Healthy Buildings 2017 Europe July 2-5, 2017, Lublin, Poland Paper ID 0279 ISBN: 978-83-7947-232-1 Evaluation of Risk of Mold Damages Based on a Novel Dust Sampling Method Mari Sand Austigard 1, Ingeborg Bjorvand Engh 1, * 1 Mycoteam AS, Oslo, Norway * Corresponding email: ibe@mycoteam.no SUMMARY We have studied occurrence of mold in settled dust from preschool buildings from all over Norway. We were able to identify buildings with a risk of moisture damage from a range of independently chosen buildings. Buildings with a high proportion of fungi associated with moisture damages are believed to have higher risk of mold damage. We hypothesize that regular building inspections protects the building from mold damages. Our aim is to improve the understanding amongst practitioners on how to analyze and evaluate dust samples when assessing IAQ and answering IAQ questions. We conclude that qpcr of dust samples is an effective method for evaluating IAQ when dust is allowed to settle for a certain time period. We found that more molds associated with fungal building damages are present in dust samples from buildings with long inspection and maintenance intervals. KEYWORDS Building microbiome, Building ecology, Microbial damages, DNA based techniques, qpcr analysis. 1 INTRODUCTION Microbial exposure, including viruses, bacteria and fungi, are important in development of the human microbiome. As humans spend most of their time indoors, there is no surprise that the microbial exposure is mostly dependent on the indoor microbiome. Exposure to a diverse indoor microbiome will effect human health, and increase in mold exposure through sources such as dust and airborne spores is well known to have a negative effect on health (Bornehag et al., 2001) In this study we performed sampling of dust to evaluate exposure to both dust and molds deposited in dust. Our survey concentrates on buildings from all over Norway, and we chose preschools in order to sample buildings geographically evenly distributed through populated areas. We chose to sample dust directly from surfaces, instead of sampling dust from rugs or smooth floors by vacuum cleaning as performed in other studies of mold contamination of indoor environment (Täubel et al., 2016).
Dust cover assembled on surfaces represents the history of what has been airborne since last cleaning, and studying the dust cover will reveal amount and diversity of molds the residents have been exposed to in their indoor environment. In preschools, cleaning is done regularly and expected to follow quite similar routines. Surfaces not easy to access are more rarely cleaned than easily accessible surfaces, which in turn allows dust to settle for some time. Our sampling methods are based on molecular and morphological techniques available for dust sampling. The tape lifts cover a surface of approximately 10 cm 2. Dust cover in the range from 1.5% to 5% is expected on surfaces with reduced availability if regularly cleaned (Standard Norge AS, 2010). Information about last building inspection and building maintenance was collected to study the correlation with diversity and amount of mold in dust cover. Our hypothesis is that buildings undergoing regular inspections are subject to less mold damages than buildings that are rarely inspected. 2 MATERIALS/METHODS A subset of 100 preschools were sampled, representing five buildings from the ten largest cities of Norway and fifty buildings from fifty different municipalities representing all parts of Norway. Dust samples were taken by tape lifts to examine the dust cover in different parts of the preschool while in regular use. Sampling surfaces had low availability and were chosen among horizontal surfaces at least 0.5 meter above floor level and away from windows and ventilation. One dust cover sample was selected from each preschool. Samples were analyzed using microscopy and molecular techniques. We performed both microscopy and a scanning method in combination to evaluate the dust coverage in the sample. Tape lift samples from surfaces were scanned using direct optical microscopy and computerized dust coverage analysis using Olympus Stream 1.9 (Olympus Corporation, 2013). Subsequently, we performed qpcr of the collected dust on the tape samples from surfaces employing a qpcr technique with a panel consisting of 20 primer pairs provided by HouseTest ApS, Odense, Denmark to identify and quantify the amount of fungal contamination fungi. Blank tapes were included as negative template control, and negative qpcr controls were performed on a regular basis to ensure no contamination. We assessed the qpcr results based on proportion originating from species associated with moisture damages versus naturally occurring fungi from outdoor air (table 1). Table 1. Grouping of fungi and actinobacteria from the qpcr test (HouseTest ApS) Species, Genera or group of fungi Most probable source All fungi (universal primer) Alternaria alternata Cladosporium cladosporioides Cladosporium herbarum Cladosporium sphaerospermum Aspergillus niger Chaetomium globosum Penicillium, Aspergillus, Paecilomyces Stachybotrys chartarum
Streptomyces sp. Aspergillus versicolor Acremonium strictum Aspergillus glaucus Mucor sp. and Rhizopus sp. Penicillium chrysogenum Rhizopus stolonifer Aspergillus fumigatus Trichoderma viride Ulocladium chartarum Wallemia sebi Based on assessment criteria, the preschool buildings were grouped into four categories: Category 0 represents buildings with no signs of mold damage based on the results from the qpcr analysis, 1 represents a minor risk of mold damage, 2 represents moderately elevated risk of mold damage, and 3 represents a high risk of mold damage in the building. We collected information about the buildings and the building maintenance through a questionnaire to the employees in the preschools. We performed a oneway analysis of variance on the data using the statistical software JMP from SAS institute Inc. 3 RESULTS Results from analyses of dust cover shows that percentage dust on horizontal surfaces varied between 0.26 to 56.18%. Eighty-two of the samples had a result between 0 and 5% dust cover as expected, while 18 had a dust cover percentage exceeding 5%. qpcr proved to be an efficient tool for identifying contaminants in dust samples. Amount of dust settled on the surface had impact on the amount of mold present in the samples. qpcr identified total fungi in each sample and additionally up to 19 other fungal and actinobacteria contaminants. Results from qpcr were categorized based on proportion originating from species associated with moisture damages versus naturally occurring fungi from outdoor air, giving a distribution of the preschool buildings into category 0 to 3 as shown in figure 1.
