Mould growth can have severe consequences both on the health of occupants and on constructions’ durability. Mould growth is a very complex process that depends on many factors such as temperature and relative humidity, presence of nutrients, and exposure time. Several mould prediction models, which allow estimating mould growth in building components and performing risk analysis, are available in the literature, such as the updated VTT model or the Biohygrothermal model. A Portuguese typical wall configuration was used for a sensitivity analysis. The importance of insulation (with and without insulation), orientation (north and south), and finishing coating (gypsum-based rendering, medium density fibreboard (mdf), and untreated wood) for the mould growth phenomenon was tested using both the updated VTT model and the Biohygrothermal model. A total of 12 case studies were investigated. The influence of indoor climate was evaluated by simulating 200 scenarios previously generated using the Monte Carlo method. Each of the scenarios has been applied to the 12 case studies, and 2400 hygrothermal simulations were carried out. Initially, the case studies were simulated using WUFI 1D since both mould growth models require the superficial temperature and relative humidity as input. Simulations were carried out for a one-year period. The updated VTT model produced results (mould index—
Mould growth is a very common problem in dwellings and has been steadily increasing in the last decades due to the growing concerns about energy efficiency of buildings. In fact, higher airtightness of the envelopes and lower ventilation rates provide favourable conditions to enable mould growth. On the other hand, new materials used as interior coatings may also increase the problem [
Several studies performed by the scientific community pointed that mould growth affects not only the durability and performance of the materials but has also a main impact on the health and well-being of occupants. Respiratory infections, asthma, allergies, and cough are reported by several authors as respiratory diseases related to inhalation of mould spores. Coating detachment, materials deterioration, and decrease of thermal, hygric, and mechanical performance are the most common drawbacks of mould growth from the building point of view [
Although more than 100,000 mould species can be found in nature, only about 200 occur inside buildings. In dwellings, the transmission by air is the one that plays a relevant role on the health of occupants. Critical concentrations in rooms have been defined, although the information available in different legal directives/guidelines often differs among them. In addition, no accurate and widely applicable information exists about which concentrations do represent a hazard to health [
For mould growth to occur, certain conditions are required. Although these conditions depend on the species, it is possible to generally state that temperature, relative humidity (and the combination of these two), the existence of nutrients and oxygen, and exposure time play a major role in mould development [
In the last decades, several models have been developed in order to assess mould growth in buildings [
The VTT model [
To classify the materials, the model establishes four mould growth sensitivity classes: (a) very sensitive, which includes pine sapwood; (b) sensitive, which includes glued wooden boards, PUR with paper surface and spruce; (c) medium resistant, which includes concrete (aerated and cellular concrete), glass wool, and polyester wool; and (d) resistant, which includes PUR polished surface. The model also considers a decrease function in the mould index, when relative humidity and/or temperature are unfavourable for mould growth, as a function of materials characteristics [
The Biohygrothermal model was developed by Sedlbauer et al. [
According to the Biohygrothermal model, if the course of the moisture content within a spore, which depends on ambient relative humidity, achieves the critical water content inside the spore, which depends on temperature and moisture retention curve for each substrate, mould growth will begin. The growth is expressed in millimetres and, in the beginning of the simulation, it describes the increase of the mycel length. However, this model allows continuous growth as long as there are suitable boundary conditions and, with ongoing growth, unrealistic values of several hundred millimetres can be reached. Therefore, these values can only be regarded for a comparative assessment of the risk of mould development, but not as a realistic growth [
Some studies about the comparison of these two models are available in the literature [
These models have recently been applied in mould risk evaluation. The risk of mould growth when adding interior thermal insulation to a log wall in a cold climate was analysed by Alev and Kalamees [
Although there are existing studies that use and compare the updated VTT and the Biohygrothermal models, no detailed information exists about their application to Mediterranean countries. In fact, in southern European countries, not only the exterior climate has specific particularities but also the interior climate is much more dependent on exterior conditions as no heating habits exist and “adventitious ventilation” is a common strategy. The main objective of this paper is to present the results of a sensitivity analysis to evaluate mould risk in a wall, using a probabilistic approach based on Monte Carlo simulation.
In this work, a sensitivity analysis of the updated VTT and the Biohygrothermal models was made. Three parameters were assessed, coating material, orientation, and the existence of a layer with thermal insulation characteristics, resulting in 12 cases analysed (3 coatings, 2 orientations, and existence or not of thermal insulation). The main aim of the analysis was to evaluate the influence of these three parameters on mould growth. Additionally, a comparison between the results produced by each model was attempted.
