Issue |
E3S Web Conf.
Volume 172, 2020
12th Nordic Symposium on Building Physics (NSB 2020)
|
|
---|---|---|
Article Number | 20001 | |
Number of page(s) | 6 | |
Section | Microbial damage | |
DOI | https://doi.org/10.1051/e3sconf/202017220001 | |
Published online | 30 June 2020 |
Predicting mould growth on building materials- the PJ-model
1 RISE Research Institutes of Sweden, Built Environment, Box 857, S50115 Borås, Sweden
2 Thomas Svensson-Ingenjörstatisk, Boviksvägen 21, S50493, Borås, Sweden
* Corresponding author: pernilla.johansson@ri.se
Mould growth in buildings is a complex process, affected by moisture and temperature, the properties of the building material as well as characteristics of the mould fungi. The complexity poses challenges when assessing the risk of mould growth in buildings. Mathematical models are often used to predict whether mould will grow in a part of building with expected RH and temperature conditions. The models can be described as static or dynamic. In a previous round-robin study, comparing results from models with observations from field studies, the outcome of the dynamic models evaluated depended on the user of the model. Also, the models often underestimated the risk of mould growth. A better agreement was found for static models, especially for the PJ-model. It is a part of a standardised technical specification (SIS-TS 41:2014) and has not previously been described as a model. The critical moisture level (RHcrit), determined by tests according to the method, is used as input. Thus, the subjectivity in the predictions is reduced. RHcrit is the lowest moisture level at which mould can grow and is temperature-dependent. The PJ-model provides an equation to estimate RHcrit at typical temperatures in buildings. If RH in a building section exceeds the limit values at the current temperature, growth is predicted. This paper describes the PJ-model version 1.0, some of the extensive work performed during the development and validation of the model and the ongoing work to refine the model to include considering transient conditions and measurement uncertainties.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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