A Smart CO2-Based Ventilation Control Framework to Minimize the Infection Risk of COVID-19 In Public Buildings

This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance. © International Building Performance Simulation Association, 2022

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.

Towards enabling accurate measurements of CO2 exposure indoors

Indoor climate standards recommend maximum CO2 concentration levels in rooms. At present the CO2 exposure of occupants is assessed by measurements in a room's exhaust air or near the walls. However, most often room air is not perfectly mixed and CO2 emitted in air exhaled by occupants is non-uniformly distributed. It is more reliable to assess CO2 concentration in the air inhaled by occupants by measurements in the breathing zone as close as possible to the face. In this work the importance of the location of air sampling in front of the face, the time and frequency of sampling, and the breathing mode, for the accuracy of CO2 measurements were studied. For this a breathing thermal manikin was used. The CO2 concentration in the air exhaled by the manikin was adjusted to be the same as that for an average person. The results show that synchronization of the air sampling with the inhalation period of breathing is the most accurate method. The air sampling locations positioned between the centre of the chin and the mouth, or at the left (or right) corner of the mouth, or next to and above the nostrils, are the most representative for assessing CO2 concentration in the inhaled air. The obtained results can be used for the development of wearable devices for accurate assessment of exposure to CO2 and other indoor pollution, as well as advanced air distribution methods, such as personalised ventilation that supplies clean air to the breathing zone.

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

Gymnasiums, fitness rooms and alike places offer exercise services to citizens, which play positive roles in promoting health and enhancing human immunity. Due to the high metabolic rates during exercises, supplying sufficient ventilation in these places is essential and extremely important especially given the risk of infectious respiratory diseases like COVID-19. Traditional ventilation control methods rely on a single CO2 sensor (often placed at return air duct), which is often difficult to reflect the human metabolic rates accurately, and thus can hardly control the infection risk instantly. Thus, to ensure a safe and healthy environment in places with high metabolism, a real-time metabolism-based ventilation control method is proposed. A computer vision algorithm is developed to monitor human activities (regarding human motion amplitude and speed) and an artificial neural network is established for metabolic prediction. Case studies show that the proposed metabolism-based ventilation control method can reduce the infection probability down to 4.3-6.3% while saving 13% of energy in comparison with the strategy of fixed-fresh-air ventilation. In the development of healthy and sustainable society, gymnasiums and alike exercise places are essential and the proposed ventilation control method is a promising solution to decrease the risk of COVID-19 while preserving features of energy saving and carbon emission reduction.