RESUMEN
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.
RESUMEN
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 system 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 number 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% with number of negative ion generators was 10. This work can promote intelligent operation, maintenance, and control systems considering infection prevention and energy sustainability.
RESUMEN
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.
RESUMEN
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.
RESUMEN
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.
RESUMEN
Mental health literacy (MHL) plays an important role in public health. Improving MHL can promote mental health at the individual and public levels. To date, no published studies have assessed the effectiveness of MHL curriculum interventions among undergraduate public health students. The participants in this study were undergraduate public health students (n = 48) who were enrolled in an 18-week MHL curriculum for 100 min per week. MHL was assessed using the Mental Health Literacy Scale for Healthcare Students. A paired sample t-test was performed to examine the immediate and delayed effects of the MHL curriculum. The total MHL score significantly improved, and a moderate effect size was found directly after the intervention and six weeks later. There were significant differences in the recognition of mental illness (p < 0.01), help-seeking efficacy (p < 0.05), and help-seeking attitude (p < 0.05) in the five components of MHL between pre- and post-test. Furthermore, significant improvements were obtained for the maintenance of positive mental health (p < 0.05) and reduction of mental illness stigma (p < 0.001) between the pre-test and follow-up. Our findings provide evidence for the development and implementation of an MHL curriculum for public health education.
RESUMEN
During the normalization phase of the COVID-19 epidemic, society has gradually reverted to using building space, especially for public buildings, e.g., offices. Prevention of airborne pollutants has emerged as a major challenge. Ventilation strategies can contribute to mitigating the spread of airborne disease in an indoor environment, including increasing supply air rate, modifying ventilation mode, etc. The larger ventilation rate can inevitably lead to high energy consumption, which may be also ineffective in reducing infection risk. As a critical factor affecting the spread of viral contaminant, the potential of ventilation modes for control of COVID-19 should be explored. This study compared several ventilation strategies in the office, including mixing ventilation (MV), zone ventilation (ZV), stratum ventilation (SV) and displacement ventilation (DV), through analyzing ventilation performance and infection risk for the optimal one. By using ANSYS Fluent, the distributions of airflow and pollutant were simulated under various ventilation modes and infected occupants. The SV showed greater performance in mitigating infection disease spread than MV, ZV and DV, with an air distribution performance index (ADPI) of 90.5% and minimum infection risk of 13%. This work can provide a reference for development of ventilation strategies in public space oriented the prevention of COVID-19.
RESUMEN
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.