How to remove Dampness and Mould?


Fungal growth in damp or water-damaged buildings worldwide is an increasing problem, adversely affecting the occupants and the facilities. Air sampling alone in mouldy buildings does not reveal the diversity of fungal species growing on building materials. This study aimed to estimate the qualitative and quantitative variety of fungi growing on damp or water-damaged building materials. Another way 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 employing V8 contact plates from materials with visible fungal growth. Fungal identifications and information on material building components were analysed using multivariate statistical methods to determine associations between fungi and material details. 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. Analyses show that associated mycobiota 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.


Most water damage indoors is due to natural disasters (e.g., flooding) or human error (e.g., disrepair). Water can seep into a building due to melting snow, heavy rain, or sewer system overflow. Water vapour 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 (mould 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 building material is the determining factor for fungal growth and varies with the temperature and the type of material (). The longer a material’s aw is over 0.75, the greater the risk of fungal growth (), though different fungi have different aw preferences (). Some filamentous fungi can grow on material when the aw is as low as 0.78 (), while others can survive 3 weeks at an aw of 0.45 (). The severity of indoor dampness varies with the climate, but WHO () estimates that in Australia, Europe, India, Japan, and North America, dampness is a problem in 10 to 50% of the buildings. Sivasubramani et al. () estimate that fungal growth is a problem in 15 to 40% of North American and Northern European homes.

The adverse 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 (), which concluded that there is sufficient epidemiological evidence to show that occupants of damp or mouldy buildings are at increased risk of respiratory problems, respiratory infections, and the exacerbation of asthma. The causality between fungal exposure and the development of type I allergy has been proven (), but clinical evidence linking specific fungal spores, hyphal fragments, and metabolites to particular health complaints are lacking. The symptoms reported by occupants in mouldy buildings are many and diverse (, ), as are the fungal species found on mouldy building materials (, ). The fact that some people are hypersensitive to fungi while others do not react further complicates the issue.

Detection and species identification of all fungi present in a mouldy building are the first steps toward resolving the cause and effect of building-related illness (sick building syndrome), so sampling methods are essential. Air and dust samples have been taken to associate fungal exposure and health problems (e.g., , , ), but no conclusive links have been found. This may be because spore liberation from a surface is sporadic (), and spore distribution in the air is random (). Toxic fungi (e.g., Stachybotrys spp. and Chaetomium spp.) growing on damp building materials do not readily become airborne and lose their culturability soon after liberation (, , , ). They may therefore not be detected during air or dust sampling. Correct species identification of the fungi is also essential since new research has indicated that species-specific metabolites, like atranone C produced by Stachybotrys chlorohalonata (), are cytotoxic or immunotoxic or induce inflammatory responses when inhaled (, , ). Therefore, this study aimed 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 of V8 contact plates from building materials with visible fungal growth in Denmark and Greenland. The aim was to determine if an association exists between the most common fungi found and particular types of water-damaged building materials.




Sample collection and treatment.

Samples from building materials with visible fungal growth were taken utilising 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 (). 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 was taken from each damaged site. Approximately 75% of all samples were taken on-site by the building inspectors utilising contact plates and mailed overnight to ML. The remaining 25% were mouldy building materials sent to ML by the occupant after thorough instruction. The materials were then sampled by ML utilising contact plates.

Fungal identification.

Identification of fungi in a sample was made directly on the V8 contact plates after 7 days of incubation at 26°C in darkness. Whenever possible, fungi were identified as species using direct microscopy and identified according to the methods of Domsch et al. (), de Hoog et al. (), and Samson et al. (). Fungi present in the sample were determined qualitatively (taxon present) and quantitatively (number of colonies).

Since different Penicillium species can be challenging 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. ().

Data compilation.

The samples were evaluated based on the reliability of the information on material type and fungal identification. Each piece contained information on the sort 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 on “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 to minimise analytical noise (e.g., Karlit ceiling tiles, Botrytis cinerea, Doratomyces spp., or Epicoccum nigrum). Examples with the 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 material building 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 pieces, 25 material building components, and 41 fungi.

Multivariate statistics.

