HOUSING CHARACTERISTICS PREDICTING MOULD GROWTH IN BATHROOMS

Size: px

Start display at page:

Download "HOUSING CHARACTERISTICS PREDICTING MOULD GROWTH IN BATHROOMS"

Gavin Wells
6 years ago
Views:

1 Proceedings: Indoor Air 2 HOUSING CHARACTERISTICS PREDICTING MOULD GROWTH IN BATHROOMS JTV Ginkel *, E Hasselaar OTB Research Institute for Housing, Urban and Mobility Studies. P.O. Box 3, 26 GA Delft, The Netherlands ABSTRACT Mould on interior surfaces is correlated with adverse health effects. The aim of this study was to formulate measures to avoid moulds in bathrooms. These measures were based on the relationship between housing characteristics and mould growth. We investigated 7 housing characteristics and the occurrence of mould in 186 dwellings in The Netherlands. The relationship between housing characteristics and mould was investigated by means of bivariate correlation and logistic regression analysis. The number of showers (> 14 per week) taken by the occupants and the age of the ventilation box (> 6 years) were found to be the most important predictors of mould growth in bathrooms. The results show that 8% of the values predicted by the logistic model agreed with the observations. To keep the risk of mould growth at a minimum level, the maintenance frequency of the ventilation fan box should be at least every years and the number of showers per week per bathroom should not exceed 14. INDEX TERMS Mould, Bathroom, Housing characteristic, Number of showers, Age of ventilation box INTRODUCTION Moisture problems occur in approximately 2% of Dutch dwellings (Anon., 1993). Dampness generally gives rise to mould growth. Besides aesthetic deterioration of interior finishes, mould growth is also correlated with health effects such as allergic reactions and respiratory infections (Samson et al 1994). The species Stachybotrys chartarum can lead to pulmonary haemorrhage (Dearborn et al. 1999, Novotny and Dixit 2, Vesper et al. 2). The adverse health effects result from inhalation of viable as well as non-viable fungal particles. Among these, fungal spores are of considerable importance, since they tend to serve as a reservoir of low molecular weight toxins (mycotoxins) produced by the fungi. Moulds should therefore be avoided in the indoor environment. Nowadays, restructuring of the post-war housing stock is taking place in The Netherlands. Besides normal maintenance programs, this restructuring process offers a good opportunity to reduce indoor exposure to fungal particles. In view of this, knowledge about the relationship between mould growth and dwelling characteristic is of great practical value and can be used to formulate measures to avoid mould in dwellings. Models to predict mould growth on interior surfaces in buildings (Sedlbauer et al. 23, Moon and Augenbroe 23) are not completely tested and scarce. This paper describes a statistical relationship between mould growth and technical and behavioural housing characteristics. It focuses on the bathroom since this room has generally the highest humidity in dwellings, thus also the highest risk of mould. The key to the relationship between dwelling characteristics and mould lies in the abiotic factors that determine fungal growth: water, temperature and nutrients. Normal indoor temperatures permit optimum fungal growth (Brock and Madigan 1988). The nutritional requirements are met by the material constituents or by dust and other deposits on indoor surfaces (Grinsbergs et al. 1993). Therefore, the amount of available water is the limiting factor of fungal growth. Fungal growth ceased if relative humidity decreases below 8% (Adan 1994). This humidity pertains to the air in close contact with the moulds; thus at the surface on which mould growth occurs. The relative humidity at interior wall and floor surfaces is determined by the indoor vapour concentration, the thermal insulation properties of the building envelope and the indoor/outdoor temperatures. The indoor vapour concentration varies along a daily pattern due to ventilation and dwellers behaviour such as laundering, cooking and taking showers. Due to this, the relative humidity at interior surfaces shows daily periods with values below * Corresponding author j.v.ginkel@otb.tudelft.nl 242

