A novel approach for quantifying elongated airborne mineral particles (EMPs) using an automated scanning electron microscope (SEM)

Our plain-English summary of A novel approach for quantifying elongated airborne mineral particles (EMPs) using an automated scanning electron microscope (SEM), by A. Elangasinghe, H. Patel, K.N. Dirks, et al (Atmospheric Environment, 2025).

The study

• Authors: A. Elangasinghe, H. Patel, K.N. Dirks, A. Hamilton, W.W. Fan, S. Chen, N. Talbot, S. Lim, J. Januch, M. Brook, B. Wells, D.E. Williams, P. Davy, W. Pattinson, J.A. Salmond
• Journal: Atmospheric Environment
• Published: 2025;354:121217
• Cited by: 1 article, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Exposure to carcinogenic elongated mineral particles (EMPs), such as erionite, found in rocks and released into the air by construction, quarrying, or roading activities, poses a significant possible health risk due to their respirable size and potential for airborne dispersion. The detection of EMPs in the air is typically achieved by filter sampling and subsequent examination using a range of microscopic methods, including phase contrast microscopy (PCM) and scanning electron microscope (SEM). Such analyzes require the manual searching for fibers through many image fields and are both labor-intensive and time-consuming. Moreover, these methods do not result in conclusive particle identification, limiting their effectiveness in large-scale monitoring programmes. This paper introduces a novel methodology for the automated detection and quantification of EMPs using an automated SEM with energy dispersive spectroscopy (EDS) to identify fibers on pre-sampled polycarbonate (PC) filters. This method provides a streamlined workflow for fiber identification based on their size, morphology, and elemental composition. Performance evaluation (PE) standards were prepared by spiking filters with a series of known concentrations of one EMP, namely erionite, and fiber concentrations were measured using the automated SEM-EDS approach. Our results demonstrate a linear relationship (R2 = 0.98***) between the erionite mass percentage in a bulk sample and the fiber counts in an aerosolized air volume, with a detection limit of 7.4 f/cc. The approach can be optimized based on the time available for analysis and the choice of detection limit suitable for the specific site and application. Additionally, the automated SEM-EDS method has been applied to real-world air samples collected from Auckland, New Zealand, showing promising results for fiber detection in complex environmental matrices.

In plain English

This laboratory study developed and tested an automated method for finding and counting elongated mineral particles on air-sampling filters, with a focus on erionite. The method used a benchtop scanning electron microscope with a motorized stage and automated chemical analysis (EDS). To test it, the authors aerosolized mixtures of silica and an erionite-bearing sample from Timber Bay, New Zealand, at six erionite levels from 0% to 1.7% by mass, and collected the air onto polycarbonate filters. In repeated trials on the same filter area, fiber counts were higher at greater magnification (128 at 2000X versus 36 at 400X), and 2000X was used. Estimated fiber concentrations rose in a straight line with erionite content (R2 of 0.98, p less than 0.001). For these spiked laboratory standards, the detection limit was 7.4 fibers per cubic centimeter, which the authors attributed to the small air volume sampled to make them. In air samples from Auckland, the method found fibers that could not be definitively confirmed as erionite. The authors said accuracy at lower concentrations needs further validation.

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 method-development and validation study of an automated scanning electron microscope (SEM) with energy dispersive spectroscopy (EDS), tested on filters spiked with erionite and then applied to real-world air samples
• Who was studied: Performance evaluation filters (PC1 to PC6) made by aerosolizing 3 g of silica mixed with erionite at 0%, 0.02%, 0.15%, 0.37%, 0.89% and 1.7% by mass, plus one filter (SPC1) spiked with the erionite sample alone. The authors also tested real-world air samples from Auckland, New Zealand: a fixed-site campaign at Riverhead and a mobile campaign at Te Henga. Sampling dates are not given in the text.
• Exposure: Erionite from Timber Bay, New Zealand, in a sample containing 20% erionite by XRD, mixed with microcrystalline quartz (silica) and aerosolized in a closed chamber. This was a laboratory test material, not a human exposure.
• Main results:
  • On the erionite-only filter (SPC1), three repeated trials on the same field of view gave fiber counts of 36 (plus or minus 4) at 400X, 53 (plus or minus 11) at 1000X and 128 (plus or minus 24) at 2000X magnification
  • For the same filter area, analysis time rose from 15 minutes at 400X to over 3 hours at 2000X
  • In the spiked performance evaluation standards, the estimated fiber concentration in the aerosolized air correlated with the erionite mass percentage in the bulk sample, with R2 of 0.98 (p less than 0.001)
  • Estimated fiber concentrations in the performance evaluation standards ranged from 0 to 716 fibers per cubic centimeter
  • The detection limit for the spiked laboratory samples was 7.4 fibers per cubic centimeter
  • The authors attributed this high detection limit to the low volume of air sampled to create the standards
  • In the spiked standards, fibers were shorter than 5 micrometers, and the mechanical agitation had broken fibers into small pieces
  • For 308 fibers in one field of view of a road sample, fiber lengths measured with ImageJ software were lower than those from the SEM software (PA3); the regression line was 7.58% below perfect agreement
  • The method detected elongated mineral fibers in real-world Auckland samples, but these fibers could not be definitively confirmed as erionite
  • Except for very few fibers, zeolite fibers on the road-sampled air filters did not consistently show the silicon-to-aluminum (Tsi) ratio characteristic of erionite
• The authors' conclusion: The authors concluded that the 7.4 fibers per cubic centimeter detection limit indicates the automated method can achieve high sensitivity. They added that further assessment is needed for detecting fibers at lower erionite concentrations, and that fibers flagged by this screening would need a secondary technique such as TEM to confirm erionite.
• Limitations the authors state:
  • Overloaded filters can hide fibers in the SEM image
  • If the electron beam drifts during automated analysis, some fibers may be missed
  • The method identifies only straight fibers or fiber bundles, so it may miss fibers attached to other particles or crossed with them
  • Fibers smaller than one pixel at the chosen image resolution cannot be detected
  • Standards at extremely low concentrations are difficult to prepare, and accuracy at lower fiber concentrations needs further validation
  • The silicon-to-aluminum (Tsi) ratio from SEM-EDS alone cannot distinguish between different fibrous zeolites, so it served only as an initial screen

Topics

Medical terms in this study, explained in our Lexicon: Carcinogenic, erionite, phase contrast microscopy (PCM), energy dispersive X-ray spectroscopy (EDS), Fiber Identification.

Related studies

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• Oxygen incineration and electron microscope x-ray microanalysis of mineral particles in biological tissues — W.J. Henderson, C. Melville-Jones, et al. (1978)
• Preparation of extrapulmonary tissues and body fluids for quantitative transmission electron microscope analysis of asbestos and other mineral particle concentrations — P.M. Cook (1979)
• Reanalysis of historic elemental speciation filters to investigate the presence of fibrous mineral particles using microscopy techniques — N. Talbot, K.N. Dirks, et al. (2022)

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