COVID-19 Impact on Operation and Energy Consumption of Heating, Ventilation and Air-Conditioning (HVAC) Systems

Energy efficient ventilation and indoor air quality in the context of COVID-19 - A systematic review

New COVID-19 ventilation guidelines have resulted in higher energy consumption to maintain indoor air quality (IAQ), and energy efficiency has become a secondary concern. Despite the significance of the studies conducted on COVID-19 ventilation requirements, a comprehensive investigation of the associated energy challenges has not been discussed. This study aims to present a critical systematic review of the Coronavirus viral spreading risk mitigation through ventilation systems (VS) and its relation to energy use. COVID-19 heating, ventilation and air conditioning (HVAC)-related countermeasures proposed by industry professionals have been reviewed and their influence on operating VS and energy consumption have also been discussed. A critical review analysis was then conducted on publications from 2020 to 2022. Four research questions (RQs) have been selected for this review concerning i) maturity of the existing literature, ii) building types and occupancy profile, iii) ventilation types and effective control strategies and iv) challenges and related causes. The results reveal that employing HVAC auxiliary equipment is mostly effective and increased fresh air supply is the most significant challenge associated with increased energy consumption due to maintaining IAQ. Future studies should focus on novel approaches toward solving the apparently conflicting objectives of minimizing energy consumption and maximizing IAQ. Also, effective ventilation control strategies should be assessed in various buildings with different occupancy densities. The implications of this study can be useful for future development of this topic not only to enhance the energy efficiency of the VS but also to enable more resiliency and health in buildings.

New ventilation design criteria for energy sustainability and indoor air quality in a post Covid-19 scenario

The Covid-19 outbreak raised great attention to the importance of indoor air quality in buildings. Even if the Covid-19 epidemic is nearing an end, all stakeholders agree that increasing outside air flow rates is beneficial for decreasing the likelihood of contagion, lowering the risk of future pandemics, and enhancing the general safety of the interior environment. Indeed, diverse concerns raised about whether the ventilation standards in place are still adequate. In this context, this research intends to assess the suitability of current ventilation standards in addressing the current pandemic scenario and to offer novel criteria and guidelines for the design and operation of HVAC systems, as well as useful guidance for the creation of future ventilation standards in a post-Covid-19 scenario. To that end, a comprehensive analysis of the ANSI/ASHRAE 62.1 is carried out, with an emphasis on its effectiveness in reducing the risk of infection. Furthermore, the efficacy of various ventilation strategies in reducing the likelihood of contagion has been investigated. Finally, because building ventilation is inextricably linked to energy consumption, the energy and economic implications of the proposed enhancements have been assessed. To carry out the described analysis, a novel method was developed that combines Building Energy Modelling (BEM) and virus contagion risk assessment. The analyses conducted produced interesting insights and criteria for ventilation system design and operation, as well as recommendations for the development of future standards.

Assessment of natural ventilation strategy to decrease the risk of COVID 19 infection at a rural elementary school

Natural ventilation in low-budget elementary schools is the main focus to ensure the health and comfort of its occupants, specifically when looking at the global pandemic related to SARS-COV-2. This paper presents an experimental and novel study of natural ventilation in a public elementary school (Los Zumacales), with a particularly low economic budget. The study was carried out during the winter months of the Covid 19 pandemic. The school is located in the rural area of Castilla y León (North-Western Spain) far from high traffic roads. In this study, a methodology of measuring CO2 concentration was applied in nine classrooms in a school. The experimental study shows the level of natural ventilation in each classroom, expressed in Air Changes per Hour (ACH), using the Decay CO2 concentration method. The method is proven by comparing the experimental values of the obtained ACH with those determined by the most powerful methods to achieve appropriate ventilation levels. Thus, ensuring health protection protocol in rural schools, against the COVID 19 pandemic. Harvard guide and Spanish regulations (RITE), two widely recognized methods have been used together with the experimentally obtained standard by Rey et al. Only one classroom showed a value lower than 3 indicating poor ventilation. In this study, the degree of thermal comfort in the nine classrooms were also analyzed according to the EN15251 standard. An average indoor temperature of approximately 19 °C was obtained, and the relative humidity was stable and correct according to Spanish regulations. In addition, the risk of infection in each classroom was estimated following the international method recommended by the federation of European Heating, Ventilation, and Air Conditioning Associations (REHVA). The probability of infection in all the cases studied was less than 14%. Therefore, this study provides a strong response against infections illnesses, such as Covid 19, in educational buildings where economic budgets of their facilities are low in both, maintenance and investment.

