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
Eighty million people in Europe live in dwellings with indoor mould contamination [1], with 15% of homes in the UK affected [2]
Our results show that the most accurate predictions about mould and a mouldy odour were made from mould levels, Mm, that were generated by a model that was conducted on data at 5-min intervals
Best perfor mance was achieved when the model was implemented using a sensi tivity higher than the highest sensitivity in the original VTT model, a default critical relative humidity (RH) (RHcrit) of 50%, which is lower than the 80% in the original model, coefficients and constant in Equation (2) that promoted a greater change in the mould index, M, and a consistent decline rate (Non-wood), rather than one that varied based on the time spent below RHcrit (Wood)
Summary
Eighty million people in Europe live in dwellings with indoor mould contamination [1], with 15% of homes in the UK affected [2]. In the UK, around 31% of home have been found to have humidity problems (48% of whom do not use air conditioning, fans or dehumidifier) [3,4], which increases the risk of condensation. Mould spores are ubiquitous in outdoor environments [5,6,7]. Out door concentrations of mould spores vary seasonally and can influence the indoor environment [8]. The presence of indoor dampness (caused by water ingress, rising damp and condensation) can lead to increased mould contamination [9]. The extent of indoor dampness and resultant mould contamination increases in homes that are suffering fuel poverty [10]. Housing interventions to alleviate risk of fuel poverty (energy ef ficiency measures) can suffer from condensation and mould contamination, unless there is adequate ventilation and heating [11,12]
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