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Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain)
Journal of Cultural Heritage 11 (2010) 339–344
Original article
Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain) Fernando-Juan García-Diego a,∗ , Manuel Zarzo b,1 a b
Department of Applied Physics (U.D. Agrónomos), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
a r t i c l e
i n f o
Article history: Received 25 March 2009 Accepted 12 June 2009 Available online 23 March 2010 Keywords: Humidity Temperature Art conservation Diagnosis Sensor
a b s t r a c t The renaissance frescoes of the metropolitan cathedral of Valencia, located at the vault of the apse, were restored in 2006. We describe a microclimate monitoring system that was implemented for the preventive conservation of the paintings. It is comprised by a set of temperature and relative humidity sensors positioned at different points of the vault. This system is rather unique because some of these sensors were inserted inside the paintings during the restoration process. A principal components analysis was applied to the data of relative humidity recorded in February 2007. The analysis was repeated in three additional months of 2007. The resulting loading plots highlight the most relevant similarities and dissimilarities among sensors. These plots can be considered as some sort of control maps that could be used to detect abnormal conditions in the future. Actually, moisture problems at certain zones of the frescoes are causing the formation of efflorescence, and the sensors located close to these zones are the ones recording the highest values of relative humidity. © 2010 Elsevier Masson SAS. All rights reserved.
Research aims This work describes a monitoring system of the microclimate environment surrounding fresco paintings for preventive conservation. The system is comprised by temperature and humidity sensors. During the restoration process, some of these sensors were installed inside the paintings and others outside, as described below. The main goal of this work was to analyse the data obtained during the first months of monitoring in order to assess the feasibility of the system for an early detection of abnormal conditions that may be harmful for the paintings.
relative humidity, specific humidity, dew point) inside museums [2–5]. Similar studies have monitored the temperature and relative humidity (RH) in churches [6–9]. These buildings contain valuable works of art, and the microclimate requirements are similar as those in museums. Temperature and humidity changes can affect the conservation of fresco paintings. Reported works have characterised the distribution of thermal and hygrometric parameters in order to study the interactions between the indoor atmosphere and walls supporting frescoes or mural paintings [10–12], even Michelangelo’s frescoes at the Sistine Chapel of the Vatican [13] and the famous mural painting of Leonardo’s “Last Supper” [14].
1. Introduction 1.2. Renaissance frescoes of the cathedral of Valencia, Spain 1.1. Microclimate monitoring for preventive conservation The internal environment of a museum should be appropriate for the conservation and display of the collections inside [1]. Attempting to assess the complexity of potential risks related to imbalance in temperature and humidity, different works have monitored thermohygrometric parameters (e.g., air temperature,
∗ Corresponding author. Tel.: +34 963877520; fax: +34 963877529. E-mail addresses: [email protected] (F.-J. García-Diego), [email protected] (M. Zarzo). 1 Tel.: +34 963877490; Fax: +34 963877499. 1296-2074/$ – see front matter © 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.culher.2009.06.002
The metropolitan basilica cathedral of St. Mary in Valencia started in 2004 the restoration of the main chapel and the renaissance fresco paintings (Fig. 1) that nicely decorate the severies of the vaulted roofs of the presbytery (http://www.frescosdelacatedral.com). The restoring team observed the presence of efflorescence that had to be removed in different zones of the frescoes. Certain parts with problems of deterioration due to high humidity were also identified [15]. Since the moisture was restricted to specific zones, it was suspected that the problem could be caused by infiltration of rainwater through the roof above the apse. Attempting to prevent this kind of problems in the future, this roof was remodelled and an asphaltic roofing sheet
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Fig. 1. View of the renaissance frescos at the cathedral of Valencia as they can be observed from the presbytery. The seven ribs converge to the vault keystone, which appears in the centre of the image. Position of the 29 probes that were located during the restoration process for the monitoring of indoor air: seven of them on the ribs (squares), two probes at the cornice (diamonds), 10 probes on the walls below the severies (triangles) and 10 probes on the frescoes (circles). Each probe contains a sensor of temperature and relative humidity (RH).
was laid below the tiles. The presence of any joint not properly sealed or any crack in the sheet might become a source of water infiltration. Small amounts of water that may accumulate during future decades would diffuse by capillarity down to the frescoes and cause problems of moisture in the paintings. The importance of waterproofing in the roof above frescoes to prevent humidity dropping down through capillary action is discussed in the literature [16]. Taking into account that it is very difficult to ensure a long-term impermeability of the roof over these paintings that are to be conserved over centuries, the restoring team decided to implement a monitoring system comprised by RH and temperature sensors. These sensors were installed during the restoration process at different points of the vault, some of them inside the paintings and others outside. This control system is rather unique because sensors are rarely inserted into the precious walls supporting the frescoes. The main purpose of this system is the detection of abnormally higher values of humidity at specific parts of the vault, which might indicate infiltration of water through the roof. The present work describes the monitoring system and reports the analysis of the data obtained during the first months of monitoring. 2. Materials and methods 2.1. Description of probes: characteristics and installation During the restoration process, specialists had to remove and reintegrate the layer of plaster supporting the paintings in zones highly deteriorated due to moisture problems. Ten porous ceramic tubes were inserted in these zones by drilling in the mortar between two bricks of the severy. The exact position of bricks and joints was identified by means of an ultrasound device as described elsewhere [17]. One probe was located inside each tube so that the RH sensor remains on the surface of the painting (Fig. 2). Other probes of the same type, 19 in total, were implemented in order to get supplementary information that might help the interpretation of data recorded from the frescoes. For this purpose, seven probes were located on the ribs that separate the severies of the apse vault, at about 2 m from the keystone (Fig. 1). These sensors measure the RH and temperature of the air at about 5 cm from the
surface of the frescoes. Moreover, 10 probes were installed on the walls of the apse at a distance of about 120 cm below the severies (see Fig. 1). These probes were introduced in a ceramic tube of the type described above that was inserted into the wall by drilling in the mortar between two ashlars. Two additional probes were located at the cornice of the apse, at 3.5 m below the wall ones. All 29 probes contain a small-outline integrated circuit, model DS2438 (Maxim Integrated Products, Inc.) that incorporates a direct-to-digital temperature sensor with an accuracy of ± 2 ◦ C as well as an analogue-to-digital voltage converter. This converter measures the output voltage of a humidity sensor (HIH-4000, Honeywell International, Inc.). A set of 30 sensors HIH-4000 were purchased from the manufacturer, who provided the second order calibration curve determined experimentally for one of them. This equation allows the estimation of RH according to the voltage output with an accuracy of ± 3.5%RH. The following calibration procedure was applied in laboratory. All 30 RH sensors were introduced in a small chamber equilibrated with a saturated solution of a salt, and the voltage output of all 30 sensors was measured. The saturated solution was changed and, after reaching the equilibrium, the voltage output was measured again. This experiment was repeated with different saturated solutions. Next, calibration equations were obtained for each sensor in order to relate the voltage output and the RH, which was estimated from the reference sensor calibrated by the manufacturer. Each RH sensor receives a 5 V DC input supplied by an integrated circuit TL7805. This circuit presents certain variability, and the actual voltage supply ranges from 4.9 to 5.1, which affects the RH estimation. Actually, according to the manufacturer, the voltage output of HIH-4000 sensors is proportional to voltage supply. Thus, in order to improve the accuracy of RH measurements, the exact value of the voltage supply was measured for each RH sensor once all probes and connections were installed in the apse vault, and the calibration curves of each individual sensor were corrected accordingly. 2.2. Data acquisition system Four electric wires come out from each probe: two for 12-volt DC power supply and two wires for data transfer. All sensors are
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Fig. 2. Scheme depicting the installation of probes in the frescoes. The porous ceramic tube was introduced after drilling in specific zones of the plaster where the original painting was not present. In this figure, the porous tube is partially sectioned to see the interior.