Matrices A and B were analysed by principal component analysis (PCA) using the program Unscrambler v. 9.2 (CAMO Process A/S, Oslo, Norway). PCA is a bilinear modelling method giving an interpretable overview of the preliminary information. All variables (components and fungi) were standardised (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 standardised. 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. The fungal count or the number of colonies for each fungus was summarised for each material. This resulted in two tables, contingency table A, based on the qualitative data (5,353 samples summarised in table A [42 rows with fungi and 30 columns with materials]), and contingency table B, based on the quantitative data (4,241 samples summarised 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 specific 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 a 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 analysed by correspondence analysis (CA) using the program NTSYS version 2.21c (Exeter Software, Setauket, NY) (). Chi-square distances were used to judge the row and column variables’ proximities.

The data in matrix A were also converted into a fungal species distance matrix, where the count for each of the 42 fungi was summarised on the other 41 fungi. This was done to analyse whether any fungal species cooccurred independently of material preferences. This resulted in matrix C, a qualitative 42-by-42 symmetric matrix, which was then analysed by principal coordinate (PCO) analysis using NTSYS v. 2.21c. Matrix C was double-centred, 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 ().


Building materials.

Table 1 shows the material-building components most often affected by fungal growth. As can be seen, plaster and concrete were the material components most likely to support the 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, vapour barriers, carpets, cork, medium-density fiberboard (MDF), vinyl, felt, grout, filler, Eternit, textiles, and tar-treated materials.

Table 1.

Qualitative (qual) and quantitative (Quan) frequencies of building material components with fungal growth from water-damaged buildings

Material component Frequency (%)a

qual (n = 5,353) Quan (n = 4,241)
Plaster 23.5 22.1
Concrete 19.0 24.1
Wood 18.4 19.0
Wallpaper 12.6 6.3
Gypsum 7.7 8.1
Paint 5.2 5.8
Mineral wool 3.8 3.3
Glass fibre 3.6 3.6
Plywood 3.0 4.5
Brick 2.1 2.5
Chipboard 2.0 2.5
Linoleum 2.0 3.5


black mould

Elimination of Black Mould

The raw data showed that 45 fungal genera or species were identified on the samples of water-damaged building materials. Table 2 shows fungi’s qualitative and quantitative presence 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. Aspergillus Versicolor was the most prevalent fungal species (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-atrium, 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 Frequency (%)a

qual (n = 5,353) Quan (n = 287,169)
Penicillium spp. 69.5 39.7
Aspergillus versicolor 26.5 15.6
Chaetomium spp. 16.5 3.1
Acremonium spp. 14.9 7.8
Ulocladium spp. 8.0 2.1
Cladosporium sphaerospermum 7.4 4.9
Mucor plumbeus (syn. M. spinosus) 7.2 0.3
Trichoderma spp. 6.7 0.4
Cladosporium herbarum 6.6 1.5
Alternaria tenuissima 6.5 0.7
Sporothrix spp. 6.4 3.3
Aspergillus niger 6.1 0.5
Yeasts 5.1 2.6
Rhodotorula mucilaginosa 5.1 2.3
Aspergillus ochraceus 5.0 0.9
Penicillium chrysogenumb (syn. P. notatum) 4.5 2.5
Rhizopus stolonifer (syn. R. nigricans) 4.1 0.1
Stachybotrys spp. 3.9 1.9
Aspergillus fumigatus 3.8 0.2
Aspergillus spp. 3.6 1.2
Mucor spp. 3.3 0.2
Mycelia Sterilia 3.1 c
Aspergillus wentii 3.1 1.3
Calcarisporium arbuscula 2.7 1.2
Scopulariopsis brevicaulis 2.1 0.5
Fusarium spp. 2.0 0.4
Mucor racemosus 2.0 0.1
aAn another 15 fungi occurred in fewer than 2% of the samples. A sample may contain more than one fungus.
Underestimated, as several of the Penicillium spp. It may also be Penicillium chrysogenum.
Mycelia Sterilia was not quantified in matrix B.

The isolation and identification of 120 penicilliums 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. At the same time, Penicillium brevicompactum, Penicillium corylophilum, Penicillium crustose, Penicillium olsonii, Penicillium palitans, and Penicillium solatium 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, the other components of building material, and the 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 other building material components: plaster, wallpaper, and painted surfaces cooccur in the plot in Fig. 1 because most Danish houses have brick walls levelled 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 fibre. 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 exact negative correlation can be seen for wallpaper and Aspergillus niger, concrete and Cladosporium sphaerospermum, plywood, and Aspergillus ochraceus. Fungi or components lying close to the centroid in the plot infrequently occur (<4%) and have very loose or little association with each other.

