Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

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

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

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.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

Analysis on the thermal performance of low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) for space heating

With increasing energy use and outbreaks of respiratory infectious diseases (such as COVID-19) in buildings, there is a growing interest in creating healthy and energy-efficient indoor environments. A novel heating system named low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) is proposed in this study. Thermal performance, indoor air quality, energy and exergy performance were investigated and compared with conventional radiant floor heating (CRFH) and conventional radiant floor heating with mixing ventilation (CRFH + MV). The results indicated that LTR-ISV had a more uniform operative temperature distribution and overall thermal sensation, and air mixing was enhanced without generating additional draft sensation. Compared with CRFH and CRFH + MV, the indoor CO₂ concentration in LTR-ISV can be reduced by 1355 ppm and 400 ppm, respectively. Airborne transmission risk can also be reduced by 5.35 times. The coefficient of performance for CRFH, CRFH + MV, and LTR-ISV during working hours was 4.2, 2.5, and 3.4, respectively. The lower value of LTR-ISV was due to the high energy usage of the primary air handing unit. In the non-working hours, LTR-ISV was 0.6 and 1.3 higher compared to CRFH and CRFH + MV, respectively. The exergy efficiency of LTR-ISV, CRFH, and CRFH + MV was 81.77 %, 76.43 %, and 64.71 %, respectively. Therefore, the LTR-ISV system can meet the requirements of high indoor air quality and thermal comfort and provides a reference for the energy-saving use of low-grade energy in space heating.

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%.

Ventilation improvement and evaluation of its effectiveness in a Japanese manufacturing factory

A coronavirus disease 2019 (COVID-19) cluster emerged in a manufacturing factory in early August 2021. In November 2021, we conducted a ventilation survey using the tracer gas method. Firstly, we reproduce the situation at the time of cluster emergence and examined whether the ventilation in the office was in a condition that increased the risk of aerosol transmission. Secondly, we verified the effectiveness of the factory's own countermeasure implemented immediately after the August cluster outbreak. Furthermore, we verified the effectiveness of several additional improvement measures on the factory's own countermeasures already installed in August. Under the conditions of the cluster emergence, the air changes per hour (ACH) value was 0.73 ACH on average. The ACH value was less than 2 ACH recommended by the Ministry of Health, Labour, and Welfare, suggesting an increased risk of aerosol transmission. The factory's own countermeasures taken immediately in August were found to be effective, as the ACH value increased to 3.41 ACH on average. Moreover, it was confirmed that additional improvement measures on the factory's own countermeasures increased the ACH value to 8.33 ACH on average. In order to prevent the re-emergence of COVID-19 clusters due to aerosol infection in the office, it was found that while continuing the factory's own countermeasure, additional improvement measures should also be added depending on the number of workers in the room. In a company, it is important that workers themselves continue to take infection control measures autonomously, and confirming the effectiveness of the measures will help maintain workers' motivation. We believe it is helpful that external researchers in multiple fields and internal personnel in charge of the health and safety department and occupational health work together to confirm the effectiveness of conducted measures, such as in this case.

A modelling method for large-scale open spaces orientated toward coordinated control of multiple air-terminal units

The temperature distribution is always assumed to be homogeneous in a traditional single-input-single-output (SISO) air conditioning control strategy. However, the airflow inside is more complicated and unpredictable. This study proposes a zonal temperature control strategy with a thermal coupling effect integrated for air-conditioned large-scale open spaces. The target space was split into several subzones based on the minimum controllable air terminal units in the proposed method, and each zone can be controlled to its own set-point while considering the thermal coupling effect from its adjacent zones. A numerical method resorting to computational fluid dynamics was presented to obtain the heat transfer coefficients (HTCs) under different air supply scenarios. The relationship between heat transfer coefficient and zonal temperature difference was linearized. Thus, currently available zonal models in popular software can be used to simulate the dynamic response of temperatures in large-scale indoor open spaces. Case studies showed that the introduction of HTCs across the adjacent zones was capable of enhancing the precision of temperature control of large-scale open spaces. It could satisfy the temperature requirements of different zones, improve thermal comfort and at least 11% of energy saving can be achieved by comparing with the conventional control strategy.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

The Appendix is available in the online version of this article at 10.1007/s12273-022-0942-8.

Ventilation reconstruction in bathrooms for restraining hazardous plume: Mitigate COVID-19 and beyond

Converging evidence reports that the probability of vertical transmission patterns via shared drainage systems, may be responsible for the huge contactless community outbreak in high-rise buildings. Publications indicate that a faulty bathroom exhaust fan system is ineffective in removing lifted hazardous virus-laden aerosols from the toilet bowl space. Common strategies (boosting ventilation capability and applying disinfection tablets) seem unsustainable and remain to date untested. Using combined simulation and experimental approaches, we compared three ventilation schemes in a family bathroom including the traditional ceiling fan, floor fan, and side-wall fan. We found that the traditional ceiling fan was barely functional whereby aerosol particles were not being adequately removed. Conversely, a side-wall fan could function efficiently and an enhanced ventilation capability can have increased performance whereby nearly 80.9% of the lifted aerosol particles were removed. There exists a common, and easily-overlooked mistake in the layout of the bathroom, exposing occupants to a contactless vertical pathogen aerosol transmission route. Corrections and dissemination are thus imperative for the reconstruction of these types of family bathrooms. Our findings provide evidence for the bathroom and smart ventilation system upgrade, promoting indoor public health and human hygiene.

Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities

The COVID-19 epidemic has disrupted the normal teaching and learning in universities, which poses significant challenges to higher education. The traditional face-to-face learning mode has been switched to online (distance) learning, causing various influences on students' academic performance, physical and psychological well-being. As higher education plays a central role in technology innovation and society development, it is of great importance to investigate and improve online learning in the context of COVID-19. This study distributed online questionnaires to undergraduate and postgraduate students from 30 provinces or municipalities in China (covering 88% of the whole country). Results indicate that online learning mode is more likely to reduce the academic performance of lower-grade students (e.g., freshman and sophomore). The learning environment could be one of essential factors affecting the academic performance during online education. Studying at home or dormitory is more evidently correlated with academic performance decline. Regarding the physical and mental health during online learning, most students had experienced eye strain (84%) and cervical stiff (79%), while anxiety is the most prominent mental issue (66% of occurrence). Several coping strategies are suggested to improve the online learning in post-pandemic era, which is essential for higher education and promoting a civilized and sustainable society.

Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks

Ventilation plays a noteworthy role in maintaining a healthy, comfortable and energy-efficient indoor environment and mitigating the risk of aerosol transmission and disease infection (e.g., SARS-COV-2). In most commercial and office buildings, demand-controlled ventilation (DCV) systems are widely utilized to conserve energy based on occupancy. However, as the presence of occupants is often inherently stochastic, accurate occupancy prediction is challenging. This study, therefore, proposes an autoencoder Bayesian Long Short-term Memory neural network (LSTM) model for probabilistic occupancy prediction, taking account of model misspecification, epistemic uncertainty, and aleatoric uncertainty. Performances of the proposed models are evaluated using real data in an educational building at the University of Cambridge, UK. The models trained on data of one open-plan space are used to predict occupant numbers for other spaces (with similar layout and function) in the same building. The probabilistic occupant profiles are then used for estimating optimal ventilation rates for two scenarios (i.e., normal DCV mode for energy conservation and anti-infection mode for virus transmission prevention). Results show that, during the test period, for the 1-h ahead prediction, the proposed model achieved better performance with up to 5.8% mean absolute percentage error reduction than the traditional LSTM model. More flexible alternatives for ventilation can be offered by the proposed risk-aware decision-making schemes serving different purposes under real operation. The findings from this study provide new occupancy forecasting solutions and explore the potential of probabilistic decision making for building ventilation optimization.

A novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in the indoor environment

Ventilation is of critical importance to containing COVID-19 contagion in indoor environments. Keeping the ventilation rate at high level is recommended by many guidelines to dilute virus-laden respiratory particles and mitigate airborne transmission risk. However, high ventilation rate will cause high energy use. Demand-controlled ventilation is a promising technology option for controlling indoor air quality in an energy-efficient manner. This paper proposes a novel CO₂-based demand-controlled ventilation strategy to limit the spread of COVID-19 in indoor environments. First, the quantitative relationship is established between COVID-19 infection risk and average CO₂ level. Then, a sufficient condition is proposed to ensure COVID-19 event reproduction number is less than 1 under a conservative consideration of the number of infectors. Finally, a ventilation control scheme is designed to make sure the above condition can be satisfied. Case studies of different indoor environments have been conducted on a testbed of a real ventilation system to validate the effectiveness of the proposed strategy. Results show that the proposed strategy can efficiently maintain the reproduction number less than 1 to limit COVID-19 contagion while saving about 30%-50% of energy compared with the fixed ventilation scheme. The proposed strategy offers more practical values compared with existing studies: it is applicable to scenarios where there are multiple infectors, and the number of infectors varies with time; it only requires CO₂ sensors and does not require occupancy detection sensors. Since CO₂ sensors are very mature and low-cost, the proposed strategy is suitable for mass deployment in most existing ventilation systems.

Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption

The risk assessment system of virus infection probability and the prevention measures for virus transmission are keys to controlling epidemics. In the context of university resumption, this study identifies the risk elements in terms of the mechanism of virus transmission. The effect of two recognized effective measures, i.e., occupancy constraints and ventilation intervention, on the infection risk are quantified and compared using the improved Wells–Riley model. Considering the priority of these two measures, the controlling quantity are determined, and the optimal schemes are proposed based on the targeted infection risk. The results show that the effect of reducing infection risk by constraining occupancy within 25% of all public campus buildings is better than that achieved by increasing the ventilation rate alone. If the ventilation system of the building type is operated by occupiers, it is a priority to prevent the risk of virus infection by restricting occupancy and ensuring the distance between occupants, while if the ventilation system of the building type is centrally controlled, it is a priority to increase the ventilation rate and then limit the occupancy rate during peak periods to 75%.

Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm

Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause energy waste or reduce human comfort. In this study, a two-stage leakage fault diagnosis (LFD) method based on an Adam optimization BP neural network algorithm, which locates leakage faults based on the change values of monitoring data from flow meters and pressure sensors in air conditioning water systems, is proposed. In the proposed LFD method, firstly, the ACWS network’s hydraulic model is built on the Dymola platform. At the same time, a cuckoo algorithm is used to identify the pipe network’s characteristics to modify the model, and the experimental results show that the relative error between the model-simulated value and the actual values is no more than 1.5%. Secondly, all possible leakage conditions in the network are simulated by the model, and the dataset is formed according to the change rate of the observed data, and is then used to train the LFD model. The proposed LFD method is verified in a practical project, where the average accuracy of the first-stage LFD model in locating the leaking pipe is 86.96%; The average R2 of the second-stage LFD model is 0.9028, and the average error between the predicted location and its exact location with the second-stage LFD model is 6.3% of the total length of the leaking pipe. The results show that the proposed method provides a feasible and convenient solution for timely and accurate detection of pipe network leakage faults in air conditioning water systems.