Characterizing and discriminating the shape of asbestos particles

Our plain-English summary of Characterizing and discriminating the shape of asbestos particles, by H.G. Siegrist, A.G. Wylie (Environmental Research, 1980).

The study

• Authors: H.G. Siegrist, A.G. Wylie
• Journal: Environmental Research
• Published: 1980;23(2):348-61
• Cited by: 33 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

The lengths and widths of approximately 1000 particles from each of four asbestos samples, two nonfibrous amphiboles, and one talc-serpentine sample were measured utilizing the SEM. The asbestos samples are commercially available and include chrysotile from Quebec, chrysotile from California, amosite from South Africa, and crocidolite from South Africa. The tremolite and talc-serpentine are from New York, and the riebeckite is from California. Beneficiation, including milling of amosite, crocidolite, and tremolite and air classifications of the two chrysotile samples, was done under commercial conditions; the riebeckite was milled in the laboratory. For comparison, the same measurements were made on the California chrysotile using TEM. Shape characterizations of the samples are compared using regression techniques. The usefulness of the various shape definitions including length, width, and aspect ratio (length/width) in characterizing and discriminating between samples is explored and evaluated. Significant results include: (i) Frequency distribution of log length, log width, and log aspect ratio show very apparent differences between asbestos and nonasbestos populations. (ii) Dimensional differences and accurate classification according to dimensions are enhanced by regressing log width and/or log aspect ratio against log length. (iii) Discriminant function analysis is able to quantify the distinction between asbestos and nonasbestos particle dimensions such that over 95% of the population assignments are correct. (iv) Log width is a more efficient classifier than log aspect ratio using either linear regression of discriminant function analysis for these particular samples. (v) The choice of instrumentation, i.e., TEM vs SEM, may affect the sample characterization. (vi) Quantitative descriptions of the dimensions of small particles may be related to the habit of the mineral and the structure of the mineral groups to which the particles belong. However, such mineralogical distinctions are probably not valid for particles whose longest dimension is less than about 1 µm.

PubMed has no abstract for this study. This is the authors' abstract, copied from the article.

In plain English

This laboratory study examined the shape of mineral particles, not human health. Using scanning electron microscopy (SEM), the authors measured the length and width of about 1000 particles from each of seven samples. Four were commercially available asbestos: chrysotile from Quebec, chrysotile from California, and amosite and crocidolite from South Africa. Three were nonasbestos minerals: tremolite and talc-serpentine from New York, and riebeckite from California. The California chrysotile was also measured by transmission electron microscopy (TEM). Compared with the nonasbestos particles, the asbestos particles had longer and more varied lengths, thinner and less varied widths, and greater aspect ratios (length divided by width). A statistical sorting method correctly assigned asbestos particles to the asbestos group 95.9% of the time using width alone and 97.8% using width plus aspect ratio. For nonasbestos particles the figures were 74.7% and 87.9%. The authors found width a more sensitive classifier than aspect ratio or length. They said separating the groups would be very difficult if all particles were shorter than about 1 micrometer. The authors concluded that these populations could be classified by shape. They cautioned that results from bulk samples should be applied to airborne or waterborne particles only with extreme caution.

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 mineralogical study measuring particle length and width by electron microscopy, analyzed with linear regression and discriminant function analysis
• Who was studied: Mineral particles, not people: about 1000 particles from each of seven samples. The four asbestos samples were long-fiber chrysotile from Thetford, Quebec; short-fiber chrysotile from Idria, California; amosite from South Africa; and crocidolite from South Africa. The three nonasbestos samples were tremolite and talc-serpentine from St. Lawrence County, New York, and riebeckite from California.
• Exposure: Not an exposure study. The materials measured were chrysotile, amosite (about 95% grunerite asbestos and 5% actinolite asbestos) and crocidolite asbestos, compared with nonfibrous tremolite, talc-serpentine and riebeckite. The short-fiber chrysotile was examined by both SEM and TEM.
• Main results:
  • Compared with nonasbestos particles, asbestos particles had longer and more varied lengths, thinner and less varied widths, and greater, more varied aspect ratios.
  • Regression lines of log aspect ratio or log width against log length fell into two groups, asbestos and nonasbestos, based on their slope.
  • Nonasbestos particles were correctly assigned to the nonasbestos group 74.7% of the time using log width alone, and 87.9% using log width plus log aspect ratio.
  • Asbestos particles were correctly assigned to the asbestos group 95.9% of the time using log width alone, and 97.8% using log width plus log aspect ratio.
  • Log width was a more sensitive classifier than log aspect ratio or log length.
  • If all particles in a population were shorter than about 1 micrometer, telling asbestos from nonasbestos would be very difficult if not impossible.
  • For the short-fiber chrysotile, TEM showed many more particles of smaller widths than SEM did. The authors considered this probably a real instrumentation effect.
• The authors' conclusion: The authors concluded that the particle populations they studied could be classified as asbestos or nonasbestos based on shape.
• Limitations the authors state:
  • Many of the size distributions were not normal, so the comparisons of means and variability were only visual.
  • Sample preparation may have slightly altered the particle populations by breaking up some fibers or fiber bundles.
  • The results come from bulk samples and should be extended to airborne or waterborne particles only with extreme caution.
  • The samples were not pure. The amosite included grunerite cleavage fragments that were not told apart from grunerite asbestos.
  • The SEM and TEM measurements of the short-fiber chrysotile used different data-collection methods (particle volume versus particle number), which complicates comparing the two instruments.
  • Other mineral particle populations may not be as clearly distinguished as those studied.

Topics

Medical terms in this study, explained in our Lexicon: Amphibole Group, Talc, Serpentine Group, Chrysotile, Amosite, Crocidolite, Tremolite, Riebeckite.

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