A new PM2.5-based CADR method to measure air infiltration rate of buildings

Abstract

Air infiltration is an important way to exchange indoor air with outdoors. It significantly impacts energy consumption and air quality of buildings. Fine particles (PM2.5) in the outdoor atmosphere environment are a potential natural tracer for the measurement of air infiltration rate, especially in the long term field measurement. In this study, a PM2.5-based method, named as CADR (clean air delivery rate) method, is developed to supplement traditional tracer gas method in order to make routine measurement in realistic environments possible and convenient. An air cleaner is installed indoors to reduce indoor PM2.5 concentration. Air infiltration is determined by fitting a model to the decreasing concentration data. Comparison with CO2 decay method in four different indoor environments gives a normalized mean error of 19% and a correlation coefficient of 0.80 for this method. This justifies the CADR method as a feasible option to measure air infiltration rate. Although subject to several constraints, the proposed method would facilitate field measurement under realistic conditions by being combined with current tracer gas methods.

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References

  1. Batterman S (2017). Review and extension of CO2-based methods to determine ventilation rates with application to school classrooms. International Journal of Environmental Research and Public Health, 14: 145.

    Google Scholar 

  2. Bekö G, Gustavsen S, Frederiksen M, Bergsøe NC, Kolarik B, et al. (2016). Diurnal and seasonal variation in air exchange rates and interzonal airflows measured by active and passive tracer gas in homes. Building and Environment, 104: 178–187.

    Google Scholar 

  3. Cao S-J, Ren C (2018). Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network. Building and Environment, 144: 316–333.

    Google Scholar 

  4. Castillo JA, Huelsz G, van Hooff T, Blocken B (2019). Natural ventilation of an isolated generic building with a windward window and different windexchangers: CFD validation, sensitivity study and performance analysis. Building Simulation, 12: 475–488.

    Google Scholar 

  5. Cheng PL, Li X (2018). Air infiltration rates in the bedrooms of 202 residences and estimated parametric infiltration rate distribution in Guangzhou, China. Energy and Buildings, 164: 219–225.

    Google Scholar 

  6. Diapouli E, Chaloulakou A, Koutrakis P (2013). Estimating the concentration of indoor particles of outdoor origin: A review. Journal of the Air & Waste Management Association, 63: 1113–1129.

    Google Scholar 

  7. Dong B, Yan D, Li Z, Jin Y, Feng X, et al. (2018). Modeling occupancy and behavior for better building design and operation—A critical review. Building Simulation, 11: 899–921.

    Google Scholar 

  8. Fisk WJ (2018). How home ventilation rates affect health: A literature review. Indoor Air, 28: 473–487.

    Google Scholar 

  9. Fortenberry C, Walker M, Dang A, Loka A, Date G, et al. (2019). Analysis of indoor particles and gases and their evolution with natural ventilation. Indoor Air, 29: 761–779.

    Google Scholar 

  10. Gołofit-Szymczak M, Górny RL (2018). Microbiological air quality in office buildings equipped with different ventilation systems. Indoor Air, 28: 792–805.

    Google Scholar 

  11. Hänninen OO, Lebret E, Ilacqua V, Katsouyanni K, Künzli N, et al. (2004). Infiltration of ambient PM2.5 and levels of indoor generated non-ETS PM2.5 in residences of four European cities. Atmospheric Environment, 38: 6411–6423.

    Google Scholar 

  12. Hou J, Zhang Y, Sun Y, Wang P, Zhang Q, et al. (2018). Air change rates at night in northeast Chinese homes. Building and Environment, 132: 273–281.

    Google Scholar 

  13. Hou J, Sun Y, Chen Q, Cheng R, Liu J, et al. (2019). Air change rates in urban Chinese bedrooms. Indoor Air, 29: 828–839.

    Google Scholar 

  14. JGJ/T461-2019 (2019). Design Standard for Controlling Indoor Air Quality of Public Building. Ministry of Housing and Urban-Rural Development of China. Beijing: China Architecture & Building Press. (in Chinese)

    Google Scholar 

  15. Ji W, Zhao B (2015). Contribution of outdoor-originating particles, indoor-emitted particles and indoor secondary organic aerosol (SOA) to residential indoor PM2.5 concentration: A model-based estimation. Building and Environment, 90: 196–205.

    Google Scholar 

  16. Johnston CJ, Andersen RK, Toftum J, Nielsen TR (2020). Effect of formaldehyde on ventilation rate and energy demand in Danish homes: Development of emission models and building performance simulation. Building Simulation, 13: 197–212.

    Google Scholar 

  17. Li A, Hou Y, Yang J (2019). Attached ventilation based on a curved surface wall. Building Simulation, 12: 505–515.

    Google Scholar 

  18. Licina D, Tian Y, Nazaroff WW (2017). Emission rates and the personal cloud effect associated with particle release from the perihuman environment. Indoor Air, 27: 791–802.

    Google Scholar 

  19. Liu C, Zhao B, Zhang Y (2010). The influence of aerosol dynamics on indoor exposure to airborne DEHP. Atmospheric Environment, 44: 1952–1959.

    Google Scholar 

  20. Liu C, Zhang Y, Benning JL, Little JC (2015). The effect of ventilation on indoor exposure to semivolatile organic compounds. Indoor Air, 25: 285–296.

    Google Scholar 

  21. Liu C, Yang J, Ji S, Lu Y, Wu P, Chen C (2018a). Influence of natural ventilation rate on indoor PM2.5 deposition. Building and Environment, 144: 357–364.

    Google Scholar 

  22. Liu Y, Misztal PK, Xiong J, Tian Y, Arata C, et al. (2018b). Detailed investigation of ventilation rates and airflow patterns in a northern California residence. Indoor Air, 28: 572–584.

