Experimental study on the control effect of different ventilation systems on fine particles in a simulated hospital ward

Kong, Xiangming; Guo, Chenli; Lin, Zhang; Duan, Shasha; He, Junjie; Ren, Yue; Ren, Jing

doi:10.1016/j.scs.2021.103102

Cited by 89 publications

(44 citation statements)

References 47 publications

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“…Nishiura et al, 2020 [ [ 20 ]] found that transmission of COVID-19 in a confined space was 18.7 times greater compared to an open-air environment. In enclosed spaces, the airflow pattern induced by the ventilation systems has large influence on the airborne particle transmission [ [ [21] , [22] , [23] ]]. Therefore, the ventilation system of the elevator is important for efficiently diluting or removing the aerosol concentration [ [ 24 ]].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of airborne particle exposure for riding elevators

Liu

Zhao

Nichols

et al. 2022

Building and Environment

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Section: Introductionmentioning

confidence: 99%

Evaluation of airborne particle exposure for riding elevators

Liu

Zhao

Nichols

et al. 2022

Building and Environment

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“…The World Health Organization recommends to increase the supply of fresh indoor air as an effective way to reduce the concentration of virus particles in enclosed spaces, thereby reducing the risk of infection. Therefore, effective building ventilation is essential to ensure the health and safety of occupants ( Cao et al, 2020a ; Kong et al, 2021 ; Srivastava et al, 2021 ;).…”

Section: Introductionmentioning

confidence: 99%

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

Wang

Huang

et al. 2022

Sustainable Cities and Society

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Gymnasiums, fitness rooms and alike places offer exercise services to citizens, which play positive roles in promoting health and enhancing human immunity. Due to the high metabolic rates during exercises, supplying sufficient ventilation in these places is essential and extremely important especially given the risk of infectious respiratory diseases like COVID-19. Traditional ventilation control methods rely on a single CO 2 sensor (often placed at return air duct), which is often difficult to reflect the human metabolic rates accurately, and thus can hardly control the infection risk instantly. Thus, to ensure a safe and healthy environment in places with high metabolism, a real-time metabolism-based ventilation control method is proposed. A computer vision algorithm is developed to monitor human activities (regarding human motion amplitude and speed) and an artificial neural network is established for metabolic prediction. Case studies show that the proposed metabolism-based ventilation control method can reduce the infection probability down to 4.3-6.3% while saving 13% of energy in comparison with the strategy of fixed-fresh-air ventilation. In the development of healthy and sustainable society, gymnasiums and alike exercise places are essential and the proposed ventilation control method is a promising solution to decrease the risk of COVID-19 while preserving features of energy saving and carbon emission reduction.

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“…While for stratum ventilation (SV), by placing supply air outlets at the middle height of the side‐wall (e.g., 1.3 m for seated occupants and 1.5 m for standing occupants), 9 the air movement in the breathing zone is higher, 10 and a non‐uniform indoor environment is rendered. The high air movement efficiently dilutes the contaminant in the breathing zone, leading to a better indoor air quality 11–13 and less airborne infection risk 14–18 without compromising thermal comfort and energy efficiency 10 …”

Section: Introductionmentioning

confidence: 99%

Zonal model for predicting contaminant distribution in stratum ventilated rooms

Zhang

2022

Indoor Air

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Accurate prediction of the non‐uniform contaminant distribution under stratum ventilation (SV) is crucial for optimal design for reducing contaminant exposure risks. Compared with experiments and computational fluid dynamics, zonal models are convenient to implement. This study proposes a zonal model for predicting dynamic non‐uniform contaminant distribution in the stratum ventilated room. The zoning method is based on the unique airflow pattern under SV, and the room is divided into the jet zone, entrainment zone, and the mixing zone. The interzonal airflow rate is derived from the profile of the supply air jet. The results show that the proposed zonal model can predict the dynamic contaminant distribution in the stratum ventilated room. Compared with the experimental measurement, the predictions show good accuracy with the mean absolute error (MAE) at 0.51–2.36 ppm and root mean squared error (RMSE) at 0.64–2.53 ppm. The error of the proposed zonal model is influenced by the degree of mixing in each subzone. The proposed zonal model shows better accuracy for non‐uniform air distribution under stratum ventilation compared with the existing zonal model.

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Experimental study on the control effect of different ventilation systems on fine particles in a simulated hospital ward

Cited by 89 publications

References 47 publications

Evaluation of airborne particle exposure for riding elevators

Evaluation of airborne particle exposure for riding elevators

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

Zonal model for predicting contaminant distribution in stratum ventilated rooms

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