Application of biregressional designs to electrodialytic removal of heavy metals from contaminated matrices

Alexandra B. Ribeiro; Eduardo P. Mateus

Discussiones Mathematicae Probability and Statistics (2010)

  • Volume: 30, Issue: 1, page 123-143
  • ISSN: 1509-9423

Abstract

top
Given a base design with quantitative factors and a primary linear regression to each of the treatments, we may adjust secondary regressions of linear combinations of the adjusted coefficients on the primary regressions on the factor levels, thus obtaining a biregressional model. A biregressional design was established for a set of treatments, defined from quantitative factors and a linear regression in the same variables. Afterwards the action of the regression coefficients and their linear correlations was analysed. This approach was used to study the electrodialytic process (ED), a decontamination technique for removal of heavy metals from polluted matrices. The method uses a low-level direct current as the "cleaning agent", combining the electrokinetic movement of ions in the matrix with the principle of electrodialysis. The authors have studied the removal of heavy metals from industrially heavy-metal-contaminated soil, preserved wood waste and fly ash from municipal solid waste incinerators using the application of the electrodialytic process. In this paper we show how statistics may support the development of a research line. The removal of heavy metals was found to be described, in all studies, by low degree polynomials with null independent terms. The coefficient [twice the coefficient] of the first [second] degree terms measuring the initial rate [acceleration] of removal. Our approach enabled the study of the action of the factors defining the treatments on these, and other, coefficients of the polynomials.

How to cite

top

Alexandra B. Ribeiro, and Eduardo P. Mateus. "Application of biregressional designs to electrodialytic removal of heavy metals from contaminated matrices." Discussiones Mathematicae Probability and Statistics 30.1 (2010): 123-143. <http://eudml.org/doc/277034>.

@article{AlexandraB2010,
abstract = { Given a base design with quantitative factors and a primary linear regression to each of the treatments, we may adjust secondary regressions of linear combinations of the adjusted coefficients on the primary regressions on the factor levels, thus obtaining a biregressional model. A biregressional design was established for a set of treatments, defined from quantitative factors and a linear regression in the same variables. Afterwards the action of the regression coefficients and their linear correlations was analysed. This approach was used to study the electrodialytic process (ED), a decontamination technique for removal of heavy metals from polluted matrices. The method uses a low-level direct current as the "cleaning agent", combining the electrokinetic movement of ions in the matrix with the principle of electrodialysis. The authors have studied the removal of heavy metals from industrially heavy-metal-contaminated soil, preserved wood waste and fly ash from municipal solid waste incinerators using the application of the electrodialytic process. In this paper we show how statistics may support the development of a research line. The removal of heavy metals was found to be described, in all studies, by low degree polynomials with null independent terms. The coefficient [twice the coefficient] of the first [second] degree terms measuring the initial rate [acceleration] of removal. Our approach enabled the study of the action of the factors defining the treatments on these, and other, coefficients of the polynomials. },
author = {Alexandra B. Ribeiro, Eduardo P. Mateus},
journal = {Discussiones Mathematicae Probability and Statistics},
keywords = {contaminated soil; preserved wood; fly ash; electro-remediation; biregressional design},
language = {eng},
number = {1},
pages = {123-143},
title = {Application of biregressional designs to electrodialytic removal of heavy metals from contaminated matrices},
url = {http://eudml.org/doc/277034},
volume = {30},
year = {2010},
}

TY - JOUR
AU - Alexandra B. Ribeiro
AU - Eduardo P. Mateus
TI - Application of biregressional designs to electrodialytic removal of heavy metals from contaminated matrices
JO - Discussiones Mathematicae Probability and Statistics
PY - 2010
VL - 30
IS - 1
SP - 123
EP - 143
AB - Given a base design with quantitative factors and a primary linear regression to each of the treatments, we may adjust secondary regressions of linear combinations of the adjusted coefficients on the primary regressions on the factor levels, thus obtaining a biregressional model. A biregressional design was established for a set of treatments, defined from quantitative factors and a linear regression in the same variables. Afterwards the action of the regression coefficients and their linear correlations was analysed. This approach was used to study the electrodialytic process (ED), a decontamination technique for removal of heavy metals from polluted matrices. The method uses a low-level direct current as the "cleaning agent", combining the electrokinetic movement of ions in the matrix with the principle of electrodialysis. The authors have studied the removal of heavy metals from industrially heavy-metal-contaminated soil, preserved wood waste and fly ash from municipal solid waste incinerators using the application of the electrodialytic process. In this paper we show how statistics may support the development of a research line. The removal of heavy metals was found to be described, in all studies, by low degree polynomials with null independent terms. The coefficient [twice the coefficient] of the first [second] degree terms measuring the initial rate [acceleration] of removal. Our approach enabled the study of the action of the factors defining the treatments on these, and other, coefficients of the polynomials.
LA - eng
KW - contaminated soil; preserved wood; fly ash; electro-remediation; biregressional design
UR - http://eudml.org/doc/277034
ER -

