Association of the infection probability of COVID-19 with ventilation rates in confined spaces

A COVID-19 cluster analysis in an office: Assessing the long-range aerosol and fomite transmissions with infection control measures

Simulated exposure to severe acute respiratory syndrome coronavirus 2 in the environment was demonstrated based on the actual coronavirus disease 2019 cluster occurrence in an office, with a projected risk considering the likely transmission pathways via aerosols and fomites. A total of 35/85 occupants were infected, with the attack rate in the first stage as 0.30. It was inferred that the aerosol transmission at long-range produced the cluster at virus concentration in the saliva of the infected cases on the basis of the simulation, more than 108 PFU mL-1. Additionally, all wearing masks effectiveness was estimated to be 61%-81% and 88%-95% reduction in risk for long-range aerosol transmission in the normal and fit state of the masks, respectively, and a 99.8% or above decline in risk of fomite transmission. The ventilation effectiveness for long-range aerosol transmission was also calculated to be 12%-29% and 36%-66% reductions with increases from one air change per hour (ACH) to two ACH and six ACH, respectively. Furthermore, the virus concentration reduction in the saliva to 1/3 corresponded to the risk reduction for long-range aerosol transmission by 60%-64% and 40%-51% with and without masks, respectively.

Viral infection transmission and indoor air quality: A systematic review

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

The Wells-Riley model revisited: Randomness, heterogeneity, and transient behaviours

The Wells-Riley model has been widely used to estimate airborne infection risk, typically from a deterministic point of view (i.e., focusing on the average number of infections) or in terms of a per capita probability of infection. Some of its main limitations relate to considering well-mixed air, steady-state concentration of pathogen in the air, a particular amount of time for the indoor interaction, and that all individuals are homogeneous and behave equally. Here, we revisit the Wells-Riley model, providing a mathematical formalism for its stochastic version, where the number of infected individuals follows a Binomial distribution. Then, we extend the Wells-Riley methodology to consider transient behaviours, randomness, and population heterogeneity. In particular, we provide analytical solutions for the number of infections and the per capita probability of infection when: (i) susceptible individuals remain in the room after the infector leaves, (ii) the duration of the indoor interaction is random/unknown, and (iii) infectors have heterogeneous quanta production rates (or the quanta production rate of the infector is random/unknown). We illustrate the applicability of our new formulations through two case studies: infection risk due to an infectious healthcare worker (HCW) visiting a patient, and exposure during lunch for uncertain meal times in different dining settings. Our results highlight that infection risk to a susceptible who remains in the space after the infector leaves can be nonnegligible, and highlight the importance of incorporating uncertainty in the duration of the indoor interaction and the infectivity of the infector when estimating risk.

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.

Indoor Air Quality and COVID-19: A Scoping Review

Objectives: The COVID-19 pandemic has been a major public health concern for the past 3 years. Scientific evidence on the relationship between SARS-CoV-2 infection and indoor air quality still needs to be demonstrated. This scoping review aims to study the association between air quality indoors and COVID-19. Methods: A scoping review analyzing the association between indoor air quality and epidemiological outcomes was conducted. Papers published between 1 January 2020 and 31 October 2022 were included. Hospital settings were excluded from the study. Results: Eight relevant articles met the inclusion criteria. Indoor settings included workplaces, schools, restaurants, and public transport. Types of ventilation used to improve indoor air quality were dilution methods (opening windows) and mechanical systems with or without filtration or purifier. CO2 sensors were employed in one study. All the studies showed a positive association between indoor air quality and its improvement and epidemiological indicators. Conclusion: The findings of this scoping review indicate that indoor air quality, which can be improved with ventilation methods, may reduce the risk of developing COVID-19. Ventilation could thus be viewed as a possible effective mitigating method.

Measuring the effects of respiratory protective equipment and other protectors in preventing the scattering of vocalization droplets

This study was conducted to quantitatively examine the effects of respiratory protective equipment (respirators) and various other types of protectors in preventing the scattering of vocalization droplets. Each of 12 adult male volunteers was asked to vocalize intermittently for 1 min at a target intensity of approximately 100 dBA in an experimental room adjusted to a humidity of approximately 60-70%. The subjects vocalized while wearing respirators, other types of protectors, or no protectors at all. The droplet concentration in a particle size range of 0.3 to 10 μm was measured under each experimental condition, and the transmitted particle concentration and penetration were calculated. The concentration and penetration of particles transmitted from the respirators were lower than those transmitted from the other protectors examined. The probability of infection reduction through the use of the protectors was estimated from the data obtained on the effectiveness of the protectors in preventing the scattering of droplets. We concluded that there is no need for additional droplet scattering prevention in various work settings when appropriate respirators are used under optimal conditions.

Optimizing indoor air quality: CFD simulation and novel air cleaning methods for effective aerosol particle inhibition in public spaces

In contemporary building ventilation, displacement and mixing ventilation demand high air volumes for rapid virus elimination, resulting in elevated energy consumption. To minimize the spread of viruses and decrease energy consumption for ventilation, this study employed CFD to explore the efficacy of a downward uniform flow field in impeding the transmission of aerosol particles in a high-traffic public facility, like a supermarket. The findings indicate that the downward uniform flow field proves insufficient when individuals remain static for extended periods. A wind speed of 0.1 m/s or higher becomes essential to overpower the stationary thermal plume, which disrupts this flow field. In areas with human presence, however, this technique is found to be particularly efficient since mobile heat sources do not generate a fixed thermal plume. A 0.05 m/s downward uniform flow field can settle 90% of particles within just 22 s. This flow pattern contributes to the swift settling of aerosol particles and effectively diminishes their dispersion. Employing this flow pattern in public places with increased foot traffic, like supermarkets, can lower the risk of contracting novel coronavirus without augmenting energy consumption. In order to implement the flow field in a part of the domain, a new air purification device is proposed in this study. The device combined with shelves can optimize the flow field uniformity through the MLA (PSO-SVR) algorithm and alteration of the air distribution structure. The uniformity of the final flow field increased to 0.925. The combination of data-driven MLA with CFD showed good performance in predicting the flow field uniformity. These findings offer valuable insights and practical applications for the prevention and control of respiratory diseases, particularly in post-epidemic scenarios.