Analysis of direct expansion heat recovery ventilation devices by orthogonal optimization method

Heat recovery ventilation devices include rectangular plate cross-flow, hexagonal plate combined counter and cross-flow, rotary wheel sensible, sorption rotor hybrid sensible and latent heat exchanger. Currently, existing studies have produced no clear findings on which climatic conditions latent heat recovery would be optimal, and therefore sought to determine in which climatic conditions it would be suitable to use devices that perform latent heat recovery. This study analysed the performance of different heat recovery devices in different climatic conditions in a ventilation project of a sample hotel building. In the case study, while there is a useful heat recovery between 44.01 and 58.68 kW at low outdoor temperatures in devices with only sensible heat transfer, this value increases up to 158.42 kW, as the outdoor temperature rises. In the heat recovery device providing latent heat transfer, the amount of useful heat recovery varies between 51.34 and 352.16 kW at low outdoor temperatures, depending on the outdoor relative humidity, while this amount increases to 411.26 from 773.25 kW at high outdoor temperatures. Outdoor temperature and humidity levels required for latent heat recovery was also determined by orthogonal optimization method. By using the orthogonal optimization, the study found that under conditions of high temperature that exceeds 35 °C in outdoor ambient temperature, and high humidity that exceeds 60% relative humidity, usage of latent heat recovery devices caused significant differences in total heat recovery ratio. Analysis also concludes that these devices can be used under these conditions.

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.

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85 % accuracy using a one-person offset. The accuracy of 15 min of the ahead prediction could reach 95 % with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2 % for 93 % of the day. This OBMPC model could reduce the coil load by 52.44 % and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9 % to 26 % difference in demand ventilation, a 0.3 °C to 2.4 °C difference in zone temperature, a 28.5 % to 44.5 % difference in outdoor airflow rate, and a 10.7 % to 28.2 % difference in coil load.

A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID-19 and future pandemics scenarios

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

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.

Modeling impacts of ventilation and filtration methods on energy use and airborne disease transmission in classrooms

Lowering the potential of airborne disease transmission in school buildings is especially important in the wake of the COVID-19 pandemic. The benefits of increased ventilation and filtration for reducing disease transmission compared to drawbacks of reduced thermal comfort and increased energy consumption and electricity demand are not well described. A comprehensive simulation of outdoor air ventilation rates and filtration methods was performed with a modified Wells-Riley equation and EnergyPlus building simulation to understand the trade-offs between infection probability and energy consumption for a simulated classroom in 13 cities across the US. A packaged heating, ventilation, and air conditioning unit was configured, sized, and simulated for each city to understand the impact of five ventilation flow rates and three filtration systems. Higher ventilation rates increased energy consumption and resulted in a high number of unmet heating and cooling hours in most cities (excluding Los Angeles and San Francisco). On average, across the 13 cities simulated, annual energy consumed by an improved filtration system was 31% lower than the energy consumed by 100% outdoor air ventilation. In addition, the infection probability was 29% lower with improved filtration. An economizer, which activates cooling based on an outdoor temperature setpoint, increased ventilation and reduced both energy consumption and infection probability. It was also concluded that ventilation and filtration measures better reduced absolute infection probability when the quanta generation rate for an infectious disease was higher. Dynamic outdoor airflow rate controls and filtration technologies that consider both health and energy consumption are an important area for further research.

