Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis

Norman Schreiber; Emanuel Garcia; Aart Kroon; Peter Carsten Ilsøe; Kurt H. Kjær; Thorbjørn Joest Andersen

doi:10.1007/s11270-014-2221-5

Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis

Norman Schreiber, Emanuel Garcia, Aart Kroon, Peter Carsten Ilsøe, Kurt H. Kjær, Thorbjørn Joest Andersen

Geography

4 Citations (Scopus)

Abstract

Principal component analysis (PCA) was performed on chemical data of two sediment cores from an urban freshwater lake in Copenhagen, Denmark. X-ray fluorescence (XRF) core scanning provided the underlying datasets on 13 variables (Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Cd, and Pb). Principal component analysis helped to trace geochemical patterns and temporal trends in lake sedimentation. The PCA models explained more than 80 % of the original variation in the datasets using only two or three principal components. The first principal component (PC1) was mostly associated with geogenic elements (Si, K, Fe, Rb) and characterized the content of minerogenic material in the sediment. In the case of both cores, PC2 was a good descriptor emphasized as the contamination component. It showed strong linkages with heavy metals (Cu, Zn, Pb), disclosing changing heavy-metal contamination trends across different depths. The sediments featured a temporal association with contaminant dominance. Lead contamination was superseded by zinc within the compound pattern which was linked to changing contamination sources over time. Principal component analysis was useful to visualize and interpret geochemical XRF data while being a straightforward method to extract contamination patterns in the data associated with temporal elemental trends in lake sediments.

Original language	English
Article number	2221
Journal	Water, Air and Soil Pollution
Volume	225
Issue number	12
Number of pages	11
ISSN	0049-6979
DOIs	https://doi.org/10.1007/s11270-014-2221-5
Publication status	Published - 16 Nov 2014

Keywords

Faculty of Science
urban lake sediment
contamination
heavy metals
XRF
PCA

Access to Document

10.1007/s11270-014-2221-5

Cite this

@article{f1e2c0183c314396901e3e2071c782e0,

title = "Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis",

abstract = "Principal component analysis (PCA) was performed on chemical data of two sediment cores from an urban freshwater lake in Copenhagen, Denmark. X-ray fluorescence (XRF) core scanning provided the underlying datasets on 13 variables (Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Cd, and Pb). Principal component analysis helped to trace geochemical patterns and temporal trends in lake sedimentation. The PCA models explained more than 80 % of the original variation in the datasets using only two or three principal components. The first principal component (PC1) was mostly associated with geogenic elements (Si, K, Fe, Rb) and characterized the content of minerogenic material in the sediment. In the case of both cores, PC2 was a good descriptor emphasized as the contamination component. It showed strong linkages with heavy metals (Cu, Zn, Pb), disclosing changing heavy-metal contamination trends across different depths. The sediments featured a temporal association with contaminant dominance. Lead contamination was superseded by zinc within the compound pattern which was linked to changing contamination sources over time. Principal component analysis was useful to visualize and interpret geochemical XRF data while being a straightforward method to extract contamination patterns in the data associated with temporal elemental trends in lake sediments.",

keywords = "Faculty of Science, urban lake sediment, contamination, heavy metals, XRF, PCA",

author = "Norman Schreiber and Emanuel Garcia and Aart Kroon and Ils{\o}e, {Peter Carsten} and Kj{\ae}r, {Kurt H.} and Andersen, {Thorbj{\o}rn Joest}",

year = "2014",

month = nov,

day = "16",

doi = "10.1007/s11270-014-2221-5",

language = "English",

volume = "225",

journal = "Water, Air and Soil Pollution",

issn = "0049-6979",

publisher = "Springer",

number = "12",

}

TY - JOUR

T1 - Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis

AU - Schreiber, Norman

AU - Garcia, Emanuel

AU - Kroon, Aart

AU - Ilsøe, Peter Carsten

AU - Kjær, Kurt H.

AU - Andersen, Thorbjørn Joest

PY - 2014/11/16

Y1 - 2014/11/16

N2 - Principal component analysis (PCA) was performed on chemical data of two sediment cores from an urban freshwater lake in Copenhagen, Denmark. X-ray fluorescence (XRF) core scanning provided the underlying datasets on 13 variables (Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Cd, and Pb). Principal component analysis helped to trace geochemical patterns and temporal trends in lake sedimentation. The PCA models explained more than 80 % of the original variation in the datasets using only two or three principal components. The first principal component (PC1) was mostly associated with geogenic elements (Si, K, Fe, Rb) and characterized the content of minerogenic material in the sediment. In the case of both cores, PC2 was a good descriptor emphasized as the contamination component. It showed strong linkages with heavy metals (Cu, Zn, Pb), disclosing changing heavy-metal contamination trends across different depths. The sediments featured a temporal association with contaminant dominance. Lead contamination was superseded by zinc within the compound pattern which was linked to changing contamination sources over time. Principal component analysis was useful to visualize and interpret geochemical XRF data while being a straightforward method to extract contamination patterns in the data associated with temporal elemental trends in lake sediments.

AB - Principal component analysis (PCA) was performed on chemical data of two sediment cores from an urban freshwater lake in Copenhagen, Denmark. X-ray fluorescence (XRF) core scanning provided the underlying datasets on 13 variables (Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Cd, and Pb). Principal component analysis helped to trace geochemical patterns and temporal trends in lake sedimentation. The PCA models explained more than 80 % of the original variation in the datasets using only two or three principal components. The first principal component (PC1) was mostly associated with geogenic elements (Si, K, Fe, Rb) and characterized the content of minerogenic material in the sediment. In the case of both cores, PC2 was a good descriptor emphasized as the contamination component. It showed strong linkages with heavy metals (Cu, Zn, Pb), disclosing changing heavy-metal contamination trends across different depths. The sediments featured a temporal association with contaminant dominance. Lead contamination was superseded by zinc within the compound pattern which was linked to changing contamination sources over time. Principal component analysis was useful to visualize and interpret geochemical XRF data while being a straightforward method to extract contamination patterns in the data associated with temporal elemental trends in lake sediments.

KW - Faculty of Science

KW - urban lake sediment

KW - contamination

KW - heavy metals

KW - XRF

KW - PCA

U2 - 10.1007/s11270-014-2221-5

DO - 10.1007/s11270-014-2221-5

M3 - Journal article

SN - 0049-6979

VL - 225

JO - Water, Air and Soil Pollution

JF - Water, Air and Soil Pollution

IS - 12

M1 - 2221

ER -

Pattern recognition on X-ray fluorescence records from Copenhagen lake sediments using principal component analysis

Abstract

Keywords

Access to Document

Fingerprint

Cite this