Evaluation of intervention measures in reducing the driver's exposure to respiratory particles in a taxi with infected passengers

In the fifth wave of the COVID-19 epidemic in Hong Kong in early 2022, the large number of infected persons caused a shortage of ambulances and transportation vehicles operated by the government. To solve the problem, taxi drivers were recruited to transport infected persons to hospitals in their taxis. However, many of the drivers were infected after they began to participate in the plan. To tackle this issue, the present study numerically evaluated the effectiveness of several intervention measures in reducing the infection risk for taxi drivers. First, experiments were conducted inside a car to validate the large-eddy simulation (LES)-Lagrangian model for simulation of particle transport in a car. The validated model was then applied to calculate the particle dispersion and deposition in a Hong Kong taxi with intervention measures that included opening windows, installing partitions, and using a far-UVC lamp. The results show that opening the windows can significantly reduce the driver's total exposure by 97.4 %. Installing partitions and using a far-UVC lamp can further reduce the infection risk of driver by 55.9 % and 32.1 %, respectively. The results of this study can be used to support the implementation of effective intervention measures to protect taxi drivers from infection.

Optimization of cabin seating arrangement strategies based on the Wells-Riley risk theory

Civil aviation transport is an important source of global respiratory disease spread due to the closely-spaced environment. In order to reduce the probability of infection of passengers, an improved Wells-Riley model for cabin passenger risk assessment have been given in this work, the cabin ventilation and passenger nose and mouth orientation were considered. The model's effectiveness has been verified with published data. Finally, how the load factor and use of an empty seat scheme are associated with the number of infected people was assessed. The results demonstrated that the number of infected people positively correlates with the passenger load factor, and the most suitable load factor can be determined by controlling the final number of infected people with the condition of the epidemic situation in the departure city. Additionally, infection risk was found to be lower among passengers in window seats than in those in aisle seats and middle seats, and keeping empty seats in the middle or aisle could reduce the cabin average probability of infection by up to 37.47%. Using the model developed here, airlines can determine the optimal load factor threshold and seating arrangement strategy to improve economic benefits and reduce the probability of passenger infection.

Facility design and worker justice: COVID-19 transmission in meatpacking plants

BACKGROUND

Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output.

METHODS

Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions.

RESULTS

Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space.

CONCLUSION

Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO₂ concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Role of face masks and ventilation rates in mitigating respiratory disease transmission in ICU

Indoor environments are major contributing locations where the respiratory virus transmission occurs. Higher air change rate (ACH) values (up to 12) have been recommended in hospital environments to reduce virus transmission. In the present study, the Large Eddy Simulation (LES) data of particle transport in a typical intensive care unit (ICU) is used to calculate the infection risk in close proximity interaction. Three different ACH (6, 9, 12) rates with face masks and one case with a healthy person wearing a face shield are considered. The average resident time of the droplets in the ICU is calculated to find the optimal ACH rate. Of the different types of masks analyzed in the present study, the triple-layer mask has shown the most resistance ([Formula: see text] probability of infection) to the penetration of virus-laden droplets, while the single-layer mask has shown the highest risk of infection (up to [Formula: see text]. The results show that the ACH rate has little effect on close proximity transmission. The ACH 9 case provided optimal value for the particle removal, while the ACH 12 has inferior performance to that of ACH 9. From an energy consumption view, our results recommend not using higher ACH in similar indoor environments. Inside indoor environments, it is advised to wear a three-layer face mask and face shield to reduce the risk of infection.

SARS-CoV-2 airborne infection probability estimated by using indoor carbon dioxide

Airborne transmission is one of the main routes of SARS-CoV-2 spread. It is important to determine the circumstances under which the risk of airborne transmission is increased as well as the effective strategy to reduce such risk. This study aimed to develop a modified version of the Wells-Riley model with indoor CO₂ to estimate the probability of airborne transmission of SARS-CoV-2 Omicron strains with a CO₂ monitor and to evaluate the validity of this model in actual clinical practices. We used the model in three suspected cases of airborne transmission presented to our hospital to confirm its validity. Next, we estimated the required indoor CO₂ concentration at which R₀ does not exceed 1 based on the model. The estimated R₀ (R₀, basic reproduction number) based on the model in each case were 3.19 in three out of five infected patients in an outpatient room, 2.00 in two out of three infected patients in the ward, and 0.191 in none of the five infected patients in another outpatient room. This indicated that our model can estimate R₀ with an acceptable accuracy. In a typical outpatient setting, the required indoor CO₂ concentration at which R₀ does not exceed 1 is below 620 ppm with no mask, 1000 ppm with a surgical mask and 16000 ppm with an N95 mask. In a typical inpatient setting, on the other hand, the required indoor CO₂ concentration is below 540 ppm with no mask, 770 ppm with a surgical mask, and 8200 ppm with an N95 mask. These findings facilitate the establishment of a strategy for preventing airborne transmission in hospitals. This study is unique in that it suggests the development of an airborne transmission model with indoor CO₂ and application of the model to actual clinical practice. Organizations and individuals can efficiently recognize the risk of SARS-CoV-2 airborne transmission in a room and thus take preventive measures such as maintaining good ventilation, wearing masks, or shortening the exposure time to an infected individual by simply using a CO₂ monitor.

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.

CO2 concentration as an indicator of indoor ventilation performance to control airborne transmission of SARS-CoV-2

BACKGROUND

The Wells-Riley equation has been extensively used to quantify the infection risk of airborne transmission indoors. This equation is difficult to apply to actual conditions because it requires measurement of the outdoor air supply rate, which vary with time and are difficult to quantify. The method of determining the fraction of inhaled air that has been exhaled previously by someone in a building using a CO₂ concentration measurement can solve the limitations of the existing method. Using this method, the indoor CO₂ concentration threshold can be determined to keep the risk of infection below certain conditions.

METHODS

Based on the calculation of the rebreathed fraction, an appropriate mean indoor CO₂ concentration and required air exchange rate to control SARS-CoV-2 airborne transmission was calculated. The number of indoor occupants, ventilation rate, and the deposition and inactivation rates of the virus-laden aerosols were considered. The application of the proposed indoor CO₂ concentration-based infection rate control was investigated through case studies in school classrooms and restaurants.