An external file that holds a picture, illustration, etc. Object name is zam9991021830001.jpg

Loadings plot from the principal component analysis (PCA) based on the qualitative matrix A [5,353 samples × (30 materials and 42 fungi)]. The plot shows associations between building materials and fungi (e.g., wood and Alternaria tenuissima, Cladosporium herbarum, Rhodotorula mucilaginosa, and yeasts). Fungi and building materials encircled are particularly associated. Fungi or components close to the centroid have little or no association, infrequently occur (<4%), and have little or no influence on the PCA model. Axes are principal components, PC 1 and PC 2, with loading values.

Matrices A and B were converted to contingency tables A and B based on the qualitative data (table A, 42 rows with fungi and 30 columns with materials) and quantitative data (table B, 41 rows with fungi and 25 columns with materials), respectively. Table 3 is an extract of contingency tables A and B and shows the distribution of each 17 fungi on 7 different fabrics. The table gives the qualitative and quantitative frequencies and percentage distributions of fungi on various building materials, where the sum of all counts or colonies of one fungal species on all 30 or 25 materials constitutes 100%. Some fungi are overrepresented on some fabrics (row values in bold) compared to the statistically predicted value if the fungi were randomly or evenly distributed on all materials. For example, Sporothrix spp. has a strong association with plaster; i.e., 39% (162 counts) of all material samples with Sporothrix spp. were plaster and 37% (4,118 colonies) of all the Sporothrix spp. Colonies counted were found on plaster samples. However, had Sporothrix spp. Having been randomly distributed, the statistically predictive values would have been 81 counts and 1,865 colonies, respectively. Sporothrix spp. also has an association with concrete; 18% of all concrete samples were colonised with Sporothrix spp., and 21% of all Sporothrix spp. Colonies originated from concrete examples. However, Sporothrix spp. are rarely found on plywood (0 and 2%, respectively). Similar strong associations were found between Stachybotrys spp. and gypsum, wallpaper, and glass fibre and Ulocladium spp. and wallpaper, plaster, and gypsum. Phoma spp. also had a preference for glass fibre. Alternaria tenuissima, Cladosporium herbarum, Trichoderma spp., and yeasts were the associated fungal species on water-damaged wood, while Mucor racemosus, Aspergillus ochraceus, and Aspergillus niger were associated with wet concrete. It can also be seen that members of the most dominant fungal genus in water-damaged buildings, Penicillium, were evenly distributed on all the examined building materials and without any pronounced preference. Likewise, some fungal species are underrepresented in some materials. For example, Aspergillus Versicolor had a low association with plywood; i.e., 1% (20 counts) of all material samples with Aspergillus Versicolor were plywood, and 1% (776 colonies) of all the Aspergillus Versicolor colonies counted were found on plywood samples. However, had Aspergillus Versicolor been randomly distributed, the statistically predictive values would have been 41 counts and 4,307 colonies, respectively. Table 3 also shows that Aspergillus niger, Aspergillus ochraceus, and Mucor racemosus were not associated with wallpaper and that Cladosporium sphaerospermum and yeasts were uncommon on concrete.

Table 3.

Qualitative (qual) and quantitative (Quan) distribution (%) of associated fungi on different building materials

Fungus Distribution (%)


Glass fibre






qual Quan qual Quan qual Quan qual Quan qual Quan qual Quan qual Quan
Acremonium spp. 13 14 3 3 8 8 25 22 3 5 8 6 14 15
Aspergillus niger 32 35 2 0 7 5 11 10 1 0 4 1 18 23
Aspergillus ochraceus 33 52 2 4 4 3 20 6 1 0 5 5 12 8
Aspergillus versicolor 20 26 3 3 4 4 25 21 1 1 11 7 11 10
Aureobasidium pullulans 3 0 0 9 6 0 14 11 9 6 3 0 46 37
Chaetomium spp. 25 35 2 2 6 9 15 11 1 2 10 3 17 18
Cladosporium herbarum 11 15 1 1 5 5 14 15 5 9 6 3 24 27
Cladosporium sphaerospermum 8 10 4 3 3 4 22 24 6 11 12 6 17 16
Mucor racemosus 36 54 1 0 3 3 17 14 0 0 5 1 15 10
Paecilomyces variotii 13 8 0 1 13 15 10 8 19 15 2 3 27 17
Penicillium spp. 19 26 3 2 6 6 20 16 2 3 10 6 17 18
Penicillium chrysogenum 12 18 2 2 11 14 21 18 3 4 16 5 13 15
Phoma spp. 9 12 9 12 4 1 15 9 2 4 12 6 12 11
Sporothrix spp. 18 21 3 2 3 4 39 37 0 2 7 4 7 7
Stachybotrys spp. 10 4 2 10 25 39 22 13 1 0 10 8 9 7
Trichoderma spp. 14 16 2 2 9 9 15 13 4 8 6 2 25 30
Ulocladium spp. 9 3 5 2 7 15 21 34 1 1 29 16 12 9
Yeasts 8 7 2 3 8 5 10 11 7 9 5 2 28 39
values in bold give the materials on which the fungus is overrepresented (the associated mycobiota). Row sums are ≤100%.

