Simulating the impact of ventilation on COVID-19 infection probability from aerosol transmission in enclosed spaces

Most people spend 80-90% of their lives indoors. This makes controlling the airborne transmission of respiratory viruses such as influenza, rhinovirus, SARS, and COVID-19 in indoor environments important for healthy building outcomes. Though direct transmission from droplets and surfaces is usually a more effective means of infection transfer, buildings need to operate assuming aerosol transmission can be a serious risk. This study used simulations to assess the impacts of occupant density and ventilation rates as control measures to reduce the risk of aerosol transmission of COVID-19 in large and small offices. The simulation outputs were selected to correspond with in situ CO2 sensors and control points. The results of the simulation can be used to set targets for CO2 and other parameters that can be measured by low-cost sensors to manage risk of infection due to aerosol transmission. © International Building Performance Simulation Association, 2022

Air-permeable redox mediated transcutaneous CO2 sensor.

Standard clinical care of neonates and the ventilation status of human patients affected with coronavirus disease involves continuous CO2 monitoring. However, existing noninvasive methods are inadequate owing to the rigidity of hard-wired devices, insubstantial gas permeability and high operating temperature. Here, we report a cost-effective transcutaneous CO2 sensing device comprising elastomeric sponges impregnated with oxidized single-walled carbon nanotubes (oxSWCNTs)-based composites. The proposed device features a highly selective CO2 sensing response (detection limit 155 ± 15 ppb), excellent permeability and reliability under a large deformation. A follow-up prospective study not only offers measurement equivalency to existing clinical standards of CO2 monitoring but also provides important additional features. This new modality allowed for skin-to-skin care in neonates and room-temperature CO2 monitoring as compared with clinical standard monitoring system operating at high temperature to substantially enhance the quality for futuristic applications.

IoT platform for level detection of CO2 in closed environments

In this research seeks to implement a prevention system that measures CO2 levels in closed environments, in order to reduce the probability of contagion of SARS-COV-2. This system interacts with a microcontroller (ESP32) which will obtain the CO2 concentrations in ppm from a gas sensor (MQ-135) which in the first instance will proceed to calibrate the sensor using established formulas so that it gives exact data and can convert them to ppm. Finally, the system based on IoT technology will sfhow the concentration levels obtained in real time and thus, with already established ranges, it will indicate to the user the level of probability of contagion to which they will be exposed, making the stay in a closed environment safer. © 2022 IEEE.

A Machine Learning Approach for Optimal Ventilation based on Data from CO2Sensors

Window ventilation is important in everyday life. The COVID-19 pandemic in particular has shown that air exchange is necessary to minimize the spread of viruses. Efficient ventilation can be supported with the help of sensors and intelligent data processing. A CO2 sensor, for example, can be used to measure CO2 levels and, together with IoT hardware, indicating when ventilation is needed. By combining these components together with algorithms, an assessment of such window ventilation can be made. This paper presents a measurement setup to measure CO2. The measured values in the form of time series are used in the setup to learn the time points ventilation. Two approaches are taken to analyze these time series. The first approach is based on the simple K-Means and K nearest neighbors algorithm, the second approach uses Dynamic Time Warp (DTW) Barycenter Averaging (DBA). Both approaches are compared in this work in the detection of ventilation events. © 2022 IEEE.

A portable system for CO2and pressure monitoring inside the dead space of face masks

This paper presents a portable system designed to monitor physical and chemical quantities inside the dead space of face masks that are commonly used to prevent the diffusion of SARS-CoV-2 infection. The system is a 'side stream' analyzer that continuously samples the gas inside the gap between the mask and the user mouth and provides, in real time, the temporal evolution of CO2 concentration, differential pressure and temperature. These quantities are related to the mask behaviour in term of breath resistance, CO2 accumulation and overheating that, in turn, can cause physiological side effects and a feeling of discomfort. These effects become more relevant when the masks are employed during physical activities, such as team sports, and for this reason the system has been designed to have a reduced weight and dimensions as well as a reduced invasiveness of the sampling line. The paper also proposes a measurement procedure for the evaluation of the CO2 inspired volume in real conditions during the physical activity. At the current stage, the proposed procedure is only able to provide qualitative measurements, but it can work in real conditions and it does not require bulky and expensive facilities as the ones required by the actual international standards for the certification of respiratory protective devices. © 2022 IEEE.

