Journal of Health and Safety at Work

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Introduction: Hydrogen sulfide (H2S) is a toxic gas that has adverse effects on human health and equipment. One of the methods for eliminating of H2S gas is the use of adsorbent substrate. In this study, the effect of adding iron oxides including ferric (Fe2O3) and magnetite (Fe3O4) nanoparticles to ZSM-5 zeolite substrate was investigated on the efficiency of H2S elimination from the air stream.
Methods: In this study, Fe2O3 and Fe3O4 nanoparticles were impregnated in ZSM-5 zeolite in two weight ratios of 3% and 5%. The structural properties of the substrate were studied using XRD, BET and SEM. Then, the efficiency of substrate in removing H2S from air was studied while H2S gas was injected in to a pilot setup, in concentrations of 30, 60, 90 and 120 ppm at three bed temperatures of 100, 200 and 300 o C.
Results: The accuracy of combination and the morphology of inoculated zeolite was confirmed using XRD and SEM. The BET test also showed that the loading of iron oxide nanoparticles on the substrate educed the substrate surface area. The results revealed that increasing the percentage of nanoparticles and increasing the temperature from 100 ° C to 300 ° C increase the time of breakthrough point. The maximum adsorption capacity was obtained equal to 44.449 (mgH2S/g zeolite) for ZSM-5/Fe3O4-5% substrate at 120 ppm concentration.
Conclusion: Iron oxide  nanoparticles  inoculated  in  ZSM-5  zeolite  substrate  increase  the  capability of eliminating of H2S gas at high temperatures and therefore can be used as a suitable method for the elimination of similar pollutants.

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Introduction: Several extraction techniques have been developed in recent years to determine the concentration of volatile metabolites in a biological sample. This study conducted with the aim of using the needle trap device- molecularly imprinted polymer (NTD-MIP) for the co-extraction of n-hexane and methyl ethyl ketone (MEK) in the urine matrix.
Material and Methods: Characterization of MIP was investigated by fourier-transfer infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FE-SEM), and brunauer–emmett–teller (BET). The response surface methodology - central composite design (RSM-CCD) was used to optimize the extraction conditions of n-hexane and MEK with the input variables of absorption temperature, absorption time, salt percent, and stirring speed. Method validation was performed with determination of the precision, accuracy, the limit of detection (LOD), limit of quantification (LOQ), and dynamic linear range.
Results: The optimum conditions were an extraction time of 60 min, an absorption temperature of 65 °C, 22% of salt, and a stirring speed of 250 rpm. The linear ranges of n-hexane and MEK were determined in ranges of 30-500 and 30-4000 µg/L, respectively. The intra-day and enter-day relative standard deviation were evaluated in the range of 3 to 10 and 1 to 7, respectively. The average recovery of n-hexane and MEK were estimated 99.3 ± 0.8 and 99 ± 0.9, respectively.
Conclusion: The HS-NTD method is suggested as a suitable method for determining very low amounts of MEK in urine along with n-hexane.


 

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Introduction: With the advancement of industries and increased use of metalworking fluids, controlling pollutants generated by machining operations has become increasingly challenging. This study aimed to address these challenges by designing an air filtration system designed specifically for this purpose.
Material and Methods: A local exhaust ventilation system was developed based on the VS-80-12 ACGIH standard, tailored to the working conditions and air sampling of the environment. The filtration system includes an aluminum pre-filter, an E11 class filter, and a nanofiber filter incorporating a metal-organic framework. The performance of the system was evaluated by measuring the numerical concentration of particles and the mass concentration of oil mist at both the inlet and outlet. The results were then compared to those obtained from an E1 class filter.
Results: The results obtained from XRD and FTIR analyses showed that ZIF-8 had high crystallinity and was successfully incorporated into the structure of the fibrous media filter containing metal-organic framework. The evaluation revealed that the filtration system effectively removed pollutant particles at their source. Notably, the initial efficiency for larger particles reached 100%, while the average removal efficiency for particles smaller than 2.5 microns was 99%.  
Conclusion: In conclusion, the combination of nanofiber filters with a metal-organic framework and aluminum pre-filters presents an effective solution for controlling particulate pollutants from machining operations. However, further research is necessary to comprehensively assess the system’s performance, particularly regarding dust loading capacity. Future studies should also explore the effects of various factors, such as airflow rate and the type of metalworking fluid, on the system’s efficacy.

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Introduction: With the rapid development of new chemicals across various industries and the growing need for efficient and accurate toxicity assessments, in silico methods have emerged as a screening tool due to their cost-effectiveness, time efficiency, and reduction in animal testing. The aim of this review is to examine the existing studies on the application of in silico methods in predicting the toxicity of chemical compounds in occupational and industrial settings.
Material and Methods: This systematic review follows established protocols and is based on data extracted from reputable scientific databases such as PubMed, Scopus, and Web of Science. The review analyzes articles published between 2000 and 2024 that utilized in silico methods for toxicity prediction in occupational toxicology. Inclusion criteria focused on studies that applied modeling, simulation, and prediction methods primarily to chemical toxicity in workplace environments. Also, the quality assessment of the articles was done using the STROBE form.
Results: This study surveyed 13 articles on computer simulation of chemical compounds from 2000 to 2024. The majority of research was conducted between 2020 and 2024. The reviewed articles, based on the STROBE form, had a moderate to high quality. Various methods, including Quantitative Structure-Activity Relationship (QSAR), machine learning, and molecular dynamics, were widely used to predict the toxicity of chemical compounds, with the predictive accuracy of these models generally being high. The results also indicated that QSAR methods had the most application in studies predicting the toxicity of chemical compounds used in industries.
Conclusion: In silico methods, using molecular descriptors and structural data, have shown high accuracy in predicting toxicity. However, challenges such as limitations in reliable data, the need for model improvement, lack of experimental data, and the complexity of chemical interactions exist. The results indicated that the use of computational methods can significantly reduce the need for animal testing and improve risk assessment. These studies also emphasize the importance of improving and developing predictive models to enhance their accuracy and applicability. Overall, it can be said that modeling can serve as an effective tool in reducing costs and improving safety in workplace environments.