    Google Scholar 

  23. Liu C, Miao X, Li J (2019a). Outdoor formaldehyde matters and substantially impacts indoor formaldehyde concentrations. Building and Environment, 158: 145–150.

    Google Scholar 

  24. Liu C, Wang H, Guo H (2019b). Redistribution of PM2.5-associated nitrate and ammonium during outdoor-to-indoor transport. Indoor Air, 29: 460–468.

    Google Scholar 

  25. Liu C, Zhang Y (2019). Relations between indoor and outdoor PM2.5 and constituent concentrations. Frontiers of Environmental Science & Engineering, 13: 5.

    Google Scholar 

  26. Marley W G (1935). The measurement of the rate of air change. Journal of the Institution of Heating & Ventilation Engineers, 2: 499–504.

    Google Scholar 

  27. Ni PY, Jin HC, Wang XL, Xi GN (2018). A new method for measurement of air change rate based on indoor PM2.5 removal. International Journal of Environmental Science and Technology, 15: 2561–2568.

    Google Scholar 

  28. Ohlsson KEA, Yang B, Ekblad A, Boman C, Nyström R, et al. (2017). Stable carbon isotope labelled carbon dioxide as tracer gas for air change rate measurement in a ventilated single zone. Building and Environment, 115: 173–181.

    Google Scholar 

  29. Persily AK (1997). Evaluating building IAQ and ventilation with indoor carbon dioxide. Transactions-American Society of Heating Refrigerating and Air Conditioning Engineers, 103: 193–204.

    Google Scholar 

  30. Persily A (2015). Challenges in developing ventilation and indoor air quality standards: The story of ASHRAE Standard 62. Building and Environment, 91: 61–69.

    Google Scholar 

  31. Persily AK (2016). Field measurement of ventilation rates. Indoor Air, 26: 97–111.

    Google Scholar 

  32. Qi MW, Li XF, Weschler LB, Sundell J (2014). CO2 generation rate in Chinese people. Indoor Air, 24: 559–566.

    Google Scholar 

  33. Qian M, Yan D, An J, Hong T, Spitler JD (2020). Evaluation of thermal imbalance of ground source heat pump systems in residential buildings in China. Building Simulation, 13: 585–598.

    Google Scholar 

  34. Remion G, Moujalled B, El Mankibi M (2019). Review of tracer gas-based methods for the characterization of natural ventilation performance: Comparative analysis of their accuracy. Building and Environment, 160: 106180.

    Google Scholar 

  35. Ren J, Cao S-J (2019). Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems. Sustainable Cities and Society, 47: 101498.

    Google Scholar 

  36. Renbourn ET, Angus TC, Ellison JM, Croton LM, Jones MS (1949). The measurement of domestic ventilation: An experimental and theoretical investigation with particular reference to the use of carbon dioxide as a tracer substance. Journal of Hygiene, 47: 1–38.

    Google Scholar 

  37. Salvador CM, Bekö G, Weschler CJ, Morrison G, Le Breton M, et al. (2019). Indoor ozone/human chemistry and ventilation strategies. Indoor Air, 29: 913–925.

    Google Scholar 

  38. Sherman MH (1990). Tracer-gas techniques for measuring ventilation in a single zone. Building and Environment, 25: 365–374.

    Google Scholar 

  39. Shi Y, Li X, Li H (2017). A new method to assess infiltration rates in large shopping centers. Building and Environment, 119: 140–152.

    Google Scholar 

  40. Sundell J, Levin H, Nazaroff WW, Cain WS, Fisk WJ, et al. (2011). Ventilation rates and health: Multidisciplinary review of the scientific literature. Indoor Air, 21: 191–204.

    Google Scholar 

  41. Thatcher TL, Layton DW (1995). Deposition, resuspension, and penetration of particles within a residence. Atmospheric Environment, 29: 1487–1497.

    Google Scholar 

  42. Tian E, Mo J (2019). Toward energy saving and high efficiency through an optimized use of a PET coarse filter: The development of a new electrostatically assisted air filter. Energy and Buildings, 186: 276–283.

    Google Scholar 

  43. Tredgold T (1824). Principles of Warming and Ventilating Public Buildings: Dwelling Houses, Manufactories, Hospitals, Hot-houses, Conservatories, and of Constructing Fire-places, Boilers, Steam Apparatus, Grates, and Drying Rooms, with Illustrations Experimental, Scientific, and Practical, 2nd edn. London: Printed for J. Taylor.

    Google Scholar 

  44. Xu Z (2003). Principles of Air Cleaning Technology, 3rd edn. Beijing: Science press. (in Chinese)

    Google Scholar 

  45. Zhai S, Li Z, Zhao B (2014). State-space analysis of influencing factors on airborne particle concentration in aircraft cabins. Building and Environment, 74: 13–21.

    Google Scholar 

Download references

Acknowledgements

Financial support was provided by the National Natural Science Foundation of China (No. 51808107), the National Key R&D Program of China (No. 2017YFC0702700), the Science and Technology Plan of Changzhou (No. CJ20190018), and the Fundamental Research Funds for the Central Universities (No. FRF-TP-18-025A2). The authors declared no potential conflicts of interest.

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Correspondence to Cong Liu or Xiaoliang Shao.

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Liu, C., Ji, S., Zhou, F. et al. A new PM2.5-based CADR method to measure air infiltration rate of buildings. Build. Simul. 14, 693–700 (2021). https://doi.org/10.1007/s12273-020-0676-4

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Keywords

  • natural ventilation
  • outdoor-indoor-connection
  • haze
  • formaldehyde
  • VOCs
  • exposure