) fields on human health. Consequently, this study aims to measure and evaluate the effect of ELF fields on blood parameters and liver enzymes of personnel working in high-voltage power stations in the petrochemical industry. Methods: This cross-sectional, descriptive-analytical study was done in a petrochemical industry in southern Iran in 2021. Two groups of 50 people were selected to attain the research goals and classify jobs with the risk of exposure to extremely low-frequency electromagnetic fields (ELF-EMF) and the time spent on work. One group was exposed to ELF-EMF, and the other was regarded as the control group. Electromagnetic fields were measured using the HI-3604 device and the standard method of IEEE 1994-644. The workers' demographic data, blood parameters, and liver enzymes were also obtained from the workers' medical records. The course of changes in each of the blood parameters and liver enzymes and demographic variables in the control and case groups were then examined. In the last stage, the effect of the electric field and magnetic field on the blood parameters and liver enzymes of the two control and subject groups was conducted using the multiple regression model. Results: The measured points in all stations are 200, and the minimum and maximum values of the magnetic field was 0.8 and 2019 mG, respectively. The minimum and maximum values of the electric field intensity was 0.003 and 215 V/m, respectively. The average results of the demographic variables and blood and liver indices in the control and case groups from 2018 to 2020 revealed that the average variables were not significantly different in the case and control groups (p-value>0.05). Moreover, workers' exposure to ELF-EMF was below the permissible limit. Conclusion: Based on the results of the present study, ELF-EMF does not have any significant effects on demographic factors, blood parameters, and liver enzymes.

The noise absorption prediction of a combined and independent absorber under different conditions and at different frequencies, using the new Engineering Noise Control Software (ENC).

Noise is one of the most harmful factors in the work environment, which is very important to control. There are various techniques to achieve this goal. One of the most important of them is the use of noise absorbers. Absorbent materials are often used to counteract the effects of reflected noise from hard surfaces and reduce their level. This is an experimental-applied study conducted in the physical factor laboratory of the Faculty of Health. The purpose was to predict the Noise absorption rate of combined and independent absorbers under different conditions and frequencies using the new ENC (engineering noise control) software. The sound absorption determination was carried out in 5 stages, including sound frequency analysis for the source, measurement of the dominant frequency, measurement of the absorption coefficient of absorbent materials in different conditions, measurement of the limit frequency (peak frequency of noise absorption) and comparison of the software results with the findings of the impedance tube in Real conditions. The best absorption mode for combined and independent absorbents is using a 5 cm rock wool absorbent with a 2 cm thick air layer behind it without a polyurethane absorbent layer and a 10 cm wide rock wool absorbent with a 1 cm air layer behind it without polyurethane layer. A polyurethane layer on the stone wool absorber decreased the amount of noise absorption for high frequencies. The results obtained from the best absorption conditions in the ENC software were consistent with the findings from the impedance tube device in real situations. The results of this study showed that suitable and optimal conditions of sound absorption could be achieved by using the ENC software, correct design, use of suitable absorbers, changes in the physical parameters of the absorber, and the use of a combined absorber.

Evaluating the effect of heat stress on cognitive performance of petrochemical workers: A field study.

INTRODUCTION: Heat stress disrupts blood hormones and reduces workers' cognitive performance. To further shed light on the dysfunction of heat stress, the present study aimed to evaluate its effect on cognitive performance of petrochemical workers. MATERIALS AND METHODS: This descriptive-analytical cross-sectional study was conducted in 2020 in one of the Iranian petrochemical companies. Participants were divided into 2 case groups and 1 control group. They worked 12 h and their shift entialed one week working day and one week working night. According to the ISO 7243 standard, the heat stress index of employees was measured at the beginning, in the middle and at the end of the shift separately. Continuous Performance Test (CPT) and N-back cognitive performance tests were performed at the beginning, in the middle and at the end of the shift to determine the level of cognitive performance. The data were analyzed using SPSS software version 20 and the significance level was set at 0.05. RESULTS: Comparison of the results in the continuous performance test showed significant differences between the three groups with regard to the omission error and response time at the end of the shift. Moreover, according to the working memory test, participants reaction time during the shift significantly increased. Besides, average correct responses significantly reduced during the shift. Finally, the heat stress throughout the shift had a significant effect on the commission error and the response time of individuals. CONCLUSION: Heat stress affects people's cognitive performance in such a way that it can decrease their cognitive performance by increasing the commission error and response time and reducing the average correct response of site operators, generally reducing the cognitive performance of people at the end of the shift.

