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Estimation of Outdoor PM 2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

Author

Listed:
  • Bo Ram Park

    (Department of Architectural Engineering, Graduate School, University of Seoul, Seoul 02504, Korea)

  • Ye Seul Eom

    (Department of Architectural Engineering, Graduate School, University of Seoul, Seoul 02504, Korea)

  • Dong Hee Choi

    (Department of Architectural Engineering, College of Engineering, Kyungil University, Gyeongsan 38428, Korea)

  • Dong Hwa Kang

    (Department of Architectural Engineering, College of Urban Sciences, University of Seoul, Seoul 02504, Korea)

Abstract

The purpose of this study was to evaluate outdoor PM 2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM 2.5 . The field tests revealed that the average PM 2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM 2.5 infiltration factor revealed that building airtightness metrics (ACH 50 , ELA/FA, and NL) had a statistically significant ( p < 0.05) positive correlation ( r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM 2.5 infiltration based on the ACH 50 airtightness index was proposed. The study confirmed that the outdoor-origin PM 2.5 concentration in sufficiently leaky units could be up to 1.59 times higher than that in airtight units.

Suggested Citation

  • Bo Ram Park & Ye Seul Eom & Dong Hee Choi & Dong Hwa Kang, 2021. "Estimation of Outdoor PM 2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5708-:d:557948
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    References listed on IDEAS

    as
    1. Dong Hee Choi & Dong Hwa Kang, 2018. "Indoor/Outdoor Relationships of Airborne Particles under Controlled Pressure Difference across the Building Envelope in Korean Multifamily Apartments," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    2. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
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