Associations between Fungal Species and Water-Damaged Building Materials ▿
Jens C. Frisvad
Ib Søndergaard
Ib S. Rasmussen
Lisbeth S. Larsen
2Danish Technological Institute, Gregersensvej 1, DK-2630 Taastrup, Denmark
Corresponding author. Mailing address: CMB, DTU Systems Biology, Søltofts Plads, Building 221, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. Phone: (45) 4525 2726. Fax: (45) 4588 4922. E-mail: @ab
Received 2010 Oct 25; Accepted 2011 Apr 17.
Copyright © 2011, American Society for Microbiology
This article has been cited by other articles in PMC.
Abstract
Fungal growth in damp or water-damaged buildings worldwide is an increasing problem, which has adverse effects on both the occupants and the buildings. Air sampling alone in moldy buildings does not reveal the full diversity of fungal species growing on building materials. One aim of this study was to estimate the qualitative and quantitative diversity of fungi growing on damp or water-damaged building materials. Another was to determine if associations exist between the most commonly found fungal species and different types of materials. More than 5,300 surface samples were taken by means of V8 contact plates from materials with visible fungal growth. Fungal identifications and information on building material components were analyzed using multivariate statistic methods to determine associations between fungi and material components. The results confirmed that Penicillium chrysogenum and Aspergillus versicolor are the most common fungal species in water-damaged buildings. The results also showed Chaetomium spp., Acremonium spp., and Ulocladium spp. to be very common on damp building materials. powell tn flood cleanup show that associated mycobiotas exist on different building materials. Associations were found between (i) Acremonium spp., Penicillium chrysogenum, Stachybotrys spp., Ulocladium spp., and gypsum and wallpaper, (ii) Arthrinium phaeospermum, Aureobasidium pullulans, Cladosporium herbarum, Trichoderma spp., yeasts, and different types of wood and plywood, and (iii) Aspergillus fumigatus, Aspergillus melleus, Aspergillus niger, Aspergillus ochraceus, Chaetomium spp., Mucor racemosus, Mucor spinosus, and concrete and other floor-related materials. These results can be used to develop new and resistant building materials and relevant allergen extracts and to help focus research on relevant mycotoxins, microbial volatile organic compounds (MVOCs), and microparticles released into the indoor environment.
INTRODUCTION
Most water damage indoors is due to natural disaster (e.g., flooding) or human error (e.g., disrepair). Water can seep into a building as a result of melting snow, heavy rain, or sewer system overflow. Water vapor can be produced by human activities like cooking, laundering, or showering and then condense on cold surfaces like outer walls, windows, or furniture. Damp or water-damaged building materials are at high risk of fungal growth (mold growth), possibly resulting in health problems for the occupants and the deterioration of the buildings. The water activity (aw) (aw × 100 = % relative humidity at equilibrium) of a building material is the determining factor for fungal growth and varies with the temperature and the type of material ( 27 ). The longer a material's aw is over 0.75, the greater the risk of fungal growth ( 49 ), though different fungi have different aw preferences ( 11 ). Some filamentous fungi can grow on a material when the aw is as low as 0.78 ( 26 ), while others can survive 3 weeks at an aw of 0.45 ( 30 ). The severity of indoor dampness varies with the climate, but WHO ( 52 ) estimates that in Australia, Europe, India, Japan, and North America, dampness is a problem in 10 to 50% of the buildings, and Sivasubramani et al. ( 41 ) estimate that fungal growth is a problem in 15 to 40% of North American and Northern European homes.
The negative health effects of damp building materials and fungal growth in homes, institutions, and workplaces have been reported in many publications, including the WHO guidelines Dampness and Mould ( 52 ), which concluded that there is sufficient epidemiological evidence to show that occupants of damp or moldy buildings are at increased risk of respiratory problems, respiratory infections, and the exacerbation of asthma. The causality between fungal exposure and development of type I allergy has been proven ( 18 ), but clinical evidence linking specific fungal spores, hyphal fragments, and/or metabolites to particular health complaints is lacking. The symptoms reported by occupants in moldy buildings are many and diverse ( 20 , 23 ), as are the fungal species found on moldy building materials ( 14 , 19 ). The fact that some people are hypersensitive to fungi while others do not react at all further complicates the issue.