Prioritizing the noise control methods by using the Analytical Hierarchy Process (AHP) method in an Iranian tire factory.

BACKGROUND: Noise is a common harmful physical factor in the work environment. OBJECTIVE: This study sought to prioritize noise control methods using the analytical hierarchy process (AHP) in a tire factory. METHODS: The study, which adopted a cross-sectional, descriptive, analytical design, was conducted in the baking hall of an Iranian tire manufacturing factory in 2018. 4 criteria (namely implementation and maintenance cost, method applicability, method effectiveness and efficiency, and intervention in the process) and 8 alternatives (including reducing individuals' noise exposure time, designing and installing sound isolation chamber for operators, using of earmuffs and earplug simultaneously, changing processes or operational procedures in machinery with excessive noise generation, forming noise control engineering teams, requiring people in charge to quickly fix the leaks and change baking press washers on time, using acoustic panels in the ceiling and walls, and designing and manufacturing silencer and nuzzle for the steam and compressed air outlet of baking press machinery) were selected. Then, to prioritize noise control methods based on objectives, criteria, and alternatives, an AHP questionnaire was developed and completed by domain experts and noise control specialists. Data analysis was performed using Expert Choice V. 11 and Excel. RESULTS: The results showed that the inconsistency rate in all cases was less than 10%, hence the consistency of responses was approved. Based on experts' opinion about the selected criteria, "implementation and maintenance cost" had the highest weight (0.481), while "method effectiveness and efficiency" recorded the lowest one (0.046). With regard to the alternatives, "change in the process" registered the greatest weight (0.193), whereas "individuals' noise exposure time" had the lowest weight (0.046). CONCLUSIONS: Based on the final weights, the most appropriate noise control methods in this industry are changing processes in machinery with excessive noise generation, forming noise control engineering team, and manufacturing silencer and nuzzle for the steam and compressed air outlet of baking press machinery. Furthermore, AHP is a suitable approach for prioritizing decisions related to noise control.

The Relation between Hearing Loss and Smoking among Workers Exposed to Noise, Using Linear Mixed Models.

INTRODUCTION: Noise is one of the most common and harmful physical factors in the working environment and has physical and psychological effects on individuals. In this study, the audiometry results of industrial workers were modeled and the effect of noise and other factors on hearing loss was examined. MATERIALS AND METHODS: This was a longitudinal study based on the records of workers who had worked over 10 years in the industry and had recorded audiometries since their employment. Data was analyzed through linear mixed models. RESULTS: During each year of noise exposure, hearing loss was 1.9 db at 4000 Hz; 0.059 in low frequencies and 0.62 db in high frequencies. At 8000 Hz the effect of the age at employment on hearing loss was significant (P=0.014). At low frequencies the interaction of smoking and age at employment was significantly related to hearing loss (P˂0.001). CONCLUSION: This study showed that despite acquaintance with safety measures, workers still face hearing loss in industry and employers should put workers under more surveillance for using protective gear. Smoking might be another risk factor for hearing loss.

Predicting and Weighting the Factors Affecting Workers' Hearing Loss Based on Audiometric Data Using C5 Algorithm.

INTRODUCTION: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. OBJECTIVE: This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers' hearing loss based on the audiometric data. METHODS: This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too. RESULTS: The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70-80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%. CONCLUSIONS: During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.