Modelling mould growth using relative humidity and temperature time-series data
Dr Tamaryn Menneer, Dr Markus Mueller, Dr Richard Sharpe, Professor Stuart Townley
Introduction & aim
Mould has an adverse affect on health, e.g., asthma and other respiratory diseases (Sharpe et al., 2015). The VTT model predicts mould growth from relative humidity (RH) and temperature (Hukka & Viitanen, 1999). It was developed using surface readings on wood in a controlled laboratory setting.
We test the generalisability of the laboratory-based VTT model to less controlled domestic environments on an unprecedented scale by comparing model predictions for 274 homes with occupants’ responses about mould.
Conclusions
The domestic air measurements represent complex interactions between built environment and human behaviours. The model can predict mould growth when the RHcrit default value is reduced from 80%.
Real-time predictions could inform:
Early targeted interventions to improve public health and living environments.
Smart control to provide the minimum targeted intervention necessary to minimise mould growth and:
reduce its impact on health;
avoid unintended consequences in homes with reduced ventilation (e.g. energy efficient homes);
maintain human comfort;
avoid unnecessary power expenditure.
Smart monitoring to:
alleviate costs of repair associated with mould;
be combined with monitoring for other damaging conditions such as cold or damp.
Poster: Accepted to PHE Public Health Research and Science Conference - 31st March to 1st April 2020
