Development and validation of a dynamic mass-balance prediction model for indoor particle concentrations in an office

Soo Bhin Park1, Ju-Hyeong Park2, Young Min Jo3

  • 1Department of Mechanical Engineering, College of Engineering, Kyung Hee University, Yong-In, Gyung-Gi Do, South Korea.

Building and Environment
|April 15, 2024
PubMed

Insights

This study developed a dynamic model to predict indoor air quality. The model guides optimal intermittent operation of air purifiers (APs) to effectively reduce fine particulate matter (PM2.5) while meeting air quality standards.

Area of Science:

  • Environmental Science
  • Indoor Air Quality
  • Aerosol Science

Background:

  • Intermittent air purifier (AP) operation is recommended by the Korean government to control indoor particulate matter (PM) concentrations.
  • Guidelines for optimal AP operation timing and duration are lacking to meet mandatory air quality standards and reduce exposure to PM2.5.

Purpose of the Study:

  • To develop and validate a dynamic mass-balance model for predicting indoor PM concentrations in an office environment.
  • To assess the impact of AP operation, occupant presence, and particle infiltration on indoor PM levels.
  • To provide a basis for developing operational guidelines for APs.

Main Methods:

  • A dynamic mass-balance model was created to simulate indoor PM concentrations.
  • Model performance was validated using ASTM D 5157 criteria and k-fold cross-validation.
  • Factors considered included outdoor particle penetration, occupant dynamics, and AP operational status.

Main Results:

  • Indoor PM2.5 concentrations were primarily influenced by outdoor infiltration and indoor generation/resuspension by occupants.
  • Larger particulate matter (PM2.5-10) levels were affected by door access and indoor sources.
  • AP operation effectively reduced indoor PM2.5 but had minimal impact on PM2.5-10 concentrations.

Conclusions:

  • The developed model accurately predicts indoor PM concentrations.
  • A guideline can be established using the model: initiate AP operation when predicted PM2.5 nears 90% of the standard and cease when it drops below 80%.