RESULTS

In a typical school classroom environment with 20-25 occupants and an exposure time of 6-8 h, the average indoor CO₂ concentration should be kept below 700 ppm to control the risk of airborne infection indoors. The ASHRAE recommended ventilation rate is sufficient when wearing a mask in classrooms. For a typical restaurant with 50-100 occupants and an exposure time of 2-3 h, the average indoor CO₂ concentration should be kept below about 900 ppm. Residence time in the restaurant had a significant effect on the acceptable CO₂ concentration.

CONCLUSION

Given the conditions of the occupancy environment, it is possible to determine an indoor CO₂ concentration threshold, and keeping the CO₂ concentration lower than a certain threshold could help reduce the risk of COVID-19 infection.

Animal experiments on respiratory viruses and analogous studies of infection factors for interpersonal transmission

Air pollution caused by SARS-CoV-2 and other viruses in human settlements will have a great impact on human health, but also a great risk of transmission. The transmission power of the virus can be represented by quanta number in the Wells-Riley model. In order to solve the problem of different dynamic transmission scenarios, only a single influencing factor is considered when predicting the infection rate, which leads to large differences in quanta calculated in the same space. In this paper, an analog model is established to define the indoor air cleaning index RL and the space ratio parameter. Based on infection data analysis and rule summary in animal experiments, factors affecting quanta in interpersonal communication were explored. Finally, by analogy, the factors affecting person-to-person transmission mainly include viral load of infected person, distance between individuals, etc., the more severe the symptoms, the closer the number of days of illness to the peak, and the closer the distance to the quanta. In summary, there are many factors that affect the infection rate of susceptible people in the human settlement environment. This study provides reference indicators for environmental governance under the COVID-19 epidemic, provides reference opinions for healthy interpersonal communication and human behavior, and provides some reference for accurately judging the trend of epidemic spread and responding to the epidemic.

Safe CO2 threshold limits for indoor long-range airborne transmission control of COVID-19

CO₂-based infection risk monitoring is highly recommended during the current COVID-19 pandemic. However, the CO₂ monitoring thresholds proposed in the literature are mainly for spaces with fixed occupants. Determining CO₂ threshold is challenging in spaces with changing occupancy due to the co-existence of quanta and CO₂ remaining from previous occupants. Here, we propose a new calculation framework for deriving safe excess CO₂ thresholds (above outdoor level), C _t, for various spaces with fixed/changing occupancy and analyze the uncertainty involved. We categorized common indoor spaces into three scenarios based on their occupancy conditions, e.g., fixed or varying infection ratios (infectors/occupants). We proved that the rebreathed fraction-based model can be applied directly for deriving C _t in the case of a fixed infection ratio (Scenario 1 and Scenario 2). In the case of varying infection ratios (Scenario 3), C _t derivation must follow the general calculation framework due to the existence of initial quanta/excess CO₂. Otherwise, C _t can be significantly biased (e.g., 260 ppm) when the infection ratio varies greatly. C _t can vary significantly based on specific space factors such as occupant number, physical activity, and community prevalence, e.g., 7 ppm for gym and 890 ppm for lecture hall, indicating C _t must be determined on a case-by-case basis. An uncertainty of up to 6 orders of magnitude for C _t was found for all cases due to uncertainty in emissions of quanta and CO₂, thus emphasizing the role of accurate emissions data in determining C _t.

Evaluation of infection probability of Covid-19 in different types of airliner cabins

According to the World Health Organization (https://covid19.who.int/), more than 651 million people have been infected by COVID-19, and more than 6.6 million of them have died. COVID-19 has spread to almost every country in the world because of air travel. Cases of COVID-19 transmission from an index patient to fellow passengers in commercial airplanes have been widely reported. This investigation used computational fluid dynamics (CFD) to simulate airflow and COVID-19 virus (SARS-CoV-2) transport in a variety of airliner cabins. The cabins studied were economy-class with 2-2, 3-3, 2-3-2, and 3-3-3 seat configurations, respectively. The CFD results were validated by using experimental data from a seven-row cabin mockup with a 3-3 seat configuration. This study used the Wells-Riley model to estimate the probability of infection with SARS-CoV-2. The results show that CFD can predict airflow and virus transmission with acceptable accuracy. With an assumed flight time of 4 h, the infection probability was almost the same among the different cabins, except that the 3-3-3 configuration had a lower risk because of its airflow pattern. Flying time was the most important parameter for causing the infection, while cabin type also played a role. Without mask wearing by the passengers and the index patient, the infection probability could be 8% for a 10-h, long-haul flight, such as a twin-aisle air cabin with 3-3-3 seat configuration.

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO₂, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO₂ and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO₂ concentrations, with 22.11% and 21.27% of the total time exceeding 1000 ppm in spring and autumn respectively. In-cabin PM_2.5 mass concentration exceeded 35 μm/m³ by 57.35% and 86.42% in spring and autumn, respectively. CO₂ concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM_2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM_2.5 during a one-way trip in autumn was up to 43.13 μg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Experimental study on the control effects of different strategies on particle transportation in a conference room: Mechanical ventilation, baffle, portable air cleaner, and desk air cleaner

To control the spread and transmission of airborne particles (especially SARS-CoV-2 coronavirus, recently) in the indoor environment, many control strategies have been employed. Comparisons of these strategies enable a reasonable choice for indoor environment control and cost-effectiveness. In this study, a series of experiments were conducted in a full-scale chamber to simulate a conference room. The control effects of four different strategies (a ventilation system (320 m³/h) with and without a baffle, a specific type of portable air cleaner (400 m³/h) and a specific type of desk air cleaner (DAC, 160 m³/h)) on the transportation of particles of different sizes were studied. In addition, the effects of coupling the ventilation strategies with five forms of indoor airflow organization (side supply and side or ceiling return, ceiling supply and ceiling or side return, floor supply and ceiling return) were evaluated. The cumulative exposure level (CEL) and infection probability were selected as evaluation indexes. The experimental results showed that among the four strategies, the best particle control effect was achieved by the PAC. The reduction in CEL for particles in the overall size range was 22.1% under the ventilation system without a baffle, 34.3% under the ventilation system with a baffle, 46.4% with the PAC, and 10.1% with the DAC. The average infection probabilities under the four control strategies were 11.3-11.8%, 11.1-11.8%, 9.1-9.5%, and 18.2-19.7%, respectively. Among the five different forms of airflow organization, the floor supply and ceiling return mode exhibited the best potential ability to remove particles.