CO2-measuring dongle.

The compact pocketable CO2-measuring device is built on a small printed circuit board (PCB) with the dimensions of ca. 8.5 × 3 cm. It is plugged into the universal serial bus (USB) port of a personal computer (PC) which serves to provide power and for downloading the measurements. The measurements can be viewed on the computer display where they also can be plotted as a graph to recognize trends. As the level of CO2 rises indoors when people are present and the air is not exchanged adequately the device can be employed as a personal device for monitoring indoor air quality. This is of particular interest when airborne pathogens might be present such as during the COVID-19 pandemic. The device utilizes a novel compact CO2 sensor which has only become available commercially recently, and is based on the photoacoustic measuring principle. A graphical user interface on the PC programmed in Python allows easy interaction with the user. A MSP430FR2433 microcontroller on the board controls the sensor and establishes the communication to the software application on the computer. This was facilitated by the choice of Forth as the programming framework for the microcontroller.

Trial of Building a Resilient Face-To-Face Classroom Based on CO2-Based Risk Awareness

The COVID-19 pandemic forced many schools to switch to online classes. Although there has been a movement to return to face-to-face classes since then, many schools are still struggling to ensure safety during classes and subsequent examinations in a face-to-face environment. In this study, we attempted to visualize the relationship between class usage and building air conditioning management by installing CO2 sensors at fixed points in classrooms and also applied them to environmental monitoring during examinations to grasp the risks in real time and provide a response. © 2022, IFIP International Federation for Information Processing.

Impact of COVID-19 countermeasures on the energy and water consumption and CO2 concentration of municipal buildings

As a disease, COVID-19 has shaken the world in 2020/21. Countermeasures have been taken by governments to protect against infection, which have also affected the water and energy consumption of buildings. In particular, buildings managed by public authorities (e.g. kindergartens, schools and administrative offices) have been closed and reopened with different concepts. In this paper, the impact of countermeasures on the electrical and thermal energy and water consumption of municipal buildings is investigated using monitoring data of 100 buildings from 2018 to 2021 of a city in the state of North Rhine-Westphalia (NRW) in Germany. Where available, data of CO2 sensors from building automation systems are included in the evaluation. The CO2 concentration is in a range of concern (1500-2000 ppm) for 2 % of presence periods. A value in the unacceptable range (>2000 ppm) is not found in the CO2 data. Since only ventilation-conditioned spaces are equipped with CO2 sensors, no conclusions can be drawn about non-ventilated buildings. We investigate to which extent the measures taken have changed the energy and water consumption. On total average, the heat demand increased lightly by 5 % and water and electricity demand decreased by 15 % and 12 % respectively. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

A Miniaturised, Fully Integrated NDIR CO2 Sensor On-Chip.

In this paper, we present a fully integrated Non-dispersive Infrared (NDIR) CO2 sensor implemented on a silicon chip. The sensor is based on an integrating cylinder with access waveguides. A mid-IR LED is used as the optical source, and two mid-IR photodiodes are used as detectors. The fully integrated sensor is formed by wafer bonding of two silicon substrates. The fabricated sensor was evaluated by performing a CO2 concentration measurement, showing a limit of detection of ∼750 ppm. The cross-sensitivity of the sensor to water vapor was studied both experimentally and numerically. No notable water interference was observed in the experimental characterizations. Numerical simulations showed that the transmission change induced by water vapor absorption is much smaller than the detection limit of the sensor. A qualitative analysis on the long term stability of the sensor revealed that the long term stability of the sensor is subject to the temperature fluctuations in the laboratory. The use of relatively cheap LED and photodiodes bare chips, together with the wafer-level fabrication process of the sensor provides the potential for a low cost, highly miniaturized NDIR CO2 sensor.