Air Sampling vs. Dispersion Modeling During the COVID-19 Pandemic

By: Bernard L. Fontaine, Jr., CIH, CSP, FAIHA, Contributor

As the American economy recovers and workers return to work, there is concern the air may contain the SARS CoV-2 virus. This is especially of concern if it is found there is no long-term immunity from recovered individuals. People in various industries may work and/or live in close quarters, such as meat processing, canaries, fisheries, onboard military ships and submarines, schools, agricultural settings, etc., or use public transportation.

The SARS CoV-2 virus has a reproductive rater higher than other similar contagions, especially during speaking, suggesting it is a super spreader in the environment. The infectivity rate appears low, as represented by the Ro and K factors, suggesting the viral load for causing infection may be 1,000 log copies or as low as 100 log copies in air.

Preliminary air sample results are mixed, based on study limitations to healthcare and the number of total number of samples collected during the investigation. Compounding this is the fact that there are no established air sampling protocols for viral loads in air. Moreover, the level of risk of respiratory infection has not been determined.

In certain limited workplaces and work practices, air monitoring may be appropriate to evaluate worker exposure and determine the level of risk. The spread of the SARS-CoV-2 virus through the air has become a controversial topic among scientists. Airborne transmission calculators have modeled the dynamics of risk, based on building management system parameters, but there are limitations in the available information and assumptions.

Air sampling methods vary by sample size and cost, while analytical test method for the SARS CoV-2 virus is limited presently to reverse transcriptase polymerase chain reaction (RT-PCR) for precision and accuracy. Reliability and cost issues remain elusive on whether or not to perform air monitoring in work areas of concern or use a risk-assessment dispersion model. This article attempts to investigate the business value for collecting air samples, in collaboration with modeling, to protect the health of the workforce in specific or nontraditional workplaces.

Monitoring Air in Indoor Environments

Without air monitoring in workspaces with high-density occupancy or workspaces with a lack of building or room ventilation, workers are at risk of exposure to the virus. (photo courtesy Adobe Stock Images)

In the working environment, people may be exposed a variety of noxious gases, vapors, dusts, mists and fumes. Besides these potential airborne contaminants, infectious agents like bacteria, viruses, mold spores and fungi can be found in the indoor environment. Some air contaminants may be liberated from a work process or operation, while other indirect exposure may arise as a result of dry sweeping, vacuuming or cleaning contaminated surfaces. Of most concern today is the presence of the SARS CoV-2 novel coronavirus, which is attributed to the generation of aerosols by infected individuals during the COVID-19 pandemic.

Airborne contaminants are inhalable particles suspended in the air that may enter the body when breathed. Most large particles are stopped in the upper respiratory tract (e.g., nose and back of the throat), but smaller respirable particles can reach deep inside the lungs. The mass and particle size will determine how far the aerosolized particle can reach the bronchioles or the terminal alveolar sacs. Inhaled contaminants may cause respiratory issues, including tissue damage, tissue reaction, disease or physical obstruction. New scientific evidence is available on the transmission of SARS-CoV-2 that suggests that, not only can larger droplets become airborne, but respiratory aerosols can function as potential transmission pathways for COVID-19.

The novel coronavirus may be aerosolized by coughing, singing, shouting, sneezing, or by playing sports or a musical instrument. The airborne concentration is based on the rate of generation and suspension of the respiratory droplets or aerosols in air over a period of time. Large respiratory droplets in the near field are typically greater than 50µm in size with water and mucous; medium-sized respiratory droplets are greater than 10µm; and small, micro-droplet nuclei are less than 5µm—which can spread to the far field. Respiratory droplets can cause infection when they are inhaled or deposited on mucous membranes in sufficient quantities over time, such as those that line the inside of the nose and mouth, bronchiole passages and alveolar spaces in the deep lung.

Healthcare workers are at risk of contracting COVID-19, especially those who might become infected while treating patients having procedures and surgeries that are Aerosol Generating Medical Procedures (AGMP). (photo courtesy Adobe Stock Images)

As the respiratory droplets travel further from an infected person, the airborne concentration of these droplets decreases. Larger droplets fall out of the air due to gravity. Smaller droplets and particles spread in the air, depending on the airflow pattern and amount of building ventilation. With passing time, the amount of infectious virus in respiratory droplets can decrease with fewer people in the room. In most instances, the building ventilation should continue to operate even with nobody in the room and for a period after normal work hours.

Other potential modes of transmission may include medical procedures, such as bronchoscopy, tracheotomy, tracheal tube intubation, noninvasive ventilation and dental work. Viral aerosols may become aerosolized by flushing toilets in COVID-19 patient hospital rooms; and vacuuming carpets and cleaning other porous surfaces, such as bed linen and surgical gowns in healthcare facilities. For example, otolaryngologists and supporting healthcare workers (HCWs) are particularly at high risk of becoming infected while treating patients, as many in-
office procedures and surgeries are Aerosol Generating Medical Procedures (AGMP).

Based on a review of the literature and various guidelines, engineering and administrative controls help mitigate the risk to healthcare workers of becoming infected with SARS-CoV-2 while providing clinical care. Without air monitoring in workspaces with high-density occupancy or workspaces with a lack of building or room ventilation, workers are at risk of exposure to the virus.

Risk of Airborne Infectious Disease

One approach to quantifying the risk associated with airborne transmission aerosols and respiratory disease has been the Wells-Riley model (Riley et al., 1978). The model is based on the concept of “quantum of infection,” whereby the generation rate of infectious airborne particles (or quanta) can be used to model steady state conditions in a well-mixed and ventilated indoor environment. The model was constructed to evaluate exposure to infectious particles in air and potentially succumbing to infection. In 1997, the original Wells-Riley model was enhanced to consider dynamic or time-varying exposure.

The premise of the Wells-Riley model examined the probability of infection based on assumptions like the number of infected cases; number of susceptible individuals; pulmonary ventilation rate; quanta generation rate; room air ventilation with clean air; and exposure time. The model did not look at the person-to-person variability of those who may be infected, but not expressing clinical symptoms, or changes in room occupancy density because people move about in different workspaces throughout the day. Finally, the model did not evaluate the size or mass for aerosol dispersion based on source of exposure, i.e., coughing, sneezing, speaking etc., and evaporation of moisture, which may affect transmission near vs. far field.

[Editors note: This article is an excerpt/shortened version of an article that appears in its entirety on IHW’s website. For the full article and a complete list of references/sources, visit https://industrialhygienepub.com/industrial-hygiene-in-the-workplace/air-sampling-verses-dispersion-modeling-during-the-covid-19-pandemic/ .]

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