Colliding respiratory jets as a mechanism of air exchange and pathogen transport during conversations

Abstract

Air exchange between people has emerged in the COVID-19 pandemic as the important vector for transmission of the SARS-CoV-2 virus. We study the airflow and exchange between two unmasked individuals conversing face-to-face at short range, which can potentially transfer a high dose of a pathogen, because the dilution is small when compared to long-range airborne transmission. We conduct flow visualization experiments and direct numerical simulations of colliding respiratory jets mimicking the initial phase of a conversation. The evolution and dynamics of the jets are affected by the vertical offset between the mouths of the speakers. At low offsets the head-on collision of jets results in a `blocking effect', temporarily shielding the susceptible speaker from the pathogen carrying jet, although, the lateral spread of the jets is enhanced. Sufficiently large offsets prevent the interaction of the jets. At intermediate offsets (8-10 cm for 1 m separation), jet entrainment and the inhaled breath assist the transport of the pathogen-loaded saliva droplets towards the susceptible speaker's mouth. Air exchange is expected, in spite of the blocking effect arising from the interaction of the respiratory jets from the two speakers.

Virus transmission by aerosol transport during short conversations

“…We therefore do not Flow E13-7 incorporate the thermodynamics of evaporation in this study, and treat small droplets and droplet nuclei as interchangeable; we refer to both as 'aerosols'. This is consistent with Abkarian et al (2020), Yang et al (2020) and Giri et al (2022), who have not considered effects of droplet evaporation in their speechflow simulation/modelling. The resulting governing equations, namely the Boussinesq-Navier-Stokes equations, the continuity equation and the scalar transport equations, are solved using the finite volume DNS solver, Megha-5 (for further details, see appendix A).…”

“…It is worth noting that our grid resolution is better than that used in the DNS of studies on cough flows by Chong et al (2021) and Rosti et al (2021) when adjusted for Reynolds number, since our Reynolds number is much lower than that used in these studies (see table S1 (supplementary material)). While the DNS of conversation by Giri et al (2022) uses a grid size of 2 mm which is smaller than ours, in view of the above discussion, we see our grid size of 256 × 128 × 128 to be adequate. This exercise serves us well in reducing the computational cost, and enables us to report results from a large number of simulations (nearly 25).…”

“…A recent study by Poydenot et al. (2021) estimates to be as high as , and we have included this value in ‘set 3’, with and same as those in set 2. Incidentally, the parameters in set 3 are the same as used by Giri et al. (2022) for estimating the probability of infection when the speaker is a ‘super-titerer’ (i.e. a person who has very high viral load in their saliva).…”

“…The other factors that influence the risk of infection are the duration of speech as discussed in § 3.3 and the measure of total viral exposure used in calculating the probability of infection. For example, the method used by Giri et al. (2022), which is essentially the same as that proposed by Yang et al. (2020), considers only the inhaled virions and therefore results in probabilities lower than our estimates, which also include viral exposure to eyes and mouth. Furthermore, the results presented here are relevant for poorly ventilated spaces whereas a cross-ventilation can make a difference in the number of ingested virions.…”

“…Furthermore, N II,i /max(N II,i ) is significantly reduced for y s /L s > 0.3, implying that for a given separation distance between two people, the viral load during a short conversation can be expected to be low if the condition y s L s /3 is satisfied. Giri et al (2022) found the 'critical' vertical separation for a maximum exposure to infected aerosols during a conversation to be 2y s /(L s tan(𝛼)) ≈ 0.83 (where 𝛼 is the jet half-angle), which translates into y s /L s ≈ 0.087, using 𝛼 = 11.8 • (Giri et al, 2022). They did this exercise for L s = 1 m. This value is somewhat smaller than the y s /L s ≈ 0.12 that we obtained for maximum aerosol exposure to person 2 for L s = 1.2 m (figure 6b).…”