Figure 1. Distribution of preschools into categories 0 to 3 based on assessment of qpcr results. Categories 0 = normal levels of molds, 1 = minor increase in level of molds, 2 = moderate increase of moisture damage associated molds, 3 = distinct increase in moisture damage associated molds. After calculating proportion of fungi originating from species associated with moisture damages versus naturally occurring fungi from outdoor air, we chose to exclude three outliers with a proportion above 0.5. Results were grouped in buildings that did not undergo inspection through the last three years and buildings that did undergo such inspection. Buildings not subject to inspection last three years had a higher proportion of fungi associated with moisture damages than those that had been inspected as found by oneway analysis of variance on the data (N = 81, p=0.0029). Distribution of preschools according to inspection is displayed in figure 2.
Figure 2. Distribution of proportion of fungi associated with moisture damages (y axis) in the two categories; Left side: Building inspected last 3 years, right side: Building not inspected last 3 years. There was also a significantly (p= 0.0029) higher chance for a building to be assessed at higher risk of mold damage when the building was not inspected during the last three years. 4 DISCUSSION Several studies support the importance of exposure to a wide diversity of microorganisms through childhood in order to protect against development of childhood asthma (Dannemiller et al., 2014), whereas exposure to molds associated with moisture damages have more negative effects. In order to detect mold damages at an early stage, a dust sample is useful. Our study shows that molds originating from both natural air movements from outdoors to indoors, and also molds associated with moisture damages on building materials, can be detected through a simple sample of settled dust. Scanning of the tape lifts provides a quick method to measure dust coverage (percent dust cover). In our opinion, this method is both faster and safer than collection of dust by vacuum cleaning. The tape lift sample is easy to perform, and the area sampled is small and restricted to the area of the tape itself. Vacuum cleaning or sampling dust by other methods like swabs allows for sampling errors like sampling from a too small or too large area. In the case of tape lifts, the area sampled can be verified by scanning the tape. When dust is allowed to settle for some time, we bring evidence that airborne units are deposited together with dust, and that qpcr of dust sampled directly from horizontal surfaces (Engh, Carlson and Mattsson, 2015) can predict the health of the building. Sampling from
surfaces that are cleaned on a regular basis, but not too often, seems to be a good starting point for analyzing risk of mold damage in a building. Samples with low dust cover, typically less than 1%, is at risk of providing too little dust and microorganisms to produce a useful qpcr result. Less accessible horizontal surfaces provides dust to settle for several weeks, and makes qpcr analysis possible and easy to perform. Sampling from surfaces up from the floor level works well and avoids contamination of microorganisms brought into the building from shoes. 5 CONCLUSIONS We were able to identify buildings with a risk of moisture damage and subsequent elevated risk of mold damage based on sampling of dust from surfaces. In future research, preschools provides an even distribution throughout populated areas and are thus suitable to regional studies of the building microbiome. In order to address the building microbiome, the spatiotemporal variation in the indoor mycobiome within single buildings is necessary. 6 ACKNOWLEDGEMENT We would like to thank the The Norwegian Directorate of Health for financial support, The Norwegian Public Broadcasting Corporation for supplying samples and Mycoteam s laboratory for providing analyses. 7 REFERENCES Bornehag, C. G., Blomquist, G., Gyntelberg, F., Järvholm, B., Malmberg, P., Nordvall, L., Nielsen, A., Pershagen, G. and Sundell, J. (2001) Dampness in buildings and health. Nordic interdisciplinary review of the scientific evidence on associations between exposure to dampness in buildings and health effects (NORDDAMP)., Indoor air, 11(2), pp. 72 86. Available at: http://www.ncbi.nlm.nih.gov/pubmed/11394014 (Accessed: 20 January 2015). Dannemiller, K. C., Mendell, M. J., Macher, J. M., Kumagai, K., Bradman, A., Holland, N., Harley, K., Eskenazi, B. and Peccia, J. (2014) Next-generation DNA sequencing reveals that low fungal diversity in house dust is associated with childhood asthma development, Indoor Air, 24(3), pp. 236 247. doi: 10.1111/ina.12072. Engh, I. B., Carlson, O. E. and Mattsson, J. (2015) EVALUATION OF MOULD SAMPLING METHODS IN ASSESSMENT OF A BUILDING, in Proceedings of Healthy Buildings 2015 Europe. Eindhoven, pp. 1 7. SAS Institute Inc. (1989-2007) JMP, Version 9.0.2. Cary, NC. Standard Norge AS (2010) Cleaning quality. Measuring system for assessment and rating of cleaning quality. Täubel, M., Karvonen, A. M., Reponen, T., Hyvärinen, A., Vesper, S. and Pekkanen, J. (2016) Application of the Environmental Relative Moldiness Index in Finland, Applied and Environmental Microbiology. Edited by C. A. Elkins. American Society for Microbiology, 82(2), pp. 578 584. doi: 10.1128/AEM.02785-15.