Direct Infrared Spectroscopy for the Size-Independent Identification and Quantification of Respirable Particles Relative Mass in Mine Dusts

Details

• Personal Author:
  Barone T ; Cauda, Emanuele G. ; Daboss S ; Krebs P ; Mizaikoff B ; Pejcic B ; Stach R
• Description:
  Due to the global need for energy and resources, many workers are involved in underground and surface mining operations where they can be exposed to potentially hazardous crystalline dust particles. Besides commonly known alpha quartz, a variety of other materials may be inhaled when a worker is exposed to airborne dust. To date, the challenge of rapid in-field monitoring, identification, differentiation, and quantification of those particles has not been solved satisfactorily, in part because conventional analytical techniques require laboratory environments, complex method handling, and tedious sample preparation procedures and are in part limited by the effects of particle size. Using a set of the three most abundant minerals in limestone mine dust (i.e., calcite, dolomite, and quartz) and real-world dust samples, we demonstrate that Fourier transform infrared (FTIR) spectroscopy in combination with appropriate multivariate data analysis strategies provides a versatile tool for the identification and quantification of the mineral composition in relative complex matrices. An innovative analytical method with the potential of in-field application for quantifying the relative mass of crystalline particles in mine dust has been developed using transmission and diffuse reflection infrared Fourier transform spectroscopy (DRIFTS) within a unified multivariate model. This proof-of-principle study shows how direct on-site quantification of crystalline particles in ambient air may be accomplished based on a direct-on-filter measurement, after mine dust particles are collected directly onto PVC filters by the worker using body-mounted devices. Without any further sample preparation, these loaded filters may be analyzed via transmission infrared (IR) spectroscopy and/or DRIFTS, and the mineral content is immediately quantified via a partial least squares regression (PLSR) algorithm that enables the combining of the spectral data of both methods into a single robust model. Furthermore, it was also demonstrated that the size regime of dust particles may be classified into groups of hazardous and less hazardous size regimes. Thus, this technique may provide additional essential information for controlling air quality in surface and underground mining operations. [Description provided by NIOSH]
• Subjects:
  Dust Infrared Rays Mining Occupational Health Particulate Matter Safety Silicon Dioxide Spectrum Analysis
• Keywords:
  Author Keywords: Alpha Quartz Chemometrics Crystalline Silica Diffuse Reflectance Direct-reading Methods DRIFTS Dust Particles Dust Sampling Fourier Transform Infrared Spectroscopy FTIR Infrared Spectroscopy Inhalable Particles IR Mineral Dust Multivariate Data Analysis Occupational Safety Partial Least Squares Regression Particle Size Particulates PLSR Respirable Dust Spectrographic Analysis Surface Mining

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• Series:
  Mining Publications
• ISSN:
  1618-2642
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  PMRD - Pittsburgh Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  412
• Issue:
  14
• NIOSHTIC Number:
  nn:20059446
• Citation:
  Anal Bioanal Chem 2020 May; 412(14):3499-3508
• Contact Point Address:
  Boris Mizaikoff, Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
• Email:
  boris.mizaikoff@uni-ulm.de
• CAS Registry Number:
  Quartz (SiO2) (CAS RN 14808-60-7)
• Federal Fiscal Year:
  2020
• NORA Priority Area:
  Mining
• Peer Reviewed:
  True
• Source Full Name:
  Analytical and Bioanalytical Chemistry
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:dc491812973799f569208eb263753be7b8a1c6bf602858a1afadea0ba375a94a7e6b419ab9013a1221e9c71c661636d01c1ed7ac8a432b92e44dce1d3e2c6ea2
• Download URL:
  https://stacks.cdc.gov/view/cdc/215297/cdc_215297_DS1.pdf
• File Type:
  [PDF - 1.49 MB ]