Musculoskeletal symptoms often occur in more than one anatomical site. The present study aimed to define specific patterns of multisite musculoskeletal disorders and examine how these patterns are related to common psychological problems. Using the data from an interview-based health survey of 358 samples of the industrial manufacturing male employees, we derived major patterns of musculoskeletal complaints using latent class analysis and investigated its association with psychological problems score extracted from depression, anxiety, and stress measured by Depression/Anxiety/Stress Scale (DASS-21). Musculoskeletal disorders were assessed by Nordic Musculoskeletal Questionnaire (NMQ). The statistical analysis was carried out by Mplus 8. Complaints in the lower back (42.1%) and neck (30.7%) had the highest prevalence, and in the hip (15.0%) and ankle (12.2%) the lowest. Three major patterns of musculoskeletal disorders were extracted using latent class analysis. Class 1 (12.9%) was characterized by a high rate of complaints in upper musculoskeletal sites, such as the neck, shoulder, and joints; class 2 (38.2%) was identified by a higher rate of complaints in the lower and upper back; and class 3 (48.9%) was marked by low rates of complaints in all musculoskeletal sites. After adjustment for confounding variables and specifying class 3 as the reference, it turned out that there was a statistically significant association between the psychological problems score and the chance of being in class 1 (OR = 2.47, 95% CI 1.66–3.68), but not a significant association with the chance of being in class 2 (OR = 1.51, 95% CI 0.83–2.72). Musculoskeletal disorders can be summarized in the latent class-derived patterns in the adult study population and provide additional prognostics. Common psychological problems are significantly associated with the type of musculoskeletal disorder patterns. The findings in this study could be useful for dealing with prevention and treatment programs.

Background: The present study aimed to define specific patterns of multisite MSDs of office employees and to examine how derived patterns are related to the ergonomic chair design. Methods: in this cross-sectional study of 254 office employees, major patterns of MSDs were extracted using latent class analysis and investigated its association with the ergonomic chair design. Results: Four major patterns of MSDs were extracted using latent class analysis. Class 1 (12.1%) was characterized by individuals with high probabilities of MSDs in the neck, shoulders, back, and wrists. Class 2 (36.6%) was marked by individuals with near-zero probabilities of MSDs across all sites. Class 3 (14.1%) included high probabilities of complaints in the back, hips, and knees. Class 4 (38.2%) accounted for those with high probabilities of MSDs across all sites. Considering class 2 as the reference, there was an inverse significant association between seat comfort and membership to class 3 (OR=0.94, 95%CI [0.89-0.99]) and class 4 (OR=0.94, 95%CI [0.91-0.98]) and a significant inverse relationship between body support and membership to class 4 (OR=0.95, 95%CI [0.92-0.99]). Conclusion: MSDs can be summarized with latent class-derived patterns among the office workers. Ergonomic chair design was significantly associated with the type of MSDs patterns.

This paper describes the preparation of a single-wall carbon nanotube silica composite based on sol-gel technique that was packed in a needle trap device to determine low concentrations of toluene in urine. Optimal conditions for the extraction of toluene from aquatic matrices were investigated. Needle trap devices were used with purge and trap and the performance was compared to needles packed with polydimethylsiloxane, a commercial sorbent. After the optimization of the conditions for toluene standards, spiked and real urine samples were analyzed. The results showed that the single-wall carbon nanotube/silica composite–packed needle trap device had better performance than polydimethylsiloxane. The limit of detection was below 1 µgL⁻¹.

Introduction: Petrochemical industries are known as one of the most important sources of organic pollutants. In this study, the relationship between inhaled benzene, toluene, ethylbenzene and xylene (BTEX) concentration with some urine metabolites was investigated in a petrochemical complex Methods: This cross-sectional study was an analytical type with 60 inhalation air ...

Computational fluid dynamics (CFD) is an indispensable simulation tool for predicting the emission of pollutants in the work environment. Welding is one of the most common industrial processes that might expose the operators and surrounding workers to certain hazardous gaseous metal fumes. In the present study, we used computational fluid dynamics (CFD) methodology for simulating the emission of iron fumes from the shielded metal arc welding (SMAW) procedure. A galvanized steel chamber was fabricated to measure the pollutant concentration and identify the size of the fume created by the SMAW. Then, the emission of welding aerosol was simulated using a method of computational fluid-particle dynamics with the ANSYS 2020 R1 software. The highest amount of welding fumes concentration was related to iron fumes (i.e., 3045 μg/m³ with a diameter of 0.25 μm). The results of the current study indicated that the local exhaust and general ventilation system can prevent the spreading of welding fumes to the welder’s breathing zone and the surrounding environment. CFD was also found to be an efficient method for predicting the emission of the iron fumes created by SMAW as well as for selecting an appropriate ventilation system. However, further studies that take the modeling of welding-generated emission of additional metal particles and gases into account will need to be undertaken.