Journal of Health and Safety at Work

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Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities.
Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software.
Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations.
Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air.

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Introduction: Nurses of the emergency department experience stressful events that affect their mental health and reduce the quality of their work life. Career adaptability is considered a personal capability that enables employees to adapt to changes and avoid the negative consequences of job mismatch. This study was conducted to study career adaptability and its correlation with the quality of work life in the emergency department.
Material and Methods: This descriptive-correlational study was conducted on 104 nurses in the emergency department of teaching–therapeutic hospitals in Tabriz who were selected using random stratified sampling. Data was collected using a demographic checklist, the Career Adapt-Abilities Scale (CAAS) by Savickas, and the Quality of Nursing Work Life (QNWL) scale by Brooks & Anderson. Data were analyzed in SPSS using descriptive statistics (frequency, mean and standard deviation) and inferential statistics (independent t-test, one-way analysis of variance, Pearson’s correlation coefficient, and linear regression analysis).
Results: The total scores of career adaptability and the quality of nursing work life were 88.55±25.01 from the achievable range of 24-120 and 141.15±22.56 from the achievable range of 42-252, respectively, which were moderate. In this study, 85.6% of nurses enjoyed a moderate quality of work life. Furthermore, the results of Pearson’s correlation indicated a significant positive correlation between career adaptability and scopes with the quality of work life score (p=0.05). Regression analysis results indicated that career adaptability significantly predicts the quality of nursing work life (p=0.000).
Conclusion: In this study, nurses experienced moderate career adaptability and quality of work life. Given the above factors, and considering adaptability as a variable predicting quality of work life, it is suggested to take measures to increase career adaptability in nurses through training or consulting interventions to improve the quality of nursing work life.

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Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.