Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection.

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as 'A' to 'H'). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability.

Refined design of ventilation systems to mitigate infection risk in hospital wards: Perspective from ventilation openings setting.

To prevent respiratory infections between patients and medical workers, the transmission risk of airborne pollutants in hospital wards must be mitigated. The ventilation modes, which are regarded as an important strategy to minimize the infection risk, are challenging to be systematically designed. Studies have considered the effect of ventilation openings (inlets/outlets) or infected source locations on the airflow distribution, pollutant removal, and infection risk mitigation. However, the relationship (such as relative distance) between ventilation openings and infected sources is critical because it affects the direct exhaust of exhaled pollutants, which has not been thoroughly studied. To explore pollutant removal and infection prevention in wards, different ventilation modes (with varying ventilation openings) and infected patient locations must be jointly considered. This study investigated displacement ventilation (DV), downward ventilation (DWV), and stratum ventilation (SV) with 4, 6, and 10 scenarios of ventilation openings, respectively. The optimal ventilation mode and relative distance between outlets and infected patients were analyzed based on the simulated pollutant concentration fields and the evaluated infection risk. The pollutant removal effect and infection risk mitigation of SV in the ward were largely improved by 75% and 59% compared with DV and DWV, respectively. The average infection risk was reduced below 7% when a non-dimensional relative distance (a ratio of the actual distance to the cubic root of the ward volume) was less than 0.25 between outlets and infected patient. This study can serve as a guide for the systematic ventilation system design in hospitals during the epidemic.

Intelligent operation, maintenance, and control system for public building: Towards infection risk mitigation and energy efficiency.

During the post-COVID-19 era, it is important but challenging to synchronously mitigate the infection risk and optimize the energy savings in public buildings. While, ineffective control of ventilation and purification systems can result in increased energy consumption and cross-contamination. This paper is to develop intelligent operation, maintenance, and control systems by coupling intelligent ventilation and air purification systems (negative ion generators). Optimal deployment of sensors is determined by Fuzzy C-mean (FCM), based on which CO2 concentration fields are rapidly predicted by combing the artificial neural network (ANN) and self-adaptive low-dimensional linear model (LLM). Negative oxygen ion and particle concentrations are simulated with different numbers of negative ion generators. Optimal ventilation rates and number of negative ion generators are decided. A visualization platform is established to display the effects of ventilation control, epidemic prevention, and pollutant removal. The rapid prediction error of LLM-based ANN for CO2 concentration was below 10% compared with the simulation. Fast decision reduced CO2 concentration below 1000 ppm, infection risk below 1.5%, and energy consumption by 27.4%. The largest removal efficiency was 81% when number of negative ion generators was 10. This work can promote intelligent operation, maintenance, and control systems considering infection prevention and energy sustainability.

Impact of ionizers on prevention of airborne infection in classroom.

Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used. Electronic Supplementary Material ESM: the Appendix is available in the online version of this article at 10.1007/s12273-022-0959-z.

Ventilation impacts on infection risk mitigation, improvement of environmental quality and energy efficiency for subway carriages.

Subway carriages are enclosed for extended periods of time, with a high density of passengers. Providing a safe, healthy, and comfortable cabin environment is a great challenge, particularly during the COVID-19 pandemic. An increase in ventilation rate can potentially reduce infection probability, which may result in worsening environmental quality (e.g., thermal comfort) and larger energy consumption. Thus, exploring the trade-off among infection risk, environmental quality (with regard to ventilation, thermal comfort, and air quality), and energy consumption is important to optimize ventilation systems for carriages. The effect of different supply air parameters (e.g., velocity and temperature) and ventilation modes of mixing ventilation (MV) & Supply air from the Floor and Return air from the Ceiling (SFRC) was studied. The questionnaires were analyzed to explore passenger dissatisfaction with the carriage environment using a MV system. Simulations were performed to predict the velocity, temperature, and CO2 concentration fields. In addition, the comprehensive benefit was evaluated by analytic hierarchy process (AHP), based on infection probability from the revisited Wells-Riley equation, Air Diffusion Performance Index (ADPI), Predicted Mean Vote (PMV), Pollutant Removal Effectiveness (PRE) and energy consumption estimated by cooling load (Lcool). Compared with MV, the optimized SFRC provided softer draft sensation and decreased CO2 concentration by 42%. The SFRC achieved better comprehensive benefits, with an infection risk reduced to 0.4%, ADPI of 80%, PMV approaching zero, PRE up to 16, and energy efficiency increased by 30%. This work contributes to the optimal design of subway carriage ventilation systems in the post-epidemic era.

Removal of SARS-CoV-2 using UV+Filter in built environment.