2 Proceedings: Indoor Air 2 and above 8%. Then, the question arises about the minimum daily wet period that is necessary to allow fungal growth. The minimum wet spell is expressed in terms of the time-of-wetness (TOW) that is defined by the ratio of the cyclic wet period (RH surface > 8%) and the cyclic wet-dry period (Adan 1994, Pasanen et al. 1992). For gypsum plaster Adan (1994) found that TOW <. does not influence the growth rate, whereas a TOW >. will accelerate this rate. Thus, the duration of the dry period should exceed the duration of the wet period. The number of showers per week and the ventilation capacity determine TOW. The capacity of the ventilation box varies with the selected fan speed. At speed levels 1, 2 and 3 the capacity is equal to 7, 1 and 22 m 3 /h, respectively. In general, Dutch dwellers select fan speed level 1 during 23 hours of the day; speed level 3 is used for one hour during cooking and bathing. Since this ventilation habit is very common in the Netherlands, the ventilation capacity mainly varies in time due to deterioration. This means that the age of the fan box and the numbers of showers taken per week are expected to be the most important indicators of mould growth. The housing characteristics that might predict the occurrence of moulds are related to the water balance pertaining to the mouldy spot. The following categories of housing characteristics can be distinguished: production (e.g. laundering, showers, leakage), accumulation (thermal bridges, condensation) and transfer (capillary rise, ventilation) of water. These categories will be used in the analyses given below. RESEARCH METHODS In the context of several studies carried out by the OTB institute, a total of 186 dwellings were visited. During these visits data on building characteristics and behaviour of the occupants were collected on the basis of visual inspection and by means of a questionnaire. If mould was visually detected or the occupants answered that they removed the mouldy spots by chemical treatment, the occurrence of mould was accepted. A complete list of collected characteristics and more details about the inspection are available from the authors. The relationship between housing characteristics and mould growth was investigated by means of bivariate correlation and logistic regression analysis (SPSS). RESULTS About 6% of the dwellings in our survey was less than 3 years old (figure 1A), 69 % was rented, 31% owned by the occupant, 49% belonged to the category of single family and 1% multi family. In 41% moulds were detected in the bathroom. This is twice as much as the reported moisture problems found in the national survey (1993). In 76% of the houses the number of occupants was 4 or less (figure 1B). The distributions of the number of launderings and showers depend on the number of occupants (figures 1C and 1D). The number of showers is peaking every multiple of 7 times a week, suggesting that most people take one shower each day. The distribution of the age of the fan box (ventilation system) shows a peak at the level of 6 years (figure 1E). This is due to a bias in our survey: a significant number of dwellings in the age category of - years were 6 years old. A number of housing characteristic were of nominal type. For this type of variables regression analysis has no meaning. We therefore started our investigation of the relationship between housing characteristics and mould with the computation of bivariate correlation coefficients. Table 1 lists housing characteristics that are statistically significant. Only out of 7 characteristics had statistically significant correlation coefficients, whereas none of these correlations were very strong. Subsequent logistic regression with characteristics of ordinal type and statistically significant correlation coefficients resulted in the following model describing the chance that mould growth will occur: ( * (Age fan box >6) * (number of showers > 14)) e P mould = e ( * (Age fan box >6) * (number of showers > 14)) + 1 (1) The results calculated with this statistical model are in good agreement with 8% of the observations obtained in this survey. Prior to the logistic regression analysis the observations of mould (dependent variable) were dichotomized; this means that the variable mould was set to 1 if the occurrence of moulds was accepted and set to zero if not accepted. Although not necessary from a statistical point of view, also the independent variables Age of fan box and number of showers were dichotomized since this resulted in regression coefficients with higher statistical significance. The best results were obtained if the threshold value of the Number of showers was 14 and the Age of the fan box was 6. This means that the Number of showers was set to 1 if this number exceeded 14, else it was set to ; the Age of fan box was set to 1 if it exceeded 6 years, else it was set to. Then, the Odd Ratios (OR) of these regression coefficients were 7 ( ) and 6.3 ( ), respectively. 2426

