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Estimation of surface area concentration of workplace incidental nanoparticles based on number and mass concentrations

  • J. Y. Park
  • G. RamachandranEmail author
  • P. C. Raynor
  • S. W. Kim
Research Paper

Abstract

Surface area was estimated by three different methods using number and/or mass concentrations obtained from either two or three instruments that are commonly used in the field. The estimated surface area concentrations were compared with reference surface area concentrations (SAREF) calculated from the particle size distributions obtained from a scanning mobility particle sizer and an optical particle counter (OPC). The first estimation method (SAPSD) used particle size distribution measured by a condensation particle counter (CPC) and an OPC. The second method (SAINV1) used an inversion routine based on PM1.0, PM2.5, and number concentrations to reconstruct assumed lognormal size distributions by minimizing the difference between measurements and calculated values. The third method (SAINV2) utilized a simpler inversion method that used PM1.0 and number concentrations to construct a lognormal size distribution with an assumed value of geometric standard deviation. All estimated surface area concentrations were calculated from the reconstructed size distributions. These methods were evaluated using particle measurements obtained in a restaurant, an aluminum die-casting factory, and a diesel engine laboratory. SAPSD was 0.7–1.8 times higher and SAINV1 and SAINV2 were 2.2–8 times higher than SAREF in the restaurant and diesel engine laboratory. In the die casting facility, all estimated surface area concentrations were lower than SAREF. However, the estimated surface area concentration using all three methods had qualitatively similar exposure trends and rankings to those using SAREF within a workplace. This study suggests that surface area concentration estimation based on particle size distribution (SAPSD) is a more accurate and convenient method to estimate surface area concentrations than estimation methods using inversion routines and may be feasible to use for classifying exposure groups and identifying exposure trends.

Keywords

Surface area concentration Surface area estimation Nanoparticles Inversion method Occupational health and safety 

Notes

Acknowledgments

We thank TSI, Inc. for the instrumentation and technical support for this study, in particular Dr. Avula Sreenath’s help with calibration data and the Campus Club at the University of Minnesota, QX Inc., and the Center for Diesel Research in Department of Mechanical Engineering at University of Minnesota for allowing us to collect samples in their workplaces. Financial support for this project was provided by 3 M Company and the Midwest Center for Occupational Health and Safety (MCOHS) paper.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • J. Y. Park
    • 1
  • G. Ramachandran
    • 1
    Email author
  • P. C. Raynor
    • 1
  • S. W. Kim
    • 1
  1. 1.Division of Environmental Health Sciences, School of Public HealthUniversity of MinnesotaMinneapolisUSA

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