A cost-effectiveness assessment of the operational parameters of central HVAC systems during pandemics

The present study develops a cost-effectiveness assessment model to analyze the performance of major operational parameters of central HVAC systems in terms of airborne transmission risk, energy consumption, and medical and social cost. A typical multi-zone building model with a central HVAC system is built numerically, and the effect of outdoor air (OA) ratio (from 30% to 100%) and filtration level (MERV 13, MERV 16, and HEPA) are assessed under the conditions of five climate zones in China. Compared with the baseline case with 30% OA and MERV 13 filtration, the airborne transmission risk in zones without infector is negligibly reduced with the increase in OA ratio and the upgrade of filtration level, owing to their slight modification on the equivalent ventilation rate of virus-free air. However, depending on climate zone, a 10% increase in OA ratio results in 12.5%-78.6% and 0.1%-8.6% increase in heating and cooling energy consumption, respectively, while an upgrade of filtration level to MERV 16 and HEPA results in an increase of 0.08%-0.2% and 1.4%-2.6%, respectively. Overall, when compared to the use of 100% OA ratio and HEPA filtration, the application of 30% or 40% OA ratio and MERV 13 filtration would save annually an energy and facility related cost of $29.4 billion in China, though giving an increase of approximately $0.1 billion on medical and social cost from the increased number of confirmed cases. This study provides basic method and information for the formulation of cost-effective operational strategies of HVAC systems coping with the airborne transmission, especially in resource-limited regions.

CFD Simulations of Ventilation and Interunit Dispersion in Dormitory Complex: A Case Study of Epidemic Outbreak in Shanghai

Since the beginning of March 2022, a new round of COVID-19 outbreaks in Shanghai has led to a sharp increase in the number of infected people. It is important to identify possible pollutant transmission routes and predict potential infection risks for infectious diseases. Therefore, this study investigated the cross-diffusion of pollutants caused by natural ventilation, including external windows and indoor ventilation windows, under three wind directions in a densely populated building environment with the CFD method. In this study, CFD building models were developed based on an actual dormitory complex and surrounding buildings under realistic wind conditions to reproduce the airflow fields and transmission paths of pollutants. This paper adopted the Wells-Riley model to assess the risk of cross-infection. The biggest risk of infection was when a source room was located on the windward side, and the risk of infection in other rooms on the same side as the source room was large in the windward direction. When pollutants were released from room 8, north wind resulted in the highest concentration of pollutants in room 28, reaching 37.8%. This paper summarizes the transmission risks related to the indoor and outdoor environments of compact buildings.

Measurement and rapid assessment of indoor air quality at mass gathering events to assess ventilation performance and reduce aerosol transmission of SARS-CoV-2

To assess risk factors for COVID-19 transmission and address the closure of mass gathering events since March 2020, the UK Government ran the Events Research Programme (ERP), following which it reopened live events in sports, music, and culture in July 2021. We report the rapid post-occupancy evaluation of Indoor Air Quality (IAQ) and associated long-range airborne transmission risk conducted in the Environmental Study of the ERP. Ten large venues around the UK were monitored with CO2 sensors at a high spatial and temporal resolution during 90 events. An IAQ Index based on CO2 concentration was developed, and all monitored spaces were classified in bands from A to G based on their average and maximum CO2 concentrations from all events. High resolution monitoring and the IAQ Index depicted the overall state of ventilation at live events, and allowed identification of issues with ventilation effectiveness and distribution, and of spaces with poor ventilation and the settings in which long-range airborne transmission risk may be increased. In numerous settings, CO2 concentrations were found to follow patterns relating to event management and specific occupancy of spaces around the venues. Good ventilation was observed in 90% of spaces monitored for given occupancies. Practical applications: High-resolution monitoring of indoor CO2 concentrations is necessary to detect the spatial variation of indoor air quality (IAQ) in large mass gathering event venues. The paper summarises COVID-19 ventilation guidance for buildings and defines a methodology for measurement and rapid assessment of IAQ during occupancy at live events that can be implemented by venue managers. Comparisons of the CO2 concentrations measured during the events identified the spaces at high risk of long-range transmission of airborne pathogens. Building operators should be mindful of the ventilation strategies used relative to the total occupancy in different spaces and the occupant's activities.

Control measure implications of COVID-19 infection in healthcare facilities reconsidered from human physiological and engineering aspects

The Wells-Riley model invokes human physiological and engineering parameters to successfully treat airborne transmission of infectious diseases. Applications of this model would have high potentiality on evaluating policy actions and interventions intended to improve public safety efforts on preventing the spread of COVID-19 in an enclosed space. Here, we constructed the interaction relationships among basic reproduction number (R0) - exposure time - indoor population number by using the Wells-Riley model to provide a robust means to assist in planning containment efforts. We quantified SARS-CoV-2 changes in a case study of two Wuhan (Fangcang and Renmin) hospitals. We conducted similar approach to develop control measures in various hospital functional units by taking all accountable factors. We showed that inhalation rates of individuals proved crucial for influencing the transmissibility of SARS-CoV-2, followed by air supply rate and exposure time. We suggest a minimum air change per hour (ACH) of 7 h-1 would be at least appropriate with current room volume requirements in healthcare buildings when indoor population number is < 10 and exposure time is < 1 h with one infector and low activity levels being considered. However, higher ACH (> 16 h-1) with optimal arranged-exposure time/people and high-efficiency air filters would be suggested if more infectors or higher activity levels are presented. Our models lay out a practical metric for evaluating the efficacy of control measures on COVID-19 infection in built environments. Our case studies further indicate that the Wells-Riley model provides a predictive and mechanistic basis for empirical COVID-19 impact reduction planning and gives a framework to treat highly transmissible but mechanically heterogeneous airborne SARS-CoV-2.

Systematic Review of the Key Factors Influencing the Indoor Airborne Spread of SARS-CoV-2

The COVID-19 pandemic due to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been plaguing the world since late 2019/early 2020 and has changed the way we function as a society, halting both economic and social activities worldwide. Classrooms, offices, restaurants, public transport, and other enclosed spaces that typically gather large groups of people indoors, and are considered focal points for the spread of the virus. For society to be able to go “back to normal”, it is crucial to keep these places open and functioning. An understanding of the transmission modes occurring in these contexts is essential to set up effective infection control strategies. This understanding was made using a systematic review, according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PRISMA) 2020 guidelines. We analyze the different parameters influencing airborne transmission indoors, the mathematical models proposed to understand it, and discuss how we can act on these parameters. Methods to judge infection risks through the analysis of the indoor air quality are described. Various mitigation measures are listed, and their efficiency, feasibility, and acceptability are ranked by a panel of experts in the field. Thus, effective ventilation procedures controlled by CO2-monitoring, continued mask wearing, and a strategic control of room occupancy, among other measures, are put forth to enable a safe return to these essential places.

