Showing 3 results for Pourhassan
Bahman Pourhassan, Farideh Golbabaei, Mohammad Reza Pourmand, Somayeh Farhang Dehghan, Ensieh Masoorian,
Volume 8, Issue 3 (9-2018)
Abstract
Introduction: Indoor air environments contain a wide variety of microorganisms such as bacteria, fungi, and viruses in which some of them can affect the human health. Filtration is considered as one of the most common methods to remove microorganisms in these environments. The purpose of current study was to investigation the neat and photocatalytic HEPA filters performance at different face velocities and various intensity of UVC light source on the reduction of airborne microorganisms.
Material and Method: After installation of the neat and photocatalytic HEPA filters in a closed–loop chamber, suspension of Staphylococcus epidermidis and Bacillus subtilis bacteria with a concentration of 107 CFU / ml were sprayed into the closed–loop chamber by nebulizer. Sampling of penetrated microorganisms from filters were performed using the NIOSH 0800 method under ambient temperature 22±3oC, relative humidity 35±5%, and different air velocity (0.1 m/s and 0.3 m/s) and UVC different radiation intensity (1 mW/cm2, 1.8 mW/cm2 and no radiation (dark)) at 30 minutes time period. penetrated microorganisms density from filters was determined in term of CFU/m3.
Result: There were no significant differences in the penetration rates of microorganisms at the dark mode between the two neat and photocatalytic HEPA filters (p>0.05). The penetration rate of bacteria was significantly decreased in the neat and photocatalytic HEPA filters at UVC radiation mode with various intensities than dark mode (p<0.05). In addition, comparison of the filters in the illuminance modes of 1 mW/cm2 and 1.8 mW/cm2 were statistically significant (P <0.05). Also, UVC radiation with the 1.8mW/cm2 illuminance compared to the 1 mW/cm2 illuminance resulted in a greater reduction in the bacterial penetration from both types of filters, which is statistically significant(p<0.05). The bacteria penetration rate dramatically increased by increasing the face velocity from 0.1 m/s to 0.3 m/s under UVC radiation at an illuminance of 1mW/cm2, 1.8mW/cm2 and as well as in no radiation mode in both types of HEPA filters (P <0.05).
Conclusion: Photocatalytic HEPA filters and increasing UVC illuminance, especially at lower surface velocities, have a significant positive effect on reducing airborne microorganisms and increasing the efficiency of HEPA filters
Zahra Beigzadeh, Mehran Pourhossein, Sajjad Samiei, Reza Pourbabaki, Bahman Pourhassan, Hamed Motamedi Nejad,
Volume 8, Issue 4 (12-2018)
Abstract
Introduction: Construction industry plays a major role in the economic development of all countries and among the various occupations, this industry is one of the most dangerous industries, particularly respiratory contaminants, around the world. The aim of this study was to evaluate the respiratory capacity of construction workers, working in different workshops in Tehran city and developing a regression model to examine the relationship between pulmonary capacities with the type of occupation, work experience and tobacco smoking.
Material and Method: This study was a cross-sectional descriptive study conducted among 628 construction workers in Tehran city in 2017. After data collection, data analyses were performed using statistical independent t-test, one way ANOVA and correlation tests by SPSS software version 22. Also, multiple backward regression was used to check the effect of independent variables on lung function.
Result: According to the results of this study, a significant relationship was found between age and work history with the pulmonary function indexes (FVC, FEV1, FEV1/FVC and FEF25-75%) (P-value<0.001). The average of FEV1/FVC% was significantly different among various occupational groups (p-value<0.001). In the analysis of the findings of the pulmonary function test in the exposed group a separate model was made using multiple linear regression for each of the pulmonary functions, and the independent variables including age, work experience, job type and cigarette addiction were entered into the model.
Conclusion: The present study showed a significant change in the pulmonary function parameters of the construction workers and the chance of pulmonary disorders might be high among these individuals.
Saba Kalantary, Bahman Pourhassan, Zahra Beigzadeh, Vida Shahbazian, Ali Jahani,
Volume 14, Issue 1 (3-2024)
Abstract
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.
