36 C O N T E X T 1 3 7 : N O V E M B E R 2 0 1 4 JOHN FOWLER Environmental monitoring in a historic building The monitoring of humidity in a chapel in Downside Abbey in Somerset evolved into a project logging data throughout the building to find out how best to protect its fabric. Downside Abbey is a Grade I listed building located on the eastern end of the Mendip Hills in Somerset. It was constructed between 1872 and 1938 from Doulting and Bath limestone. Its location leaves it exposed to high levels of rainfall. The original brief was to monitor the gradual decrease in humidity and condensation anticipated in the Lady Chapel following a major project to replace a very leaky copper roof. The humidity data was expected to prove that the water ingress and condensation had been addressed, and that the very substantial financial outlay had been justified. It was later decided that collecting humidity and temperature data at other locations throughout the abbey, and external weather data, would provide a much better idea of the abbey’s overall performance. The following data were logged: humidity, air temperature and masonry surface temperature in three locations with condensation and damp problems; humidity and air temperature in the vaulting ceiling to explore temperature stratification; surface temperature in various locations elsewhere in the abbey; and humidity and air temperature on the north elevation. It proved necessary to collect data over the whole year in order to understand the seasonal relationships between external and internal environmental conditions, and the implications of these on historic fabrics, energy-saving possibilities and healthy environments. The abbey’s historic masonry and plaster, wood, paint, ceramics, textiles and books are susceptible to high humidity levels. It is vital to retain a balanced level of humidity, for example to ensure the longevity of lime-based mortars and plasters. Artefacts may have to be removed if environmental conditions can not be controlled well enough. The risk of condensation was determined using environmental data on matters such as the proximity of building temperatures to dew point and the temperatures at which condensation will form. A historic fabric repair strategy will be formulated when periods of high risk (especially during seasonal changes) have been eliminated by paying close attention to the heating. The analysis of data collected for condensation risk analysis has made it possible to identify considerable heat energy savings (and consequent financial savings that can offset the costs of this type of project as well as avoiding condensation). Energy and financial savings may also be found through optimising the efficiency of the building’s thermal envelope (walls, doors, windows and so on), and through the frugal use of internal and external lighting. Environments experiencing high humidity levels for prolonged periods are likely to experience mould growth and associated risks to health. Collecting humidity data in this project has made it possible to identify mould risks in the abbey, although most of the mould is expected to be remedied by the replacement roof. Any further mitigation strategies through heating or ventilation changes will be considered once the results of the roofing project are known, and the relationships between this, condensation risk analysis and energy saving measures have been considered. The advent of more complex monitoring systems with large data-storing capabilities has made it possible to collect much higher resolution data on many variables in a method known as big data analysis. Data on multiple variables can be recorded at frequent intervals of, say, one minute, allowing very close analysis of correlations using data analysis software. The term ‘correlation’, used frequently in big data circles, refers to looking for relationships between variables in large data sets (as opposed to looking for direct causes). This offers the opportunity to enter projects with an open mind about the relationships between variables and issues, instead of starting with a hypothesis and then trying to prove it by identifying causes. Correlational analysis could allow us to focus on variables or events that had not previously been recognised as issues. Big data also offers the analysis of ‘predictions by proxy’.Where a strong correlation is known to exist between environmental factors, but only one is known or has been predicted, it is possible to predict the second. Big data analysis also makes it possible to discover previously unknown anomalies in building performance by analysing the relationships between large numbers of environmental variables. John Fowler is the founder of Heritage Monitoring. For this project Heritage Monitoring operated under the guidance of architect Mark Taylor of Beech Tyldesley and Partners, and as a subcontractor for Sally Strachey Historic Conservation. The first month’s data was analysed with the assistance of the Centre for Business and Climate Solutions Team at Exeter University, funded by the European Regional Development Fund. Downside Abbey’s historic fabric is susceptible to high humidity levels.
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