dc.contributor.author | Rowan, Neil J. | |
dc.contributor.author | Johnstone, Cameron M. | |
dc.contributor.author | McLean, R. Craig | |
dc.contributor.author | Anderson, John G. | |
dc.contributor.author | Clarke, Joe A. | |
dc.date.accessioned | 2019-07-02T09:50:16Z | |
dc.date.available | 2019-07-02T09:50:16Z | |
dc.date.copyright | 1999 | |
dc.date.issued | 1999-11 | |
dc.identifier.citation | Rowan, N.J., Johnstone, C.M., McLean, R.C., Anderson, J.G., Clarke, J.A. (1999) | en_US |
dc.identifier.issn | 0099-2240 | |
dc.identifier.other | Faculty of Science & Health -Nursing and Healthcare-Articles-Nursing and Healthcare | en_US |
dc.identifier.uri | http://research.thea.ie/handle/20.500.12065/2762 | |
dc.description.abstract | There is growing concern about the adverse effects of fungal bioaerosols on the occupants of damp dwellings.
Based on an extensive analysis of previously published data and on experiments carried out within this study,
critical limits for the growth of the indoor fungi Eurotium herbariorum, Aspergillus versicolor, and Stachybotrys
chartarum were mathematically described in terms of growth limit curves (isopleths) which define the minimum
combination of temperature (T) and relative humidity (RH) at which growth will occur. Each growth limit
curve was generated from a series of data points on a T-RH plot and mathematically fitted by using a
third-order polynomial equation of the form RH 5 a3T3 1 a2T2 1 a1T 1 a0. This fungal growth prediction
model was incorporated within the ESP-r (Environmental Systems Performance [r stands for “research”])
computer-based program for transient simulation of the energy and environmental performance of buildings.
For any specified location, the ESP-r system is able to predict the time series evolution of local surface
temperature and relative humidity, taking explicit account of constructional moisture flow, moisture generation
sources, and air movement. This allows the predicted local conditions to be superimposed directly onto
fungal growth curves. The concentration of plotted points relative to the curves allows an assessment of the risk
of fungal growth. The system’s predictive capability was tested via laboratory experiments and by comparison with monitored data from a fungus-contaminated house. | en_US |
dc.format | PDF | en_US |
dc.language.iso | en | en_US |
dc.publisher | American Society for Microbiology | en_US |
dc.relation.ispartof | Applied and Environmental Microbiology | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | * |
dc.subject | Fungi | en_US |
dc.subject | Fungal growth | en_US |
dc.subject | Dampness in buildings | en_US |
dc.title | Prediction of toxigenic fungal growth in buildings by using a novel modelling system. | en_US |
dc.type | Article | en_US |
dc.description.peerreview | yes | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-1228-3733 | |
dc.rights.access | Open Access | en_US |
dc.subject.department | Faculty of Science and Health | en_US |