Introduction: High concentration of inhalable airborne particles can increase the risk of lung disease in exposed people. This study aimed to determine the respirable particulate matter (PM5) concentration in traditional bakeries of Saveh in 2020.
Materials and Methods: This cross-sectional descriptive study was conducted among 25 bakeries where the samples were collected by cyclone and teflon filter equipped by air sampling pump. Later, the respirable particulate matter concentration was measured using gravimetric method. The collected PM5 was scanned using a FTIR (Fourier-transform infrared spectroscopy) with regard to flour dust. In addition, size and shape of the collected PM5 were analyzed using a scanning electron microscope (SEM).
Results: Findings showed that the Lavash bakery had the highest PM5 concentration (9.15 mg/m3) in comparison with two other bakeries (Sangak and Barbari). However, an inverse relationship was observed between RH and particle concentration. In addition, the results demonstrated that increasing RH decreased the particle concentration, but the relationship was not significant (P = 0.052, Spearman's rho = -0.393). Furthermore, Lavash bakery had the lowest average size of PM5 (0.63 ± 0.32 μm). However, the FTIR scans confirmed that the flour dust had the predominant amount of PM5.
Conclusion: Based on the findings, the density of respirable PM5 has a high level in Saveh bakeries and workers are exposed to high levels of PM.
In this article, we consider a two-phase tandem queueing model with a second optional service. In this model, the service is done by two phases. The first phase of service is essential for all customers and after the completion of the first phase of service, any customer receives the second phase of service with probability , or leaves the system with probability 1 − . Also, there are two heterogeneous servers which work independently, one of them providing the first phase of service and the other a second phase of service. In this model, our main purpose is to estimate the parameters of the model, traffic intensity, and mean system size, in the steady state, via maximum likelihood and Bayesian methods. Furthermore, we find asymptotic confidence intervals for mean system size. Finally, by a simulation study, we compute the confidence levels and mean length for asymptotic confidence intervals of mean system size with a nominal level 0.95.
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