AAAR 36th Annual Conference October 16 - October 20, 2017 Raleigh Convention Center Raleigh, North Carolina, USA
Abstract View
Laboratory Evaluation of a Novel Real-time Respirator Seal Integrity Monitor
BINGBING WU, Michael Yermakov, Yan Liu, Jonathan Corey, Sergey A. Grinshpun, University of Cincinnati
Abstract Number: 186 Working Group: Aerosol Exposure
Abstract Firefighters are exposed to high concentrations of toxic particles, of which >99% are submicron (< 1 micro-meter) and >70% are ultrafine (< 0.1 micro-meter). Elastomeric respirators equipped with P-100 filters worn by firefighters during the fire overhaul stage provide extremely high protection against relatively large particles (~1 micro-meter). Therefore, significant increases in the concentration of these particles inside a well-fit, high-efficiency respirator can be considered as a fair indicator of its performance failure. This idea was utilized for the development of a novel Respirator Seal Integrity Monitor (ReSIM), which deploys a portable optical Particle Sensor Unit that detects particles size of ≳0.5 micro-meter and can identify respirator leakage in real time. In this study, the ReSIM was subjected to laboratory testing. First, the prototype was exposed to pre-determined concentrations of different aerosols, including monodisperse polystyrene latex (PSL) spheres and polydisperse NaCl particles in a controlled flow-through setting. The ReSIM responses in a concentration range of 0.5 to 300 cm-3 were calibrated against a reference optical size spectrometer (Grimm PAS 1.108: 0.3–20 micro-meter). Second, the effectiveness of the prototype was tested in an aerosol exposure chamber using a respirator-wearing manikin with controlled leaks. Results show that the ReSIM responded rapidly with sufficient sensitivity and accuracy. The aerosol number concentration measured with ReSIM strongly correlated with that measured with Grimm (R2=0.936) in the chosen concentration range, which represents various levels of aerosol contamination. The ReSIM prototype revealed a leak detection accuracy of 92% over 1080 data processing intervals. This investigation demonstrates that ReSIM is capable of rapidly detecting a respirator performance failure and thus can be deployed to alert a respirator wearer of a sudden increase in their aerosol inhalation exposure during the work activity. This is a particularly important feature for firefighters, first responders and other groups.