A review of different ventilation modes on thermal comfort, air quality and virus spread control

Shape-dependent aerosol dynamics in indoor environments: Penetration, deposition, and dispersion

Particle shape exerts a significant influence on their dynamic behavior, and it is imperative to elucidate these effects given the potential for severe environmental toxicity associated with shaped particles. Despite extensive research on the dynamical processes of spherical particles, the behaviors of non-spherical particles have been insufficiently investigated. In this study, we have developed a suite of computation-based models that account for particle shape and have reported on the typical dynamical behaviors of non-spherical particles within indoor environments. We have explored three typical scenarios, i.e., particle penetration into indoor spaces through building cracks, indoor particle deposition, and indoor particle dispersion. The shape-induced deviations are associated with dynamical processes, showing a decrease trend among penetration, deposition, and dispersion of the non-spherical particles. The maximum discrepancy due to particle shape during the penetration process exceeds 1000 %, observed with particles of approximately 0.02 μm in diameter interacting with straight cracks 4.5 cm in length and 0.25 mm in height. Moreover, there is a discrepancy of more than 70 % in the deposition of particles with a diameter of approximately 10 μm on side walls when using side air supply ventilation. Similarly, a discrepancy of nearly 11 % is noted for particles around 0.02 μm in diameter during dispersion under displacement ventilation within indoor settings. The interaction between shape-related particle dynamics, particularly their diffusion characteristics, and the properties of the flow field leads to these shape-dependent dynamical discrepancies. These findings offer a comprehensive understanding of how the shape of particles affects their indoor dynamic behavior, thereby supporting the control of hazardous particles in indoor environments.

Field measurement study of indoor thermal environment of badminton halls in a hot summer and cold winter region in different seasons in China

The indoor thermal environment has a direct impact on human thermal comfort and health. In order to assess the status of the indoor thermal environment of typical sports buildings in hot summer and cold winter climate zones in China, 14 badminton halls in 10 cities in Hubei Province (including 5 venues in Wuhan) in this climate zone are chosen as research objects for field testing of indoor thermal environment parameters in 4 seasons. All the tested stadiums are naturally ventilated in non-event conditions. The results reveal that the average indoor temperature of badminton halls in summer is excessively high (i.e., 31.89 °C), which is higher than the regulation specified in JGJ31-2003 or GB-T18883-2022 on the reference interval of the indoor air temperature of venues in summer, (i.e., (26-28 °C) or (22-28 °C), respectively). The average indoor temperature of badminton halls in winter is too low (i.e., 12.95 °C), and it is lower than the recommendations of JGJ31-2003 or GB-T18883-2022 on the reference interval of the indoor air temperature of venues in winter (i.e., (16-18 °C) or (16-24 °C), respectively), relative humidity and air velocity are in the thermal comfort interval for all seasons, and the indoor thermal environment factors of badminton courts in spring and autumn meet the comfort requirements. The indoor and outdoor temperatures and the relative humidity of badminton courts are highly correlated. The indoor temperature and relative humidity vary according to changes in those factors outdoors, whereas the air velocity is not affected by outdoor changes. In the hot summer and cold winter climate zones, some discrepancies in the indoor temperature variation patterns of badminton halls at various altitudes are detectable. The results of this study aim to provide a solid basis for the development of indoor thermal-comfort standards for sports stadiums in China.

Indoor overheating: A review of vulnerabilities, causes, and strategies to prevent adverse human health outcomes during extreme heat events

The likelihood of exposure to overheated indoor environments is increasing as climate change is exacerbating the frequency and severity of hot weather and extreme heat events (EHE). Consequently, vulnerable populations will face serious health risks from indoor overheating. While the relationship between EHE and human health has been assessed in relation to outdoor temperature, indoor temperature patterns can vary markedly from those measured outside. This is because the built environment and building characteristics can act as an important modifier of indoor temperatures. In this narrative review, we examine the physiological and behavioral determinants that influence a person's susceptibility to indoor overheating. Further, we explore how the built environment, neighborhood-level factors, and building characteristics can impact exposure to excess heat and we overview how strategies to mitigate building overheating can help reduce heat-related mortality in heat-vulnerable occupants. Finally, we discuss the effectiveness of commonly recommended personal cooling strategies that aim to mitigate dangerous increases in physiological strain during exposure to high indoor temperatures during hot weather or an EHE. As global temperatures continue to rise, the need for a research agenda specifically directed at reducing the likelihood and impact of indoor overheating on human health is paramount. This includes conducting EHE simulation studies to support the development of consensus-based heat mitigation solutions and public health messaging that provides equitable protection to heat-vulnerable people exposed to high indoor temperatures.

