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Journal article
Inactivating airborne pathogens indoors with 222-nm far-UVC systems: Insights from a CFD-based analysis and a predictive performance model
Building and environment, v 283, p113336
01 Sep 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
•Indoor far-UVC inactivation was modeled with CFD and Monte Carlo sampling.•Inactivation may vary by orders of magnitude within a room.•Effectiveness strongly depends on virus type, irradiance, and ventilation.•Many low-intensity lamps are more effective than few high-intensity lamps.•Reduced-order models can predict log reductions and equivalent-ACH.
Far-UVC (222 nm) systems, an emerging indoor air disinfection technology, are advantageous over traditional 254 nm systems because they can directly irradiate occupied zones without causing significant damage to human tissue. Whole-room far-UVC implementations can effectively inactivate aerosolized pathogens, but the existing literature only encapsulates a small pool of experimental and computational case studies. To produce guidelines and facilitate predictions that are accessible to design practitioners, a much larger data set is required. This need is addressed here by leveraging computational fluid dynamics (CFD) simulations with a Monte Carlo sampling of input parameters. 575 in-silico experiments were carried out, varying room size, room occupancy, lamp layout, lamp power, viral susceptibility to far-UVC, and room supply airflow. Inactivation was characterized with log reductions and equivalent air changes per hour (eACH) by simulating each case with and without UV light. Locally, inactivation can span several orders of magnitude within a room, with the magnitude of the spread correlated with its average. The system’s eACH was well-predicted by a simple multiplicative model between the UV power density and the viral susceptibility, with a residual standard error (RSE) of 0.22 logs with respect to the CFD results. Room-averaged log reductions were well-predicted by a support vector model—released with this publication—with a RSE = 0.12. The RSEs enable designers to use quantitative safety factors with the respective reduced-order models.
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Details
- Title
- Inactivating airborne pathogens indoors with 222-nm far-UVC systems: Insights from a CFD-based analysis and a predictive performance model
- Creators
- Bryan E. Cummings - Drexel University, Civil, Architectural, and Environmental EngineeringCharles N. Haas - Drexel University, Civil, Architectural, and Environmental EngineeringL. James Lo - Drexel University, Department of Civil Architectural and Environmental Engineering, Philadelphia, PA 19104, USAChristopher M. Sales - Drexel University, C. and J. Nyheim Plasma InstituteMichael S. Waring - Drexel University, Civil, Architectural, and Environmental Engineering
- Publication Details
- Building and environment, v 283, p113336
- Publisher
- Elsevier Ltd
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- C. and J. Nyheim Plasma Institute; Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001537566500001
- Scopus ID
- 2-s2.0-105010293480
- Other Identifier
- 991022061654304721
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- Web of Science research areas
- Construction & Building Technology
- Engineering, Civil
- Engineering, Environmental