Detection and species identification of all fungi present in a moldy building are the first step toward resolving the cause and effect of building-related illness (sick building syndrome), so the choice of sampling methods is essential. Air and dust samples have been taken in order to associate fungal exposure and health problems (e.g., 10 , 17 , 48 ), but no conclusive links have been found. This may be because spore liberation from a surface is sporadic ( 41 ) and spore distribution in the air is random ( 21 ). Toxic fungi (e.g., Stachybotrys spp. and Chaetomium spp.) growing on damp building materials do not readily become airborne and/or lose their culturability soon after liberation ( 21 , 29 , 45 , 47 ) and may therefore not be detected during air or dust sampling. Correct species identification of the fungi is also important, since new research has indicated that species-specific metabolites, like atranone C produced by Stachybotrys chlorohalonata ( 37 ), are cytotoxic or immunotoxic or induce inflammatory responses when inhaled ( 24 , 33 , 34 ). The purpose of this study was therefore to estimate the qualitative and quantitative diversity of fungi growing on damp or water-damaged building materials. The study was based on more than 5,300 surface samples taken by means of V8 contact plates from building materials with visible fungal growth in Denmark and Greenland. The aim was to determine if there exists an association between the most common fungi found and particular types of water-damaged building materials.
MATERIALS AND METHODS
Sample collection and treatment.
Samples from building materials with visible fungal growth were taken by means of 65-mm contact plates (VWR International) containing V8 agar with antibiotics (200 ml Campbell's original V8 100% vegetable juice, 3 g CaCO3 Merck, 20 g agar VWR International) and 800 ml water. Penicillin (100,000 IU/liter Sigma) and streptomycin (1 g/liter Sigma) were added after autoclaving ( 12 ). The plates were subjected to fungal analysis at the Mycological Laboratory (ML) at the Danish Technological Institute (DTI). Samples were collected from June 2005 to February 2008 and originated from private residences (houses, apartments, and holiday cottages) and private businesses (shops and offices) as well as from public buildings (kindergartens, schools, and offices) from all parts of Denmark and Greenland. Samples were taken from buildings where either professional building inspectors had reported visible fungal growth or water damage or occupants had contacted DTI with self-reported fungal or health problems. Several samples may have been taken from the same building, but only one sample was taken from each damage site. Approximately 75% of all samples were taken on site by the building inspectors by means of contact plates and mailed overnight to ML. The remaining 25% were moldy building materials sent to ML by the occupant after thorough instruction. The materials were then sampled by ML by means of contact plates.
Fungal identification.
Identification of fungi in a sample was done directly on the V8 contact plates after 7 days of incubation at 26°C in darkness. Whenever possible, fungi were identified to species using direct microscopy and identified according to the methods of Domsch et al. ( 7 ), de Hoog et al. ( 6 ), and Samson et al. ( 36 ). Fungi present in the sample were determined qualitatively (taxon present) and quantitatively (number of colonies).
Since different Penicillium species can be difficult to identify on V8 medium, special attention was given to this genus in the spring of 2010. DTI randomly selected 80 V8 contact plates with Penicillium growth and delivered them to the Center for Microbial Biotechnology (CMB) at the Technical University of Denmark (DTU). At CMB, all different Penicillium colonies on each plate were isolated, resulting in 120 Penicillium cultures. After transfer to Czapek yeast extract agar (CYA) for purity control, the isolates were inoculated onto CYA, malt extract agar (MEA), yeast extract sucrose agar (YES), and creatine sucrose agar (CREA) and identified to species level after 7 days at 25°C in the dark by methods reported by Samson et al. ( 37 ).
Data compilation.
