Reconstruction of historical hygrometric time series for the application of the European standard EN 15757:2010 and its comparison with current time series

The quality and quantity of thermo-hygrometric data are essential to carry out an appropriate assessment of the microclimate from a preventive conservation standpoint in those spaces where the artefacts to be preserved are located. These analyses are fundamental for long-term preventive conservation plan to assess chemical, biological or fracture risks. However, many small and medium-sized museums as well as heritage buildings have only a limited amount of historical data, with various problems that hinder the evaluation of microclimatic conditions. Two of the most common problems are short monitoring time periods, usually less than one year, and low sampling rates of measurements. In many of these situations, guidelines such as the European standard EN 15757:2010 cannot be applied because they require a monitoring period of at least 13 months and a minimum sampling frequency of one measurement per hour. In addition to these issues, there are other drawbacks such as missing values or lack of regularity in data collection. This paper proposes a procedure for the reconstruction of historical thermo-hygrometric data using multivariate statistical methods. The methodology allows the arrangement of long historical series of sufficient quality, enabling museums to restore their datasets for further analysis regarding the application of guidelines for preventive conservation. The methodology has been validated on the basis of real data. The application of the European standard EN 15757:2010 is presented as a practical example of the procedure using historical data collected at a partner museum of the H2020 CollectionCare project, together with data currently being collected for some months by a set of wireless sensor nodes.

Automated summary

The quality and quantity of thermo-hygrometric data are crucial for assessing microclimate conditions in spaces where artifacts are preserved. However, many small and medium-sized museums lack sufficient historical data, making it difficult to evaluate microclimatic conditions. This paper proposes a method for reconstructing historical thermo-hygrometric data using multivariate statistical methods, allowing museums to restore their datasets for further analysis and application of preventive conservation guidelines.