connected to a microcontroller that recorded the measurements in a digital format. Data were weekly downloaded to a laptop. Taking into account that the manufacturer recommends a correction of the RH estimation according to temperature [18], the proposed correction was applied in order to improve the accuracy. The restoration works finished in December 2006, and the data acquisition started in February 2007. The microcontroller was programmed to record one datum per hour from each sensor, and hence we expected to get each month about 720 time observations (i.e., 30 days × 24 hours/day). Unfortunately, due to different problems, it was only possible to record the measurements at 90 time observations in February, 62 in September, 108 in October, and 172 in November. Sensor F was broken down and it was disregarded. Data from sensors M and N were only available for February. In November, data from six additional sensors (C, D, J, L, Q, and U) were abnormal and they had to be also discarded (see Fig. 1 for label codes). 2.3. Multivariate data analysis
achieve a tight interchangeability which, according to the manufacturer, reduces or eliminates calibration of individual sensors [18]. Nevertheless, all RH sensors used in this monitoring system were calibrated individually prior to their installation in the apse vault. Calibration curves were obtained taking into account the information provided by a reference sensor calibrated by the manufacturer. All calibration curves resulted were very similar for the 30 sensors, which is in accordance with the high interchangeability claimed by the manufacturer. Signal correction according to temperature and to the actual voltage supply of each sensor was also implemented, as described above. 3.2. Data pretreatment prior to PCA We plotted the RH recorded from all sensors versus time and it was observed that the daily variations were clearly marked, as shown in Fig. 3 for four representative sensors. These variations are higher than the accuracy of the RH sensors. We also observed that the signal evolution along the time was rather parallel for all sensors. Thus, the average RH recorded by each sensor, that will be
Data of RH recorded in February were arranged in a matrix comprised by 90 observations (instants of time, in rows) by 28 variables (RH sensors, in columns). This matrix was row-centred as described below and, next, a principal components analysis (PCA) was carried out using the software SIMCA-P 10.0 (http://www.umetrics.com). The same analysis was repeated with data obtained in September, October and November 2007. The results from these four models were compared in order to check if the relationships among sensors were maintained month after month. The same procedure was also applied to the matrices of temperature. 3. Results and discussion 3.1. Accuracy of the RH sensors Sensor HIH-4000 is a thermoset polymer capacitive sensing element with on-chip integrated signal conditioning. This model was selected because it has a better accuracy (± 3.5%), interchangeability (from ± 5% to ± 8%RH, see data sheet), hysteresis (± 0.5%RH) and risetime (15 s), compared with other capacitive RH sensors. The dielectric element of these sensors are laser trimmed in order to
Fig. 3. Evolution of relative humidity recorded during 7 consecutive days (November 2007) corresponding to four sensors: H (upper thicker line), T (lower thicker line), U (upper thinner line), and D (lower thinner line). Sensor codes according to Fig. 1. The horizontal scale indicates hours (7 days ≈ 170 values). DP stands for discrimination period.
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Fig. 4. (left) Simulated evolution of relative humidity versus time corresponding to eight sensors (s1 to s8). (right) Loading plot (p[2] vs p[1]) obtained by applying PCA to the simulated relative humidity (RH) matrix. Data were row-centred prior to the analysis.
referred to as RH j (i.e., the mean value of column j) is the parameter that best describes the different performance among sensors. PCA is a useful technique for explaining the data variability of a matrix, as well as for the interpretation of relationships among observations and variables. Different pretreatment methods can be applied before conducting a PCA. If the interest relies on the identification of correlation structures among variables, which is commonly the case, data are column-centred prior to the PCA by subtracting the mean value of each column, so that all centred variables have a null average. This pretreatment seems appropriate a priori in this case because the target is to understand the similarities and dissimilarities among sensors. We applied PCA after this pretreatment and it was obtained that the results basically explain the differences among sensors according to RH j , because all variables are strongly correlated due to the parallel evolution of RH. Consequently, we decided to try other alternatives. The mean value of RH recorded by all sensors in a given instant of time ti will be referred to as RH i . The evolution versus time of RH i is of relevant interest for the identification of excessive diurnal or seasonal variations that might be harmful for the frescoes. This common pattern of RH i is followed by all sensors, and the goal in this case is to diagnose the slight variations of each sensor respect this average performance. Thus, if the data variability caused by time is eliminated from the matrix, the subsequent PCA will reflect more clearly the relationships among sensors. For this purpose, we centred the data by row (i.e., subtracting RH i to the elements of row i), so that all centred rows have a null average. The idea of applying this special pretreatment comes from the literature on the multivariate statistical control of batch chemical processes [19–23]. In a batch reaction, process variables such as temperatures or pressures follow a particular pattern along the course of the reaction, and the goal is to control properly the process so that the deviations respecting the target pattern are as low as possible. In these cases, PCA can be applied for process diagnosis in order to identify abnormal batches that deviate from the normal operative conditions. For this purpose, the data variability caused by time is usually eliminated from the matrix prior to applying PCA [19,20]. Many successful applications of this methodology have been reported [21–23]. 3.3. PCA of simulated data The first principal component (PC1) can be interpreted as the linear combination of the original variables that explains the highest
amount of the total data variability. Similarly, PC2 is the correlation structure that explains as much as possible the remaining variability not described by PC1. The contributions of variables in the formation of a given component are called loadings, being p[1] the loadings corresponding to PC1, and so on. In order to understand the advantages of the proposed data pretreatment, a previous study was conducted. Taking into account the daily cycles observed in Fig. 3, simulated RH data were generated for eight sensors (Fig. 4, left), resulting a matrix of eight variables by 170 observations. The evolution of RH versus time is nearly parallel for all sensors; the only difference is that the lower part of the cycle is sharper in sensors s2, s4, s6, and s8, but flatter for the rest. PCA was carried out after applying the row-centring pretreatment. The scatter plot of p[2] versus p[1], which is referred to as PC1-2 loading plot (Fig. 4, right), clearly reflects the relationships among sensors. Regarding p[1], sensors are arranged according to their average signal, while p[2] discriminates them according to the different signal shape. This example illustrates that the statistical methodology is very powerful to highlight slight differences when the pattern of a set of sensors is rather parallel, which occurs in the present case. 3.4. PCA of RH data The RH matrix corresponding to February was row-centred as described, and a PCA was applied next. The same analysis was repeated with data of September, October and November. Given that all sensors follow a parallel pattern, centring by row leads to values that are nearly constant for each sensor. Consequently, PC1 explains a large amount of the total data variability: 98.2% in February, 95.8% in September, 94.3% in October and 99.6% in November. The p[1] loadings (i.e., horizontal axis in Fig. 5) discriminate sensors according to RH j because this average accounts for the main data variability of the centred matrix, as already explained in the simulated case. In order to determine if PC2 provides relevant information, we inspected the PC1-2 loading plots corresponding to each model. If the four plots are overlapped (Fig. 5), a reasonable consistency is observed for PC1 and PC2, and hence both components can be regarded as relevant. We also compared the loading plots corresponding to PC2-3 for the four models, but no consistency was observed for PC3. Thus, the information of this component was not considered.