The samples were evaluated on the basis of the reliability of information on material type and fungal identification. Each sample contained information on type of building (e.g., private home), type of water-damaged construction (e.g., wallpaper on plaster, outer wall), qualitative fungal analysis (e.g., Aspergillus niger dominant, Chaetomium sp.”), and quantitative fungal analysis (e.g., 30 Aspergillus versicolor, 1 Ulocladium sp.”). The information of type of water-damaged material” was divided into categories. If an entry contained two or more components, it was split into component categories (e.g., painted wallpaper on plaster” into paint,” wallpaper” and plaster”).
The sample set containing qualitative data was transformed into a binary matrix consisting of 5,532 samples in rows and 51 different component categories and 57 fungi in columns. Fungi and component categories that constituted less than 0.5% of the total 5,532 samples were deleted in order to minimize analytical noise (e.g., Karlit ceiling tiles, Botrytis cinerea, Doratomyces spp., or Epicoccum nigrum). Samples with growth of dry or wet rot fungi (Serpula lacrymans or Coniophora puteana, respectively) were also deleted. This resulted in a qualitative (binary) matrix (matrix A) with 5,353 valid samples where the association between 30 building material components and 42 fungi was unambiguous. A similar process was repeated on the original sample set to extract the samples with quantitative data, resulting in a matrix (matrix B) with 4,241 samples, 25 building material components, and 41 fungi.
Multivariate statistics.
Matrices A and B were analyzed by principal component analysis (PCA) using the program Unscrambler v. 9.2 (CAMO Process A/S, Oslo, Norway). PCA is a bilinear modeling method giving an interpretable overview of the main information. All variables (components and fungi) were standardized (x − average/sdev), thus giving all the variables the same chance to influence the estimation of the components. In PCA, proximities among the objects were judged using Euclidean distances and among the variables using covariance (or correlation) since the variables have been standardized. The information carried by the original variables was projected onto a smaller number of underlying (latent”) variables called principal components.
The data in matrices A and B were then converted into two contingency tables of observed occurrences, where either the fungal count or the number of colonies for each fungus was summarized for each material. This resulted in two tables, contingency table A, based on the qualitative data (5,353 samples summarized into table A 42 rows with fungi and 30 columns with materials), and contingency table B, based on the quantitative data (4,241 samples summarized in table B 41 rows with fungi and 25 columns with materials). From the contingency tables of observed occurrences, predicted values were calculated for a particular fungus on a particular material: (sum of counts or colonies for fungus A on all materials × sum of counts or colonies of all fungi on material B)/(sum of counts or colonies of all fungi on all materials). For example, 7,452 Ulocladium colonies were counted in total, 19,100 fungal colonies were counted on all wallpaper samples, and 366,304 fungal colonies were counted in total, giving a predicted number of Ulocladium colonies on wallpaper of (7,452 × 19,100)/366,304 = 388, compared with the observed number of 1,208 Ulocladium colonies counted on all wallpaper. Contingency tables A and B were then analyzed by correspondence analysis (CA) using the program NTSYS version 2.21c (Exeter Software, Setauket, NY) ( 15 ). Chi-square distances were used to judge proximities both for the row and for the column variables.
The data in matrix A were also converted into a fungal species distance matrix, where the count for each of the 42 fungi was summarized on the other 41 fungi. This was done to analyze if any of the fungal species cooccurred independently of material preferences. This resulted in matrix C, a qualitative 42-by-42 symmetric matrix, which was then analyzed by principal coordinate (PCO) analysis using NTSYS v. 2.21c. Matrix C was double-centered, and an eigenvector analysis was performed. The correlation coefficient was used, and a minimum spanning tree analysis was superimposed upon the operational taxonomic units (OTUs) in the PCO score plot ( 42 ).
RESULTS
Building materials.
Table 1 shows the building material components that were most often affected by fungal growth. As can be seen, plaster and concrete were the material components most likely to support fungal growth of the total material components. Together with wood, wallpaper, and gypsum, they constitute ca. 80% of materials and construction parts damaged by dampness, condensation, or liquid water. The other 18 building materials that occurred in fewer than 2% of cases were Masonite, cardboard, gas concrete, glue, wood-wool cement, bitumen, paper, vapor barriers, carpets, cork, medium-density fiberboard (MDF), vinyl, felt, grout, filler, Eternit, textiles, and tar-treated materials.