The Influence of Ventilation Measures on the Airborne Risk of Infection in Schools: A Scoping Review

OBJECTIVES

To review the risk of airborne infections in schools and evaluate the effect of intervention measures reported in field studies.

BACKGROUND

Schools are part of a country's critical infrastructure. Good infection prevention measures are essential for reducing the risk of infection in schools as much as possible, since these are places where many individuals spend a great deal of time together every weekday in a small area where airborne pathogens can spread quickly. Appropriate ventilation can reduce the indoor concentration of airborne pathogens and reduce the risk of infection.

METHODS

A systematic search of the literature was conducted in the databases Embase, MEDLINE, and ScienceDirect using keywords such as school, classroom, ventilation, carbon dioxide (CO2) concentration, SARS-CoV-2, and airborne transmission. The primary endpoint of the studies selected was the risk of airborne infection or CO2 concentration as a surrogate parameter. Studies were grouped according to the study type.

RESULTS

We identified 30 studies that met the inclusion criteria, six of them intervention studies. When specific ventilation strategies were lacking in schools being investigated, CO2 concentrations were often above the recommended maximum values. Improving ventilation lowered the CO2 concentration, resulting in a lower risk of airborne infections.

CONCLUSIONS

The ventilation in many schools is not adequate to guarantee good indoor air quality. Ventilation is an important measure for reducing the risk of airborne infections in schools. The most important effect is to reduce the time of residence of pathogens in the classrooms.

The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events

This study aimed to analyze the impact of hosting large events on the spread of pandemics, taking Tokyo Olympics 2020 as a case study. A risk assessment method for the whole organization process was established, which could be used to evaluate the effectiveness of various risk mitigation measures. Different scenarios for Games participants and Japanese residents during the Tokyo Olympics were designed based on the infection control protocols proposed by the Olympic Committee and local governments. A modified Wells-Riley model considering the influence of social distance, masking and vaccination, and an SIQRV model that introduced the effect of quarantine and vaccination strategies on the pandemic spread were developed in this study. Based on the two models, our predicted results of daily confirmed cases and cumulative cases were obtained and compared with reported data, where good agreement was achieved. The results show that the two core infection control strategies of the bubble scheme and frequent testing scheme curbed the spread of the COVID-19 pandemic during the Tokyo Olympics. Among Games participants, Japanese local staff accounted for more than 60% of the total in positive cases due to their large population and most relaxed travel restrictions. The surge in positive cases was mainly attributed to the high transmission rate of the Delta variant and the low level of immunization in Japan. Based on our simulation results, the risk management flaws for the Tokyo Olympics were identified and improvement measures were investigated. Moreover, a further analysis was carried out on the impact of different preventive measures with respect to minimizing the transmission of new variants with higher transmissibility. Overall, the findings in this study can help policymakers to design scientifically based and practical countermeasures to cope with pandemics during the hosting of large events.

New dose-response model and SARS-CoV-2 quanta emission rates for calculating the long-range airborne infection risk

Predictive models for airborne infection risk have been extensively used during the pandemic, but there is yet still no consensus on a common approach, which may create misinterpretation of results among public health experts and engineers designing building ventilation. In this study we applied the latest data on viral load, aerosol droplet sizes and removal mechanisms to improve the Wells Riley model by introducing the following novelties i) a new model to calculate the total volume of respiratory fluid exhaled per unit time ii) developing a novel viral dose-based generation rate model for dehydrated droplets after expiration iii) deriving a novel quanta-RNA relationship for various strains of SARS-CoV-2 iv) proposing a method to account for the incomplete mixing conditions. These new approaches considerably changed previous estimates and allowed to determine more accurate average quanta emission rates including omicron variant. These quanta values for the original strain of 0.13 and 3.8 quanta/h for breathing and speaking and the virus variant multipliers may be used for simple hand calculations of probability of infection or with developed model operating with six size ranges of aerosol droplets to calculate the effect of ventilation and other removal mechanisms. The model developed is made available as an open-source tool.

Multi-objective optimization of mechanical ventilation with the aid of purifiers in two scenarios: Regular operation and mitigating the spread of respiratory infectious diseases

COVID-19 and its impact on society have raised concerns about scaling up mechanical ventilation (MV) systems and the energy consequences. This paper attempted to combine MV and portable air cleaners (PACs) to achieve acceptable indoor air quality (IAQ) and energy reduction in two scenarios: regular operation and mitigating the spread of respiratory infectious diseases (RIDs). We proposed a multi-objective optimization method that combined the NSGA-II and TOPSIS techniques to determine the total equivalent ventilation rate of the MV-PAC system in both scenarios. The concentrations of PM_2.5 and CO₂ were primary indicators for IAQ. The modified Wells-Riley equation was adopted to predict RID transmissions. An open office with an MV-PAC system was used to demonstrate the method's applicability. Meanwhile, a field study was conducted to validate the method and evaluate occupants' perceptions of the MV-PAC system. Results showed that optimal solutions of the combined system can be obtained based on various IAQ requirements, seasons, outdoor conditions, etc. For regular operation, PACs were generally prioritized to maintain IAQ while reducing energy consumption even when outdoor PM_2.5 concentration was high. MV can remain constant or be reduced at low occupancies. In RID scenarios, it is possible to mitigate transmissions when the quanta were < 48 h^-1. No significant difference was found in the subjective perception of the MV and PACs. Moreover, the effects of infiltration on the optimal solution can be substantial. Nonetheless, our results suggested that an MV-PAC system can replace the MV system for offices for daily use and RID mitigation.

Electronic Supplementary Material ESM

The Appendix is available in the online version of this article at 10.1007/s12273-023-0999-z.

