⚡ electrical engineering

Residence Time Distribution Analysis of Patient-Emitted Air in a Mock Hospital Isolation Room: A CFD Study

This study demonstrates that while Air Changes per Hour (ACH) alone cannot predict contaminant removal efficiency due to airflow topology, Residence Time Distribution (RTD) analysis via CFD reveals that optimizing exhaust placement to create a direct patient-to-exhaust pathway significantly outperforms increased ventilation rates in minimizing healthcare worker exposure to patient-emitted air.

Original authors: Anthony J. Pérez, Juan Peñaloza-Gutiérrez, Tauhidur Rahman, Andrés E. Tejada-Martínez

Published 2026-09-24

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CC BY 4.0

Original authors: Anthony J. Pérez, Juan Peñaloza-Gutiérrez, Tauhidur Rahman, Andrés E. Tejada-Martínez

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the quiet hum of a hospital isolation room, the air is constantly moving, carrying invisible particles exhaled by a patient. For decades, engineers have relied on a single number to judge whether that air is being cleaned effectively: the air changes per hour. This metric counts how many times the total volume of air in a room is replaced with fresh air in sixty minutes. The underlying assumption is simple and comforting: if you pump enough fresh air in, the old, contaminated air will vanish uniformly, as if the room were a giant, perfectly stirred cup of coffee. In this idealized view, the location of the air vents and the path the air takes do not matter; only the volume of air moving through the space counts.

However, real rooms are not cups of coffee. They are filled with beds, people, and furniture that disrupt the flow. Air can rush straight from a vent to an exhaust without ever touching the corners where a nurse might stand, or it can get trapped in swirling pockets that linger for a long time. This means that the same number of air changes can result in very different levels of safety for the people inside. Understanding exactly how air moves, where it gets stuck, and how quickly it carries away what a patient breathes out is crucial for protecting healthcare workers from infectious diseases.

Researchers at the University of South Florida and the University of California San Diego set out to look beyond the simple count of air changes. They used powerful computer simulations to map the journey of air emitted from a patient's mouth in a mock hospital isolation room. Instead of just counting how much air moved, they tracked a pulse of invisible tracer gas released from the patient, watching how long it took to reach the exhaust vent and how long it took to clear the room entirely. They tested two very different room layouts: one where the exhaust vent was placed directly above the patient to create a direct path for the air to escape, and another where the vent was on the opposite side of the room, forcing the air to travel across the space. They ran these simulations at two common ventilation rates: six air changes per hour and twelve.

The results revealed that the simple rule of "more air is always better" depends entirely on the room's layout. When the ventilation rate was low, at six air changes per hour, the two room designs behaved similarly. The air moved slowly enough that the differences in vent placement mattered little; the tracer gas took roughly the same amount of time to leave the room in both cases. But when the researchers doubled the ventilation rate to twelve air changes per hour, the two designs split apart dramatically. In the room with the direct path, the air from the patient shot straight to the exhaust, clearing the room much faster. In the room with the distant vent, the air got caught in large, slow-moving swirls, taking significantly longer to escape.

This difference had a profound effect on the exposure of a healthcare worker standing in the room. In the design with the direct path, doubling the air changes reduced the worker's long-term exposure to the patient's air by seventy-five percent. In the design with the distant vent, the same increase in air changes only reduced exposure by thirty-nine percent. More surprisingly, in the room with the distant vent, increasing the air changes actually made the short-term exposure worse for a brief period. The faster air flow pushed the contaminated air into a recirculating loop that kept it near the worker's head for longer before it finally escaped. The study showed that simply pumping more air into a room does not guarantee safety if the air is not guided along a clear path away from the people inside.

The researchers found that the time it took for the first ten percent of the contaminated air to reach the exhaust was a key indicator of safety. In the room with the direct path at high ventilation, this breakthrough happened almost instantly. In the other room, it was delayed by several minutes, allowing the contaminated air to spread and linger. By measuring these specific times and the total time the air spent in the room, the team could predict how much exposure a worker would face. They concluded that hospital designers should stop relying solely on the count of air changes. Instead, they should use these time-based measurements to ensure that the air is not just moving, but moving in a way that carries contaminants away from people as quickly as possible. The study suggests that a well-placed vent that creates a direct highway for the air to exit is far more effective than a high volume of air that gets lost in the room's corners.

Technical Summary: Residence Time Distribution Analysis of Patient-Emitted Air in a Mock Hospital Isolation Room

Problem Statement
Ventilation performance in hospital isolation rooms is traditionally specified by Air Changes per Hour (ACH). However, ACH presumes the room behaves as a perfectly mixed reactor, an assumption that fails to account for complex airflow topologies such as short-circuiting pathways and recirculation (dead) zones. These flow structures significantly influence the transport and removal of patient-discharged air, potentially leading to substantial spatial and temporal variations in contaminant concentration and healthcare worker (HCW) exposure. While previous Computational Fluid Dynamics (CFD) studies have demonstrated that direct patient-to-exhaust pathways can reduce exposure, a formal framework to quantify these transport mechanisms and their relationship to exposure metrics has been lacking. This study addresses the need to move beyond bulk ACH metrics to characterize the specific transport dynamics of patient-emitted air.