Table 1.
Material component
Frequency (%)a
Fungi.
The raw data showed that 45 fungal genera or species in total were identified on the samples of water-damaged building materials. Table 2 shows the qualitative and the quantitative presence of fungi on 5,353 and 4,241 samples, respectively. As can be seen, Penicillium was the most dominant fungal genus (3,720 counts and 114,143 colonies) on water-damaged building materials. The most dominant fungal species was Aspergillus versicolor (1,421 counts and 44,665 colonies). Together with Chaetomium spp., Acremonium spp., Ulocladium spp., and Cladosporium sphaerospermum, they constituted the most frequently detected fungi on damp or water-damaged building materials. The other 15 fungi that occurred in fewer than 1% of cases were Phoma spp., Paecilomyces lilacinus, Aspergillus ustus, Arthrinium phaeospermum, Aspergillus melleus, Alternaria spp., Scopulariopsis brumptii, Verticillium albo-atrum, Aspergillus flavus, Paecilomyces variotii, Aspergillus sydowii, Absidia spp., Gliocladium spp., Guehomyces pullulans (syn. Trichosporon pullulans), and Aureobasidium pullulans.
Table 2.
Qualitative (qual) and quantitative (quan) frequencies of fungal species and genera on water-damaged building materials
Fungus
7.2
0.3
4.5
2.5
4.1
0.1
bUnderestimated, as several of the Penicillium spp. may also be Penicillium chrysogenum.
cMycelia Sterilia was not quantified in metrix B.
The isolation and identification of 120 penicillia from 80 water-damaged building materials (not the same samples described above) showed that between 70 and 75% of all Penicillium isolates were identified as Penicillium chrysogenum, while Penicillium brevicompactum, Penicillium corylophilum, Penicillium crustosum, Penicillium olsonii, Penicillium palitans, and Penicillium solitum constituted the last 25 to 30% and were found in almost equal amounts.
Associations between fungi and building materials.
The result of a principal component analysis (PCA) of the qualitative (binary) data (matrix A, 5,353 samples × 72 variables 42 fungi and 30 material components) is shown in Fig. 1 The result of the PCA of the quantitative data (matrix B, 4,241 samples × 66 variables 41 fungi and 25 material components) gave a very similar result and is not shown. The first four PCA axes described 3%, 2%, 2%, and 2% of the variation in both matrix A and matrix B. By plotting the first two principal component axes (PC1 against PC2), the interrelationships between all variables (fungi and material components) can be seen. The plot in Fig. 1 shows the qualitative associations between the different fungi, between the different components of building material, and between fungi and material. The more often two fungal species occurred in the same sample, the closer they are together in the plot: Alternaria tenuissima, Cladosporium herbarum, Rhodotorula mucilaginosa, and other yeasts, together with Aureobasidium pullulans, Fusarium spp., Trichoderma spp., and Arthrinium phaeospermum, are often found together on different types of water-damaged wood and therefore lie close together. The same is seen for the different building material components: plaster, wallpaper, and painted surfaces cooccur in the plot in Fig. 1 , because most Danish houses have brick walls leveled with plaster, coated with wallpaper, and then painted. As can be seen, Acremonium spp., Penicillium chrysogenum, Stachybotrys spp., and Ulocladium spp. often occur together and are highly associated with water-damaged walls with painted wallpaper or glass fiber. On the other hand, Chaetomium spp., Penicillium spp., and different Aspergillus species were often found on water-damaged concrete. Figure 1 also shows that Aspergillus versicolor, Calcarisporium arbuscula, and Sporothrix spp. are placed opposite wood (negative correlated) in the plot, meaning that Aspergillus versicolor, Calcarisporium arbuscula, and Sporothrix spp. occurred rarely on wood. The same negative correlation can be seen for wallpaper and Aspergillus niger, concrete and Cladosporium sphaerospermum, and plywood and Aspergillus ochraceus. Fungi or components lying close to the centroid in the plot occur infrequently (<4%) and have very loose or little association with each other.