Improving the foundation for particulate matter risk assessment by individual nanoparticle statistics from electron microscopy analysis | Health & Environmental Research Online (HERO)

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Citation
Tags

HERO ID

6774068

Reference Type

Journal Article

Title

Improving the foundation for particulate matter risk assessment by individual nanoparticle statistics from electron microscopy analysis

Author(s)

Brostrom, A; Kling, KI; Koponen, IK; Hougaard, KS; Kandler, K; Molhave, K; ,

Year

2019

Is Peer Reviewed?

Journal

Scientific Reports
EISSN: 2045-2322

Publisher

NATURE PUBLISHING GROUP

Location

LONDON

PMID

31147577

DOI

10.1038/s41598-019-44495-7

Web of Science Id

WOS:000469418100039

Abstract

Air pollution is one of the major contributors to the global burden of disease, with particulate matter (PM) as one of its central concerns. Thus, there is a great need for exposure and risk assessments associated with PM pollution. However, current standard measurement techniques bring no knowledge of particle composition or shape, which have been identified among the crucial parameters for toxicology of inhaled particles. We present a method for collecting aerosols via impaction directly onto Transmission Electron Microscopy (TEM) grids, and based on the measured impactor collection efficiency and observed impact patterns we establish a reproducible imaging routine for automated Scanning Electron Microscopy (SEM) analysis. The method is validated by comparison to scanning mobility particle sizer (SMPS) measurements, where a good agreement is found between the particle size distributions (PSD), ensuring a representative description of the sampled aerosol. We furthermore determine sampling conditions for achieving optimal particle coverage on the TEM grids, allowing for a statistical analysis. In summary, the presented method can provide not only a representative PSD, but also detailed statistics on individual particle geometries. If coupled with Energy-dispersive X-ray spectroscopy (EDS) analysis elemental compositions can be assessed as well. This makes it possible to categorize particles both according to size and shape e.g. round and fibres, or agglomerates, as well as classify them based on their elemental composition e.g. salt, soot, or metals. Combined this method brings crucial knowledge for improving the foundation for PM risk assessments on workplaces and in ambient conditions with complex aerosol pollution.