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Fig. 5. Overlap of four loading plots corresponding to PC1 and PC2 (i.e., p[2] vs p[1]). Each plot was obtained by applying PCA to a different relative humidity (RH) matrix: February 2007 (diamonds); September (squares); October (triangles); November 2007 (circles). In all cases, matrices were row-centred prior to the PCA. Variables joined together correspond to the same sensor (labels as in Fig. 1). Filled points highlight those sensors located at the vault ribs or at the cornice. Dotted lines correspond to sensors located on the frescoes. Continuous lines joining empty points indicate sensors located on the walls.
˜ D, and C) as well Sensors located at the vault ribs (I, J, X, S, N, as at the cornice (A, B) appear clustered together at the left of the plot (Fig. 5). Interestingly, there is a correspondence between the position of probes in this cluster and in the vault (Fig. 1). These results suggest that the environment surrounding the frescoes is not the same at all points of the vault. Sensors positioned on walls or in the paintings tend to record higher values than those in the vault ribs. Taking into account this result and the parallel evolution of RH versus time, we suggest that the former are measuring the RH of the indoor air at the apse plus a contribution coming from the ceramic tube. All wall probes except M and V are also close to each other in Fig. 5. By contrast, sensors on the frescoes span over a wider range of RH values. The volume delimited by two adjacent severies converging to the same vault rib, the wall and the apse roof is filled with a lime-sand material that can retain water. If this material is dry, it is expected that neither of the severies enclosing the volume will present moisture problems, and their corresponding sensors would reflect drier conditions. This is probably the case for T and O, because their RH data are lower than wall sensors. No efflorescence problems are currently observed on this part of the vault, as well as in sensors K and E, that performed similarly (Fig. 5). Sensor H is somewhat abnormal because it presents the lowest p[2] loadings (Fig. 5). Thus, it is of interest to discuss the arrangement of sensors according to PC2. Fig. 3 shows the evolution of sensors T, U, H, and D. Two of them, T and U, are characterised by p[2] > 0, which is not the case for H and D (Fig. 5). We carefully inspected the evolution of RH for these four sensors, and we found certain daily cycles with a different pattern. Fig. 3 indicates two of these discriminating periods, reflecting a similar performance of H and D different from T and U. Further studies will be necessary to understand why the pattern is slightly different in certain sensors at certain periods. Sensors R, H and N recorded the highest values of RH (Fig. 5). Interestingly, H and N are located on severies with moisture problems during the restoration. Efflorescence is appearing again basically at the same zones where it was identified after the first restoration works. It is important to point out that R and H belong to two severies converging at the same rib. The filling material enclosed by these severies is probably retaining more water than in
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other parts of the vault, perhaps due to small amounts of rainwater infiltrated through the roof along the years. Regarding Y and W, which also belong to two converging severies, the RH measurements are slightly higher than wall probes. Nonetheless, we speculate that the risk of moisture problems is probably lower than in the case of R and H because their loadings in PC2 are positive, which is not the case for R, H and N. The loading plot for PC1-2 (Fig. 5) highlights the relationships among sensors and it can be regarded as some sort of control map. The approach that we propose for the preventive conservation of frescoes based on RH measurements is to repeat the same multivariate analysis month after month, obtain the loading plot for PC1-2 and compare it with Fig. 5. We obtained consistent results for four months although the number of time observations was different in each case. Thus, an important advantage of this method is that it can easily deal with missing data, which often occurs in practice. We expect that the relative position in the plot of sensors installed at the vault ribs or walls will be more or less maintained over time, but it might not be the case for those ones on the frescoes. Thus, if the analysis reveals a sustained increase of RH month after month for a particular sensor, that signal would warn against likely problems of moisture that should be further investigated with visual inspection and additional tests. 3.5. PCA of temperature data Preliminary analyses of the temperature matrices were also carried out with PCA, and it was obtained that PC1 explains the mean value of temperature. Actually, as in the case of RH, the recorded temperatures follow a parallel pattern along the time that is maintained month after month. We checked that the mean values of temperature recorded by the different sensors in February were not significantly correlated with the mean values of RH (p = 0.092), and similar results were obtained in September (p = 0.48), October (p = 0.91) and November (p = 0.79). In the case of PC2, the comparison of loading plots suggested that this component was not meaningful and it was significantly different from month to month. The reasons are uncertain, but we speculate that one datum per hour recorded by the system was probably not enough for the purpose of identifying slight differences among the common pattern of temperature followed by all sensors. The frequency of data acquisition was initially set at one datum per hour because sensors are located at 18 m from the ground level and significant variations of the microclimate surrounding the frescoes were not expected a priori for periods lower than one hour. Nevertheless, this sampling time interval seems too long and the best solution would be to set it equal to the response time of the monitoring system under real conditions. This value will be higher than 15 s, which is the response time of the RH sensors, but the optimum is uncertain and no literature is available for this case. Thus, at the end of 2007, the frequency of data acquisition was set at one datum per minute. One minute is a high resolution and the system will sample many times the same value. Nonetheless, recording such redundant data will allow further studies in order to determine a posteriori the optimum data sampling time. This optimisation is of interest because the size of the resulting database will increase throughout the years, and an excessive data overloading should be avoided. 4. Conclusions An overview of RH data recorded by sensors installed in the vault shows that: (i) the microclimate environment surrounding the fresco paintings undergoes considerable variations along the time, and (ii) the signals follow a parallel evolution. The differ-