Methodology
The study employs CFD simulations of a mock hospital isolation room, modeled after geometries investigated by Thatiparti et al. [8, 9]. Two distinct exhaust configurations were analyzed:

Normal Design (NMD): The exhaust is displaced from the patient, representing a standard layout.
Short-Circuiting Design (SCD): The exhaust is positioned to preserve a direct pathway from the patient's mouth to the exhaust, acting as a form of source capture.

Simulations were conducted at two ventilation rates: 6 ACH and 12 ACH. The fluid dynamics were solved using Reynolds-Averaged Navier-Stokes (RANS) equations with a four-equation transition SST turbulence model to capture low free-stream turbulence and swirl flows. Thermal plumes generated by the patient (312 K) and HCW (309 K) were included to simulate buoyancy effects.

To characterize transport, the study applies Residence Time Distribution (RTD) analysis. A passive tracer pulse (representing a ~2 L cough over 0.2 s) was released at the patient's mouth. The tracer concentration at the exhaust was tracked over time to generate RTD curves, $E(t)$ , and cumulative RTD curves, $F(t)$ . Key metrics derived included:

Breakthrough time ( $t_{10}$ ): Time for 10% of the tracer to exit.
Spread window ( $t_{90} - t_{10}$ ): Duration for the middle 80% of tracer removal.
Mean Residence Time ( $\tau_{CFD}$ ): The first moment of the RTD curve.
Morrill Index ( $t_{90}/t_{10}$ ): A measure of the combination of early breakthrough and late-time persistence.
Apparent Dead Volume Fraction ( $\phi_d$ ): Quantifying the deviation from theoretical mean residence time ( $\tau_{theor}$ ).

These metrics were compared against the Perfectly Mixed Assumption (PMA) and correlated with HCW exposure, defined as the time integral of tracer concentration near the HCW's head.

Key Results
The study reveals that ventilation performance is highly dependent on the interaction between ACH and exhaust placement, with RTD metrics capturing differences that ACH alone cannot predict.

Low Ventilation (6 ACH): Both NMD and SCD configurations yielded RTD curves close to the PMA prediction. Normalized breakthrough times and spreads were similar, and mean residence times were close to the theoretical value. While SCD reduced long-term HCW exposure by approximately 20% compared to NMD, the overall transport behavior remained dominated by mixing and diffusion rather than strong short-circuiting.
High Ventilation (12 ACH): The configurations diverged sharply.
- SCD: Established a strong direct pathway, reducing the normalized breakthrough time ( $t_{10}/\tau_{theor}$ ) from 0.311 (NMD) to 0.0283. The normalized spread narrowed from 2.29 to 1.64, and the mean residence time dropped from 6.23 min to 2.64 min. The Morrill index increased significantly (58.88 vs. 8.36 for NMD), driven by the extremely rapid early breakthrough.
- NMD: Exhibited delayed breakthrough and broader retention, with a mean residence time (6.23 min) exceeding the theoretical value (4.96 min), indicating significant dead zones.
HCW Exposure:
- Short-term: Doubling ACH from 6 to 12 reduced short-term exposure only in the SCD configuration (where the pathway was preserved). In the NMD configuration, doubling ACH actually increased the short-term exposure rate compared to the 6 ACH case and the PMA, demonstrating that higher flow rates without a direct pathway can accelerate contaminant transport to the HCW before dilution occurs.
- Long-term: Increasing ACH reduced long-term exposure in both designs, but the reduction was substantially greater for SCD (75% reduction) than for NMD (39% reduction). At 12 ACH, the SCD configuration reduced long-term exposure to 0.42 times the PMA value, while NMD increased it to 1.27 times the PMA value.
Correlations: Long-term HCW exposure was strongly correlated with the CFD mean residence time ( $\tau_{CFD}$ ). Short-term exposure rates correlated systematically with RTD spread, breakthrough time, and the Morrill index.

Significance and Claims
The paper claims to be the first to apply RTD analysis formally to evaluate patient-discharged air transport in hospital isolation rooms. The authors argue that RTD metrics provide a quantitative framework to translate complex airflow topology into physically interpretable time scales.

Key claims include:

Limitations of ACH: ACH specifies the volume of air supplied but not its destination. The study demonstrates that ACH alone is an inadequate predictor of HCW exposure; the effect of increasing ACH depends entirely on whether the airflow topology preserves a direct patient-to-exhaust pathway.
Validation of the "Path Principle": The results quantitatively support the "path principle" (Memarzadeh and Xu [5]), showing that a direct pathway acts as a source capture mechanism that drastically reduces exposure, particularly at higher ventilation rates.
Design Utility: RTD analysis (via CFD or physical tracer testing) offers a portable, ACH-agnostic basis for comparing exhaust placements. The authors suggest that designers should prioritize layouts with low normalized breakthrough times ( $t_{10}/\tau_{theor}$ ) and low normalized spreads ( $W$ ) to ensure rapid clearance, rather than relying solely on meeting ACH targets.
Screening Tool: The study positions RTD analysis as an inexpensive screening tool for design and commissioning, capable of identifying optimal configurations before resorting to higher-fidelity multiphase simulations or complex aerosol testing.

The authors maintain a modest scope, noting that the relationships observed are descriptive based on four specific configurations and that future work is needed to assess larger particles, different patient postures, and broader ranges of ACH.

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