Energy, economic, and environmental impacts of enhanced ventilation strategies on railway coaches to reduce Covid-19 contagion risks

In the last years, the Covid-19 outbreak raised great awareness about ventilation system performance in confined spaces. Specifically, the heating, ventilation, and air conditioning system design and operating parameters, such as air change per hour, air recirculation ratio, filtration device performance, and vents location, play a crucial role in reducing the spread of viruses, moulds, bacteria, and general pollutants. Concerning the transport sector, due to the impracticability of social distancing, and the relatively loose requirements of ventilation standards, the SARS-COV-19 outbreak brought a reduction of payload (up to 50%) for different carriers. Specifically, this has been particularly severe for the railway sector, where train coaches are typically characterized by relatively elevated occupancy and high recirculation ratios. In this framework, to improve the Indoor Air Quality and reduce the Covid-19 contagion risk in railway carriages, the present paper investigates the energy, economic and environmental feasibility of diverse ventilation strategies. To do so, a novel dynamic simulation tool for the complete dynamic performance investigation of trains was developed in an OpenStudio environment. To assess the Covid-19 contagion risk connected to the investigated scenarios, the Wells-Riley model has been adopted. To prove the proposed approach's capabilities and show the Covid-19 infection risk reduction potentially achievable by varying the adopted ventilation strategies, a suitable case study related to an existing medium-distance train operating in South/Central Italy is presented. The conducted numerical simulations return interesting results providing also useful design criteria.

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.

Optimization of COVID-19 prevention and control with low building energy consumption

COVID-19 is a global threat. Non-pharmaceutical interventions were commonly adopted for COVID-19 prevention and control. However, during stable periods of the pandemic, energy would be inevitably wasted if all interventions were implemented. The study aims to reduce the building energy consumption when meet the demands of epidemic prevention and control under the stable period of COVID-19. Based on the improved Wells-Riley model considering dynamic quanta generation and pulmonary ventilation rate, we established the infection risk - equivalent fresh air volume - energy consumption model to analyze the infection risk and building energy consumption during different seasons and optimized the urban building energy consumption according to the spatio-temporal population distribution. Shopping centers and restaurants contributed the most in urban energy consumption, and if they are closed during the pandemic, the total infection risk would be reduced by 25%-40% and 15%-25% respectively and the urban energy consumption would be reduced by 30%-40% and 13%-20% respectively. If people wore masks in all public indoor environments (exclude restaurants and KTV), the infection risk could be reduced by 60%-70% and the energy consumption could be reduced by 20%-60%. Gyms pose the highest risk for COVID-19 transmission. If the energy consumption kept the same with the current value, after the optimization, infection risk in winter, summer and the transition season could be reduced by 65%, 53% and 60%, respectively. After the optimization, under the condition of R t  < 1, the energy consumption in winter, summer, and the transition season could be reduced by 72%, 64%, and 68% respectively.

Energy efficiency in large office buildings post-COVID-19 in Europe's top five economies

Since the World Health Organization announced the COVID-19 pandemic, indoor airflows became a synonym for virus super-spreaders and the focus point for the scientific community and professional associations across the globe, disrupting all daily life dimensions. Europe's quick response to control the disease led the REHVA board to address mitigation guidelines, reassessed by each member association's following national specifics. The present study aims to quantify the energy consumption and CO₂ emissions of "large office" buildings in top-five European economies under the COVID-19 guidelines under the post-pandemic telework forecast. Methodology resorted to a standard model under Building Energy Simulation assessment to compare prior and posterior scenarios. The latter displays a tendency to increase energy and CO₂ emissions in all locations, in the first form 10.18% (Rome) to 69.48% (Paris); and second 5.80% (Rome) and 120.61% (Paris), which will affect national energy production and imports, urban pollution and business competitiveness. On a different scope, future HVAC guidelines need to address the incoming figures, particularly in highly dense urban areas. Also, to comply with the goals set by the Paris Accord.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Investigation on household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic

