A novel approach for quantifying elongated airborne mineral particles (EMPs) using an automated scanning electron microscope (SEM)
Anushka Elangasinghe, Hamesh Patel, Kim N. Dirks, Ayrton Hamilton, Wenxia Fan, Shuoyu Chen, Nick P. Talbot, Shanon Lim, Jed Januch, Martin Brook, Brett Wells, David E. Williams, Perry Davy, Woodrow Pattinson, Jennifer Salmond
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 (R 2 = 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.