Fixed and Adaptive Concentration Threshold for Particle Filtration Systems

Alexander Mendell, Alexander Olson, JEFFREY SIEGEL, University of Toronto

     Abstract Number: 506
     Working Group: Indoor Aerosols

Abstract
Particle filtration can effectively reduce indoor concentrations of particulate matter (PM), but may incur high energy use. One approach to minimize energy use is to operate particle filters only when concentrations exceed a threshold. However, such approaches are complicated by the diversity of indoor environments and contexts limiting the applicability of universal thresholds. To address this need, this study evaluates fixed and adaptive concentration thresholds to automate the operation of filtration systems. Simulated environments were derived from week-long continuous PM measurements from two types of low-cost particle counters deployed in apartments (n=204) in Toronto. A fixed threshold of 4.0 μg∙m-3 resulted in a mean air cleaner runtime of 6.9% to 21.0% depending on clean air delivery rate (CADR) and particle sensor, while providing mean concentration reductions of 67% to 71% compared to operating the air cleaner constantly (runtime = 100%). In most environments, runtime could be further reduced by raising the fixed threshold while resulting in only a modest decrease in absolute and normalized mean exposure reduction. These results were generally insensitive to cleaning power and the monitor used to measure particle concentrations. Using an adaptive threshold derived from a k-means clustering approach generally further increased exposure reduction while preventing high runtimes, further increasing benefit. Reducing the energy usage of particle filter systems will make them a more viable and sustainable means of improving occupant health.