The coating materials were selected according to the most common Portuguese construction practices and taking into account the substrate classes in the Biohygrothermal model (Classes I and II) and the sensitivity classes in the updated VTT model (very sensitive, sensitive, and medium resistant), in order to obtain at least one material representative of each group. Table
Selected materials and their classification according to the updated VTT and biohygrothermal models.
Material | Sensitivity class (updated VTT model) | Substrate class (Biohygrothermal model) |
---|---|---|
Gypsum-based rendering (M1) | Medium resistant | II |
Medium density fibreboard (mdf) (M2) | Sensitive | I |
Untreated wood (M3) | Very sensitive | I |
Surface hygrothermal conditions are also fundamental for mould growth. These conditions depend essentially on the interior climate, whose variability is highly dependent on the actions and behaviours of the users [
Typical fluctuation of the interior air (black line) and indoor climates used in the hygrothermal simulations: (a) temperature; (b) relative humidity.
After defining a base case, the Monte Carlo method was used to generate 200 new scenarios (Figure
Methodology and cases Id.
The exterior climate was the one of Porto (Portugal), generated by the commercial software Meteonorm in an hourly base [
Porto climate (generated by Meteonorm 6.0).
Climatic parameter | Annual average | Annual accumulated |
---|---|---|
Temperature | 14.8°C | — |
Relative humidity | 78% | — |
Global radiation emitted by the sun | 343 W/m2 | — |
Wind velocity | 2.6 m/s | — |
Wind direction (north: 0°; east: 90°; south: 180°; west: 270°) | 195° | — |
Rain | — | 779 mm |
Material properties used in the hygrothermal simulations.
Material |
|
|
|
|
|
|
---|---|---|---|---|---|---|
Exterior rendering | 0.02 | 1219 | 0.3 | 850 | 0.25 | 10.8 |
Concrete | 0.2 | 2200 | 0.18 | 850 | 1.6 | 92 |
XPS (extruded polystyrene insulation) | 0.06 | 30 | 0.95 | 1500 | 0.04 | 50 |
Gypsum-based rendering (M1) | 0.015 | 850 | 0.65 | 850 | 0.2 | 8.3 |
Medium density fibreboard (mdf) (M2) | 0.015 | 508 | 0.66 | 1700 | 0.12 | 15 |
Untreated wood (M3) | 0.02 | 550 | 0.66 | 1700 | 0.18 | 70 |
Schematic representation of the wall section.
The first step of this research was simulating the interior superficial temperature and relative humidity for the 2400 scenarios. As an example, Figure
Superficial temperature and relative humidity for the scenario M1_NI_N using the base case as indoor climate.
Average, maximum, and minimum for the entire dataset: (a) temperature; (b) relative humidity.
Results reveal that the presence of an insulation layer is the most important factor as a clear difference between I and NI cases can be observed. As expected, NI cases present lower temperature and higher relative humidity (differences of 1°C and 5%, approximately). Moreover, a larger variability can be found in NI cases, confirmed by an average coefficient of variation of 13.8% and 15.9% in temperature results, for I and NI cases, respectively, and of 10.3% and 12.5% in the relative humidity results.
In the I scenarios, the effect of finishing coating and orientation is almost imperceptible. On the contrary, in the NI cases, the effect of the finishing coating is more obvious in the north oriented scenarios.
Figure
Maximum mould index, considering the base case as indoor climate.
VTT model: sensitivity analysis (base case as indoor climate): (a) finishing coating material; (b) insulation layer; (c) orientation.
Figure
The introduction of variability in indoor climate has confirmed the importance of this parameter in the evaluation of the risk of mould growth. Figure
Maximum value of mould index cumulative relative frequency: (a) case studies with insulation layer; (b) case studies without insulation layer.
In addition to the already known importance of the sensitivity class of the coating material, it is observed that in cases with insulation layer, the importance of the variability of the interior climate is more evident. In fact, in the cases without thermal insulation, the value of the mould index has a much lower dispersion, that is, the introduction of the layer reduces the impact of the exterior climate, thus maximizing the importance of the indoor climate. It is also worth noting that the cases with an untreated wood coating and without insulation layer are clearly limited by the model itself (
The Biohygrothermal model was applied to the entire dataset using Wufi Bio, and the maximum growth was used as the comparison output. The same approach used in the VTT model was applied and Figure
Maximum mould growth, considering the base case as indoor climate.