Association between the infection probability of COVID-19 and ventilation rates: An update for SARS-CoV-2 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current coronavirus disease 2019 (COVID-19) pandemic, is evolving. Thus, the risk of airborne transmission in confined spaces may be higher, and corresponding precautions should be re-appraised. Here, we obtained the quantum generation rate (q) value of three SARS-CoV-2 variants (Alpha, Delta, and Omicron) for the Wells-Riley equation with a reproductive number-based fitted approach and estimated the association between the infection probability and ventilation rates. The q value was 89-165 h^-1 for Alpha variant, 312-935 h^-1 for Delta variant, and 725-2,345 h^-1 for Omicron variant. The ventilation rates increased to ensure an infection probability of less than 1%, and were 8,000-14,000 m³ h^-1, 26,000-80,000 m³ h^-1, and 64,000-250,000 m³ h^-1 per infector for the Alpha, Delta, and Omicron variants, respectively. If the infector and susceptible person wore N95 masks, the required ventilation rates decreased to about 1/100 of the values required without masks, which can be achieved in most typical scenarios. An air purifier was ineffective for reducing transmission when used in scenarios without masks. Preventing prolonged exposure time in confined spaces remains critical in reducing the risk of airborne transmission for highly contagious SARS-CoV-2 variants.

Automated infection risks assessments (AIRa) for decision-making using a blockchain-based alert system: A case study in a representative building

Indoor air quality (IAQ) is an important parameter in protecting the occupants of an indoor environment. Previous studies have shown that an indoor environment with poor ventilation increases airborne virus transmission. Existing research has concluded that high ventilation rates can reduce the risk of individuals in indoor environments being infected. However, most existing ventilation systems are designed to be efficient under non-pandemic conditions. Ultimately, indoor environments will become hotspots for the transmission of airborne viruses. Current infection risk assessments can estimate virus transmission via airborne routes, but with limited information sharing among stakeholders. Our own research did not identify any systems that integrate risk assessments with smart sensors in order to support information sharing with experts in indoor environments in their decision-making process. To fill this gap, we designed a blockchain-based prototype (AIRa) that integrates CO₂ smart sensor data with infection risk assessments from a post-pandemic perspective. This system generates two types of alerts: (1) P-Alert and (2) R0-Alert for decision-making by building owners, such as increasing the ventilation rate or track and trace, as needed. AIRa shows various benefits over three existing infection-control alert systems. Our solution stores and shares information such as the timestamp and room number, instead of storing building user's personal information. Our approach does not require a QR code to be scanned or a mobile app to be downloaded in order to enable track and trace. However, AIRa is still an early prototype for evaluating the risks of airborne virus transmission in smart building environments. Multidisciplinary knowledge and technological research will be vital in formulating different alerts in the future.

Effects of mask wearing duration and relative humidity on thermal perception in the summer outdoor built environment

During the pandemic, face masks are one of the most significant self-protection necessities, but they also cause heat stress. By using the ERA5 (ECMWF Reanalysis 5th Generation) database and the local weather bureau data, the effect of mask wearing on outdoor thermal sensation has been investigated by a survey conducted in the hot summer and cold winter region of eastern China in the summer of 2020. Results show that wearing a face mask for a longer period result in a higher level of discomfort, and the primary source of discomfort is hot and stuffy feelings. The effect of relative humidity is crucial for mask wearers in warm-biased thermal environments, as mean thermal sensation vote (TSV) peaks when environmental relative humidity reaches the range of 70% to 80% and decreases after this range due to the evaporation within the microclimate created by a face mask. Meanwhile, prolonged mask wearing increases participants' hot feelings, especially in warm environments. Specifically, participants wearing face masks for less than 30 min feel hot at a physiological equivalent temperature (PET) value of 34.4 °C, but those who wear them for over 60 min express hot feelings even at a PET value of 24.7 °C. The participants who wear a face mask while walking slowly outdoors have similar thermal sensations to those who do not wear a mask, but are in a higher activity level. The findings demonstrate that mask wearing has a crucial impact on outdoor thermal comfort assessment in a warm-biased outdoor thermal environment.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

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.

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control.

Reducing the spread of COVID-19 transmission through analysis of the evolving building ventilation systems guidance

Purpose

The purpose of this study was to examine how data from the World Health Organization, United States Environmental Protection Agency and Center for Disease Control have evolved with relation to engineering controls for heating, ventilation and air-conditioning (HVAC) systems to mitigate the spread of spread of aerosols (specifically related to the COVID-19 pandemic) in occupied buildings.

Design/methodology/approach

A document analysis of the pandemic-focused position documents from the aforementioned public health agencies and national HVAC authorities was performed. This review targeted a range of evidence from recommendations, best practices, codes and regulations and peer-reviewed publications and evaluated how they cumulatively evolved over time. Data was compared between 2020 and 2021.

Findings

This research found that core information provided early in the pandemic (i.e. early 2020) for engineering controls in building HVAC systems did not vary greatly as knowledge of the pandemic evolved (i.e. in June of 2021). This indicates that regulating agencies had a good, early understanding of how airborne viruses spread through building ventilation systems. The largest evolution in knowledge came from the broader acceptance of building ventilation as a transmission route and the increase in publications and ease of access to the information for the general public over time.

Originality/value

The promotion of the proposed controls for ventilation in buildings, as outlined in this paper, is another step toward reducing the spread of COVID-19 and future aerosol spread viruses by means of ventilation.

Occupancy-aided ventilation for airborne infection risk control: Continuously or intermittently reduced occupancies?

Ventilation is an important engineering measure to control the airborne infection risk of acute respiratory diseases, e.g., Corona Virus Disease 2019 (COVID-19). Occupancy-aided ventilation methods can effectively improve the airborne infection risk control performance with a sacrifice of decreasing working productivity because of the reduced occupancy. This study evaluates the effectiveness of two occupancy-aided ventilation methods, i.e., the continuously reduced occupancy method and the intermittently reduced occupancy method. The continuously reduced occupancy method is determined by the steady equation of the mass conservation law of the indoor contaminant, and the intermittently reduced occupancy method is determined by a genetic algorithm-based optimization. A two-scenarios-based evaluation framework is developed, i.e., one with targeted airborne infection risk control performance (indicated by the mean rebreathed fraction) and the other with targeted working productivity (indicated by the accumulated occupancy). The results show that the improvement in the airborne infection risk control performance linearly and quadratically increases with the reduction in the working productivity for the continuously reduced occupancy method and the intermittently reduced occupancy method respectively. At a given targeted airborne infection risk control performance, the intermittently reduced occupancy method outperforms the continuously reduced occupancy method by improving the working productivity by up to 92%. At a given targeted working productivity, the intermittently reduced occupancy method outperforms the continuously reduced occupancy method by improving the airborne infection risk control performance by up to 38%.

