Department of Mechanical Engineering, University of Washington, Seattle, USA.
Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, USA.
Sci Rep. 2023 Mar 10;13(1):3992. doi: 10.1038/s41598-023-30778-7.
The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.
COVID-19 大流行引起了公众对空气传播颗粒物 (PM) 的关注,因为传染病通过呼吸道传播。需要仔细调查公共空间中潜在传染性气溶胶的持久性和医疗机构中医院感染的传播情况;然而,尚未报道一种系统的方法来描述临床环境中气溶胶的命运。本文提出了一种使用 ICU 及相邻环境中的低成本 PM 传感器网络来绘制气溶胶传播的方法,以及随后开发的数据驱动分区模型。通过模拟患者产生的气溶胶,我们生成了痕量 NaCl 气溶胶,并监测了它们在环境中的传播。在正压 (关门) 和常压 (开门) ICU 中,分别有高达 6%或 19%的所有 PM 通过门缝逸出;然而,负压 ICU 的外部传感器没有记录到气溶胶峰值。气溶胶浓度时空数据的 K-均值聚类分析表明,ICU 可以由三个不同的区域表示:(1) 气溶胶源附近,(2) 房间周边,和 (3) 房间外。数据表明存在两相羽流行为:原始气溶胶峰值在整个房间内扩散,然后是疏散阶段,其中“充分混合”的气溶胶浓度均匀衰减。计算了正压、常压和负压操作的衰减率,负压房间的清除速度几乎快了两倍。这些衰减趋势与空气交换率密切相关。这项研究展示了在医疗环境中进行气溶胶监测的方法。本研究受到相对较小数据集的限制,并且特定于单人占用的 ICU 房间。未来的工作需要评估具有传染病传播高风险的医疗环境。