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PIXE elemental characterization of air masses using a multivariate statistical technique

Swietlicki, Erik LU orcid ; Hansson, Hans Christen and Martinsson, Bengt G. LU (1987) In Nuclear Inst. and Methods in Physics Research, B 22(1-3). p.264-269
Abstract

An example is given to show the possibility of using a multivariate statistical evaluation technique in order to extract more information from a multielemental PIXE data set. Four weeks of continuous sampling was carried out at a background air pollution monitoring station in Sweden. Samples were collected both in fine and coarse mode, with a cutoff at 2 μm. In the subsequent PIXE analysis of the samples, 12-16 elements were detected in the fine fraction and 9-12 elements in the coarse fraction. The fine fraction PIXE data was further analysed using the multivariate statistical programme package SIMCA, which combines a pattern recognition technique and principal component analysis. Based on 1000 mbar back trajectories for the sampling... (More)

An example is given to show the possibility of using a multivariate statistical evaluation technique in order to extract more information from a multielemental PIXE data set. Four weeks of continuous sampling was carried out at a background air pollution monitoring station in Sweden. Samples were collected both in fine and coarse mode, with a cutoff at 2 μm. In the subsequent PIXE analysis of the samples, 12-16 elements were detected in the fine fraction and 9-12 elements in the coarse fraction. The fine fraction PIXE data was further analysed using the multivariate statistical programme package SIMCA, which combines a pattern recognition technique and principal component analysis. Based on 1000 mbar back trajectories for the sampling period, principal component class models were constructed for Easterly and North-Westerly air masses using 15 elements (S, K, Ca, Ti, V. Cr, Mn, Fe, Ni, Cu, Zn, Ga. As, Br and Pb). For these elements, mean concentration values and standard deviations for the two classes are given. A methodology is presented which excluded outliers and facilitated the calculation of classes with a restricted and definable data distribution representative of the elemental composition of the air masses originating from the two source regions.

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author
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type
Contribution to journal
publication status
published
subject
in
Nuclear Inst. and Methods in Physics Research, B
volume
22
issue
1-3
pages
6 pages
publisher
Elsevier
external identifiers
  • scopus:0022685286
ISSN
0168-583X
DOI
10.1016/0168-583X(87)90340-5
language
English
LU publication?
yes
id
83336b13-e13e-4af7-bac0-4226bde8e2e6
date added to LUP
2019-05-16 09:23:52
date last changed
2021-01-03 06:47:35
@article{83336b13-e13e-4af7-bac0-4226bde8e2e6,
  abstract     = {{<p>An example is given to show the possibility of using a multivariate statistical evaluation technique in order to extract more information from a multielemental PIXE data set. Four weeks of continuous sampling was carried out at a background air pollution monitoring station in Sweden. Samples were collected both in fine and coarse mode, with a cutoff at 2 μm. In the subsequent PIXE analysis of the samples, 12-16 elements were detected in the fine fraction and 9-12 elements in the coarse fraction. The fine fraction PIXE data was further analysed using the multivariate statistical programme package SIMCA, which combines a pattern recognition technique and principal component analysis. Based on 1000 mbar back trajectories for the sampling period, principal component class models were constructed for Easterly and North-Westerly air masses using 15 elements (S, K, Ca, Ti, V. Cr, Mn, Fe, Ni, Cu, Zn, Ga. As, Br and Pb). For these elements, mean concentration values and standard deviations for the two classes are given. A methodology is presented which excluded outliers and facilitated the calculation of classes with a restricted and definable data distribution representative of the elemental composition of the air masses originating from the two source regions.</p>}},
  author       = {{Swietlicki, Erik and Hansson, Hans Christen and Martinsson, Bengt G.}},
  issn         = {{0168-583X}},
  language     = {{eng}},
  month        = {{03}},
  number       = {{1-3}},
  pages        = {{264--269}},
  publisher    = {{Elsevier}},
  series       = {{Nuclear Inst. and Methods in Physics Research, B}},
  title        = {{PIXE elemental characterization of air masses using a multivariate statistical technique}},
  url          = {{http://dx.doi.org/10.1016/0168-583X(87)90340-5}},
  doi          = {{10.1016/0168-583X(87)90340-5}},
  volume       = {{22}},
  year         = {{1987}},
}