Direct numerical simulation of contaminant removal in presence of underfloor air distribution system

Indoor contaminant removal over 0.5 ≤ FrT ≤ 5.0, 0.5 ≤ N ≤ 5.0, and 50 ≤ Re ≤ 500 was investigated numerically, wherein FrT refers to the Froude number, N refers to the buoyancy ratio, and Re refers to the Reynolds number. As demonstrated, the ventilation effectiveness increased with increasing contaminant source intensity and air supply intensity at a constant air temperature, indicating that increase the fresh air can effectively eliminate contaminants in this case. At high air supply temperatures, the heat retention time and contaminant transport was extremely short, and the fresh air induced by strong natural convection floating lift was rapidly discharged. Additioanlly, the air supply intensity had significant effects on contaminant removal. Quantification of the ventilation effectiveness under the combined effects of air supply intensity, air supply temperature and contaminant source intensity was determined based on the results of direct numerical simulations.

A review on factors related to patient comfort experience in hospitals

The creation of a welcoming hospital atmosphere is necessary to improve patient wellbeing and encourage healing. The goal of this study was to examine the variables affecting hospitalised patients' comfort. The study procedure included a thorough search of the Web of Science and Scopus databases, as well as the use of software analytic tools to graphically map enormous literature data, providing a deeper understanding of the linkages within the literature and its changing patterns. Insights from a range of disciplines, including engineering, psychology, immunology, microbiology, and environmental science, were included into our study using content analysis and clustering approaches. The physical environment and the social environment are two crucial factors that are related to patient comfort. The study stress the need of giving patient comfort a top priority as they heal, especially by tackling indoor air pollution. Our research also emphasises how important hospital care and food guidelines are for improving patient comfort. Prioritising patients who need specialised care and attention, especially those who have suffered trauma, should be the focus of future study. Future research in important fields including trauma, communication, hospital architecture, and nursing will be built on the findings of this study. To enhance research in these crucial areas, worldwide collaboration between experts from other nations is also advised. Although many studies stress the significance of patient comfort, few have drawn conclusions from a variety of disciplines, including medicine, engineering, immunology, microbiology, and environmental science, the most crucial issue of thoroughly researching the improvement of patient comfort has not been addressed. Healthcare workers, engineers, and other professions will benefit greatly from this study's investigation of the connection between hospital indoor environments and patient comfort.

Field investigation of non-uniform environment in a Venlo-type greenhouse in Yangling, China

Non-uniform environmental conditioning has established substantial energy-saving and conditioning effects in residential buildings, however, few studies on the technology applied in greenhouses have been conducted. Semi-enclosed greenhouse development is hindered by energy consumption. To better apply non-uniform environmental conditioning technology in greenhouses, it is necessary to investigate the non-uniform characteristics of field environment parameters. Therefore, spatial and temporal measurements of indoor temperature and relative humidity in a Venlo-type greenhouse in Yangling, China, were conducted on June 5-11, 2022. Temperature and humidity sensors were arranged in the greenhouse at 4.5 m intervals, in the canopy, cultivation, center, and root areas. Temperature and humidity measurement points on the greenhouse walls were selected. The measurement results showed large fluctuations in the indoor temperature and relative humidity over time. The difference between indoor and outdoor average temperatures ranged from -5-10 °C and temperatures unsuitable for tomato growth were identified, although some passive conditioning methods such as ventilation and water spraying were employed, which indicates the necessity of active heating and cooling. Based on the measured data, the nonuniformity coefficients of temperature and relative humidity in different directions in the greenhouse were calculated. A larger non-uniformity in the vertical direction was found compared to that in the horizontal direction. These results suggest the possibility of non-uniform environmental conditioning. A rough estimation of the energy consumption by the two different condition modes, namely zone-specific and overall conditioning, was made. A huge energy saving of 69.6 % by the zone-specific conditioning mode was revealed compared to the overall conditioning. This implies a huge advantage in energy efficiency by non-unform environmental conditioning technologies applied in greenhouses. The study provides useful data for understanding non-uniform environments in greenhouses and the application of non-uniform environmental conditioning technologies.

