Engineers Track the Coronavirus's Movements Through a Supermarket

In a study of COVID-19 pathways inside supermarkets, a team of environmental engineers and modelers investigated the role that surfaces play not as infection hazards, but rather as deterrents.

Shelves, floors and ceilings proved to be attachment magnets for virus-laden particles, reducing the concentration of suspended particles in the air by as much as 50%, according to the team’s simulations. Their report was published last month in the American Society of Civil Engineers Journal of Environmental Engineering.

“Surfaces can reduce the airborne spread of disease particles substantially – and effectively. We found that their attachment on surfaces reduces their transport significantly within the supermarket,” says Michel Boufadel, director of NJIT’s Center for Natural Resources. “Large particles attach after traveling short distances, while smaller ones also end up attaching after traveling, in some cases, longer and further from their point of emission.”

Boufadel says the group supports existing approaches for reducing exposure among people through the adoption of one-way movement within supermarket aisles.

“However, we also propose placing display shelves within the aisles in a staggered way to form baffles that would both increase the surface area and block the transport of airborne particles,” he adds. Their results suggest that the type of surface is not crucial as the particles are highly adhesive and stick easily to wood, plastic and metal.

The team investigated the movement of virus-laden particles in an archetypical, mid-sized supermarket (40 meters long by 30 meters wide by 4.5 meters in height). The supermarket had air vents in the ceiling at the center part of the building, while the return vents were near the walls. In additional measures, they say that the risk of virus-laden particles being sucked into the ventilation system through return vents, posing infection risks for the buildings connected to the same ventilation system, calls for the installation of high-efficiency particulate air (HEPA) filters and highly absorbent pleated filters.

“While vaccines are the key to herd immunity and recovery in this pandemic, the study of pathogen transport in frequently traveled and densely occupied spaces will help us prepare transmission mitigation strategies in the event of future airborne diseases,” Boufadel says.

Last April, he and his NJIT colleagues secured a National Science Foundation RAPID (Rapid Response Research) grant to track the spread of SARS-CoV-2 by combining advanced statistical methods with models that incorporate environmental conditions, such as wind speed, temperature, social distancing and the physical design of interior spaces.

At that point in the pandemic, modelers were largely tracking the growth in the number of cases and adjusting their projections as the numbers changed, attributing flattening curves, for example, to social measures. The NJIT team sought to develop models that would include those and other parameters to make their predictions.

“One of the limitations to date in predicting the virus’s spread, which has led to conflicting reports on the value and time of peak outbreaks, is the lack of physicality in models. Most rely on the current caseload to predict the number of future cases, but as environmental factors change, these predictions become more difficult. That’s why we’ve seen wide swings in projections,” Boufadel observed at the time.

The team found that large droplets, on the order of a few hundred microns (100 microns is the thickness of a hair), tend to fall onto the ground within a short distance, and for these, the 6-foot social distance was appropriate. Smaller droplets, however, such as 5-micron particles, behaved similarly to dust particles in the sense that they remained in the air longer following emission and traveled further before falling to the ground.

The team, which also includes faculty from Johns Hopkins University, the University of Cincinnati and the University of Pittsburg, ran its simulations at the San Diego Supercomputer Center at the University of California-San Diego.