Bioaerosol distribution characteristics and potential SARS-CoV-2 infection risk in a multi-compartment dental clinic

Dental clinics have a potential risk of infection, particularly during the COVID-19 pandemic. Multi-compartment dental clinics are widely used in general hospitals and independent clinics. This study utilised computational fluid dynamics to investigate the bioaerosol distribution characteristics in a multi-compartment dental clinic through spatiotemporal distribution, working area time-varying concentrations, and key surface deposition. The infection probability of SARS-CoV-2 for the dental staff and patients was calculated using the Wells-Riley model. In addition, the accuracy of the numerical model was verified by field measurements of aerosol concentrations performed during a clinical ultrasonic scaling procedure. The results showed that bioaerosols were mainly distributed in the compartments where the patients were treated. The average infection probability was 3.8% for dental staff. The average deposition number per unit area of the treatment chair and table are 28729 pcs/m² and 7945 pcs/m², respectively, which creates a possible contact transmission risk. Moreover, there was a certain cross-infection risk in adjacent compartments, and the average infection probability for patients was 0.84%. The bioaerosol concentrations of the working area in each compartment 30 min post-treatment were reduced to 0.07% of those during treatment, and the infection probability was <0.05%. The results will contribute to an in-depth understanding of the infection risk in multi-compartment dental clinics, forming feasible suggestions for management to efficiently support epidemic prevention and control in dental clinics.

COVID-19 Contagion Risk Estimation Model for Indoor Environments

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO₂ concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.

Quantification of how mechanical ventilation influences the airborne infection risk of COVID-19 and HVAC energy consumption in office buildings

This paper presents an EnergyPlus-based parametric analysis to investigate the infection risk of Coronavirus Disease 2019 (COVID-19) under different mechanical ventilation scenarios for a typical medium-sized office building in various climate zones. A Wells-Riley (WR) based Gammaitoni-Nucci (GN) model was employed to quantitatively calculate the airborne infection risk. The selected parameters for the parametric analysis include the climate zone, outdoor air fraction, fraction of infectors, quanta generation rate, and exposure time. The loss and deposition of particles are not considered. The results suggest that the COVID-19 infection risk varies significantly with climate and season under different outdoor air fraction scenarios since the building heating and cooling load fundamentally impacts the supply airflow rate and thus directly influences the amount of mechanical ventilation, which determines the dilution ratio of contaminants. This risk assessment identified the climate zones that benefit the most and the least from increasing the outdoor air fraction. The climate zones such as 1A (Honolulu, HI), 2B (Tucson, AZ), 3A (Atlanta, GA), and 7 (International Falls, MN) are the most energy-efficient locations when it comes to increasing the outdoor air fraction to reduce the COVID-19 infection risk. In contrast, the climate zones such as 6A (Rochester, MN) and 6B (Great Falls, MT) are the least energy-efficient ones. This paper facilitates understanding a widely recommended COVID-19 risk mitigation strategy (i.e., increase the outdoor airflow rate) from the perspective of energy consumption.

Electronic Supplementary Material

Supplementary material is available for this article at 10.1007/s12273-022-0937-5 and is accessible for authorized users.

A multi-layered strategy for COVID-19 infection prophylaxis in schools: A review of the evidence for masks, distancing, and ventilation

Implications for the academic and interpersonal development of children and adolescents underpin a global political consensus to maintain in-classroom teaching during the ongoing COVID-19 pandemic. In support of this aim, the WHO and UNICEF have called for schools around the globe to be made safer from the risk of COVID-19 transmission. Detailed guidance is needed on how this goal can be successfully implemented in a wide variety of educational settings in order to effectively mitigate impacts on the health of students, staff, their families, and society. This review provides a comprehensive synthesis of current scientific evidence and emerging standards in relation to the use of layered prevention strategies (involving masks, distancing, and ventilation), setting out the basis for their implementation in the school environment. In the presence of increasingly infectious SARS-Cov-2 variants, in-classroom teaching can only be safely maintained through a layered strategy combining multiple protective measures. The precise measures that are needed at any point in time depend upon a number of dynamic factors, including the specific threat-level posed by the circulating variant, the level of community infection, and the political acceptability of the resultant risk. By consistently implementing appropriate prophylaxis measures, evidence shows that the risk of infection from in-classroom teaching can be dramatically reduced. Current studies indicate that wearing high-quality masks and regular testing are amongst the most important measures in preventing infection transmission; whilst effective natural and mechanical ventilation systems have been shown to reduce infection risks in classrooms by over 80%.

Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season

Since the coronavirus disease 2019, the extended time indoors makes people more concerned about indoor air quality, while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating, ventilation, and air-conditioning systems. In this study, to represent the dynamics of indoor temperature and air quality, a coupled grey-box model is developed. The model is identified and validated using a data-driven approach and real-time measured data of a campus office. To manage building energy usage and indoor air quality, a model predictive control strategy is proposed and developed. The simulation study demonstrated 18.92% energy saving while maintaining good indoor air quality at the testing site. Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones. The results showed 20%-40% energy saving in general while maintaining a predetermined indoor air quality setpoint. Although the infection risk is increased due to the reduced ventilation rate, it is still less than the suggested threshold (2%) in general.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

The Appendix is available in the online version of this article at 10.1007/s12273-022-0936-6.

Airborne infection risk of inter-unit dispersion through semi-shaded openings: A case study of a multi-storey building with external louvers

Building design for natural ventilation and indoor air quality have become increasingly important during the past decades. Investigating airflow routes of airborne transmission and evaluating the potential infection risk in the multi-storey building is helpful to the reduction of airborne transmission. Therefore, this study applies computational fluid dynamics simulations to investigate the inter-unit dispersion pattern of gaseous pollutant between different units through semi-shaded openings. The airflow exchange and pollutant dispersion in a multi-storey building is driven by wind-induced natural ventilation. External shading louvers, which are widely used in building facades to reduce heat gain from solar radiation, are chosen to establish the semi-shaded environment. Experimental validation is performed to make sure the accuracy of numerical settings in airflow investigation of semi-shaded openings. The airflow characteristics around semi-shaded openings is analyzed in the numerical simulations. The re-entry ratio of tracer gas and the airborne infection risk of COVID-19 is investigated in the cases with different louvers' locations and source units. The results show that the airflow is commonly slower in the semi-shaded space between louvers and openings. But the ventilation rate is not always consistent with the airflow speed because of the diversion effect from louver slats. The inter-unit infectious risk in the worst unit rises from 7.82% to 26.17% for windward shading, while it rises from 7.89% to 22.52% for leeward shading. These results are helpful to the further understanding of inter-unit transmission of infectious respiratory aerosols through external openings with complex structures.