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ences among sensors are nicely reflected by the loading plots corresponding to PC1 and PC2. Consistent results were obtained by repeating the data analysis month after month. Three sensors inserted in the frescoes (H, N and R) recorded higher values than those installed in the walls and, interestingly, two of them (H and R) are located at parts of the vault where moisture problems appeared during the restoration. Thus, these high signals seem to reflect an excessive moisture of the layer of plaster supporting the frescoes. According to the present results, we cannot conclude that corrective measures are required to reduce the risk of deterioration that moisture may produce in the frescoes on the long term, but at least the results stress the need to keep on with the monitoring system. The statistical methodology applied here is commonly used for the monitoring, diagnosis and fault detection of batch chemical processes, but this is probably the first time that such methodology is applied for the monitoring of pieces of art aimed at preventive conservation. In order to encourage further research in the microclimate monitoring of cultural heritage, we are creating a database of thermohygrometric measurements that will be available to the scientific community through a website that is currently under construction. Acknowledgements This research was part of a multidisciplinary project aimed at the preventive conservation of the renaissance frescoes of the cathedral of Valencia, supported by the Autonomous Valencian Government (Conselleria de Cultura y Deporte) and the Valencian Institute for Conservation and Restoration of Cultural Heritage (IVC + R). The authors wish to thank J. Pérez-Miralles for his valuable help in installing the probes during the restoration process, M.C. Pérez-García for coordinating the multidisciplinary team involved in this project and J. Alario-Genovés for the high-quality production of some figures and C. Villanueva for his friendly collaboration. References [1] D. Camuffo, Microclimate for cultural heritage, Elsevier, Amsterdam, 1998. [2] A. Bernardi, Microclimate in the British Museum, London, Museum Manag. Curatorship 9 (1990) 169–182.
[3] A. Bernardi, D. Camuffo, Microclimate in the Chiericati Palace Municipal Museum, Vicenza, Museum Manag. Curatorship 14 (1995) 5–18. [4] D. Camuffo, A. Bernardi, G. Sturaro, A. Valentino, The microclimate inside the Pollaiolo and Botticelli rooms in the Uffizi Gallery, Florence, J. Cult. Herit. 3 (2002) 155–161. [5] K. Gysels, F. Delalieux, F. Deutsch, R. Van Grieken, D. Camuffo, A. Bernardi, et al., Indoor environment and conservation in the Royal Museum of Fine Arts, Antwerp, Belgium, J. Cult. Herit. 5 (2004) 221–230. [6] Y. Tabunschikov, M. Brodatch, Indoor air climate requirements for Russian churches and cathedrals, Indoor Air 14 (Suppl. 7) (2004) 168–174. [7] D. Camuffo, G. Sturaro, A. Valentino, Thermodynamic exchanges between the external boundary layer and the indoor microclimate at the basilica of Santa Maria Maggiore, Rome, Italy: the problem of conservation of ancient works of art, Bound. Layer Meteor. 92 (1999) 243–262. [8] E. Vuerich, F. Malaspina, M. Barazutti, T. Georgiadis, M. Nardino, Indoor measurements of microclimate variables and ozone in the church of San Vincenzo (Monastery of Bassano Romano – Italy): a pilot study, Microchem. J. 88 (2008) 218–223. [9] G. Loupa, E. Charpantidou, I. Kioutsioukis, S. Rapsomanikis, Indoor microclimate, ozone and nitrogen oxides in two medieval churches in Cyprus, Atmos. Env. 40 (2006) 7457–7466. [10] D. Camuffo, Indoor dynamic climatology: investigations on the interactions between walls and indoor environment, Atmos. Env. 17 (1983) 1803–1809. [11] D. Camuffo, A. Bernardi, Study of the microclimate of the Hall of the Giants in the Carrara Palace in Padua, Stud. Conservat. 40 (1995) 237–249. [12] D. Camuffo, E. Pagan, A. Bernardi, F. Becherini, The impact of heating, lighting and people in re-using historical buildings: a case study, J. Cult. Herit. 5 (2004) 409–416. [13] D. Camuffo, A. Bernardi, The microclimate of the Sistine Chapel, Eur. Cult. Heritage Newslett. Res. 9 (1995) 7–33. [14] D. Camuffo, A. Bernardi, The microclimate of Leonardo’s Last Supper, Bollettino Geofisico 14 (3) (1991) 1–75. [15] F. Marías Franco, Angélico Borgia: Frescoes of the cathedral of Valencia, FMR 22 (2007) 49–72. [16] A. Bernardi, V. Todorov, J. Hiristova, Microclimatic analysis in St. Stephan’s church, Nessebar, Bulgaria after interventions for the conservation of frescoes, J. Cult. Herit. 1 (2000) 281–286. [17] D. Del Vescovo, A. Fregolent, Assessment of fresco detachments through a noninvasive acoustic method, J. Sound Vib. 284 (2005) 1015–1031. [18] Honeywell Inc. (2009). Data sheet HIH-4000 series humidity sensors (Retrieved from http://sensing.honeywell.com/index.cfm/ci id/140570/ la id/1/document/1/re id/0 on May 27th 2009). [19] P. Nomikos, J.F. MacGregor, Monitoring batch processes using multiway principal component analysis, AIChE J. 40 (1994) 1361–1375. [20] P. Nomikos, Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis, ISA Trans. 35 (1996) 259–266. [21] N.B. Gallagher, B.M. Wise, C.W. Stewart, Application of multi-way principal components analysis to nuclear waste storage tank monitoring, Comput. Chem. Eng. 20 (1996) S739–S744. [22] S. Rännar, J.F. MacGregor, S. Wold, Adaptive batch monitoring using hierarchical PCA, Chemom. Intell. Lab. Syst. 41 (1998) 73–81. [23] M. Zarzo, A. Ferrer, Batch process diagnosis: PLS with variable selection versus block-wise PCR, Chemom. Intell. Lab. Syst. 73 (2004) 15–27.
