Airborne pollen grains facilitate spread of COVID-19 by carrying SARS-CoV-2 particles

Computer models simulating the movement of pollen grains from trees in crowded areas could accelerate the spread of COVID-19.

Tree Pollen. Image Credit: Elisa Manzati/Shutterstock.com

Pollen particles as a new infectious agent

The general understanding of viral infection focuses on how viral particles escaping one person to infect a nearby person. However, other infectious agents are also important to factor in including the role of the environment.

From the onset of the COVID-19 pandemic, researchers developed models to explain the high rate of COVID-19 infection, and found that viral particles are able to last for prolonged periods on surfaces, facilitating virus spread.

New research now shows a new environmental agent that has not been considered previously. Published in the Journal Physics of Fluids, authors Talib Dbouk and Dimitris Drikakis investigated how pollen facilitates the spread of an RNA virus like the COVID-19 virus.

The researchers used computer model simulations to examine the role of microscopic particles in how viruses are transmitted.

The hypothesis was first developed as researchers observed a correlation between COVID-19 infection rates and the pollen concentration on the National Allergy Map. Previous studies have already shown that trees can output 1,500 pollen grains per cubic meter into the air on heavy days and that each pollen grain carries hundreds of virus particles at a time, but no one has yet to consider such infectious spread in crowded areas.

To our knowledge, this is the first time we show through modeling and simulation how airborne pollen micrograins are transported in a light breeze, contributing to airborne virus transmission in crowds outdoors,”


Drikakis

Dynamic environmental agents could limit the effectiveness of social distancing measures

The researchers simulated all the pollen-producing parts of a computational willow tree in a public area of an outdoor gathering of roughly 10 or 100 people, some of them shedding COVID-19 particles, and subjected the people to 10,000 pollen grains.

The models were then tuned for a typical spring day in the US in terms of temperature, wind speed, and humidity, which can all affect pollen transport.

The simulations then showed that it took less than a minute for pollen grains to pass through the crowd surrounding the tree, which could spread the virus rapidly and readily, infecting new individuals even when socially distanced.

Even when a 6-foot distance was maintained among individuals, it was not an adequate distance to limit the risk of disease spread in such an area with high pollen concentration in the air.

The authors, therefore, advocate basing preventative measures such as distancing on seasonal factors to better manage infection risk. Adapting measures such as social distancing in areas known for high pollen concentrations during spring could therefore mitigate at least some risk of infection.

Further studies will refine the simulations including whether specific trees, areas, or viruses, are more or less likely to be transported.

One of the significant challenges is the re-creation of an utterly realistic environment of a mature willow tree. This included thousands of tree leaves and pollen grain particles, hundreds of stems, and a realistic gathering of a crowd of about 100 individuals at about 20 meters from the tree.”


Dbouk

Although this study first and foremost demonstrates a new form of COVID-19 transmission, authors also hope to spur further studies in the fluid dynamics of plants and the interaction between airborne pollen grains and the human respiratory system under different environmental conditions. Nonetheless, this study is the first to show a new method of infection through airborne virus transmission and is particularly insightful when designing adaptive preventative measures for the current global pandemic.

Journal reference:

  • Dbouk, T. and Drikakis, D. (2021). On pollen and airborne virus transmission. Physics of Fluids, 33(6), p.063313.

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