Abstract: We present a decision-support system that guides heritage guardians in selecting mitigation actions to improve the indoor air quality and thus the preservation conditions of indoor collections in heritage buildings. This contribution shows that it is feasible to build a decision support system dedicated to preventive conservation when the following barriers are overcome: 1) simultaneous measurement of a wide range of environmental parameters in order to detect a larger number of undesired situations; 2) development of an algorithm to perform reproducible indoor air quality assessments; and 3) transformation of the air quality assessment into a graph that can be read intuitively without causing a wide variation of interpretations among stakeholders. An important aspect of the decision support system is that it reduces several sources of uncertainty that hamper reliable indoor air quality assessments. The possibilities of such a system are illustrated with a measurement campaign in a church where a heating system has been installed and used for the first time.

Abstract: X-ray radiographic images of paintings often show little or no contrast. In order to increase the contrast in radiographic images we measured the X-ray spectrum of a low power X-ray tube, after passing through the painting, with a high energy-resolution SDD detector. To obtain images, the detector is collimated with a 400 mu m diameter pinhole and the painting was moved through the beam in the x and y-direction using a dwell time of a few seconds per pixel. The data obtained consists of a data cube of, typically, 200 x 200 pixels and a 512-channel X-ray spectrum for each pixel, spanning the energy range from 0 to 40 keV. Having the absorbance spectrum available for each pixel, we are able, a posteriori, to produce images by edge subtraction for any given element. In this way high contrast, element-specific, images can be obtained. Because of the high energy-resolution a much simpler edge subtraction algorithm can be applied. We also used principal-component imaging to obtain, in a more automated way, images with high contrast. Some of these images can easily be attributed to specific elements. It turns out that preprocessing of the spectral data is crucial for the success of the multivariate image processing.