The impact of high background particle concentration on the spatiotemporal distribution of Serratia marcescens bioaerosol

Airborne transmission is a well-established mode of dissemination for infectious diseases, particularly in closed environments. However, previous research has often overlooked the potential impact of background particle concentration on bioaerosol characteristics. We compared the spatial and temporal distributions of bioaerosols under two levels of background particle concentration: heavily polluted (150-250 μg/m3) and excellent (0-35 μg/m3) in a typical ward. Serratia marcescens bioaerosol was adopted as a bioaerosol tracer, and the bioaerosol concentrations were quantified using six-stage Andersen cascade impactors. The results showed a significant reduction (over at least 62.9%) in bioaerosol concentration under heavily polluted levels compared to excellent levels at all sampling points. The temporal analysis also revealed that the decay rate of bioaerosols was higher (at least 0.654 min-1) under heavily polluted levels compared to excellent levels. These findings suggest that background particles can facilitate bioaerosol removal, contradicting the assumption made in previous research that background particle has no effect on bioaerosol characteristics. Furthermore, we observed differences in the size distribution of bioaerosols between the two levels of background particle concentration. The average bioaerosols size under heavily polluted levels was found to be higher than that under excellent levels, and the average particle size under heavily polluted levels gradually increased with time. In conclusion, these results highlight the importance of considering background particle concentration in future research on bioaerosol characteristics.

Investigating Patterns of Air Pollution in Metropolises Using Remote Sensing and Neural Networks During the COVID-19 Pandemic

The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH₄), Carbon Monoxide (CO), Carbon Dioxide (CO₂), Nitrogen Dioxide (NO₂), Ozone (O₃), Sulfur Dioxide (SO₂), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R²=0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R²=0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.

A Sensing-Based Visualization Method for Representing Pressure Distribution in a Multi-Zone Building by Floor

Airflow in a multi-zone building can be a major cause of pollutant transfer, excessive energy consumption, and occupants discomfort. The key to monitoring airflows and mitigating related problems is to obtain a comprehensive understanding of pressure relationships within the buildings. This study proposes a visualization method for representing pressure distribution within a multi-zone building by using a novel pressure-sensing system. The system consists of a Master device and a couple of Slave devices that are connected with each other by a wireless sensor network. A 4-story office building and a 49-story residential building were installed with the system to detect pressure variations. The spatial and numerical mapping relationships of each zone were further determined through grid-forming and coordinate-establishing processes for the building floor plan. Lastly, 2D and 3D visualized pressure mappings of each floor were generated, illustrating the pressure difference and spatial relationship between adjacent zones. It is expected that the pressure mappings derived from this study will allow building operators to intuitively perceive the pressure variations and the spatial layouts of the zones. These mappings also make it possible for operators to diagnose the differences in pressure conditions between adjacent zones and plan a control scheme for the HVAC system more efficiently.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Prophylaxis and Remediation for Future Pandemic Pathogens-(Lessons from a Post-COVID World)

Since influenza and coronaviruses are currently deadly and emerging threats worldwide, better treatment, remediation and prevention options are needed. In that regard, a basic understanding of severe acute respiratory syndrome (SARS)-CoV-2/COVID-19 (Betacoronaviridae) and other viral pathogen mechanisms of transmission are expected. Unfortunately, unprecedented, and growing distrust of vaccines and even masks or personal protective equipment (PPE) in the United States and elsewhere presents itself as an added challenge. We postulate that development of improved and highly effective prophylactic measures, together with new life-saving therapies that do inhibit or otherwise treat infection of SARS-CoV-2, influenza and other viral pathogens, could be an adjunct measure to globally protect vulnerable individuals from pandemic threats. In this review, we share what we learned from the past COVID experience to offer a multifactorial and improved approach to current and future pandemic infections or threats using low-cost means.

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%.

Contaminant removal and contaminant dispersion of air distribution for overall and local airborne infection risk controls

Proper air distribution is crucial for airborne infection risk control of infectious respiratory diseases like COVID-19. Existing studies evaluate and compare the performances of different air distributions for airborne infection risk control, but the mechanisms of air distribution for airborne infection risk control remain unclear. This study investigates the mechanisms of air distribution for both overall and local airborne infection risk controls. The experimentally validated CFD models simulate the contaminant concentration fields in a hospital ward based on which the airborne infection risks of COVID-19 are evaluated with the dilution-based expansion of the Wells-Riley model. Different air distributions, i.e., stratum ventilation, displacement ventilation, and mixing ventilation, with various supply airflow rates are tested. The results show that the variations of the overall and local airborne infection risks under different air distributions and different supply airflow rates are complicated and non-linear. The contaminant removal and the contaminant dispersion are proposed as the mechanisms for the overall and local airborne infection risk controls, respectively, regardless of airflow distributions and supply airflow rates. A large contaminant removal ability benefits the overall airborne infection risk control, with the coefficient of determination of 0.96 between the contaminant removal index and the reciprocal of the overall airborne infection risk. A large contaminant dispersion ability benefits the local airborne infection risk control, with the coefficient of determination of 0.99 between the contaminant dispersion index and the local airborne infection risk.