Air cleaning is an effective and reliable method in indoor airborne SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona-Virus 2) control, with ability of aerosol removal or disinfection. However, traditional air cleaning systems (e.g. fibrous filter, electrostatic removal system) have some risks in operation process, including re-aerosolization and electric breakdown. To avoid these risks, the current study proposed an UV+Filter (ultraviolet and fibrous pleated filter) system to efficiently capture airborne SARS-CoV-2 aerosols and deactivate them in filter medium. It is challenging to quantitatively design UV+Filter due to complex characteristics of SARS-CoV-2 aerosols (e.g. aerodynamic size, biological susceptibility) and hybrid filtration/disinfection processes. This study numerically investigated the overall performances of different air cleaning devices (e.g. Fibrous-filter, UV+Filter, two-stage ESP (electrostatic precipitator) et al.) for control of SARS-CoV-2 aerosols and compared them in term of filtration efficiency, energy consumption and secondary pollution. The prediction of developed models was validated with the experimental data from literature. UV+Filter is the most reliable and safest, while its energy consumption is highest. The newly proposed design method of air cleaning systems could provide essential tools for airborne diseases control.

Mitigating COVID-19 infection disease transmission in indoor environment using physical barriers.

During the normalized phase of COVID-19, droplets or aerosol particles produced by infected personnel are considered as the potential source of infection with uncertain exposure risk. As such, in densely populated open spaces, it is necessary to adopt strategies to mitigate the risk of infection disease transmission while providing sufficient ventilation air. An example of such strategies is use of physical barriers. In this study, the impact of barrier heights on the spread of aerosol particles is investigated in an open office environment with the well-designed ventilation mode and supply air rate. The risk of infection disease transmission is evaluated using simulation of particle concentration in different locations and subject to a number of source scenarios. It was found that a barrier height of at least 60 cm above the desk surface is needed to effectively prevent the transmission of viruses. For workstations within 4 m from the outlet, a 70 cm height is considered, and with a proper ventilation mode, it is shown that the barriers can reduce the risk of infection by 72%. However, for the workstations further away from the outlet (beyond 4 m), the effect of physical barrier cannot be that significant. In summary, this study provides a theoretical analysis for implementing physical barriers, as a low-cost mitigation strategy, subject to various height scenarios and investigation of their effectiveness in reducing the infection transmission probability.

Indoor airborne disinfection with electrostatic disinfector (ESD): Numerical simulations of ESD performance and reduction of computing time.

Airborne transmissions of infectious disease (e.g. SARS-CoV-2) in indoor environments may induce serious threat to public health. Air purification devices are necessary to remove and/or inactivate airborne biological species from indoor air environment. Corona discharge in an electrostatic precipitator is capable of removing particulate matter and disinfecting biological aerosols to act as electrostatic disinfector (ESD). The ions generated by ESD can effectively inactivate bacteria/viruses. However, the available research rarely investigated disinfection effect of ESD, and it is urgent to develop quantitative ESD design methods for building mechanical ventilation applications. This study developed an integrated numerical model to simulate disinfection performance of ESD. The numerical model considers the ionized electric field, electrohydrodynamic flow, and biological disinfection. The model prediction was validated with the experimental data (E. coli): Good agreement was observed. The validated model then was used to study the influences of essential design parameters (e.g. voltage, inlet velocity) of ESD on disinfection efficiency. The effects of modeling of electrophoretic force and EHD (electrohydrodynamic) flow patterns on disinfection efficiency and computing time were also analyzed. The disinfection efficiency of well-designed ESD (with space charge density of 3.6 × 10-06 C/m3) could be as high as 100%. Compared with HEPA, ESD could save 99% of energy consumed by HEPA without sacrificing disinfection efficiency.

Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission.

Ventilation plays an important role in prevention and control of COVID-19 in enclosed indoor environment and specially in high-occupant-density indoor environments (e.g., underground space buildings, conference room, etc.). Thus, higher ventilation rates are recommended to minimize the infection transmission probability, but this may result in higher energy consumption and cost. This paper proposes a smart low-cost ventilation control strategy based on occupant-density-detection algorithm with consideration of both infection prevention and energy efficiency. The ventilation rate can be automatically adjusted between the demand-controlled mode and anti-infection mode with a self-developed low-cost hardware prototype. YOLO (You Only Look Once) algorithm was applied for occupancy detection based on video frames from surveillance cameras. Case studies show that, compared with a traditional ventilation mode (with 15% fixed fresh air ratio), the proposed ventilation control strategy can achieve 11.7% energy saving while lowering the infection probability to 2%. The developed ventilation control strategy provides a feasible and promising solution to prevent transmission of infection diseases (e.g., COVID-19) in public and private buildings, and also help to achieve a healthy yet sustainable indoor environment.