Transmission and infection risk of COVID-19 when people coughing in an elevator

People in cities use elevators daily. With the COVID-19 pandemic, there are more worries about elevator safety, since elevators are often small and crowded. This study used a proven CFD model to see how the virus could spread in elevators. We simulated five people taking in an elevator for two minutes and analyzed the effect of different factors on the amount of virus that could be inhaled, such as the infected person's location, the standing positions of the persons, and the air flow rate. We found that the position of the infected person and the direction they stood greatly impacted virus transmission in the elevator. The use of mechanical ventilation with a flow rate of 30 ACH (air changes per hour) was effective in reducing the risk of infection. In situations where the air flow rate was 3 ACH, we found that the highest number of inhaled virus copies could range from 237 to 1186. However, with a flow rate of 30 ACH, the highest number was reduced to 153 to 509. The study also showed that wearing surgical masks decreased the highest number of inhaled virus copies to 74 to 155.

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.

What is suitable social distancing for people wearing face masks during the COVID-19 pandemic?

COVID-19 has caused the global pandemic and had a serious impact on people's daily lives. The respiratory droplets produced from coughing and talking of an infected patient were possible transmission routes of coronavirus between people. To avoid the infection, the US Centers for Disease Control and Prevention (CDC) advised to wear face masks while maintaining a social distancing of 2 m. Can the social distancing be reduced if people wear masks? To answer this question, we measured the mass of inhaled droplets by a susceptible manikin wearing a mask with different social distances, which was produced by coughing and talking of an index "patient" (human subject) also wearing a mask. We also used the computational fluid dynamics (CFD) technology with a porous media model and particle dispersion model to simulate the transmission of droplets from the patient to the susceptible person with surgical and N95 masks. We compared the CFD results with the measured velocity in the environmental chamber and found that the social distancing could be reduced to 0.5 m when people wearing face masks. In this case, the mass concentration of inhaled particles was less than two people without wearing masks and with a social distancing of 2 m. Hence, when the social distancing was difficult, wearing masks could protect people. We also found that the leakage between the face mask and the human face played an important role in the exhaled airflow pattern and particle dispersion. The verified numerical model can be used for more scenarios with different indoor environments and HVAC systems. The results of this study would make business profitable with reduced social distancing in transportation, education, and entertainment industries, which was beneficial for the reopening of the economy.