Original article
Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain) Fernando-Juan García-Diego a,∗ , Manuel Zarzo b,1 a b
Department of Applied Physics (U.D. Agrónomos), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain Department of Applied Statistics, Operations Research and Quality, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
a r t i c l e
i n f o
Article history: Received 25 March 2009 Accepted 12 June 2009 Available online 23 March 2010 Keywords: Humidity Temperature Art conservation Diagnosis Sensor
a b s t r a c t The renaissance frescoes of the metropolitan cathedral of Valencia, located at the vault of the apse, were restored in 2006. We describe a microclimate monitoring system that was implemented for the preventive conservation of the paintings. It is comprised by a set of temperature and relative humidity sensors positioned at different points of the vault. This system is rather unique because some of these sensors were inserted inside the paintings during the restoration process. A principal components analysis was applied to the data of relative humidity recorded in February 2007. The analysis was repeated in three additional months of 2007. The resulting loading plots highlight the most relevant similarities and dissimilarities among sensors. These plots can be considered as some sort of control maps that could be used to detect abnormal conditions in the future. Actually, moisture problems at certain zones of the frescoes are causing the formation of efflorescence, and the sensors located close to these zones are the ones recording the highest values of relative humidity. © 2010 Elsevier Masson SAS. All rights reserved.
Research aims This work describes a monitoring system of the microclimate environment surrounding fresco paintings for preventive conservation. The system is comprised by temperature and humidity sensors. During the restoration process, some of these sensors were installed inside the paintings and others outside, as described below. The main goal of this work was to analyse the data obtained during the first months of monitoring in order to assess the feasibility of the system for an early detection of abnormal conditions that may be harmful for the paintings.
relative humidity, specific humidity, dew point) inside museums [2–5]. Similar studies have monitored the temperature and relative humidity (RH) in churches [6–9]. These buildings contain valuable works of art, and the microclimate requirements are similar as those in museums. Temperature and humidity changes can affect the conservation of fresco paintings. Reported works have characterised the distribution of thermal and hygrometric parameters in order to study the interactions between the indoor atmosphere and walls supporting frescoes or mural paintings [10–12], even Michelangelo’s frescoes at the Sistine Chapel of the Vatican [13] and the famous mural painting of Leonardo’s “Last Supper” [14].
1. Introduction 1.2. Renaissance frescoes of the cathedral of Valencia, Spain 1.1. Microclimate monitoring for preventive conservation The internal environment of a museum should be appropriate for the conservation and display of the collections inside [1]. Attempting to assess the complexity of potential risks related to imbalance in temperature and humidity, different works have monitored thermohygrometric parameters (e.g., air temperature,
∗ Corresponding author. Tel.: +34 963877520; fax: +34 963877529. E-mail addresses: [email protected] (F.-J. García-Diego), [email protected] (M. Zarzo). 1 Tel.: +34 963877490; Fax: +34 963877499. 1296-2074/$ – see front matter © 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.culher.2009.06.002
The metropolitan basilica cathedral of St. Mary in Valencia started in 2004 the restoration of the main chapel and the renaissance fresco paintings (Fig. 1) that nicely decorate the severies of the vaulted roofs of the presbytery (http://www.frescosdelacatedral.com). The restoring team observed the presence of efflorescence that had to be removed in different zones of the frescoes. Certain parts with problems of deterioration due to high humidity were also identified [15]. Since the moisture was restricted to specific zones, it was suspected that the problem could be caused by infiltration of rainwater through the roof above the apse. Attempting to prevent this kind of problems in the future, this roof was remodelled and an asphaltic roofing sheet
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Fig. 1. View of the renaissance frescos at the cathedral of Valencia as they can be observed from the presbytery. The seven ribs converge to the vault keystone, which appears in the centre of the image. Position of the 29 probes that were located during the restoration process for the monitoring of indoor air: seven of them on the ribs (squares), two probes at the cornice (diamonds), 10 probes on the walls below the severies (triangles) and 10 probes on the frescoes (circles). Each probe contains a sensor of temperature and relative humidity (RH).
was laid below the tiles. The presence of any joint not properly sealed or any crack in the sheet might become a source of water infiltration. Small amounts of water that may accumulate during future decades would diffuse by capillarity down to the frescoes and cause problems of moisture in the paintings. The importance of waterproofing in the roof above frescoes to prevent humidity dropping down through capillary action is discussed in the literature [16]. Taking into account that it is very difficult to ensure a long-term impermeability of the roof over these paintings that are to be conserved over centuries, the restoring team decided to implement a monitoring system comprised by RH and temperature sensors. These sensors were installed during the restoration process at different points of the vault, some of them inside the paintings and others outside. This control system is rather unique because sensors are rarely inserted into the precious walls supporting the frescoes. The main purpose of this system is the detection of abnormally higher values of humidity at specific parts of the vault, which might indicate infiltration of water through the roof. The present work describes the monitoring system and reports the analysis of the data obtained during the first months of monitoring. 2. Materials and methods 2.1. Description of probes: characteristics and installation During the restoration process, specialists had to remove and reintegrate the layer of plaster supporting the paintings in zones highly deteriorated due to moisture problems. Ten porous ceramic tubes were inserted in these zones by drilling in the mortar between two bricks of the severy. The exact position of bricks and joints was identified by means of an ultrasound device as described elsewhere [17]. One probe was located inside each tube so that the RH sensor remains on the surface of the painting (Fig. 2). Other probes of the same type, 19 in total, were implemented in order to get supplementary information that might help the interpretation of data recorded from the frescoes. For this purpose, seven probes were located on the ribs that separate the severies of the apse vault, at about 2 m from the keystone (Fig. 1). These sensors measure the RH and temperature of the air at about 5 cm from the
surface of the frescoes. Moreover, 10 probes were installed on the walls of the apse at a distance of about 120 cm below the severies (see Fig. 1). These probes were introduced in a ceramic tube of the type described above that was inserted into the wall by drilling in the mortar between two ashlars. Two additional probes were located at the cornice of the apse, at 3.5 m below the wall ones. All 29 probes contain a small-outline integrated circuit, model DS2438 (Maxim Integrated Products, Inc.) that incorporates a direct-to-digital temperature sensor with an accuracy of ± 2 ◦ C as well as an analogue-to-digital voltage converter. This converter measures the output voltage of a humidity sensor (HIH-4000, Honeywell International, Inc.). A set of 30 sensors HIH-4000 were purchased from the manufacturer, who provided the second order calibration curve determined experimentally for one of them. This equation allows the estimation of RH according to the voltage output with an accuracy of ± 3.5%RH. The following calibration procedure was applied in laboratory. All 30 RH sensors were introduced in a small chamber equilibrated with a saturated solution of a salt, and the voltage output of all 30 sensors was measured. The saturated solution was changed and, after reaching the equilibrium, the voltage output was measured again. This experiment was repeated with different saturated solutions. Next, calibration equations were obtained for each sensor in order to relate the voltage output and the RH, which was estimated from the reference sensor calibrated by the manufacturer. Each RH sensor receives a 5 V DC input supplied by an integrated circuit TL7805. This circuit presents certain variability, and the actual voltage supply ranges from 4.9 to 5.1, which affects the RH estimation. Actually, according to the manufacturer, the voltage output of HIH-4000 sensors is proportional to voltage supply. Thus, in order to improve the accuracy of RH measurements, the exact value of the voltage supply was measured for each RH sensor once all probes and connections were installed in the apse vault, and the calibration curves of each individual sensor were corrected accordingly. 2.2. Data acquisition system Four electric wires come out from each probe: two for 12-volt DC power supply and two wires for data transfer. All sensors are
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Fig. 2. Scheme depicting the installation of probes in the frescoes. The porous ceramic tube was introduced after drilling in specific zones of the plaster where the original painting was not present. In this figure, the porous tube is partially sectioned to see the interior.
