Measurement of temperature and relative humidity in exhaled breath

Mansour, Elias; Vishinkin, Rotem; Rihet, Stéphane; Saliba, Walaa; Fish, F; Sarfati, Patrice D.; Haick, Hossam

doi:10.1016/j.snb.2019.127371

Cited by 143 publications

(91 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this analysis, we have assumed the volume of cough exhaled as 1 l, 11 temperature as 33 °C, 30 and a relative humidity of 76.7%. 30 These representative values are taken from references reporting measurements on coughing. We assume that the cloud is ejected in a room at the temperature of 25 °C and the humidity level of either 0%, 20%, or 50%.…”

mentioning

confidence: 99%

Reducing chances of COVID-19 infection by a cough cloud in a closed space

Agrawal

Bhardwaj

2020

Physics of Fluids

View full text Add to dashboard Cite

The cough of a COVID-19 infected subject contaminates a large volume of surrounding air with coronavirus due to the entrainment of surrounding air in the jet-like flow created by the cough. In the present work, we estimate this volume of the air, which may help us to design ventilation of closed spaces and, consequently, reduce the spread of the disease. Recent experiments [P. P. Simha and P. S. M. Rao, “Universal trends in human cough airflows at large distances,” Phys. Fluids 32 , 081905 (2020)] have shown that the velocity in a cough-cloud decays exponentially with distance. We analyze the data further to estimate the volume of the cough-cloud in the presence and absence of a face mask. Assuming a self-similar nature of the cough-cloud, we find that the volume entrained in the cloud varies as 0.666 , where c is the spread rate and d c is the final distance traveled by the cough-cloud. The volume of the cough-cloud without a mask is about 7 and 23 times larger than in the presence of a surgical mask and an N95 mask, respectively. We also find that the cough-cloud is present for 5 s–8 s, after which the cloud starts dissipating, irrespective of the presence or absence of a mask. Our analysis suggests that the cough-cloud finally attains the room temperature, while remaining slightly more moist than the surrounding. These findings are expected to have implications in understanding the spread of coronavirus, which is reportedly airborne.

show abstract

mentioning

confidence: 99%

Reducing chances of COVID-19 infection by a cough cloud in a closed space

Agrawal

Bhardwaj

2020

Physics of Fluids

View full text Add to dashboard Cite

show abstract

“…Clearly, whichever side is used that comes in contact with the face, the wetting will be affected by the highly humid environment that is created within the space between the nose and the mask due to the moisture from exhaled air (typically, this relative humidity value can be as high as 90%). 32 A detailed study is required to elucidate the effect of ambient humidity on the wetting signature of microbe-laden droplets on masks. In summary, this study points out the relevance of superhydrophobic coatings as a modifier to existing masks and provides some clue on how effective they are when applied on both sides of the mask (front and back), with respect to microbial droplet adhesion.…”

Section: Resultsmentioning

confidence: 99%

Wetting, Adhesion, and Droplet Impact on Face Masks

Melayil

Mitra

2021

Langmuir

View full text Add to dashboard Cite

In the present pandemic time, face masks are found to be the most effective strategy against the spread of the virus within the community. As aerosol-based spreading of the virus is considered as the primary mode of transmission, the interaction of masks with incoming droplets needs to be understood thoroughly for an effective usage among the public. In the present work, we explore the interactions of the droplets over the most commonly used 3-ply surgical masks. A detailed study of the wetting signature, adhesion, and impact dynamics of water droplets and microbe-laden droplets is carried out for both sides of the mask. We found that the interfacial characteristics of the incoming droplets with the mask are very similar for the front and the back side of the mask. Further, in an anticipated attempt to reduce the adhesion, we have tested masks with a superhydrophobic coating. It is found that a superhydrophobic coating may not be the best choice for a regular mask as it can give rise to a number of smaller daughter droplets that can linger in air for longer times and can contribute to the transmission of potential viral loads.

show abstract

“…On the one hand, the fitness gym has an AC unit and mechanical ventilation operating 24 h. It also has a capacity for 30–40 people to exercise aerobically and anaerobically. These high-intensity activities are expected to increase the occupants’ body temperature and exhalation rate, which could impact the enclosed space’s temperature and humidity saturation [ 26 ]. On the other hand, a living room is a significantly smaller space with a capacity for up to 10 or 12 people.…”

Section: Estimating Occupancy Levels In Enclosed Spaces Using Envimentioning

confidence: 99%

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Vela

Alvarado-Uribe

Davila

et al. 2020

Sensors

View full text Add to dashboard Cite

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

show abstract

Measurement of temperature and relative humidity in exhaled breath

Cited by 143 publications

References 32 publications

Reducing chances of COVID-19 infection by a cough cloud in a closed space

Reducing chances of COVID-19 infection by a cough cloud in a closed space

Wetting, Adhesion, and Droplet Impact on Face Masks

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Contact Info

Product

Resources

About