Journal of Health and Safety at Work

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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. 
 

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Introduction: Students play a key role in shaping the future of any society and spend a significant amount of time in educational environments. Creating an optimal learning environment requires close attention to factors affecting student well-being, particularly thermal comfort and indoor air quality. This study aims to systematically review the existing literature on thermal comfort and ventilation systems in schools.
Material and Methods: This systematic review was conducted based on the Cochrane methodology, involving a comprehensive search of three major databases — Scopus, Web of Science, and PubMed — for articles published between 2020 and 2024. The inclusion criteria encompassed peer-reviewed, conference, and review articles published in English that included the keywords “thermal comfort,” “ventilation,” and “school” in their title, abstract, or keywords. Studies focusing on preschools, universities, or other non-primary/secondary educational settings, as well as those conducted during the COVID-19 pandemic, were excluded.
Results: A total of 42 articles were selected after a rigorous screening process. The highest number of publications was reported in 2023. Key findings included: Most studies focused on elementary and secondary schools. The majority of research was conducted during the summer season, which may limit generalizability across seasons. There was considerable variation in CO₂ levels, with some exceeding recommended standards. In simulation studies, DesignBuilder and EnergyPlus were the most frequently used software tools. Additionally, results showed that: Indoor air quality and thermal comfort are significantly influenced by the type of ventilation system. Schools using natural ventilation often experienced higher CO₂ concentrations and lower thermal comfort than recommended. Implementation of Demand-Controlled Ventilation (DCV) has shown promise in improving indoor air quality and reducing pollutant levels.
Conclusion: This paper can contribute to the improvement of educational space design, enhancement of student learning, and promotion of indoor environmental health. It also provides insights into the latest methods for measuring and simulating thermal comfort and indoor air quality. For more practical outcomes, long-term studies with larger sample sizes across different seasons and times of day are needed. Combining computer simulations with real-world measurements can support cost-effective and optimized design of educational spaces. Future research should focus on standardizing temperature, humidity, CO₂ levels, and selecting the most appropriate ventilation strategies for classrooms.