Modelling mould growth in domestic environments using relative humidity and temperature. (15th January 2022) - Menneer, Tamaryn; Mueller, Markus; Sharpe, Richard A.; Townley, Stuart
Abstract: Damp and high levels of relative humidity (RH), typically above 70–80%, are known to provide mould-favourable conditions. Exposure to indoor mould contamination has been associated with an increased risk of developing and/or exacerbating a range of allergic and non-allergic diseases. The VTT model is a mathematical model of indoor mould growth that was developed based on surface readings of RH and temperature on wood in a controlled laboratory chamber. The model provides a mould index based on the environmental readings. We test the generalisability of this laboratory-based model to less-controlled domestic environments across different values of model parameters. Mould indices were generated using objective measurements of RH and temperature in the air, taken from sensors in a domestic setting every 3–5 min over 1 year in the living room and bedroom across 219 homes. Mould indices were assessed against self-reports from occupants regarding the presence of visible mould growth and mouldy odour in the home. Logistic regression provided evidence for relationships between mould indices and occupant responses. Mould indices were most successful at predicting occupant responses when the model parameters encouraged higher vulnerability to mould growth compared with the original VTT model. A lower critical RH level, above which mould grows, a higher sensitivity, and larger increases in the mould index all consistently increased performance. Using moment-to-moment time-series data for temperature and RH, the model and its developments could help inform smart monitoring or control of RH, for example to counter risks associated with reduced ventilation in energy efficient homes. Highlights: Domestic mould growth can be predicted from air relative humidity and temperature. Best prediction performance requires a model that is sensitive to relative humidity. Real-time predictions could inform targeted smart control to improve public health. Implications for early intervention for contamination, reducing remediation needs. The data provide a unique set of time-series air measurements and survey responses.