Table 4 is also based on contingency tables A and B but shows the distribution of 17 fungal species on each of the 7 different materials. The table gives the qualitative and quantitative occurrences and percentage distributions of fungi on other building materials, where the column sum of all counts or colonies of all the 42 fungal species on one material constitutes 100%. As can be seen, Penicillium spp. is the dominant genus and can be found on all types of materials with almost the same frequency (27 to 30% quantitatively and 27 to 46% quantitatively), corresponding to the overall occurrence seen in Table 2. Aspergillus Versicolor and Acremonium spp. are also very dominant and found evenly on most materials, except in the case of Aspergillus Versicolor, where the distribution on plywood is underrepresented (5 and 6%) compared to its overall occurrence.

Table 4.

Qualitative (qual) and quantitative (Quan) distribution (%) of dominant fungi on particular building materials

Fungus Distribution (%)


Glass fibre






qual Quan qual Quan qual Quan qual Quan qual Quan qual Quan qual Quan
Acremonium spp. 4 5 8 9 7 9 8 10 8 10 5 8 5 7
Aspergillus niger 4 1 2 0 3 0 1 0 1 0 1 0 3 1
Aspergillus ochraceus 3 2 2 1 1 0 2 0 1 0 1 1 1 0
Aspergillus versicolor 12 19 12 19 6 10 14 19 5 6 13 20 7 10
Aureobasidium pullulans 0 0 0 1 0 0 0 0 1 0 0 0 1 0
Chaetomium spp. 9 5 5 2 6 4 5 2 3 1 7 1 7 3
Cladosporium herbarum 2 1 1 1 2 1 2 1 5 3 2 1 4 2
Cladosporium sphaerospermum 1 2 4 5 2 3 3 7 7 13 4 5 3 5
Mucor racemosus 1 0 0 0 0 0 1 0 0 0 0 0 1 0
Paecilomyces variotii 0 0 0 0 1 1 0 0 2 2 0 0 1 1
Penicillium spp. 28 46 26 27 27 34 27 35 31 41 30 39 28 44
Penicillium chrysogenum 1 2 2 2 3 5 2 2 1 2 3 2 1 2
Phoma spp. 0 0 3 3 1 0 1 0 1 1 1 1 1 1
Sporothrix spp. 3 3 3 2 1 2 5 7 0 1 2 3 1 1
Stachybotrys spp. 1 0 1 7 6 12 2 1 1 0 2 3 1 1
Trichoderma spp. 2 0 2 0 4 1 2 0 4 1 2 0 4 1
Ulocladium spp. 2 0 6 2 4 5 3 4 2 1 10 6 2 1
Yeasts 1 1 1 2 3 2 1 2 6 6 1 1 4 6
values in bold give the three most dominant fungi. Column sums are ≤100%.

The correspondence analysis (CA) results based on the quantitative data (contingency table B: a 41-by-25 table based on matrix B) is shown as a biplot in Fig. 2. The first four CA axes described 20.74%, 17.85%, 14.85%, and 9.88% of the variation in the data for contingency table B. The result of the CA of the qualitative data (contingency table A: a 42-by-30 table based on matrix A) gave a very similar effect, with the first four CA axes describing 21.54%, 17.06%, 12.02%, and 8.37% of the variation in contingency table A, and is not shown. Figure 2 shows the quantitative associations between building material and fungi. As seen, wallpaper/glass fibre, gypsum, Stachybotrys spp., and Ulocladium spp. are strongly associated. Likewise, plywood is closely associated with Arthrinium phaeospermum, Rhodotorula mucilaginosa, and Trichoderma spp… At the same time, Aspergillus ochraceus, Aspergillus sydowii, and Mucor racemosus are closely associated with each other and loosely associated with Aspergillus fumigatus and Mucor spp. and concrete, glue, and cork. Being relatively close to the centroid of the plot, Penicillium spp. is associated with most of the materials. At the same time, Aspergillus Versicolor is more associated with gas concrete, cork, vinyl, and glue than wallpaper, grout/tile wood, or plywood.