connected to a microcontroller that recorded the measurements in a digital format. Data were weekly downloaded to a laptop. Taking into account that the manufacturer recommends a correction of the RH estimation according to temperature [18], the proposed correction was applied in order to improve the accuracy. The restoration works finished in December 2006, and the data acquisition started in February 2007. The microcontroller was programmed to record one datum per hour from each sensor, and hence we expected to get each month about 720 time observations (i.e., 30 days × 24 hours/day). Unfortunately, due to different problems, it was only possible to record the measurements at 90 time observations in February, 62 in September, 108 in October, and 172 in November. Sensor F was broken down and it was disregarded. Data from sensors M and N were only available for February. In November, data from six additional sensors (C, D, J, L, Q, and U) were abnormal and they had to be also discarded (see Fig. 1 for label codes). 2.3. Multivariate data analysis
achieve a tight interchangeability which, according to the manufacturer, reduces or eliminates calibration of individual sensors [18]. Nevertheless, all RH sensors used in this monitoring system were calibrated individually prior to their installation in the apse vault. Calibration curves were obtained taking into account the information provided by a reference sensor calibrated by the manufacturer. All calibration curves resulted were very similar for the 30 sensors, which is in accordance with the high interchangeability claimed by the manufacturer. Signal correction according to temperature and to the actual voltage supply of each sensor was also implemented, as described above. 3.2. Data pretreatment prior to PCA We plotted the RH recorded from all sensors versus time and it was observed that the daily variations were clearly marked, as shown in Fig. 3 for four representative sensors. These variations are higher than the accuracy of the RH sensors. We also observed that the signal evolution along the time was rather parallel for all sensors. Thus, the average RH recorded by each sensor, that will be
Data of RH recorded in February were arranged in a matrix comprised by 90 observations (instants of time, in rows) by 28 variables (RH sensors, in columns). This matrix was row-centred as described below and, next, a principal components analysis (PCA) was carried out using the software SIMCA-P 10.0 (http://www.umetrics.com). The same analysis was repeated with data obtained in September, October and November 2007. The results from these four models were compared in order to check if the relationships among sensors were maintained month after month. The same procedure was also applied to the matrices of temperature. 3. Results and discussion 3.1. Accuracy of the RH sensors Sensor HIH-4000 is a thermoset polymer capacitive sensing element with on-chip integrated signal conditioning. This model was selected because it has a better accuracy (± 3.5%), interchangeability (from ± 5% to ± 8%RH, see data sheet), hysteresis (± 0.5%RH) and risetime (15 s), compared with other capacitive RH sensors. The dielectric element of these sensors are laser trimmed in order to
Fig. 3. Evolution of relative humidity recorded during 7 consecutive days (November 2007) corresponding to four sensors: H (upper thicker line), T (lower thicker line), U (upper thinner line), and D (lower thinner line). Sensor codes according to Fig. 1. The horizontal scale indicates hours (7 days ≈ 170 values). DP stands for discrimination period.
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Fig. 4. (left) Simulated evolution of relative humidity versus time corresponding to eight sensors (s1 to s8). (right) Loading plot (p[2] vs p[1]) obtained by applying PCA to the simulated relative humidity (RH) matrix. Data were row-centred prior to the analysis.
referred to as RH j (i.e., the mean value of column j) is the parameter that best describes the different performance among sensors. PCA is a useful technique for explaining the data variability of a matrix, as well as for the interpretation of relationships among observations and variables. Different pretreatment methods can be applied before conducting a PCA. If the interest relies on the identification of correlation structures among variables, which is commonly the case, data are column-centred prior to the PCA by subtracting the mean value of each column, so that all centred variables have a null average. This pretreatment seems appropriate a priori in this case because the target is to understand the similarities and dissimilarities among sensors. We applied PCA after this pretreatment and it was obtained that the results basically explain the differences among sensors according to RH j , because all variables are strongly correlated due to the parallel evolution of RH. Consequently, we decided to try other alternatives. The mean value of RH recorded by all sensors in a given instant of time ti will be referred to as RH i . The evolution versus time of RH i is of relevant interest for the identification of excessive diurnal or seasonal variations that might be harmful for the frescoes. This common pattern of RH i is followed by all sensors, and the goal in this case is to diagnose the slight variations of each sensor respect this average performance. Thus, if the data variability caused by time is eliminated from the matrix, the subsequent PCA will reflect more clearly the relationships among sensors. For this purpose, we centred the data by row (i.e., subtracting RH i to the elements of row i), so that all centred rows have a null average. The idea of applying this special pretreatment comes from the literature on the multivariate statistical control of batch chemical processes [19–23]. In a batch reaction, process variables such as temperatures or pressures follow a particular pattern along the course of the reaction, and the goal is to control properly the process so that the deviations respecting the target pattern are as low as possible. In these cases, PCA can be applied for process diagnosis in order to identify abnormal batches that deviate from the normal operative conditions. For this purpose, the data variability caused by time is usually eliminated from the matrix prior to applying PCA [19,20]. Many successful applications of this methodology have been reported [21–23]. 3.3. PCA of simulated data The first principal component (PC1) can be interpreted as the linear combination of the original variables that explains the highest
amount of the total data variability. Similarly, PC2 is the correlation structure that explains as much as possible the remaining variability not described by PC1. The contributions of variables in the formation of a given component are called loadings, being p[1] the loadings corresponding to PC1, and so on. In order to understand the advantages of the proposed data pretreatment, a previous study was conducted. Taking into account the daily cycles observed in Fig. 3, simulated RH data were generated for eight sensors (Fig. 4, left), resulting a matrix of eight variables by 170 observations. The evolution of RH versus time is nearly parallel for all sensors; the only difference is that the lower part of the cycle is sharper in sensors s2, s4, s6, and s8, but flatter for the rest. PCA was carried out after applying the row-centring pretreatment. The scatter plot of p[2] versus p[1], which is referred to as PC1-2 loading plot (Fig. 4, right), clearly reflects the relationships among sensors. Regarding p[1], sensors are arranged according to their average signal, while p[2] discriminates them according to the different signal shape. This example illustrates that the statistical methodology is very powerful to highlight slight differences when the pattern of a set of sensors is rather parallel, which occurs in the present case. 3.4. PCA of RH data The RH matrix corresponding to February was row-centred as described, and a PCA was applied next. The same analysis was repeated with data of September, October and November. Given that all sensors follow a parallel pattern, centring by row leads to values that are nearly constant for each sensor. Consequently, PC1 explains a large amount of the total data variability: 98.2% in February, 95.8% in September, 94.3% in October and 99.6% in November. The p[1] loadings (i.e., horizontal axis in Fig. 5) discriminate sensors according to RH j because this average accounts for the main data variability of the centred matrix, as already explained in the simulated case. In order to determine if PC2 provides relevant information, we inspected the PC1-2 loading plots corresponding to each model. If the four plots are overlapped (Fig. 5), a reasonable consistency is observed for PC1 and PC2, and hence both components can be regarded as relevant. We also compared the loading plots corresponding to PC2-3 for the four models, but no consistency was observed for PC3. Thus, the information of this component was not considered.
