Browsing by Author "Yu, Minji"

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    N95 filtering facepiece respirator fit test performance for the general population attending Minnesota State Fair 2021 and 2022
    (2024-10-07) Bagheri Hosseinabadi, Majid; Yu, Minji; Petersen, Ashley; Griffin, Linsey; Durfee, William; Arnold, Susan; arnol353@umn.edu; Arnold, Susan; Division of Environmental Health Sciences, University of Minnesota
    This study aimed to investigate the performance of the general population passing quantitative fit tests for one type of N95 respirator (3M Aura Respirator 9205+) and consider the role of gender, age, race/ethnicity, and facial hair in the fit testing pass rate. The data was collected using a demographic questionnaire from the general population attending the Minnesota state fair 2021 and 2022. Demographic information included age (in years), gender, race/ethnicity, and facial hair (yes/no and type). Each participant also performed quantitative fit testing of N95 respirators (3M Aura Respirator 9205+) using a TSI PortaCount Pro+ model 8038. Fit testing was conducted according to the quantitative OSHA 29 CFR 1910.134 standard protocol with a criterion of ≥100 for pass level fit factor.
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    Predicting Fit of Filtering Facepiece Respirators Through New Face Anthropometry and 3D Face Shape Acquisition
    (2024-05) Yu, Minji
    This research investigated the relationship between face shape and respirator fit, with a focus on enhancing the fit and design of filtering facepiece respirators (FFRs). The study addressed the need for improved respirator fit, particularly in occupational settings where respiratory protection is paramount for safeguarding workers' health. Combining anthropometric analysis, three-dimensional (3D) scanning technology, quantitative fit testing, and predictive modeling, this research assessed the impact of face shape on respirator fit. It examined the limitations of traditional two-dimensional anthropometric measures in predicting FFR fit and proposed a novel framework based on 3D-derived face shapes and dimensions. Key findings highlighted the importance of predicting respirator fit based on diverse facial shapes and sizes. By integrating face anthropometric and geometric data into respirator design processes, manufacturers can develop more ergonomic and effective respiratory protective equipment. Such predictive capabilities can aid individuals in selecting respirators that are more likely to provide a secure fit, thereby enhancing the overall effectiveness of protection and reducing the risk of exposure to airborne hazards. The implications of this research extend beyond occupational safety and health, encompassing broader public health considerations, particularly in the context of infectious disease outbreaks like the COVID-19 pandemic. By advancing the understanding of respirator fit, this research contributes to the development of evidence-based practices for respiratory protection, ultimately enhancing the well-being and safety of workers worldwide.