An external file that holds a picture, illustration, etc. Object name is zam9991021830002.jpg

Biplot from the correspondence analysis (CA) based on the quantitative sum table B (matrix B [4,241 samples × (25 materials and 41 fungi)] summarised in a 41-by-25 table). The biplot shows the association between building material and fungal identity (e.g., gypsum, glass fibre, wallpaper, Ulocladium spp. and Stachybotrys spp.). The dotted lines show the distance of any fungus to the centroid, i.e., fungi farthest away from the centroid deviate the most from what would be expected based on the data table. Axes are correspondence components, DIM 1 and DIM 2, with score and loading values.

Two-dimensional plots of both PCA and CA can sometimes make variables (i.e., fungi) appear more associated than they are. A minimum spanning tree can be a reasonable control for determining whether particular variables (i.e., fungi) are as close as their similar score or loading values. To test the associations in Fig. 1 and and2,2, a principal coordinate (PCO) analysis with an overlain minimum spanning tree of matrix C (matrix C, 42 fungi by 42 fungi) was made. The PCO analysis described 42.55%, 23.58%, 8.36%, and 7.22% of the variation in the data on the first four axes, so these four axes represented a more significant proportion of the variance than expected using the broken-stick model (10.30%, 7.92%, 6.73%, and 5.94%). The PCO analysis showed cooccurrence and strong association between (i) Acremonium spp., Penicillium chrysogenum, Stachybotrys spp., and Ulocladium spp., (ii) Trichoderma spp., Alternaria tenuissima, Cladosporium herbarum, Trichosporon pullulans, and Fusarium spp., (iii) yeasts, Gliocladium spp., Arthrinium phaeospermum, and Aureobasidium pullulans, (iv) Aspergillus sydowii, Aspergillus ochraceus, Mucor racemosus, Aspergillus malleus, Aspergillus niger, Mucor spinosus Aspergillus fumigatus Chaetomium spp., and Scopulariopsis brevicaulis, and (v) Aspergillus Versicolor, Calcarisporium arbuscula, and Scopulariopsis brumptii.

A comparison of Tables 1 and and33 shows that some building materials are more prone to fungal growth after water damage and that some materials have a high fungal load (Table 3, high total column value). In contrast, others support only little fungal growth (Table 3, low total column value). For example, wallpaper often becomes fungus-ridden (Table 1, 12.6%) with a high fungal load, whereas glass fibre showed fungal growth less frequently (Table 1, 3.6%) and had a low fungal load. Comparisons of Tables 3 and and44 show that some fungi have a high occurrence but no association with particular materials (e.g., Penicillium spp.). In contrast, other fungi have a moderate occurrence and a specific association with certain materials (e.g., Stachybotrys spp. and gypsum and Ulocladium spp. and wallpaper). Others again have a low occurrence but a tight association with a particular material (e.g., Aureobasidium pullulans [blue stain fungus] and wood and Phoma spp. and glass fibre).


Sampling methods.

