Abstract
Effectiveness is commonly used to evaluate the particle removal performance of residential filtration systems. Previous investigations have used various effectiveness metrics to assess system performance through modeling efforts. However, the model assumptions often lead to an overestimation of effectiveness. Further, none of the studies considered the impact of realistic residential heating, ventilation, and air conditioning (HVAC) system operation patterns. In this investigation, we evaluated the strength and limitations of various effectiveness metrics. We identified exposure reduction as the most appropriate metric for modeling analyses because it does not rely on any steady-state assumptions. A time-varying mass balance model that accounts for system operation parameters including runtimes and cycle on-times, and the impact of indoor emission sources is used to simulate the changes in indoor particulate matter smaller than 2.5 μm (PM2.5) concentration with and without system operation. The effectiveness results show that runtime has the biggest impact on exposure reduction. At a fixed runtime, short cycle on-times and the coincidence of system operation with emission events can further improve the removal performance of the system. Thus, the particle removal performance of a filtration system can be optimized by changing its operation pattern while still maintaining similar fan energy use.
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