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Fig. 5. Overlap of four loading plots corresponding to PC1 and PC2 (i.e., p[2] vs p[1]). Each plot was obtained by applying PCA to a different relative humidity (RH) matrix: February 2007 (diamonds); September (squares); October (triangles); November 2007 (circles). In all cases, matrices were row-centred prior to the PCA. Variables joined together correspond to the same sensor (labels as in Fig. 1). Filled points highlight those sensors located at the vault ribs or at the cornice. Dotted lines correspond to sensors located on the frescoes. Continuous lines joining empty points indicate sensors located on the walls.
˜ D, and C) as well Sensors located at the vault ribs (I, J, X, S, N, as at the cornice (A, B) appear clustered together at the left of the plot (Fig. 5). Interestingly, there is a correspondence between the position of probes in this cluster and in the vault (Fig. 1). These results suggest that the environment surrounding the frescoes is not the same at all points of the vault. Sensors positioned on walls or in the paintings tend to record higher values than those in the vault ribs. Taking into account this result and the parallel evolution of RH versus time, we suggest that the former are measuring the RH of the indoor air at the apse plus a contribution coming from the ceramic tube. All wall probes except M and V are also close to each other in Fig. 5. By contrast, sensors on the frescoes span over a wider range of RH values. The volume delimited by two adjacent severies converging to the same vault rib, the wall and the apse roof is filled with a lime-sand material that can retain water. If this material is dry, it is expected that neither of the severies enclosing the volume will present moisture problems, and their corresponding sensors would reflect drier conditions. This is probably the case for T and O, because their RH data are lower than wall sensors. No efflorescence problems are currently observed on this part of the vault, as well as in sensors K and E, that performed similarly (Fig. 5). Sensor H is somewhat abnormal because it presents the lowest p[2] loadings (Fig. 5). Thus, it is of interest to discuss the arrangement of sensors according to PC2. Fig. 3 shows the evolution of sensors T, U, H, and D. Two of them, T and U, are characterised by p[2] > 0, which is not the case for H and D (Fig. 5). We carefully inspected the evolution of RH for these four sensors, and we found certain daily cycles with a different pattern. Fig. 3 indicates two of these discriminating periods, reflecting a similar performance of H and D different from T and U. Further studies will be necessary to understand why the pattern is slightly different in certain sensors at certain periods. Sensors R, H and N recorded the highest values of RH (Fig. 5). Interestingly, H and N are located on severies with moisture problems during the restoration. Efflorescence is appearing again basically at the same zones where it was identified after the first restoration works. It is important to point out that R and H belong to two severies converging at the same rib. The filling material enclosed by these severies is probably retaining more water than in
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other parts of the vault, perhaps due to small amounts of rainwater infiltrated through the roof along the years. Regarding Y and W, which also belong to two converging severies, the RH measurements are slightly higher than wall probes. Nonetheless, we speculate that the risk of moisture problems is probably lower than in the case of R and H because their loadings in PC2 are positive, which is not the case for R, H and N. The loading plot for PC1-2 (Fig. 5) highlights the relationships among sensors and it can be regarded as some sort of control map. The approach that we propose for the preventive conservation of frescoes based on RH measurements is to repeat the same multivariate analysis month after month, obtain the loading plot for PC1-2 and compare it with Fig. 5. We obtained consistent results for four months although the number of time observations was different in each case. Thus, an important advantage of this method is that it can easily deal with missing data, which often occurs in practice. We expect that the relative position in the plot of sensors installed at the vault ribs or walls will be more or less maintained over time, but it might not be the case for those ones on the frescoes. Thus, if the analysis reveals a sustained increase of RH month after month for a particular sensor, that signal would warn against likely problems of moisture that should be further investigated with visual inspection and additional tests. 3.5. PCA of temperature data Preliminary analyses of the temperature matrices were also carried out with PCA, and it was obtained that PC1 explains the mean value of temperature. Actually, as in the case of RH, the recorded temperatures follow a parallel pattern along the time that is maintained month after month. We checked that the mean values of temperature recorded by the different sensors in February were not significantly correlated with the mean values of RH (p = 0.092), and similar results were obtained in September (p = 0.48), October (p = 0.91) and November (p = 0.79). In the case of PC2, the comparison of loading plots suggested that this component was not meaningful and it was significantly different from month to month. The reasons are uncertain, but we speculate that one datum per hour recorded by the system was probably not enough for the purpose of identifying slight differences among the common pattern of temperature followed by all sensors. The frequency of data acquisition was initially set at one datum per hour because sensors are located at 18 m from the ground level and significant variations of the microclimate surrounding the frescoes were not expected a priori for periods lower than one hour. Nevertheless, this sampling time interval seems too long and the best solution would be to set it equal to the response time of the monitoring system under real conditions. This value will be higher than 15 s, which is the response time of the RH sensors, but the optimum is uncertain and no literature is available for this case. Thus, at the end of 2007, the frequency of data acquisition was set at one datum per minute. One minute is a high resolution and the system will sample many times the same value. Nonetheless, recording such redundant data will allow further studies in order to determine a posteriori the optimum data sampling time. This optimisation is of interest because the size of the resulting database will increase throughout the years, and an excessive data overloading should be avoided. 4. Conclusions An overview of RH data recorded by sensors installed in the vault shows that: (i) the microclimate environment surrounding the fresco paintings undergoes considerable variations along the time, and (ii) the signals follow a parallel evolution. The differ-
