Influence of human movement on transport of airborne infectious particles in hospital environment

Abstract

Human movement indoors can result in secondary airflow and subsequently influence the transport of airborne infectious particles. Typical examples in hospital environment are the dispersion of bacteria-carrying particles (BCPs) shed from surgical staff and respiratory droplets exhaled by infectious patients. Particles with different sizes can behave distinctly from gaseous counterparts due to gravitational settling and inertia effect etc. However, the reality of previous related researches is that for the researches considering human movement impacts these airborne particles were simply substituted by gaseous counterparts; while the researches on particle dispersion indoors lacked consideration of human movement influences, which may misinterpret the infection risk. In hospital premises such as an operating theatre the movements of surgical staff are usually unavoidable and the rigorous concentration control of airborne infectious particles is very crucial for decreasing surgical site infection. Nevertheless, the investigations on the influence of human movement on distribution of airborne infectious particles are so far insufficient, most probably due to the complexity of considering human movement and particles transport simultaneously. In this study, in order to extend the current knowledge on the impact of human movement on transport characteristics of airborne infectious particles in hospital environment, the influences of typical human movements such as walking and bending on particles transport were systematically investigated, which were through advanced numerical simulation models development, experimental measurements as well as dynamic simulations of BCPs in operating theatre. Accurate prediction of particle concentration indoors depends on the appropriate particle transport models. The performances and applicabilities of four particle transport models, namely, Lagrangian particle tracking model (LPTM), ordinary drift-flux model (ODFM), modified drift-flux model (MDFM) and passive scalar species transport model (PSSTM) were respectively compared within different particle size ranges as well as against literature data and advanced LES simulation results. The results showed that for 1-5 μm particles group all the four particle transport models could agree reasonably well with the experimental data, while for 10-50 μm particles group only LPTM and MDFM could agree relatively well with benchmark results. Experimental measurements for the influence of human walking on the distribution of coughing particles were also conducted in a full-scale ceiling-based mixing ventilated chamber. A computer-controlled walking manikin (CCWM) was invented and fabricated to mimic three different walking profiles of human. And a coughing manikin was used to model the coughing source man. When the coughing source man was controlled to cough, the CCWM was simultaneously controlled to walk back and forth respectively under three walking speed profiles. During the experimental run the dynamic airflow velocity and particle concentration were both measured at monitoring points. The mathematical models for simulating the influence of human walking on transport of particles were firstly developed, by applying the Eulerian URANS (unsteady Reynolds-averaged Navier-Stokes) model for the dynamic airflow, the MDFM for particles transport and the dynamic mesh model for modeling the human movements. The simulation results for the dynamic velocities and the particle concentrations could agree reasonably well with the measured ones. The results revealed that there obviously appear four vortexes around the walking man, with faster walking causing stronger wake. The dispersion route of coughing particles can be changed and entrained by the secondary airflow during human walking session. The longer walking disturbance duration can also cause more delay for particles recovering the normal dispersion route, which is mainly governed by the intentionally designed ventilation scheme. Based on the experimental setup, when coupled with the dynamic mesh model the Lagrangian particle tracking simulations for investigating the impact of human walking on transport of coughing particles were also carried out. The different performances between LPTM and MDFM when coupled with the dynamic mesh model were compared. It was found that both models can predict particle distribution patterns similarly. The LPTM can exhibit a more comprehensive 3D spatial distribution of particles with the disadvantage of semi-quantitative analyses, which is inconvenient for quantitative concentration validation purpose and results from not using the actual amount of initially released particles from measurements; while the MDFM coupled with the dynamic mesh model can directly obtain quantitative particle concentration distributions with the drawback of being unable to observe the 3D time-dependent evolution processes of particles. By considering three common scenarios such as human walking in the clean zone, the non-clean zone and from the non-clean zone to the clean one in an operating theatre, numerical investigations on the influences of different human walking speeds on BCPs distributions were respectively done. The results showed that walking either within the clean zone or within the non-clean one would insignificantly influence the BCPs' distribution in the surgical critical zone, but walking from the non-clean zone to the clean one could pose risks of surgical site infection depending on the walking speed. Finally, the influence of periodic bending movements of a surgeon on BCPs' distribution was numerically studied and compared with the scenario of all surgical staff standing upright motionlessly. It was found that surgeon bending movements could cause BCP's concentration in surgical critical zone exceeding the recommended 10 cfu/m3 from HTM 03-01 (Health Technical Memorandum 03-01), among which the 2-s bending back movement of surgeon could pose the highest infection risk.

Date of Award	2 Oct 2013
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Tin Tai CHOW (Supervisor) & Chung Leung Johnny CHAN (Co-supervisor)