Particle filtration can effectively reduce indoor concentrations of particulate matter (PM) but may incur high energy use. 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 Dylos DC1700 (N = 104) and Alphasense OPC-N2 (N = 100) particle counters deployed in apartments in Toronto. A fixed threshold of 4.0 μg·m⁻³ resulted in a mean air cleaner runtime of 6.9%–21.0% depending on clean air delivery rate (CADR) and particle sensor, while providing mean concentration reductions of 67%–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. Using an adaptive threshold derived from a k-means clustering approach generally provided substantial exposure reduction while preventing high runtimes. These results were generally insensitive to cleaning power and the monitor used to measure particle concentrations. Reducing the energy usage of particle filter systems will make them a more viable and sustainable means of improving occupant health.

中文翻译：

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粒子过滤系统固定和自适应浓度阈值的评估

颗粒物过滤可以有效降低室内颗粒物 (PM) 的浓度，但可能会消耗大量能源。本研究评估了固定和自适应浓度阈值，以自动运行过滤系统。模拟环境源自 部署在多伦多公寓中的 Dylos DC1700 ( N  = 104) 和 Alphasense OPC-N2 ( N = 100) 粒子计数器为期一周的连续 PM 测量值。4.0 μg·m ^{−3的固定阈值}导致平均空气净化器运行时间为 6.9%–21.0%，具体取决于洁净空气输送率 (CADR) 和颗粒传感器，同时与持续运行空气净化器（运行时间 = 100%）相比，平均浓度降低了 67%–71% . 在大多数环境中，可以通过提高固定阈值进一步减少运行时间，同时仅适度减少绝对和归一化平均曝光减少量。使用源自 k 均值聚类方法的自适应阈值通常可以显着减少曝光，同时防止高运行时间。这些结果通常对清洁能力和用于测量颗粒浓度的监视器不敏感。减少颗粒过滤系统的能源使用将使它们成为改善乘员健康的更可行和可持续的方法。