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ences among sensors are nicely reflected by the loading plots corresponding to PC1 and PC2. Consistent results were obtained by repeating the data analysis month after month. Three sensors inserted in the frescoes (H, N and R) recorded higher values than those installed in the walls and, interestingly, two of them (H and R) are located at parts of the vault where moisture problems appeared during the restoration. Thus, these high signals seem to reflect an excessive moisture of the layer of plaster supporting the frescoes. According to the present results, we cannot conclude that corrective measures are required to reduce the risk of deterioration that moisture may produce in the frescoes on the long term, but at least the results stress the need to keep on with the monitoring system. The statistical methodology applied here is commonly used for the monitoring, diagnosis and fault detection of batch chemical processes, but this is probably the first time that such methodology is applied for the monitoring of pieces of art aimed at preventive conservation. In order to encourage further research in the microclimate monitoring of cultural heritage, we are creating a database of thermohygrometric measurements that will be available to the scientific community through a website that is currently under construction. Acknowledgements This research was part of a multidisciplinary project aimed at the preventive conservation of the renaissance frescoes of the cathedral of Valencia, supported by the Autonomous Valencian Government (Conselleria de Cultura y Deporte) and the Valencian Institute for Conservation and Restoration of Cultural Heritage (IVC + R). The authors wish to thank J. Pérez-Miralles for his valuable help in installing the probes during the restoration process, M.C. Pérez-García for coordinating the multidisciplinary team involved in this project and J. Alario-Genovés for the high-quality production of some figures and C. Villanueva for his friendly collaboration. References [1] D. Camuffo, Microclimate for cultural heritage, Elsevier, Amsterdam, 1998. [2] A. Bernardi, Microclimate in the British Museum, London, Museum Manag. Curatorship 9 (1990) 169–182.
[3] A. Bernardi, D. Camuffo, Microclimate in the Chiericati Palace Municipal Museum, Vicenza, Museum Manag. Curatorship 14 (1995) 5–18. [4] D. Camuffo, A. Bernardi, G. Sturaro, A. Valentino, The microclimate inside the Pollaiolo and Botticelli rooms in the Uffizi Gallery, Florence, J. Cult. Herit. 3 (2002) 155–161. [5] K. Gysels, F. Delalieux, F. Deutsch, R. Van Grieken, D. Camuffo, A. Bernardi, et al., Indoor environment and conservation in the Royal Museum of Fine Arts, Antwerp, Belgium, J. Cult. Herit. 5 (2004) 221–230. [6] Y. Tabunschikov, M. Brodatch, Indoor air climate requirements for Russian churches and cathedrals, Indoor Air 14 (Suppl. 7) (2004) 168–174. [7] D. Camuffo, G. Sturaro, A. Valentino, Thermodynamic exchanges between the external boundary layer and the indoor microclimate at the basilica of Santa Maria Maggiore, Rome, Italy: the problem of conservation of ancient works of art, Bound. Layer Meteor. 92 (1999) 243–262. [8] E. Vuerich, F. Malaspina, M. Barazutti, T. Georgiadis, M. Nardino, Indoor measurements of microclimate variables and ozone in the church of San Vincenzo (Monastery of Bassano Romano – Italy): a pilot study, Microchem. J. 88 (2008) 218–223. [9] G. Loupa, E. Charpantidou, I. Kioutsioukis, S. Rapsomanikis, Indoor microclimate, ozone and nitrogen oxides in two medieval churches in Cyprus, Atmos. Env. 40 (2006) 7457–7466. [10] D. Camuffo, Indoor dynamic climatology: investigations on the interactions between walls and indoor environment, Atmos. Env. 17 (1983) 1803–1809. [11] D. Camuffo, A. Bernardi, Study of the microclimate of the Hall of the Giants in the Carrara Palace in Padua, Stud. Conservat. 40 (1995) 237–249. [12] D. Camuffo, E. Pagan, A. Bernardi, F. Becherini, The impact of heating, lighting and people in re-using historical buildings: a case study, J. Cult. Herit. 5 (2004) 409–416. [13] D. Camuffo, A. Bernardi, The microclimate of the Sistine Chapel, Eur. Cult. Heritage Newslett. Res. 9 (1995) 7–33. [14] D. Camuffo, A. Bernardi, The microclimate of Leonardo’s Last Supper, Bollettino Geofisico 14 (3) (1991) 1–75. [15] F. Marías Franco, Angélico Borgia: Frescoes of the cathedral of Valencia, FMR 22 (2007) 49–72. [16] A. Bernardi, V. Todorov, J. Hiristova, Microclimatic analysis in St. Stephan’s church, Nessebar, Bulgaria after interventions for the conservation of frescoes, J. Cult. Herit. 1 (2000) 281–286. [17] D. Del Vescovo, A. Fregolent, Assessment of fresco detachments through a noninvasive acoustic method, J. Sound Vib. 284 (2005) 1015–1031. [18] Honeywell Inc. (2009). Data sheet HIH-4000 series humidity sensors (Retrieved from http://sensing.honeywell.com/index.cfm/ci id/140570/ la id/1/document/1/re id/0 on May 27th 2009). [19] P. Nomikos, J.F. MacGregor, Monitoring batch processes using multiway principal component analysis, AIChE J. 40 (1994) 1361–1375. [20] P. Nomikos, Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis, ISA Trans. 35 (1996) 259–266. [21] N.B. Gallagher, B.M. Wise, C.W. Stewart, Application of multi-way principal components analysis to nuclear waste storage tank monitoring, Comput. Chem. Eng. 20 (1996) S739–S744. [22] S. Rännar, J.F. MacGregor, S. Wold, Adaptive batch monitoring using hierarchical PCA, Chemom. Intell. Lab. Syst. 41 (1998) 73–81. [23] M. Zarzo, A. Ferrer, Batch process diagnosis: PLS with variable selection versus block-wise PCR, Chemom. Intell. Lab. Syst. 73 (2004) 15–27.