The implementation of the movement control order (MCO) to curb the spread of the 2019 novel corona virus disease (COVID-19) have influenced household energy consumption patterns around the world. This study aims to investigate household energy consumption of urban residential buildings in major cities of Indonesia during COVID-19 pandemic. Three representative major cities of Indonesia were selected to investigate detailed information about household appliances and gas consumption through face-to-face interviews in 2021 (n = 311). The factors affecting household energy consumption were investigated by multiple regression analysis. The results showed that, overall, the average annual energy consumption of all samples during pandemic was approximately 23.5 GJ, 3.0 GJ larger than before pandemic. The difference was primarily attributed to the use of air conditioning and cooking. The statistical analysis clearly indicated that the increase in household income (low-to high-cost houses), which would increase household size and number of appliances including air conditioning, thus increased total household energy consumption. We recommended the following potential energy-saving strategies for urban houses in Indonesia: (a) control the number of family members, (b) use more energy efficiency standards for electrical appliances and (c) encourage energy-saving lifestyles, particularly to younger adults by adopting passive cooling techniques (window opening) whereever possible.

Optimization of energy efficiency and COVID-19 pandemic control in different indoor environments

The COVID-19 pandemic has led to considerable morbidity and mortality, and consumed enormous resources (e.g. energy) to control and prevent the disease. It is crucial to balance infection risk and energy consumption when reducing the spread of infection. In this study, a quantitative human, behavior-based, infection risk-energy consumption model for different indoor environments was developed. An optimal balance point for each indoor environment can be obtained using the anti-problem method. For this study we selected Wangjing Block, one of the most densely populated places in Beijing, as an example. Under the current ventilation standard (30 m3/h/person), prevention and control of the COVID-19 pandemic would be insufficient because the basic reproduction number (R0 ) for students, workers and elders are greater than 1. The optimal required fresh air ventilation rates in most indoor environments are near or below 60 m3/h/person, after considering the combined effects of multiple mitigation measures. In residences, sports buildings and restaurants, the demand for fresh air ventilation rate is relatively high. After our global optimization of infection risk control (R0  ≤ 1), energy consumption can be reduced by 13.7% and 45.1% on weekdays and weekends, respectively, in contrast to a strategy of strict control (R0  = 1 for each indoor environment).

A three-year dataset supporting research on building energy management and occupancy analytics

This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications-building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls-to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

The Energy Efficiency Post-COVID-19 in China’s Office Buildings

China promptly took the leading step to mitigate the spread of COVID-19, producing the first scientific guidelines assuming health above energy consumption and significantly changing HVAC/AHU operation. The research intended to fulfill the gap by measuring the impact of the guidelines on energy use intensity, CO2 emissions, and energy operation costs related to workplaces. The guidelines are long-term sector and industry trends following occupants’ health and safety concerns, and today they are applied to nursing homes. The research extended the study to post-COVID-19 scenarios by crossing those settings with published reports on telework predictions. The methodology resorts to Building Energy Simulation software to assess the Chinese standard large office building on 8 climate zones and 17 subzones between pre- and post-COVID-19 scenarios under those guidelines. The outcomes suggest an upward trend in energy use intensity (11.70–12.46%), CO2 emissions (11.13–11.76%), and costs (9.37–9.89%) for buildings located in “warm/mixed” to “subarctic” climates, especially in colder regions with high heating demands. On the other hand, the figures for “very hot” to “hot/warm” climates lower the energy use intensity (14.76–15.47%), CO2 emissions (9%), and costs (9.64–9.77%).

Comparison of Indoor Environment and Energy Consumption before and after Spread of COVID-19 in Schools in Japanese Cold-Climate Region

A report released by the WHO indicates that aerosols from infected people are one of the major sources of the spread of COVID-19. Therefore, as the COVID-19 infection caused by the SARS-CoV-2 virus spreads, it has become necessary to reconsider the design and operation of buildings. Inside school buildings in cold regions, not only is it not easy to increase ventilation during the winter, but it may also be difficult for students to attend classes while wearing masks during the summer because such buildings are not equipped with air-conditioning systems. In short, school buildings in cold climates have more problems than those in warm climates. We report on the results of indoor environmental measurement using our developed CO2-concentration meters, a questionnaire survey on students’ feeling of being hot or cold (i.e., ‘thermal sensation’), and a comparison of energy consumption before and after the spread of COVID-19 infection in schools in Sapporo, Japan, a cold-climate area. The results indicate that (1) more than 70% of the students participated in window ventilation by the CO2 meter, and (2) a relatively good indoor environment was maintained through the efforts of teachers and students. However, we also found that (1) 90% of the students felt hot in summer and (2) 40% felt cold in winter, (3) energy efficiency worsened by 7% due to increased ventilation, and (4) air quality was not as clean as desired during the coldest months of the year. Therefore, investment in insulation and air conditioning systems for school buildings is needed.

