Modeling impacts of ventilation and filtration methods on energy use and airborne disease transmission in classrooms

Abstract

Lowering the potential of airborne disease transmission in school buildings is especially important in the wake of the COVID-19 pandemic. The benefits of increased ventilation and filtration for reducing disease transmission compared to drawbacks of reduced thermal comfort and increased energy consumption and electricity demand are not well described. A comprehensive simulation of outdoor air ventilation rates and filtration methods was performed with a modified Wells-Riley equation and EnergyPlus building simulation to understand the trade-offs between infection probability and energy consumption for a simulated classroom in 13 cities across the US. A packaged heating, ventilation, and air conditioning unit was configured, sized, and simulated for each city to understand the impact of five ventilation flow rates and three filtration systems. Higher ventilation rates increased energy consumption and resulted in a high number of unmet heating and cooling hours in most cities (excluding Los Angeles and San Francisco). On average, across the 13 cities simulated, annual energy consumed by an improved filtration system was 31% lower than the energy consumed by 100% outdoor air ventilation. In addition, the infection probability was 29% lower with improved filtration. An economizer, which activates cooling based on an outdoor temperature setpoint, increased ventilation and reduced both energy consumption and infection probability. It was also concluded that ventilation and filtration measures better reduced absolute infection probability when the quanta generation rate for an infectious disease was higher. Dynamic outdoor airflow rate controls and filtration technologies that consider both health and energy consumption are an important area for further research.

Keywords: Economizers; Filtration; Indoor air quality; Infectious disease transmission; Packaged HVAC; Ventilation.

Fig. 1

Geometry for classroom model.

Fig. 2

Schematic of HVAC system model. Economizer moves dampers to supply 100% outdoor air when the outdoor temperature is below the set point and cooling is required.

Fig. 3

Filter resistance as a function of airflow for MERV8 versus MERV13 filter.

Fig. 4

Annual electricity consumption per classroom (a), unmet heating and cooling hours (b), and maximum 15-min peak demand (c) by location and ventilation rate of 3.5, 7, and 10.5 l/(s.person), economizer logic (E) (7.0–10.2 l/(s.person)) and 100% outdoor air (10.5–30.6 l/(s.person)).

Fig. 5

Annual electricity consumption per classroom (a), unmet heating and cooling hours (b), and maximum 15-min peak demand (c) by location and ventilation rate of 3.5, 7, and 10.5 l/(s.person), economizer logic (E) (7.0–10.2 l/(s.person)) and 100% outdoor air (10.5–30.6 l/(s.person)).

Fig. 6

Box and whisker plots of results for all cities for annual HVAC energy consumption (left) and infection probability (right) categorized by MERV filtration type (8 or 13), use of in-room filtration (IRF), and ventilation rate. For each filtration and ventilation combination, the following statistics are shown (outliers excluded and shown as single data points): minimum (bottom bar), 25% percentile (bottom of box), median (bar), mean (X), 75% percentile (top of box), and maximum (top bar).

Fig. 7

Impact of economizer controls on increase in ventilation rate over baseline of 7 l/(s.person), annual HVAC energy savings, and absolute reduction in infection probability.

Fig. 8

Impact of increasing quanta generation by 10 times on calculated P26 probability for all HVAC scenarios modeled. The quadratic relationship does not have a physical meaning and is used to describe the relationship only over the range shown. The infection probability has an asymptote at 100% which is not reflected by the quadratic relationship.