This paper proposes the use of simulation modelling to explore the effect of conservation strategies on the preservation of paper collections. Agent-based simulation was chosen as the simulation approach in order to capture the individual characteristics of the collections, their size, and the values of pH and degree of polymerisation (DP) for individual items. This approach enabled the simulation of the chemical degradation of different types of collections during their lifetime and under different preservation scenarios. We conducted a series of computational experiments on three types of collections, acidic, modern, and mixed, to explore the effect of slightly lowering the temperature and relative humidity in the repositories, the deacidification of part of the collection at different rates, and the delay in making the decision to start a deacidification treatment. The results indicated that a small change, lowering the storage conditions from 18°C to 16°C and from 50% to 40% RH, can protect up to 30% of the collection from reaching the critical DP of 300 within a time horizon of 500 years. On the other hand, to obtain similar results through deacidification, 45% of the mixed collection and 70% of the acidic collection should be deacidified within a period of 100 years. The experiments also indicated that better results are obtained when the priorities for deacidification are acidic records with a pH value below 5. This study shows that modelling the heterogeneity of the collections can support preservation management, particularly if the concern is not the preservation of a part of the collection but the collection as a whole.

Wear and tear is the outcome of degradation most frequently reported in assessments of archival and library collections. It is also problematic to study in controlled experiments, due to the difficulty in reproducing the conditions in which original objects are kept and used in archives and libraries. Hence, data collected from actual collections, for instance during surveys, could provide the evidence on how wear and tear occurs. However, to be useful, such data need to be purposely collected and analysed: unlike the usual collection surveys, the aim is not to quantify the prevalence of a certain damage type but to provide evidence on how such damage occurs. In this paper we explore whether two approaches used in other disciplines could be useful: reliability engineering, the method that deals with failure in complex systems, and epidemiology, which explores diseases in defined populations. We show that based on reliability engineering we can decide which data related to the causes of mechanical failure should be collected during collection surveys, while using epidemiology we can develop the study design and the data analysis needed to study the relationship between mechanical failure, and the factors that might affect the degree of failure. The results of epidemiological studies in heritage collections could provide quantitative evidence of patterns of decay in collections, and corroborate the qualitative analysis provided by reliability. The results can directly support collection management decisions or can be used in mathematical models in which the effect of preservation measures is explored.

This paper proposes a new approach to collection surveying based on epidemiology, the discipline that describes and explains disease patterns in populations. In epidemiology the focus of attention lies not only on the occurrence of a disease but also on the characteristics of the individuals which might play a role in the occurrence of the disease. To explore the applicability of epidemiology to heritage collections, we take as example the study of the occurrence and accumulation of wear and tear in archive collections, which so far has only been studied in controlled experimental studies. We designed an observational study (survey) in which the assessment of mechanical failure is understood as the outcome variable, and the factors that might affect the degree of failure are defined as exposure variables. To evaluate the relevance of the assessed factors in relation to the observed mechanical failure, exploratory data analyses were conducted by comparing groups of objects that differ regarding their level of exposure to different factors. Although highly scattered data is not unusual in this type of studies and confounding has to be taken into account during the data analysis, this paper shows that through an epidemiological approach to surveys, the factors that have a greater effect on mechanical failure can be identified. Moreover, the rate of failure can also be determined for certain groups of objects. Also patterns of decay emerge which show the vulnerability of certain groups of objects. In this paper the practical aspects of the design and analysis of observational epidemiological studies for heritage collections are discussed. As a final note, the applicability and relevance of this approach to support collection management is briefly discussed.