Evaluating risk of SARS-CoV-2 infection of the elderly in the public bus under personalized air supply

In developing countries, public transportation is the first choice for the elderly because of its convenience and cheapness. The high density population of public transportation increases the risk of passengers contracting infectious diseases, so it is extremely critical to determine healthy transportation systems to safeguard the health of passengers. The propagation characteristics of droplets in the ZK-type public bus were studied by computational fluid simulation employing the Realizable k-ε turbulence model and discrete phase model. The modified Wells-Riley model was used to quantitatively assess the infection risk of SARS-CoV-2 spread by droplets on the elderly. The risk assessment shows that when the personalized air supply angle is 30°, the number of infected passengers is the least, reaching 14, which shows that the infection risk of passengers can be reduced through the design of personalized air supply angle. Regardless of the angle of the personalized air supply, the rear seats are in a low-risk area. Therefore, it's recommended that elderly passengers choose the rear seats of the public bus during the epidemic to prevent being infected. This study can provide a reference for healthy transportation systems to construct a healthy environment inside the public bus.

A post-occupancy study of ventilation effectiveness from high-resolution CO2 monitoring at live theatre events to mitigate airborne transmission of SARS-CoV-2

Mass-gathering events were closed around the world in 2020 to minimise the spread of the SARS-CoV-2 virus. Emerging research on the transmission of SARS-CoV-2 emphasised the importance of sufficient ventilation. This paper presents the results of an indoor air quality (IAQ) monitoring study over 82 events in seven mechanically ventilated auditoria to support the UK government Events Research Programme. Indoor carbon dioxide concentration was measured at high resolution before, during, and after occupancy to allow for assessment of the ventilation systems. Generally, good indoor air quality was measured in all auditoria, with average IAQ found to be excellent or very good for 70% of spaces. In some auditoria, spatial variation in IAQ was identified, indicating poor mixing of the air. In addition, surface and air samples were taken and analysed for the presence of bacteria by culture and SARS-CoV-2 using RT-qPCR in one venue. SARS-CoV-2 RNA was detected on a small number of surfaces at very low copy numbers, which are unlikely to pose an infection risk. Under the ventilation strategies and occupancy levels investigated, it is likely that most theatres pose a low risk of long-range transmission of COVID-19.

Analysis of COVID-19 clusters involving vertical transmission in residential buildings in Hong Kong

Multiple clusters of coronavirus disease 2019 (COVID-19) in Hong Kong have involved vertical transmissions in residential buildings, wherein the flats of confirmed cases were often vertically aligned. Data on the buildings and cases associated with 19 such clusters were retrieved and compared with the corresponding data on the entirety of Hong Kong. Vertical transmissions usually occurred in old high-rise buildings with small flat areas and low estate prices during winter. In addition, infection occurred frequently among the elderly and among upstairs neighbours of index cases. Virus-laden aerosols may have been transmitted between flats mostly via shared drainpipes, and the vertical distribution of the confirmed cases in a building varied by its drainage system design. For buildings with their entire drainpipes installed indoors, both the upstairs and downstairs neighbours of the index case flats could be infected. By comparison, buildings with their drainage stacks installed outdoors had lower infection risks and demonstrated a clearer pattern of vertical transmission: most infected cases resided upstairs from the index case flats, indicating that the virus spread could be dominated by the stack effect. This study provides valuable data and analysis for developing epidemic control strategies for residential buildings.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

The Appendix is available in the online version of this article at 10.1007/s12273-022-0929-5.

Comparison of Indoor Air Quality in Summer and Winter According to Four-Way Cassette Fan Coil Unit Operation in a Four-Bed Ward

This study targeted a four-bed ward with a ventilation system and a four-way cassette fan coil unit (4-way FCU) installed on the ceiling. The indoor air quality under summer and winter conditions was comparatively analyzed. The age of air was calculated by conducting tests and simulations under diverse conditions, assuming that the ventilation system and 4-way FCU were continuously operating. The use of an air cleaner and ward curtain was investigated for its impact on the air quality in the breathing zone of a patient lying on the bed, and effects of the airflow and discharge angle of the 4-way FCU were considered. Because the 4-way FCU was installed in the central part of the ceiling, where indoor air is sucked in and subsequently discharged in four directions, the age of air at each bed was found to vary depending on the airflow and discharge angle of the 4-way FCU. When the airflow and discharge angle of the 4-way FCU was fixed, the age of air at each bed appeared to be lower during winter heating than in summer cooling mode. The age of air was significantly lowered at each bed, depending on the use of the curtain and the air cleaner along with the ventilation system and 4-way FCU, and appropriate seasonal operating conditions were identified to maintain a lower age of air at each bed.

Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption

Ventilation is an effective solution for improving indoor air quality and reducing airborne transmission. Buildings need sufficient ventilation to maintain a low infection risk but also need to avoid an excessive ventilation rate, which may lead to high energy consumption. The Wells-Riley (WR) model is widely used to predict infection risk and control the ventilation rate. However, few studies compared the non-steady-state (NSS) and steady-state (SS) WR models that are used for ventilation control. To fill in this research gap, this study investigates the effects of the mechanical ventilation control strategies based on NSS/SS WR models on the required ventilation rates to prevent airborne transmission and related energy consumption. The modified NSS/SS WR models were proposed by considering many parameters that were ignored before, such as the initial quantum concentration. Based on the NSS/SS WR models, two new ventilation control strategies were proposed. A real building in Canada is used as the case study. The results indicate that under a high initial quantum concentration (e.g., 0.3 q/m³) and no protective measures, SS WR control underestimates the required ventilation rate. The ventilation energy consumption of NSS control is up to 2.5 times as high as that of the SS control.

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 (L_cool). 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.