A CFD‐based quantitative prediction model for air change rate and particulate matter concentration in a dynamically occupied class 10,000 operating room: a case study in Dalian, China

Zhao, Yu, Wang, Kangning, Wang, Yuhan, Hu, Saihang, Hussein, Tareq, Luo, Zhiwen ORCID: https://orcid.org/0000-0002-2082-3958 and Zheng, Hong 2026. A CFD‐based quantitative prediction model for air change rate and particulate matter concentration in a dynamically occupied class 10,000 operating room: a case study in Dalian, China. Indoor Air 2026 (1) , 5838379. 10.1155/ina/5838379

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Abstract

Maintaining a healthy indoor environment in operating rooms (ORs) is critical for protecting both patients and medical staff from airborne particulate matter (PM) exposure. However, current ventilation standards typically prescribe fixed air change rates (ACH) based on static or as-built conditions, which fail to account for dynamic occupancy variations. This study is aimed at establishing a quantitative relationship between ACH and PM concentration under dynamic conditions while ensuring indoor air quality in ORs. A Class 10,000 OR, classified according to the Chinese National Standard GB 50333-2013, was investigated by integrating field measurements with computational fluid dynamics (CFD) simulations. An orthogonal experimental design was employed to generate 45 representative operating scenarios. Sobol sensitivity analysis was applied to identify key influencing factors. Bayesian optimization (BO), multiobjective optimization (MOP), and response surface methodology (RSM) were further used to optimize and calibrate the nonuniformity coefficient (ψ) and a quantitative prediction model was subsequently established, linking ACH, number of personnel, and PM concentration. The results showed that the RSM model had lower prediction errors, with a root mean square error (RMSE) of 0.032, mean absolute error (MAE) of 0.024, coefficient of variation (Cv) of 0.052, and standard deviation (σ) of 0.022. In contrast, the Cv and σ values of MOP were 0.005 and 0.002, respectively, which were the lowest among the three algorithms, indicating that MOP exhibited higher stability and robustness when handling coupled disturbances caused by ACH and personnel-generated PM. While satisfying the GB 50333-2013 Class 10,000 cleanroom requirement (PM≥0.5 ≤ 352 PC/L), the minimum required ACH for surgical teams of 5–9 personnel ranges from 9.2 to 16.6 h−1, significantly lower than the 18 h−1 recommended by current standards. It was also observed that surgical lights considerably disrupt airflow organization, thereby affecting local PM distribution. The findings provide a scientific basis for transitioning from fixed ventilation design toward dynamic control strategies while ensuring indoor air quality in ORs.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Architecture
Publisher:	Wiley
ISSN:	0905-6947
Date of First Compliant Deposit:	24 August 2026
Last Modified:	03 Sep 2026 13:40
URI:	https://orca.cardiff.ac.uk/id/eprint/189153

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