References

top
  1. [1] K. Reddy and C. Cameselle, Electrochemical Remediation Technologies for Polluted Soils, Sediments and Groundwater, John Wiley & Sons, Inc., Hoboken, New Jersey, USA, ISBN 978-0-470-38343-8, (Eds.) (2009), 732 pp. 
  2. [2] A.B. Ribeiro and J.T. Mexia, A dynamic model for the electrokinetic removal of copper from a polluted soil, Journal of Hazardous Materials 56 (3) (1997), 257-271. 
  3. [3] A.B. Ribeiro and J.M. Rodríguez-Maroto, Electroremediation of heavy metal-contaminated soils, Processes and applications, Cap. 18 In: M.N.V. Prasad, K.S. Sajwan, Ravi Naidu (Eds.), Trace elements in the environment: Biogeochemistry, Biotechnology and Bioremediation, Taylor & Francis, CRC Press, Florida, USA, ISBN 1-56670-685-8, (2006), pp. 341-368. 
  4. [4] A.B. Ribeiro, E.P. Mateus, L.M. Ottosen and G. Bech-Nielsen, Electrodialytic removal of Cu, Cr and As from chromated copper arsenate-treated timber waste, Environmental Science & Technology 34 (5) (2000), 784-788. 
  5. [5] I.V. Christensen, A.J. Pedersen, L.M. Ottosen and A.B. Ribeiro, Electrodialytic remediation of CCA-treated waste wood in a 2 m3 pilot plant, Science of the Total Environment 364 (1-3) (2006), 45-54. 
  6. [6] E. Velizarova, A.B. Ribeiro, E.P. Mateus and L.O. Ottosen, Effect of different extracting solutions în electrodialytic remediation of CCA-tråàtåd wood waste, Part 1. Behaviour of Cu ànd Ńr. Journal of Hazardous Materials 107 (3) (2004), 103-113. 
  7. [7] E. Velizarova, A.B. Ribeiro and E.P. Mateus, Removal of heavy metals from CCA-treated wood by ion exchange membrane-assisted methods, In: Edward C. Bookings (Ed.), Trends in Hazardous Materials Research, Nova Science Publishers, Inc., New York, USA, ISBN 1-60021-335-9, Cap. 6 (92007), 165-181. 
  8. [8] E. Moreira, J.T. Mexia, A.B. Ribeiro, E.P. Mateus and L.O. Ottosen, Regressional modeling of electrodialytic removal of Cu, Cr and As from CCA tråàtåd timber waste: Application to wood chips, Biometrical Letters 42 (1) (2005), 11-23. 
  9. [9] E. Moreira, A.B. Ribeiro, E.P. Mateus, J.T. Mexia and L.O. Ottosen, Regressional modeling of electrodialytic removal of Cu, Cr and As from CCA tråàtåd timber waste. Application to sawdust, Wood Science and Technology 39 (4) (2005), 291-309. 
  10. [10] A.T. Lima, L.M. Ottosen and A.B. Ribeiro, Electroremediation of straw and co-combustion ash under acidic conditions, Journal of Hazardous Materials 161(2-3) (2009), 1003-1009. 
  11. [11] C. Ferreira, P. Jensen, L.M. Ottosen and A.B. Ribeiro, Removal of selected heavy metals from MSW fly ash by the electrodialytic process, Engineering Geology 77 (3-4) (2005), 339-347. 
  12. [12] S.P. McGrath, Integrated Soil and Sediment Research: A Basis for Proper Protection, in: H.J.P. Eijsackers, T. Hamers (Eds.) Kluwer Academic Publishers, (1993) pp. 1X7-200. 
  13. [13] H.J.M. Bowen, Environmental Chemistry of the Elements, Academic Press, London, U.K. 1979. 
  14. [14] A. Varela, A.B. Ribeiro, O. Monteiro, A.T. Lima, H. Domingues, and M.A. Castelo-Branco, Caracterização inorgânica de cinza volante de uma estação de incineração de resíduos sólidos urbanos com vista à sua eventual reciclagem, Revista de Cięncias Agrárias, ISSN 0871-018X (in Portuguese) 32 (1) (2009), 207-215. 
  15. [15] L.M. Ottosen and H.K. Hansen, Electrokinetic cleaning of heavy metal polluted soil, Internal Report, Fysisk-Kemisk Institut and Institut for Geologi og Geoteknik, Technical University of Denmark, Denmark, (1992), 9 pp. (in English). 
  16. [16] D.C. Montgomery, Design and analysis of experiments, 5th edition, Wiley, New York 1997. Zbl0910.62067
  17. [17] H. Scheffé The analysis of variance, Wiley, New York 1959. 
  18. [18] J.T. Mexia, Multi-treatment regression designs, Faculty of Sciences and Technology, New University of Lisbon 1987 (in English). 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.