Biohygrothermal model: sensitivity analysis (base case as indoor climate): (a) finishing coating material; (b) insulation layer; (c) orientation.
The effect of the finishing coating material is shown in Figure
The importance of variability of the indoor climate is shown in Figure
Maximum value of mould growth cumulative relative frequency: (a) case studies with insulation layer; (b) case studies without insulation layer.
In this model, unlike the VTT model, the dispersion of the distribution is not much affected by the presence of the insulation layer. Using as example case the untreated wood (the material with the highest risk of mould growth), it is verified that for both north and south oriented cases, 80% of the maximum values (percentiles between 0.1 and 0.9) are within a range of about 100 mm. For the remaining case studies, an equivalent range is observed.
The results previously presented showed some agreement between the two models as both pointed the same cases as the extreme scenarios. Nevertheless, some differences were also identified such as the highest sensitivity to wall orientation found in the Biohygrothermal model and the more evident effect of the variability of indoor climate in the VTT model. Figure
Mould growth versus mould index (2400 simulations).
Analyzing the results, it is possible to verify that the BET function tends to overestimate the mould growth value when the mould index is high. In addition, this function presents a more interesting performance in the case studies without the insulation layer. The two approximation functions presented in this research (polynomial and exponential), for the same values of mould index, suggest lower values of mould growth. One should also stress that the high number of results with maximum mould index (
Interesting findings can be drawn if one compares the instants at which the maximum mould index and the maximum mould growth occur as illustrated in Figure
Comparison of the results calculated by both models.
Additionally, clear patterns can be identified on the results of the VTT model. In the noninsulated cases, besides the evident effect of the finishing material, a time lag due to orientation can also be observed. The south oriented scenarios reached the maximum mould index, approximately, 800 hours after the corresponding north oriented cases. This situation disappears when the insulation layer is added. A more random configuration can be observed in the results of the Biohygrothermal model. Nevertheless, a trend for an earlier maximum mould growth in the insulated scenarios can be pointed out.
In this study, the importance of insulation (with and without insulation), orientation (north and south), and finishing coating (gypsum-based rendering, medium density fibreboard (mdf), and untreated wood) for the mould growth phenomenon was tested using the two most well-known models to assess mould growth: updated VTT and Biohygrothermal. Taking into account the importance of interior climate in the phenomenon and due to its highly variable nature in Mediterranean countries, a probabilistic approach based on Monte Carlo simulation was also implemented. A typical external wall configuration was used as the case study. From the results, the following main findings can be stated: Although only slight differences among the dataset were found in the superficial hygrothermal conditions, the application of the updated VTT and Biohygrothermal models exposed great differences between the simulated scenarios (mould index ranged from 0.4 to 5.9 and mould growth ranged from 10.1 mm to 406.4 mm). From the three parameters that were assessed, the thermal insulation is the more relevant (relative differences are on average 78%), followed by the coating materials (relative differences are on average around 57%) and, finally, the orientation (relative differences are on average around 18%). The Biohygrothermal model is more sensitive to the effect of orientation than the updated VTT model (relative differences are, on average, 11% for the updated VTT model and 25% for the Biohygrothermal model). The importance of the variability of indoor climate was confirmed by the results of the Monte Carlo simulation. In the VTT model, in cases with insulation layer, the importance of this variability was more evident. This finding was not so obvious in the Biohygrothermal model. An agreement between the two models (mould index versus mould growth) was searched. A polynomial and an exponential function were tested to approximate the results of the 2400 simulations, and a coefficient of determination of 0.66 and 0.59, respectively, was attained. The maximum mould index (updated VTT model) occurs earlier than the maximum mould growth (Biohygrothermal model). Maximum mould index occurs on average at 2900 hours and maximum mould growth occurs on average at 5155 hours. This is a consequence of the reduction function included in the updated VTT model to take into account unfavourable hygrothermal conditions that are not considered in the Biohygrothermal model. Clear patterns can be identified on the results of the VTT model when analysing the instants at which the maximum mould index occur. On the contrary, a more random configuration can be observed in the results of the Biohygrothermal model.
The future research will include the use of real data to compare the results provided by the two models and assess which one is the most interesting for modelling mould growth in the Mediterranean climate.
The data are available on request from the corresponding author. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The large amount of data justifies this option.
The authors declare that they have no conflicts of interest.
This work was financially supported by Project POCI-01-0145-FEDER-007457–CONSTRUCT–Institute of R&D in Structures and Construction funded by FEDER funds through COMPETE2020–Programa Operacional Competitividade e Internacionalização (POCI).