No single sampling method can detect all fungal species in a mouldy building exists because all methods (e.g., air or surface sampling with either spore counting or cultivation) are, in one way or another, biased. Air sampling is unreliable because it favours fungi that produce large quantities of tiny, dry spores, such as Aspergillus spp., Cladosporium spp., and Penicillium spp. () and discriminates against fungi that produce small amounts of spores, large spores, or spores in slime, such as Acremonium spp., Chaetomium spp., Stachybotrys spp., Trichoderma spp., and Ulocladium spp. Besides, fungal diversity is different in outdoor air (), indoor air (), and house dust (, ) compared with each other and with mouldy building materials (). Air sampling alone may give an incorrect picture of the fungal diversity in a musty building. This study used surface sampling with contact plates containing V8 medium with antibiotics. Other agar media (e.g., water agar, MEA, or DG18) (, ) have been recommended in older literature. Still, new research () has shown important toxigenic fungi like Chaetomium spp., Stachybotrys spp. and Trichoderma spp. Do not grow or sporulate well on these media. V8, on the other hand, has been shown to support good growth and sporulation of most indoor fungi (, , , ), and V8 allows direct genus identification of indoor fungi and species identification of most Alternaria, Aspergillus, and Cladosporium species and zygomycetes. A disadvantage with V8 is that it does not allow the growth of dust fungi such as Eurotium spp. or Wallemia spp. () or identification to species level of Penicillium. A disadvantage with all sampling methods based on cultivation is that they detect only viable fungal spores. In the case of Stachybotrys spp., detection is essential because dead spores coated in toxins might still be present on mouldy materials. This can be overcome by using Sellotape (Scotch tape) to take samples directly from the surface of mouldy building materials. This method complements contact plates or swabs and detects the nonviable and nonculturable fungi (, ). The tape preparation method is cheap and quick and can be used on the sampling site before the V8 contact plates. Surface sampling using DNA detection would be ideal, but accurate DNA detection and identification of many filamentous fungi are not yet possible (). None of these sampling methods would detect hyphal fragments, MVOCs, or metabolites that might affect occupants living or working in these environments.

Fungal diversity.

Most studies on indoor fungi deal either with surveys of a few fungal species sampled on surfaces (e.g., reference ) or with many fungal genera detected using air or dilution sampling (e.g., references and , respectively). This study deals with many fungal genera (Table 2) on many surface components (Table 1). Our findings (Table 2) do not corroborate the results reported in the WHO guidelines (). In this study, Chaetomium spp., Acremonium spp., Sporothrix spp., Calcarisporium spp., and Scopulariopsis spp. were detected together with Arthrinium spp., Aureobasidium spp., and Gliocladium spp. These genera were not listed in the WHO guidelines (). WHO reports that Eurotium spp. and Wallemia Sebi are common, but this study did not detect dust fungi because V8 was used. Epicoccum spp. and Phialophora spp. were found in this study but so rarely (<0.5%) that they were deleted before analyses. Our results, however, correspond well to the findings of Gravesen et al. (), except for Stachybotrys spp. and Ulocladium spp., where our study found 3.9% Stachybotrys spp. and 8.0% Ulocladium spp. Compared to 19 and 21% in work by Gravesen et al. (). One reason could be that Gravesen et al. () used tape preparations on the mouldy materials to supplement the V8 medium, ensuring that nonviable Stachybotrys spp. and Ulocladium spp. Spores were also detected. Another reason could be that Gravesen et al. () examined more gypsum samples (11.1%) with Stachybotrys spp. And, to some extent, Ulocladium spp. are more associated than we did (7.7%).

Identification to species level is always recommended in order to know the fungal diversity (mycobiota) before building renovation commences. Large amounts of alien spores from outside sources can be introduced to a building under renovation, and knowledge of the original mycobiota can be used as a control measure of renovation quality. Identification of species level is also essential from a health perspective since several fungal genera contain species capable of producing species-specific metabolites, mycotoxins (, , , , ), and allergens (, ). Some fungal genera, however, are notoriously difficult to identify to species level without further cultivation, especially Penicillium. Identification of a set of Penicillium randomly sampled from other indoor samples showed that Penicillium chrysogenum was the most common Penicillium, constituting more than 70% of the cultivated penicillin. Using this approximation, Penicillium chrysogenum would be the most common fungal species in mouldy buildings, with ca. 50% being detected twice as often as Aspergillus Versicolor. Using the exact estimation, Penicillium brevicompactum, Penicillium citreonigrum, Penicillium corylophilum, Penicillium crustose, Penicillium olsonii, Penicillium palitans, and Penicillium soliton would constitute 4 to 5% each. Several other Penicillium spp. has been associated with indoor environments, such as Penicillium citrinum, Penicillium digitatum, Penicillium expansum, Penicillium glabrum, Penicillium italicum, and Penicillium roqueforti, but only when air sampling has been used (e.g., , ). Most of these Penicillium species are associated with foods, plants, and herbs (, ) and may be more related to mouldy food in the building than the mouldy building materials themselves.

