Ricerc@Sapienza

Asbestos was largely used in the past by several countries all over the world. From 1900 to 1990 asbestos-containing materials (ACMs) were produced in large amounts for addressed to the production of insulation, flame retardant materials, as well as to improve mechanical and chemical characteristics of construction materials. The largest producer countries were Canada (chrysotile), South Africa (amosite, crocidolite), Russia (chrysotile), Finland (anthophyllite) and Italy (chrysotile). There are still some countries (e.g., Russia, China, Kazakhstan) where asbestos is mined and processed. The extensive use of asbestos has therefore led to the presence of fibers in existing buildings and within the construction and demolition waste. For this reason, a fast, reliable and accurate recognition of ACMs represents an important target to be reached. In this paper the use of micro X-ray fluorescence (micro-XRF) technique coupled with a statistical multivariate approach was applied and discussed with reference to ACMs characterization. Different elemental maps of the ACMs were preliminary acquired in order to evaluate distribution and composition of asbestos fibers, then samples energy spectra where collected and processed using chemometric methods to perform an automatic classification of the different typologies of asbestos fibers. Spectral data were analyzed using PLSToolbox ™ (Eigenvector Research, Inc.) running into Matlab® (The Mathworks, Inc.) environment. Data pre-processing to enhance collected spectral features and data explorative analysis, based on Principal Component Analysis (PCA), was first carried out. An automatic classification model was then built and applied. For each investigated sample, a false color prediction map was obtained. Results showed that asbestos fibers were correctly identified and classified according to their chemical composition. The proposed approach, based on micro-XRF analysis combined with an automatic classification of the elemental maps, is not only effective and non-destructive, it is fast and it does not require the presence of a trained operator. The application of the developed methodology can help to correctly characterize and manage demolition waste where ACMs are present.