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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
ASHRAE (2009). ASHRAE Handbook: Fundamentals. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-conditioning Engineers.
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.
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.
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.
Chen Q, Srebric J (2002). A procedure for verification, validation, and reporting of indoor environment CFD analyses. HVAC&R Research, 8: 201–216.
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.
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.
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.
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.
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).
Guan J, Liang W, Yang X (2012). Dynamic Simulation of Long-term Indoor VOC Concentrations in A Newly Renovated Residential Unit: A Pilot Study.
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.
Haghighat F, Li Y, Megri AC (2001). Development and validation of a zonal model—POMA. Building and Environment, 36: 1039–1047.
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.
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.
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.
Kampa M, Castanas E (2008). Human health effects of air pollution. Environmental Pollution, 151: 362–367.
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.
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.
Kim M-H, Hwang J-H (2009). Performance prediction of a hybrid ventilation system in an apartment house. Energy and Buildings, 41: 579–586.
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.
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.
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.
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.
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.
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.
Megri AC, Haghighat F (2007). Zonal modeling for simulating indoor environment of buildings: Review, recent developments, and applications. HVAC&R Research, 13: 887–905.
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.
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.
Nazaroff WW, Cass GR (1986). Mathematical modeling of chemically reactive pollutants in indoor air. Environmental Science & Technology, 20: 924–934.
Nicas M (1996). Estimating exposure intensity in an imperfectly mixed room. American Industrial Hygiene Association Journal, 57: 542–550.
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.
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.
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.
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.
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.
Walton G, Dols W (2010). CONTAMW 3.0 User Manual. Gaithersburg, MD, USA: National Institute of Standards and Technology.
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.
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.
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.
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.
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.
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.
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).
About this article
Cite this article
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
- indoor air quality
- pollutant concentration
- pollutant distribution
- long-term simulation