Browsing by Subject "Respirator"

Now showing 1 - 4 of 4
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    Evaluation and development of methods for measurement of penetration of filtering facepiece respirators
    (2015-07) Satish, Swathi
    Elevated concentrations of diesel exhaust have been linked to adverse health effects. Filtering facepiece respirators (FFRs) are widely used as a form of respiratory protection against diesel particulate matter (DPM) in occupational settings. The objective of this study was to evaluate NIOSH-certified R95 and P95 electret respirators challenged with Diesel exhaust and get a better understanding of the factors that influence penetration. Two techniques were employed for the measurement of penetration: (a) particle counting technique using a Scanning Mobility Particle Sizer (SMPS, TSI Inc.) which measures particle size distribution, and (b) Gravimetric analysis using polyfluortetraethylene (PTFE) and polypropylene (PP) filters. Gravimetric measurements using PP filters were variable compared to SMPS measurements and biased high as a result of the adsorption of gas-phase semi-volatile material. Relatively inert PTFE filters adsorbed less semi-volatile material resulting in more accurate measurements. To attempt to correct for these artifacts associated with adsorption of semi-volatile material, primary and secondary filters were used in series upstream and downstream of the FFR. Correcting for adsorption by subtracting the secondary mass from the primary mass improved the result for both PTFE and PP filters but this correction is subject to “equilibrium” conditions that depend on sampling time and the concentration of particles and semi-volatile material. Overall, the results demonstrate that great care must be taken when using filters to determine filtration efficiency of FFRs challenged with diesel exhaust. Pure PTFE or other filters that minimize adsorption of semi-volatile artifacts and two filters should be used in tandem to allow correction for adsorbed artifacts. Analysis of SMPS measurements indicated that the respirators behave differently for Diesel exhaust generated at light and heavy load on engine. At light load, the penetration of the R-95 and P-95 respirators showed a steep increase with time, exceeding the maximum allowed penetration of 5% after about 40 minutes. Whereas at heavy load, the respirators were found to have a relatively unchanging penetration (less than 5%) throughout the 90-minute test duration. This difference was attributed to the presence of a high concentration of organic carbon (OC) in Diesel exhaust which has a tendency to degrade the electric charges on the respirators, thus reducing the filtration enhancement from electrostatic attraction forces. To account for the complex nature of DPM and its varying properties with changes in operating and sampling condition, an oxidation-dilution tunnel was designed to produce Diesel exhaust with a controlled set of properties: elemental carbon (EC) concentration, OC concentration, EC/OC ratio and volume flow rate. This device was used to evaluate R-95 and P-95 respirators for solid Diesel exhaust aerosol. The methodology proved to be effective in controlling the EC concentration and total volume flow rate. Results showed that the R-95 and P-95 respirators were more than 95% efficient for solid Diesel exhaust aerosol. This thesis is divided into two parts. The first focuses on the measurement of penetration of FFRs for Diesel exhaust, the second on the development of a standard DPM generator for testing filtration systems.
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    Measurement and filtration of virus aerosols
    (2014-06) Zuo, Zhili
    The potential involvement of virus aerosols (i.e., airborne virus-carrying particles) in the transmission of human respiratory diseases has led to increased public concern. This dissertation focuses on 1) measurement of laboratory generated virus aerosols as a function of particle size, virus type, and composition of nebulizer suspensions (Chapter 2 and 3) and 2) performance evaluation of filtering facepiece respirators against virus aerosols (Chapter 4 and 5) with the long term goal to better understand and better control the airborne transmission of viral diseases.
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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.