Other fungal genera can be challenging to identify species directly on V8. Newer literature suggests that the most common Chaetomium and Acremonium species found on water-damaged building materials are Chaetomium globosum and Acremonium strictum, respectively (). Ulocladium spp. are also common, and studies have shown that several species (Ulocladium Alternaria, Ulocladium atrium, and Ulocladium oudemansii) can be detected on water-damaged building materials (). Alternaria tenuissima, on the other hand, might often be confused with Ulocladium chartarum because both species produce spores in unbranched chains. The most common Trichoderma species in water-damaged buildings belong to the species complex of Trichoderma harzianum (), whereas the only Stachybotrys species found are Stachybotrys chartarum and Stachybotrys chlorohalonata ().

The findings presented in this study, that Penicillium chrysogenum, Aspergillus Versicolor, and Chaetomium globosum are the three most common fungal species on water-damaged building materials, correspond very well with the findings of Polizzi et al. (). They repeatedly detected the mycotoxins, roquefortine C, sterigmatocystin, and chaetoglobosin A in air, dust, fungal biomass, and wallpaper samples. Roquefortine C, sterigmatocystin, and chaetoglobosin A are the primary metabolites produced by Penicillium chrysogenum, Aspergillus Versicolor, and Chaetomium globosum, respectively (). Polizzi et al. () also detected original E in air samples and ochratoxin A and aflatoxins in air, dust, and fungal biomass samples. These toxins are produced by Stachybotrys spp., Aspergillus niger, Aspergillus ochraceus, and Aspergillus flavus (), which are also quite common in mouldy buildings (Table 2). Still, they were not able to see the producing fungal species.

Associations between fungi and building materials.

This study, which is the largest of its kind, shows an associated mycobiota on different building materials as an associated mycobiota on different food types (). The association between Acremonium spp., Penicillium chrysogenum, Stachybotrys spp., and Ulocladium spp. on gypsum and wallpaper was indicated by both ordination methods (PCA and CA) and the PCO/minimum spanning tree (Fig. 1 and and22 and Table 3). Production of neutral cellulases has been found in these fungi (, ) and may be a common ability of many indoor fungi that have found their niche on damp gypsum or plaster walls clad with wallpaper. A second strong association was seen between Arthrinium osteospermum, Aureobasidium pullulans, Cladosporium herbarum, Trichoderma spp., and yeasts on different types of wood and plywood in all three analyses (PCA, CA, and PCO). Alternaria tenuissima, Fusarium spp., Gliocladium spp., Rhodotorula mucilaginosa, and Trichosporon pullulans were associated with the group in two out of three analyses. These fungi are also known for their production of neutral cellulases () but differ from fungi on damp walls because they need higher water activity (). The third association was seen between Aspergillus fumigatus, Aspergillus malleus, Aspergillus niger, Aspergillus ochraceus, Chaetomium spp., Mucor racemosus, and Mucor spinosus on concrete and other floor-related materials, such as linoleum, cork, and the glue used to secure them. Concrete is mainly used to cast foundations, floors, and other horizontal structures and will hold soil, dirt, and dust better than vertical surfaces. Aspergillus, Chaetomium, and Mucor species are common in the dust (), hypersaline water, and soil (, ). They may be introduced along with dirt and may tolerate alkaline conditions in the concrete, beginning to grow when the concrete gets wet.

The results presented here can aid the building profession in choosing materials less susceptible to fungal growth (e.g., glass fibre instead of wallpaper) and by manufacturers of building materials to develop nontoxic, fungus- or water-resistant materials (e.g., coating of gypsum board against Stachybotrys spp.). Health authorities can use the results to facilitate the development of standardised allergen extracts to test for type I allergy to specific indoor fungi: Acremonium strictum, Aspergillus Versicolor, Chaetomium globosum, Stachybotrys chartarum, Stachybotrys chlorohalonata, and Ulocladium alternative. The results can also be used by the scientific community to focus research on the physiology and ecology of toxigenic species and their potential production of mycotoxins, MVOCs, and microparticles on building materials and to aid in the development of new and better detection and identification methods for fungal growth on building materials.


We thank Ole Filtenborg and Ulf Thrane, DTU Systems Biology, for fruitful discussions.

how to get rid of the damp smell, open your doors and windows and allow fresh air in, then close up your home, and dry out the air with either ( contact Marc)

effects of sleeping in a damp room are not good; again, dry it out, dry out the air, and dry out the walls.

Articles from Applied and Environmental Microbiology are provided here courtesy of the American Society for Microbiology (ASM)

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