an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU


  • Silvia Serranti - DICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, Italy
  • Giuseppe Capobianco - DICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, Italy
  • Sergio Malinconico - Department of new technologies for occupational safety of industrial plants, products and anthropic settlements, National Institute for Insurance against Accidents at Work, Italy
  • Giuseppe Bonifazi - DICMA, Department of Chemical Engineering, Materials and Environment, Sapienza - University of Rome, Italy

DOI 10.31025/2611-4135/2020.14007

Released under CC BY-NC-ND

Copyright: © 2019 CISA Publisher

Editorial History

  • Received: 21 Nov 2019
  • Revised: 28 Mar 2020
  • Accepted: 06 May 2020
  • Available online: 30 Sep 2020


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 and mainly utilized for the production of insulation, flame retardant materials, as well as to improve the mechanical and the chemical characteristics of construction materials. Its extensive use has therefore led to the presence of fibers in existing buildings and within the construction and demolition waste. 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 PLS-Toolbox™ (Eigenvector Research, Inc.) running into Matlab® (The Mathworks, Inc.) environment. An automatic classification model was then built and applied. 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.



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