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The occurrence of surface condensation and mould can lead to concerns of poor indoor air quality and adverse health implications of occupants. Remedial actions require identification of the root causes, but this can be challenging even for experts. The focus of the research is the development of a diagnostic tool that helps to streamline root cause analysis. The diagnostic method comprises a protocol with guidelines for installation of sensors, easy data collection, and a set of calculations to process environmental information. Environmental parameters collected and calculated from an environmental monitoring exercise of dwellings with and without mould, include physical properties associated with the indoor surface of external walls and surrounding air conditions. The methodology relies on linking specific surface and air environmental parameters together with critical thresholds proposed for the control and avoidance of surface condensation and mould growth in dwellings. These parameters were assessed and used to determine the likely causal factors of a moisture imbalanced environment leading to surface condensation and mould growth; poor thermal building envelope performance, an imbalanced heat-moisture regime, and/or insufficient ventilation. Examples from different scenarios are presented to show the process towards environmental data collection, post-processing to compute and assess pertinent parameters, and the display of environmental conditions in a clear and easy-to-interpret manner. The novel developed system is a time-saving method for processing and represents environmental data. It provides a straightforward building moisture index (BMI) and a systematic diagnostic procedure for environmental assessment and possible causes of mould growth. This helps to support neutral decision making, to identify rectification strategies and direct to more cost-efficient solutions to existing damp and mould problems in buildings.
The occurrence of surface condensation and mould can lead to concerns of poor indoor air quality and adverse health implications of occupants. Remedial actions require identification of the root causes, but this can be challenging even for experts. The focus of the research is the development of a diagnostic tool that helps to streamline root cause analysis. The diagnostic method comprises a protocol with guidelines for installation of sensors, easy data collection, and a set of calculations to process environmental information. Environmental parameters collected and calculated from an environmental monitoring exercise of dwellings with and without mould, include physical properties associated with the indoor surface of external walls and surrounding air conditions. The methodology relies on linking specific surface and air environmental parameters together with critical thresholds proposed for the control and avoidance of surface condensation and mould growth in dwellings. These parameters were assessed and used to determine the likely causal factors of a moisture imbalanced environment leading to surface condensation and mould growth; poor thermal building envelope performance, an imbalanced heat-moisture regime, and/or insufficient ventilation. Examples from different scenarios are presented to show the process towards environmental data collection, post-processing to compute and assess pertinent parameters, and the display of environmental conditions in a clear and easy-to-interpret manner. The novel developed system is a time-saving method for processing and represents environmental data. It provides a straightforward building moisture index (BMI) and a systematic diagnostic procedure for environmental assessment and possible causes of mould growth. This helps to support neutral decision making, to identify rectification strategies and direct to more cost-efficient solutions to existing damp and mould problems in buildings.
This research has been supported by a Grant funded by the Technology Strategy Board and Engineering & Physical Sciences Research Council (Innovate UK) and The Property Care Association (PCA) through a Knowledge Transfer Partnership (KTP) project (KTP010485) between University College London (UCL) and PCA. Special thanks go to all the PCA members that have contributed to the success of this research project.
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