Designing Post COVID-19 Buildings: Approaches for Achieving Healthy Buildings

The COVID-19 pandemic forced the accessibility, social gathering, lifestyle, and working environment to be changed to reduce the infection. Coronavirus spreads between people in several different ways. Small liquid particles (aerosols, respiratory droplets) from an infected person are transmitted through air and surfaces that are in contact with humans. Reducing transmission through modified heating, ventilation, and air conditioning (HVAC) systems and building design are potential solutions. A comprehensive review of the engineering control preventive measures to mitigate COVID-19 spread, healthy building design, and material was carried out. The current state-of-the-art engineering control preventive measures presented include ultraviolet germicidal irradiation (UVGI), bipolar ionization, vertical gardening, and indoor plants. They have potential to improve the indoor air quality. In addition, this article presents building design with materials (e.g., copper alloys, anti-microbial paintings) and smart technologies (e.g., automation, voice control, and artificial intelligence-based facial recognition) to mitigate the infections of communicable diseases.

Prediction and control of aerosol transmission of SARS-CoV-2 in ventilated context: from source to receptor

Global spread of COVID-19 has seriously threatened human life and health. The aerosol transmission route of SARS-CoV-2 is observed often associated with infection clusters under poorly ventilated environment. In the context of COVID-19 pandemic, significant transformation and optimization of traditional ventilation systems are needed. This paper is aimed to offer better understanding and insights into effective ventilation design to maximize its ability in airborne risk control, for particularly the COVID-19. Comprehensive reviews of each phase of aerosol transmission of SARS-CoV-2 from source to receptor are conducted, so as to provide a theoretical basis for risk prediction and control. Infection risk models and their key parameters for risk assessment of SARS-CoV-2 are analyzed. Special focus is given on the efficacy of different ventilation strategies in mitigating airborne transmission. Ventilation interventions are found mainly impacting on the dispersion and inhalation phases of aerosol transmission. The airflow patterns become a key factor in controlling the aerosol diffusion and distribution. Novel and personalized ventilation design, effective integration with other environmental control techniques and resilient HVAC system design to adapt both common and epidemic conditions are still remaining challenging, which need to be solved with the aid of multidisciplinary research and intelligent technologies.

Household Water and Energy Consumption Changes during COVID-19 Pandemic Lockdowns: Cases of the Kazakhstani Cities of Almaty, Shymkent, and Atyrau

The COVID-19 pandemic has changed the daily behaviors of people by forcing them to spend the majority of their time in their residences, particularly during social distancing measures. The increased time spent at home is expected to influence, among other things, the daily consumption of utilities: specifically, water and energy. A prolonged presence of residents at home during COVID-19 lockdowns might increase strain on water and energy resources, which are mostly from non-renewable sources in several countries, including Kazakhstan; however, such potentially important effects have not yet been studied for the country. The present research aims to evaluate how the COVID-19 pandemic lockdowns have affected the water and energy consumption in residential housings in cities of varying sizes in Kazakhstan, providing a novel understanding of the effect of pandemic lockdowns on household energy and water consumption. Energy and water consumption data of Almaty, Shymkent, and Atyrau have been first obtained from the local service companies, and then, the usage behavior was analyzed for the periods before, during, and after the COVID-19 pandemic lockdown. After, statistical tests were conducted to check the hypotheses regarding the effect of COVID-19 pandemic lockdowns on the consumption of energy and water. The findings indicate that residential energy and water consumption increased during the lockdown periods in large and medium cities. Nevertheless, this growth is not highly significant compared to similar non-pandemic timeframes. This result could indicate a particular risk for sustainable resources consumption and put pressure on the supply companies. Moreover, in case of further lockdown measures, current building systems are at risk of increased pressure, and eventually, of failure.

