Long-term simulation for predicting indoor air pollutant concentration considering pollutant distribution based on concept of CRPS index

Abstract

Modern people spend most of their time indoors and so are chronically exposed to indoor air pollutants. To identify the health effects of pollutant exposure, it is necessary to understand the changes over time in indoor pollutant concentrations. There are two approaches for simulating pollutant concentration changes: mass balance model, computational fluid dynamics (CFD). Although the mass balance model is suitable for long-term simulation because it is simple, there is a limit to the detailed analysis considering concentration distribution. CFD can simulate the distribution of indoor air pollutants, but long-term analyses require too many computational resources. This study proposed a novel simulation method that couples the mass balance model with the contribution ratio of pollutant sources (CRPS) index, which indicates the individual impact of all pollutant sources and is extracted from CFD result. By introducing the CRPS index, long-term pollutant concentrations can be calculated as fast as the mass balance model while considering the pollutant distribution like CFD. The method was validated using previous experimental data. The case study was conducted and simulated changes in pollutant concentrations in a new residential unit for one week. The results showed that the CRPS-coupled method was different from conventional methods in that it more realistically calculates pollutant concentrations using relatively little computational resources.

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References

  1. ASHRAE (2009). ASHRAE Handbook: Fundamentals. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-conditioning Engineers.

    Google Scholar 

  2. Bernstein JA, Alexis N, Bacchus H, Bernstein IL, Fritz P, Horner E, Li N, Mason S, Nel A, Oullette J, Reijula K, Reponen T, Seltzer J, Smith A, Tarlo SM (2008). The health effects of nonindustrial indoor air pollution. Journal of Allergy and Clinical Immunology, 121: 585–591.

    Article  Google Scholar 

  3. Boeglin ML, Wessels D, Henshei D (2006). An investigation of the relationship between air emissions of volatile organic compounds and the incidence of cancer in Indiana counties. Environmental Research, 100: 242–254.

    Article  Google Scholar 

  4. Bourdin D, Mocho P, Desauziers V, Plaisance H (2014). Formaldehyde emission behavior of building materials: On-site measurements and modeling approach to predict indoor air pollution. Journal of Hazardous Materials, 280: 164–173.

    Article  Google Scholar 

  5. Chen Q, Srebric J (2002). A procedure for verification, validation, and reporting of indoor environment CFD analyses. HVAC&R Research, 8: 201–216.

    Article  Google Scholar 

  6. Chen Q, Lee K, Mazumdar S, Poussou S, Wang L, Wang M, Zhang Z (2010). Ventilation performance prediction for buildings: Model assessment. Building and Environment, 45: 295–303.

    Article  Google Scholar 

  7. Cho J, Yoo C, Kim Y (2012). Effective opening area and installation location of windows for single sided natural ventilation in high-rise residences. Journal of Asian Architecture and Building Engineering, 11: 391–398.

    Article  Google Scholar 

  8. Davardoost F, Kahforoushan D (2018). Health risk assessment of VOC emissions in laboratory rooms via a modeling approach. Environmental Science and Pollution Research, 25: 17890–17900.

    Article  Google Scholar 

  9. Deng B, Kim C (2007). CFD simulation of VOCs concentrations in a resident building with new carpet under different ventilation strategies. Building and Environment, 42: 297–303.

    Article  Google Scholar 

  10. Garden C, Semple S, De Brouare K (2011). INTERA B4 Project. A review of existing indoor pollutant exposure data and models. Integrated Exposure for Risk Assessment in Indoor Environments (INTERA).

    Google Scholar 

  11. Guan J, Liang W, Yang X (2012). Dynamic Simulation of Long-term Indoor VOC Concentrations in A Newly Renovated Residential Unit: A Pilot Study.

    Google Scholar 

  12. Guo Z (2000). Simulation tool kit for indoor air quality and inhalation exposure (IAQX) Version 1.0 User's Guide, US Environmental Protection Agency, National Risk Management Research Laboratory.

