In situ spectroscopic identification of the six types of asbestos

Our plain-English summary of In situ spectroscopic identification of the six types of asbestos, by V. Zholobenko, F. Rutten, A. Zholobenko, et al (Journal of Hazardous Materials, 2021).

The study

• Authors: V. Zholobenko, F. Rutten, A. Zholobenko, A. Holmes
• Journal: Journal of Hazardous Materials
• Published: 2021;403:123951
• Cited by: 13 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

Exposure to asbestos fibres is related to a number of severe lung diseases, and therefore, rapid, accurate and reliable in situ or on-site asbestos detection in real-life samples is of considerable importance. This work presents a comprehensive investigation of all six types of asbestos by mid-infrared ATR-FTIR, NIR spectroscopy and Raman microspectroscopy. Our studies demonstrate that for practical applications, NIR spectroscopy is potentially the most powerful method for asbestos identification in materials utilised by the construction industry. By focusing on the narrow spectral region, 7300-7000 cm-1 (~1370-1430 nm, overtones of O‒H vibrations), which is highly specific to these materials, and optimising the sensitivity and resolution of the instrumentation, we have been able to discriminate and identify each of the six types of asbestos with the level of detection significantly better than 1 wt%. Furthermore, straightforward computational analysis has allowed for automated objective evaluation of the spectroscopic data.

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In plain English

This was a laboratory study, not a study of people or health. It tested three light-based methods for identifying asbestos: mid-infrared (ATR-FTIR), Raman and near-infrared (NIR) spectroscopy. The authors examined reference samples of all six asbestos types (chrysotile, amosite, crocidolite, actinolite, tremolite and anthophyllite), non-asbestos materials, and suspected asbestos-containing building materials, also checked by standard polarised light microscopy (PLM). Mid-infrared results agreed well with PLM for 34 of 43 suspected samples. With computer analysis, it gave a more confident identification only when asbestos made up over 20% by weight. Raman could not identify asbestos in any of the building materials studied. NIR spectra of amosite, crocidolite and chrysotile mixed into other materials showed a clear signal relative to background noise even at 0.1% by weight, and the same high-quality spectra were obtained through a plastic bag or paint. Telling amosite from crocidolite apart required high instrument resolution. A program the authors wrote, which checks for four asbestos types, was tested on 50 samples. The authors concluded that NIR is potentially the most powerful technique for identifying asbestos in construction materials.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Laboratory analytical methods study comparing mid-infrared ATR-FTIR, Raman and near-infrared (NIR) spectroscopy for identifying asbestos, plus testing of a computer program for analysing NIR spectra
• Who was studied: Samples, not people. The study used three groups of samples: geological asbestos reference samples (from the Health and Safety Laboratories, UK, and the Keele University geology department), non-asbestos matrix materials, and suspected asbestos-containing material (ACM) samples of unknown asbestos content, supplied by Greenwall Environmental with PLM results. FTIR was applied to 43 suspected ACM samples. The NIR program was evaluated on 50 samples, with 9 data sets each.
• Exposure: Detection of six asbestos types (chrysotile, crocidolite, amosite, tremolite, anthophyllite and actinolite) in matrices including bitumen, cement, plasterboard, paints, tiles and calcium carbonate, at concentrations down to 0.1 wt%
• Main results:
  • ATR-FTIR results for suspected ACM samples agreed well with PLM for 34 of 43 samples
  • For samples of unknown content, computer analysis of FTIR data gave a more confident identification only in ACM with over 20 wt% asbestos
  • Raman spectroscopy could not identify asbestos in any of the building materials studied
  • NIR spectra of amosite, crocidolite and chrysotile embedded in different matrices showed a high signal-to-noise ratio even at 0.1 wt% asbestos
  • The same high-quality NIR spectra were obtained for asbestos analysed through a plastic bag, coated with paint, wet, or heat-treated
  • NIR spectra of amosite and crocidolite could be distinguished at high resolution (2 cm-1) but became almost identical at low resolution (32 cm-1)
  • The authors reported that the NIR analysis program's outputs agreed with all available data on the composition of the tested samples
• The authors' conclusion: The authors concluded that NIR spectroscopy is potentially the most powerful technique for detecting and identifying asbestos in construction materials, and that each of the six types can be told apart with a detection limit as low as 0.1 wt%.
• Limitations the authors state:
  • The current version of the authors' NIR analysis program tests for only four asbestos types: chrysotile, actinolite, amosite and crocidolite
  • Most wide-range NIR spectrometers, particularly the portable and hyperspectral models that could be used on-site, work at a lower resolution than this method needs
  • For FTIR software matching, gaps in the spectral libraries can produce very low match values for some samples

Related studies

• In situ identification of asbestos fibres collected on membrane filters for counting — N.P. Vaughan, S.J. Rooker, et al. (1981)
• The chemical environment of iron in mineral fibres. A combined X-ray absorption and Mössbauer spectroscopic study — S. Pollastri, F. D'Acapito, et al. (2015)
• Combined use of X-ray photoelectron and Mössbauer spectroscopic techniques in the analytical characterization of iron oxidation state in amphibole asbestos — M. Fantauzzi, A. Pacella, et al. (2010)
• Adsorption of human serum albumin on the chrysotile surface: a molecular dynamics and spectroscopic investigation — R. Artali, A. Del Pra, et al. (2008)
• Asbestos health hazard: a spectroscopic study of synthetic geoinspired Fe-doped chrysotile — E. Foresti, E. Fornero, et al. (2009)
• Interaction of bovine serum albumin with chrysotile: spectroscopic and morphological studies — G. Falini, E. Foresti, et al. (2006)

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