The effect of combining a relative-humidity-sensitive ventilation system with the moisture-buffering capacity of materials on indoor climate and energy efficiency of buildings

Indoor moisture management, which means keeping the indoor relative humidity (RH) at correct levels, is very important for whole building performance in terms of indoor air quality (IAQ), energy performance and durability of the building. In this study, the effect of combining a relative-humidity-sensitive (RHS) ventilation system with indoor moisture buffering materials was investigated. Four comprehensive heat–air–moisture (HAM) simulation tools were used to analyse the performance of different moisture management strategies in terms of IAQ and of energy efficiency. Despite some differences in results, a good agreement was found and similar trends were detected from the results, using the four different simulation tools. The results from simulations demonstrate that RHS ventilation reduces the spread between the minimum and maximum values of the RH in the indoor air and generates energy savings. Energy savings are achieved while keeping the RH at target level, not allowing for possible risk of condensations. The disadvantage of this type of demand controlled-ventilation is that other pollutants (such as CO2) may exceed target values. This study also confirmed that the use of moisture-buffering materials is a very efficient way to reduce the amplitude of daily moisture variations. It was possible, by the combined effect of ventilation and wood as buffering material, to keep the indoor RH at a very stable level.

[1]  Neil J. Rowan,et al.  Prediction of Toxigenic Fungal Growth in Buildings by Using a Novel Modelling System , 1999, Applied and Environmental Microbiology.

[2]  Monika Woloszyn,et al.  WHOLE BUILDING SIMULATION TOOLS: CLIM2000 , 2004 .

[3]  Monika Woloszyn,et al.  Interzonal air and moisture transport in a test house: experiment and modelling , 2002 .

[4]  Peder Wolkoff,et al.  The dichotomy of relative humidity on indoor air quality. , 2007, Environment international.

[5]  Ocg Olaf Adan,et al.  On the fungal defacement of interior finishes , 1994 .

[6]  Jan Carmeliet,et al.  Assessment Method of Numerical Prediction Models for Combined Heat, Air and Moisture Transfer in Building Components: Benchmarks for One-dimensional Cases , 2004 .

[7]  Juha Jokisalo,et al.  Simulation of energy consumption in typical Finnish detached house , 2002 .

[8]  Paul Fazio,et al.  Modelling of indoor air humidity: the dynamic behaviour within an enclosure , 1992 .

[9]  B. Hart Life cycle and reproduction of house‐dust mites: environmental factors influencing mite populations , 1998, Allergy.

[10]  G. W. Brundrett,et al.  A review of the factors influencing electrostatic shocks in the offices , 1977 .

[11]  P. Fanger,et al.  Impact of Temperature and Humidity on Perception of Indoor Air Quality During Immediate and Longer Whole‐Body Exposures , 1998 .

[12]  Carsten Rode,et al.  The international building physics toolbox in Simulink , 2007 .

[13]  Angela Sasic Kalagasidis,et al.  HAM-Tools - An Integrated Simulation Tool for Heat, Air and Moisture Transfer Analyses in Building Physics , 2004 .

[14]  G. Pershagen,et al.  Dampness in buildings and health. Nordic interdisciplinary review of the scientific evidence on associations between exposure to "dampness" in buildings and health effects (NORDDAMP). , 2001, Indoor air.

[15]  Jaakko Paasi,et al.  Performance of ESD protective materials at low relative humidity , 2001 .

[16]  J. Korsgaard,et al.  House‐Dust Mites and Absolute Indoor Humidity , 1983, Allergy.

[17]  Lei Fang,et al.  Field study on the impact of temperature, humidity and ventilation on perceived air quality , 1999 .

[18]  L. Arlian,et al.  Reducing relative humidity to control the house dust mite Dermatophagoides farinae. , 1999, The Journal of allergy and clinical immunology.

[19]  Per Sahlin Modelling and Simulation Methods for Modular Continuous Systems in Buildings , 1996 .

[20]  Alireza Afshari,et al.  Humidity as a Control Parameter for Ventilation , 2003 .

[21]  C. Isetti,et al.  Predicting vapour content of the indoor air and latent loads for air-conditioned environments: Effect of moisture storage capacity of the walls , 1988 .

[22]  Kevin J. Lomas,et al.  Empirical validation of building energy simulation programs , 1997 .

[23]  Nathan Mendes,et al.  Moisture effects on conduction loads , 2003 .

[24]  S. Bonini,et al.  Dampness in buildings as a risk factor for health effects, EUROEXPO: a multidisciplinary review of the literature (1998-2000) on dampness and mite exposure in buildings and health effects. , 2004, Indoor air.

[25]  Lars Eriksson,et al.  Whole-building simulation with symbolic DAE equations and general purpose solvers , 2004 .

[26]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[27]  Povl Ole Fanger,et al.  Limiting criteria for human exposure to low humidity indoors , 2002 .

[28]  P. Fanger,et al.  Impact of Temperature and Humidity on the Perception of Indoor Air Quality , 1998 .