    Google Scholar 

  13. Haghighat F, Li Y, Megri AC (2001). Development and validation of a zonal model—POMA. Building and Environment, 36: 1039–1047.

    Article  Google Scholar 

  14. Huang H, Haghighat F, Lee C-S (2005). An integrated zonal model for predicting indoor airflow, temperature, and VOC distributions. ASHRAE Transactions, 111(1): 601–611.

    Google Scholar 

  15. Huang H, Kato S, Hu R, Ishida Y (2011). Development of new indices to assess the contribution of moisture sources to indoor humidity and application to optimization design: Proposal of CRI(H) and a transient simulation for the prediction of indoor humidity. Building and Environment, 46: 1817–1826.

    Article  Google Scholar 

  16. Huang H, Kato S, Hu R (2012). Optimum design for indoor humidity by coupling Genetic Algorithm with transient simulation based on Contribution Ratio of Indoor Humidity and Climate analysis. Energy and Buildings, 47: 208–216.

    Article  Google Scholar 

  17. Kampa M, Castanas E (2008). Human health effects of air pollution. Environmental Pollution, 151: 362–367.

    Article  Google Scholar 

  18. Kato S (1994). New scales for assessing contribution of heat sources and sinks to temperature distributions in room by means of numerical simulation. In: Proceedings of the 4th International Conference on Air Distribution in Rooms (ROOMVENT94), Kracow, Poland.

    Google Scholar 

  19. Kato S, Ito K, Zhu QY, Murakami S (2003). Numerical and experimental study on emission, diffusion and sorption in model room. Journal of Architecture and Planning (Transactions of AIJ), 68(564): 41–47.

    Article  Google Scholar 

  20. Kim M-H, Hwang J-H (2009). Performance prediction of a hybrid ventilation system in an apartment house. Energy and Buildings, 41: 579–586.

    Article  Google Scholar 

  21. Kim T, Kato S, Murakami S (2007). New scales for assessing contribution ratio of pollutant sources to indoor air quality. Indoor and Built Environment, 16: 519–528.

    Article  Google Scholar 

  22. Lei L, Wang SG, Zhang T (2014). Inverse determination of wall boundary convective heat fluxes in indoor environments based on CFD. Energy and Buildings, 73: 130–136.

    Article  Google Scholar 

  23. Liang W, Gao P, Guan J, Yang X (2012). Modeling volatile organic compound (VOC) concentrations due to material emissions in a real residential unit. Part I: Methodology and a preliminary case study. Building Simulation, 5: 351–357.

    Article  Google Scholar 

  24. Liu J, Li W (2011). A long-term modelling study of ventilation and VOC distribution in multi-family residential buildings in the severe cold region of China. International Journal of Ventilation, 10: 217–226.

    Article  Google Scholar 

  25. Liu W, Mazumdar S, Zhang Z, Poussou SB, Liu J, Lin C-H, Chen Q (2012). State-of-the-art methods for studying air distributions in commercial airliner cabins. Building and Environment, 47: 5–12.

    Article  Google Scholar 

  26. McDonnell WF, Abbey DE, Nishino N, Lebowitz MD (1999). Long-term ambient ozone concentration and the incidence of asthma in nonsmoking adults: The AHSMOG study. Environmental Research, 80: 110–121.

    Article  Google Scholar 

  27. Megri AC, Haghighat F (2007). Zonal modeling for simulating indoor environment of buildings: Review, recent developments, and applications. HVAC&R Research, 13: 887–905.

    Article  Google Scholar 

  28. Mölter A, Agius RM, de Vocht F, Lindley S, Gerrard W, Lowe L, Belgrave D, Custovic A, Simpson A (2013). Long-term exposure to PM10 and NO2 in association with lung volume and airway resistance in the MAAS birth cohort. Environmental Health Perspectives, 121: 1232–1238.