Exploring the potentials of personalized ventilation in mitigating airborne infection risk for two closely ranged occupants with different risk assessment models

In the context of COVID-19, new requirements are occurring in ventilation systems to mitigate airborne transmission risk in indoor environment. Personalized ventilation (PV) which directly delivers clean air to the occupant's breathing zone is considered as a promising solution. To explore the potentials of PV in preventing the spread of infectious aerosols between closely ranged occupants, experiments were conducted with two breathing thermal manikins with three different relative orientations. Nebulized aerosols were used to mimic exhaled droplets transmitted between the occupants. Four risk assessment models were applied to evaluate the exposure or infection risk affected by PV with different operation modes. Results show that PV was effective in reducing the user's infection risk compared with mixing ventilation alone. Relative orientations and operation modes of PV significantly affected its performance in airborne risk control. The infection risk of SARS-CoV-2 was reduced by 65% with PV of 9 L/s after an exposure duration of 2 h back-to-back as assessed by the dose-response model, indicating effective protection effect of PV against airborne transmission. While the side-by-side orientation was found to be the most critical condition for PV in airborne risk control as it would accelerate diffusion of infectious droplets in lateral diffusion to occupants by side. Optimal designs of PV for closely ranged occupants were hereby discussed. The four risk assessment models were compared and validated by experiments with PV, implying basically consistent rules of the predicted risk with PV among the four models. The relevance and applicability of these models were discussed to provide a basis for risk assessment with non-uniformly distributed pathogens indoor.

Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC’s yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use.

A critical review of heating, ventilation, and air conditioning (HVAC) systems within the context of a global SARS-CoV-2 epidemic

The Coronavirus disease (COVID-19) has spread over the world, resulting in more than 225 million patients, and 4.7 million deaths in September 2021. It also caused panic and terror, halted numerous activities, and resulted in the world economy deteriorates. It altered human behavior and compelled people to alter their lifestyles to avoid infection. Air conditioning systems are one of the most important sectors that must be considered because of the pandemic SARS-CoV-2 all over the world. Air is used as a heat transfer medium in heating, ventilation, and air conditioning (HVAC) systems. The air contains a variety of pollutants, viruses, and bacteria, all of which have an impact on and destroy human life. Significantly in summer, people spend more time in air conditioners which results in lower levels of vitamin D and melatonin which may affect the functioning of their immune system and are susceptible to receiving SARS-CoV-2 from other individuals. As an important component of air conditioning and ventilation systems, the air filter plays a significant role. As a result, researchers must work harder to improve its design to prevent the ultra-small particles loaded with COVID-19. This paper contributes to the design of existing HVAC systems in terms of their suitability and impact on the spread of the hybrid SARS-CoV-2 epidemic, as well as efforts to obtain a highly efficient air filter to remove super-sized particles for protection against epidemic infection. In addition, important guideline recommendations have been extracted to limit the spread of the SARS-CoV-2 throughout the world and to get the highest quality indoor air in air-conditioned places.

An Overview of the Ship Ventilation Systems and Measures to Avoid the Spread of Diseases

This article presents a review of the main aspects regarding the current rules of classification societies, standards, and practice regarding the design and construction of ventilation and air conditioning systems for different compartments in different types of ships. In the context of the COVID-19 pandemic, this paper also presents the usual practice of the actual heating ventilation and air conditioning (HVAC) systems used on large ships, which recirculate the air between living compartments, in comparison with the new requirements to avoid the risk of spreading diseases. According to the rules, the technical compartments are provided with independent ventilation systems that ensure high air flow rates; therefore, the spread of diseases through this system is not an issue. The living spaces are provided with common ventilation and air conditioning systems that recirculate the air in all compartments served. The current practice of air recirculation in various living rooms leads to the spread of diseases, which should therefore be analyzed and improved by adding high-efficiency particulate air (HEPA) filters and UV disinfection or be replaced with individual systems that provide local heating or cooling without air recirculation between different rooms and fresh air supply with complete evacuation. For existing ships, different solutions should be analyzed such as reducing or cancelling recirculation and increasing filtration.