3 Proceedings: Indoor Air 2 Table 1. Housing characteristics with statistically significant bivariate correlation coefficients(kendall s tau_b) andlevelofsignificancep(*9%;**99%) Indicator Kendall's tau_b Age of house.14 ** Number of occupants normal day/night.27 ** Number of launderings (including handwash)/week.14 * Age of fanbox.2 * Heating system.26 ** Age of heater.26 ** Cold surfaces, thermal bridges.21 ** Number of showers.18 ** Airtightness, seams in openings.24 ** Condensation on windows.31 ** DISCUSSION The statistical analysis of the observations of this survey gives a relationship between the occurrence of mould and the number of showers per week taken by the occupants and the age of the fan of the ventilation system. From this relationship it can be concluded that the chance that moulds will grow, is small if the age of the fan box is smaller than years and the number of showers taken by the occupants per week per bathroom is smaller than 14. These results are based on the existing ventilation capacities in the dwellings of our survey. This means that the threshold value of 14 showers per week may vary with the actual ventilation capacity. The results can be explained from the observations found in the literature as discussed in the introduction. Both, the capacity of the mechanical exhaust system and the number of showers taken per week per bathroom, will influence the time-of-wetness. If TOW increases, the risk of mould will also increase. It is remarkable that only two housing characteristics appear in the statistical relationship. This results from the mutual correlations between the bivariate correlations given in table 1. The two variables (fan, shower) together explain a large portion of the variance in our data. There is some bias between the distributions of the dwellings in our survey and the entire set of Dutch dwellings. Compared to the results of a recent national survey (Anon. 23) our survey contained % more dwellings of less than 3 years of age and approximately 2% more dwellings in the category of multifamily houses (49% in our survey versus 3% in the national survey). Also the number of occupants per dwelling was significantly larger in our survey compared to the national survey. The lager the number of occupants the lager the number of showers taken per week. Based on the relationship developed in our study, it seems therefore logical that we found more mould problems (41% versus 2%) than observed in the national survey (1993). The bias in the age of the dwellings at a level of 6 years, as mentioned above, may influence the significance with respect to the Age of the fan box. However, in a previous study (Van Ginkel and Hasselaar 22) we found that the ventilation capacity reduced % per year on average; after years the capacity of the mechanical exhaust system was only % of the level when the dwelling was taken into use. As the ventilation capacity becomes smaller, the exhaust of water vapour reduces whereas the time-of-wetness increases; therefore there is an increasing chance that moulds develop. CONCLUSION AND IMPLICATION From this study it can be concluded that the chance that moulds will grow, is small if the age of the fan box is smaller than years and the number of showers taken by the occupants per week per bathroom is smaller than 14. Housing institutions should set up appropriate maintenance programs for (mechanical) ventilation systems. Tenants should get the opportunity to select houses with sufficient ventilation. REFERENCES Adan OCG., On the fungal Defacement of interior finishes, Ph.D. Thesis, Eindhoven University (The Netherlands), 226 pages. Anonymous Kwalitatieve woningregistratie Resultaten landelijke steekproef, Ministerie van Volkshuisvesting, Ruimtelijke Ordening en Milieubeheer (in Dutch) 2427 P

4 Proceedings: Indoor Air 2 Anonymous 23. De kwaliteit van de Nederlandse woning en woonomgeving rond de millenniumwisseling; Basisrapportage Kwalitatieve Woningregistratie 2, Ministerie van Volkshuisvesting, Ruimtelijke Ordening en Milieubeheer (in Dutch) Ayerst G., The effect of moisture and temperature on growth and spore germination in some fungi, Journal of Stored Products Research, Vol, pp Brock TD. and Madigan MT., Biology of microorganisms, Prentice Hall int. ed., th ed., 83 pages. Dearborn DG. et al., Overview of investigations into pulmonary haemorrhage among infants in Cleveland, Ohio, Environmental Health Perspect, 7(suppl 3): Grinbergs L. et al., Wet-room wall systems- Mould resistance, in: Proceedings of the International Symposium Energy Efficient Buildings (Design, Performance and Operation) of the CIB Working Commission W67 Energy Conservation in the Built Environment and IEA-SHC Working Task Group XIII Low Energy Buildings, March 9 11, Leinfelden-Echterdingen, Germany (eds H. Erhorn, J. Reiss and M. Szerman), IRB Verlag. Moon HJ. and Augenbroe G., 23. Development of a performance indicator for mould growth risk avoidance in buildings, Proceedings: Healthy Buildings 23, pp Novotny W. and Dixit A., 2. Pulmonary Haemorrhage in an infant following 2 weeks of fungal exposure, Arch. Ped. Adolescent Med, Vol 14(3), pp Pasanen AL. et al., «Fungal micro-colonies on indoor surfaces an explanation for the base level fungal spore counts in indoor air, Atmospheric Environment, Vol 22: pp Samson RA. et al., Health implications of fungi in indoor environment, Elsevier, Amsterdam Sedlbauer K., Krus M. and Breuer K., 23. Biohygrothermal method for the prediction of mould growth: procedure and health aspects, Proceedings: Healthy Buildings 23, pp Van Ginkel JT. and Hasselaar E. 22. De ontwikkeling van afzuighoeveelheden bij mechanische woningventilatie: oorzaak en gevolg. Onderzoeksrapport OTB, 2 p. (in Dutch) Vesper SJ. et al., 2. Evaluation of Stachybotrys chartarum in house of an infant with pulmonary haemorrhage: quantitative assessment before, during, and after remediation, J. Urban Health, Vol 77(1), pp

5 Proceedings: Indoor Air 2 (N=333) (N=186) Ageofthehouse[years] Number of occupants A B (N=186) Showers/week (N=168) Launderings/week C D Percentage of dwellings(n=164) Ageofthefanbox[years] E Figure 1. Distributions of the age of the dwellings(a), number of occupants(b), showers per week(c), launderings per week(d) and age 2429

Indoor air quality influenced by ventilation system design

Workshop 5 - The Residential Context of Health Indoor air quality influenced by ventilation system design Jan van Ginkel j.v.ginkel@otb.tudelft.nl Evert Hasselaar Paper presented at the ENHR conference