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Analytical and Bioanalytical Chemistry

, Volume 385, Issue 4, pp 771–779 | Cite as

Chemometric analysis of soil pollution data using the Tucker N-way method

  • I. Stanimirova
  • K. Zehl
  • D. L. Massart
  • Y. Vander Heyden
  • J. W. Einax
Original Paper

Abstract

N-way methods, particularly the Tucker method, are often the methods of choice when analyzing data sets arranged in three- (or higher) way arrays, which is the case for most environmental data sets. In the future, applying N-way methods will become an increasingly popular way to uncover hidden information in complex data sets. The reason for this is that classical two-way approaches such as principal component analysis are not as good at revealing the complex relationships present in data sets. This study describes in detail the application of a chemometric N-way approach, namely the Tucker method, in order to evaluate the level of pollution in soil from a contaminated site. The analyzed soil data set was five-way in nature. The samples were collected at different depths (way 1) from two locations (way 2) and the levels of thirteen metals (way 3) were analyzed using a four-step-sequential extraction procedure (way 4), allowing detailed information to be obtained about the bioavailability and activity of the different binding forms of the metals. Furthermore, the measurements were performed under two conditions (way 5), inert and non-inert. The preferred Tucker model of definite complexity showed that there was no significant difference in measurements analyzed under inert or non-inert conditions. It also allowed two depth horizons, characterized by different accumulation pathways, to be distinguished, and it allowed the relationships between chemical elements and their biological activities and mobilities in the soil to be described in detail.

Keywords

Chemometrics Heavy metal pollution Tucker Sequential extraction Soil 

References

  1. 1.
    Barbieri P, Andersson CA, Massart DL, Predonzani S, Adami G, Reisenhofer E (1999) Modeling bio-geochemical interactions in the surface waters of the Gulf of Trieste by three-way principal component analysis (PCA). Anal Chim Acta 398:227–235CrossRefGoogle Scholar
  2. 2.
    Barbieri P, Adami G, Piselli P, Gemiti F, Reisenhofer E (2002) A three-way principal factor analysis for assessing the time variability of freshwaters related to a municipal water supply. Chemomet Intell Lab Syst 62:89–100CrossRefGoogle Scholar
  3. 3.
    Stanimirova I, Simeonov V (2005) Modeling of environmental four-way data from air quality control. Chemometr Intell Lab Syst 77:115–121Google Scholar
  4. 4.
    Zehl K, Einax JW (2005) Influence of atmospheric oxygen on heavy metals mobility in sediment and soil. J Soils Sediments 5:164–170CrossRefGoogle Scholar
  5. 5.
    Smilde A, Bro R, Geladi P (2004) Multi-way analysis. Applications in the chemical sciences. Wiley, ChichesterGoogle Scholar
  6. 6.
    Günther R (1990) Erhebung zur Cd—Belastung durch die Leuchtstoffe und Feinchemikalien GmbH im Raum Bad Liebenstein. Abschlussarbeit zum postgradualen Studium im Umweltschutz, Bad Salzungen, pp 1–5Google Scholar
  7. 7.
    Zehl K (2005) Schwermetalle in Sedimenten und Bödenunter besonderer Berücksichtigung der Mobilität und deren Beeinflussung durch Sauerstoff. Doctoral thesis, Jena, GermanyGoogle Scholar
  8. 8.
    Federal Soil Protection and Contaminated Sites Ordinance (BBodSchV) (1998) BGB1 I:502Google Scholar
  9. 9.
    Ure AM, Quevauviller P, Muntau H, Griepink B (1993) Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the Commission of the European Communities. Int J Environ Anal Chem 51:135–151Google Scholar
  10. 10.
    Geladi P (1989) Analysis of multi-way (multi-mode) data. Chemometr Intell Lab Syst 7:11–30CrossRefGoogle Scholar
  11. 11.
    Bro R (1998) Multi-way analysis in food industry. Models, algorithms and applications. Doctoral thesis, Copenhagen, DenmarkGoogle Scholar
  12. 12.
    Henrion R (1999) N-way principal component analysis. Theory, algorithms and applications. Chemomet Intell Lab Syst 25:295–309Google Scholar
  13. 13.
    Andersson CA, Bro R (1998) Improving the speed of multi-way algorithms. Part I. Tucker3. Chemometr Intell Lab Syst 42:93–103CrossRefGoogle Scholar
  14. 14.
    Paatero P, Andersson CA (1999) Further improvements of the speed of the Tucker3 three-way algorithm. Chemometr Intell Lab Syst 47:17–20CrossRefGoogle Scholar
  15. 15.
    Helsel DR (2005) Nondetects and data analysis. Statistics for censored environmental data. Wiley, Hoboken, NJGoogle Scholar
  16. 16.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood for incomplete data via the EM algorithm. J Roy Stat Soc B 39:1–38Google Scholar
  17. 17.
    Helsel DR, Hirsch RM (1992) Statistical methods in water resources. Elsevier, AmsterdamGoogle Scholar
  18. 18.
    Walczak B, Massart DL (2001) Dealing with missing data: Part I. Chemometr Intell Lab Syst 58:15–27CrossRefGoogle Scholar
  19. 19.
    Bro R, Smilde A (2003) Centering and scaling in component analysis. J Chemometrics 17:16–33CrossRefGoogle Scholar
  20. 20.
    Vandeginste BMG, Massart DL, Buydens LMC, de Jong S, Lewi PJ, Smeyers-Verbeke J (1998) Handbook of chemometrics and qualimetrics: Part B. Elsevier, AmsterdamGoogle Scholar
  21. 21.
    Ure AM (1996) Single extraction schemes for soil analysis and related applications. Sci Total Environ 178:3–10CrossRefGoogle Scholar
  22. 22.
    Einax JW, Nischwitz V (2001) Inert sampling and sample preparation—the influence of oxygen on heavy metal mobility in river sediments. Fresenius J Anal Chem 371:643–651Google Scholar
  23. 23.
    Paschke M, Valdecantos A, Redente E (2005) Manganese toxicity threshold for restoration grass species. Environ Pollut 135:313–322CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • I. Stanimirova
    • 1
    • 3
  • K. Zehl
    • 2
  • D. L. Massart
    • 1
  • Y. Vander Heyden
    • 1
  • J. W. Einax
    • 2
  1. 1.Department of Analytical Chemistry and Pharmaceutical TechnologyVrije Universiteit BrusselBrusselsBelgium
  2. 2.Institute of Inorganic and Analytical ChemistryFriedrich Schiller University of JenaJenaGermany
  3. 3.Department of Chemometrics, Institute of ChemistryThe University of SilesiaKatowicePoland

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