Multi-zone simulation of outdoor particle penetration and transport in a multi-story building

Possible aerosol transmission of COVID-19 associated with an outbreak in an apartment in Seoul, South Korea, 2020

BACKGROUND

Scientists have strongly implied that aerosols could be the plausible cause of coronavirus disease-2019 (COVID-19) transmission; however, aerosol transmission remains controversial.

THE STUDY

We investigated the epidemiological relationship among infected cases on a recent cluster infection of COVID-19 in an apartment building in Seoul, South Korea. All infected cases were found along two vertical lines of the building, and each line was connected through a single air duct in the bathroom for natural ventilation. Our investigation found no other possible contact between the cases than the airborne infection through a single air duct in the bathroom. The virus from the first infected case can be spread to upstairs and downstairs through the air duct by the (reverse) stack effect, which explains the air movement in a vertical shaft.

CONCLUSIONS

This study suggests aerosol transmission, particularly indoors with insufficient ventilation, which is underappreciated.

The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control

Transmission mechanics of infectious pathogen in various environments are of great complexity and has always been attracting many researchers' attention. As a cost-effective and powerful method, Computational Fluid Dynamics (CFD) plays an important role in numerically solving environmental fluid mechanics. Besides, with the development of computer science, an increasing number of researchers start to analyze pathogen transmission by using CFD methods. Inspired by the impact of COVID-19, this review summarizes research works of pathogen transmission based on CFD methods with different models and algorithms. Defining the pathogen as the particle or gaseous in CFD simulation is a common method and epidemic models are used in some investigations to rise the authenticity of calculation. Although it is not so difficult to describe the physical characteristics of pathogens, how to describe the biological characteristics of it is still a big challenge in the CFD simulation. A series of investigations which analyzed pathogen transmission in different environments (hospital, teaching building, etc) demonstrated the effect of airflow on pathogen transmission and emphasized the importance of reasonable ventilation. Finally, this review presented three advanced methods: LBM method, Porous Media method, and Web-based forecasting method. Although CFD methods mentioned in this review may not alleviate the current pandemic situation, it helps researchers realize the transmission mechanisms of pathogens like viruses and bacteria and provides guidelines for reducing infection risk in epidemic or pandemic situations.

Modeling transient particle transport by fast fluid dynamics with the Markov chain method

Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used two cases from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markov-chain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the two studied cases, the FFD-Markovchain model was 4.7 and 6.8 times faster, respectively, than the CFD-Eulerian model, and it was 137.4 and 53.3 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.

How Outdoor Trees Affect Indoor Particulate Matter Dispersion: CFD Simulations in a Naturally Ventilated Auditorium

This study used computational fluid dynamics (CFD) models, coupling with a standard k-ε model based on the Reynolds-averaged Navier-Stokes (RANS) approach and a revised generalized drift flux model, to investigate effects of outdoor trees on indoor PM1.0, PM2.5, and PM10 dispersion in a naturally ventilated auditorium. Crown volume coverage (CVC) was introduced to quantify outdoor trees. Simulations were performed on various CVCs, oncoming wind velocities and window opening sizes (wall porosities were 3.5 and 7.0%, respectively, for half and fully opened windows). The results were as follows: (1) A vortex formed inside the auditorium in the baseline scenario, and the airflow recirculation created a well-mixed zone with little variation in particle concentrations. There was a noticeable decrease in indoor PM10 with the increasing distance from the inlet boundary due to turbulent diffusion. (2) Assuming that pollution sources were diluted through the inlet, average indoor particle concentrations rose exponentially with increasing oncoming wind speed. PM10 changed most significantly due to turbulent diffusion and surface deposition reduction intensified by the increased wind velocity. (3) Increasing the window opening improved indoor cross-ventilation, thus reducing indoor particle concentrations. (4) When 2.87 m³/m² ≤ CVC ≤ 4.73 m³/m², indoor PM2.5 could meet requirements of the World Health Organization's air quality guidelines (IT-3) for 24-hour mean concentrations; and (5) average indoor particle concentrations had positive correlations with natural ventilation rates (R² = 0.9085, 0.961, 0.9683 for PM1.0, PM2.5, and PM10, respectively, when the wall porosity was 3.5%; R² = 0.9158, 0.9734, 0.976 for PM1.0, PM2.5, and PM10, respectively, when the wall porosity was 7.0%).