    Article  Google Scholar 

  29. Murakami S, Kato S, Ito K, Zhu Q (2003). Modeling and CFD prediction for diffusion and adsorption within room with various adsorption isotherms. Indoor Air, 13(s6): 20–27.

    Article  Google Scholar 

  30. Nazaroff WW, Cass GR (1986). Mathematical modeling of chemically reactive pollutants in indoor air. Environmental Science & Technology, 20: 924–934.

    Article  Google Scholar 

  31. Nicas M (1996). Estimating exposure intensity in an imperfectly mixed room. American Industrial Hygiene Association Journal, 57: 542–550.

    Article  Google Scholar 

  32. Nicolai A, Zhang J, Grunewald J (2007). Coupling strategies for combined simulation using multizone and building envelope models. In: Proceedings of the International IBPSA Building Simulation Conference, Beijing, China.

    Google Scholar 

  33. Park J, Jee NY, Jeong JW (2014). Effects of types of ventilation system on indoor particle concentrations in residential buildings. Indoor Air, 24: 629–638.

    Article  Google Scholar 

  34. Rai AC, Lin C-H, Chen Q (2014). Numerical modeling of volatile organic compound emissions from ozone reactions with human-worn clothing in an aircraft cabin. HVAC&R Research, 20: 922–931.

    Article  Google Scholar 

  35. Sasamoto T, Kato S, Zhang W (2010). Control of indoor thermal environment based on concept of contribution ratio of indoor climate. Building Simulation, 3: 263–278.

    Article  Google Scholar 

  36. Steeman HJ, Janssens A, Carmeliet J, de Paepe M (2009). Modelling indoor air and hygrothermal wall interaction in building simulation: Comparison between CED and a well-mixed zonal model. Building and Environment, 44: 572–583.

    Article  Google Scholar 

  37. Walton G, Dols W (2010). CONTAMW 3.0 User Manual. Gaithersburg, MD, USA: National Institute of Standards and Technology.

    Google Scholar 

  38. Wang L, Chen Q (2007). Theoretical and numerical studies of coupling multizone and CED models for building air distribution simulations. Indoor Air, 17: 348–361.

    Article  Google Scholar 

  39. Wurtz E (1995). Three-dimensional modeling of thermal and airflow transfers in building using an object-oriented simulation environment. PhD Thesis, Ecole Nationale des Ponts et Chaussees, France.

    Google Scholar 

  40. Wurtz E, Haghighat F, Mora L, Mendonca K, Zhao H, Maalouf C, Bourdoukan P (2006). An integrated zonal model to predict transient indoor humidity distribution. ASHRAE Transactions, 112(2): 175–186.

    Google Scholar 

  41. Zhang Z, Chen X, Mazumdar S, Zhang T, Chen Q (2009). Experimental and numerical investigation of airflow and contaminant transport in an airliner cabin mockup. Building and Environment, 44: 85–94.

    Article  Google Scholar 

  42. Zhang W, Hiyama K, Kato S, Ishida Y (2013). Building energy simulation considering spatial temperature distribution for nonuniform indoor environment. Building and Environment, 63: 89–96.

    Article  Google Scholar 

  43. Zuraimi MS, Pantazaras A, Chaturvedi KA, Yang JJ, Tham KW, Lee SE (2017). Predicting occupancy counts using physical and statistical Co2-based modeling methodologies. Building and Environment, 123: 517–528.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by a grant (18RERP-B082204-05) from Residential Environment Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1A2B3012914).

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Correspondence to Taeyeon Kim.

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Choi, H., Kim, H. & Kim, T. Long-term simulation for predicting indoor air pollutant concentration considering pollutant distribution based on concept of CRPS index. Build. Simul. 12, 1131–1140 (2019). https://doi.org/10.1007/s12273-019-0550-4

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Keywords

  • indoor air quality
  • pollutant concentration
  • pollutant distribution
  • long-term simulation
  • CRPS