Advertisement

Arabian Journal for Science and Engineering

, Volume 41, Issue 10, pp 3931–3944 | Cite as

Potential of Fuzzy-ELECTRE MCDM in Evaluation of Cyanobacterial Toxins Removal Methods

  • Animesh Debnath
  • Mrinmoy Majumder
  • Manish Pal
Research Article - Civil Engineering

Abstract

Cyanobacteria blooms and toxins released from cyanobacteria, called cyanotoxins, have become a serious environmental issue because of their potential toxicity toward human health. Several conventional and advanced water treatment methods are available for degradation of cyanotoxins from surface water, but a cost-effective and efficient water treatment technique can greatly reduce the processing time and improve the quality of treated water. Selection of an optimum treatment technique for cyanotoxins degradation is a multi-criteria decision-making problem owing to the involvement of several conflicting criteria and constraints. In this paper, an integrated Fuzzy-ELECTRE model was proposed and its potential toward evaluation of different cyanotoxins removal techniques has been explored to select the most suitable technology. In this integrated model, criteria importance weights were determined by Fuzzy process, while the ranking of alternatives was performed using ELECTRE process. The result obtained from the model shows that ‘advanced oxidation by titanium dioxide \({({\rm TiO}_{2})}\)’ is the most suitable technology among all considered technology for the removal of cyanotoxins. The developed methodological approach was also used to rank the available treatment techniques within the main group of conventional and advanced oxidation methods (AOMs). The results clearly depict that ozonation and photocatalysis by \({{\rm TiO}_{2}}\) are the best methods within the group of conventional and AOMs, respectively. The ability of the proposed model for providing complete and clear ranking of all considered alternatives confirms its potential for evaluation of cyanotoxins removal methods.

Keywords

Cyanobacteria Cyanotoxins MCDM Fuzzy-ELECTRE method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Metcalf, J.S.; Codd, G.A.: Cyanobacterial Toxins in the Water Environment: A Review of Current Knowledge. Foundation for Water Research (2004)Google Scholar
  2. 2.
    Chorus I., Bartram J.: Toxic Cyanobacteria in Water: A Guide to their Public Health Consequences, Monitoring, and Management. E & FN Spon, London (1999)CrossRefGoogle Scholar
  3. 3.
    Codd G.A., Morrison L.F., Metcalf J.S.: Cyanobacterial toxins: risk management for health protection. Toxicol. Appl. Pharmacol. 203, 264–272 (2005)CrossRefGoogle Scholar
  4. 4.
    Pantelić D., Svirčev Z., Simeunović J., Vidović M., Trajković I.: Cyanotoxins: characteristics, production and degradation routes in drinking water treatment with reference to the situation in Serbia. Chemosphere 91, 421–441 (2013)CrossRefGoogle Scholar
  5. 5.
    Metcalf J.S., Richer R., Cox P.A., Codd G.A.: Cyanotoxins in desert environments may present a risk to human health. Sci. Total Environ. 421, 118–123 (2012)CrossRefGoogle Scholar
  6. 6.
    Westrick J.A., Szlag D.C., Southwell B.J., Sinclair J.: A review of cyanobacteria and cyanotoxins removal/inactivation in drinking water treatment. Anal. Bioanal. Chem. 397, 1705–1714 (2010)CrossRefGoogle Scholar
  7. 7.
    Zamyadi A., McQuaid N., Prévost M., Dorner S.: Monitoring of potentially toxic cyanobacteria using an online multi-probe in drinking water sources. J. Environ. Monit. 14, 579–588 (2012)CrossRefGoogle Scholar
  8. 8.
    Merel S., Clément M., Thomas O.: State of the art on cyanotoxins in water and their behaviour towards chlorine. Toxicon 55, 677–691 (2010)CrossRefGoogle Scholar
  9. 9.
    Gutie’rrez-Praena D., Pichardo S., Jos A., Moreno F.J., Camean A.M.: Biochemical and pathological toxic effects induced by the cyanotoxin Cylindrospermopsin on the human cell line Caco-2. Water Res. 46, 1566–1575 (2012)CrossRefGoogle Scholar
  10. 10.
    Zamyadi A., Dorner S., Sauvé S., Ellis D., Bolduc A., Bastien C., Prévost M.: Species-dependence of cyanobacteria removal efficiency by different drinking water treatment processes. Water Res. 47, 2689–2700 (2013)CrossRefGoogle Scholar
  11. 11.
    Dai G., Quan C., Zhang X., Liu J., Song L., Gan N.: Fast removal of cyanobacterial toxin microcystin-LR by a low-cytotoxic microgel-Fe (III) complex. Water Res. 46, 1482–1489 (2012)CrossRefGoogle Scholar
  12. 12.
    Nishiwaki-Matsushima R., Ohta T., Nishiwaki S., Suganuma M., Kohyama K., Ishikawa T., Carmichael W.W., Fujiki H.: Liver tumor promotion by the cyanobacterial cyclic peptide toxin microcystin-LR. J. Cancer Res. Clin. 118, 420–424 (1992)CrossRefGoogle Scholar
  13. 13.
    Zhou L., Yu H., Chen K.: Relationship between microcystin in drinking water and colorectal cancer. Biomed. Environ. Sci. 15, 166–171 (2002)Google Scholar
  14. 14.
    WHO: Cyanobacterial Toxins: Microcystin-LR. Guidelines for Drinking-Water Quality, pp. 95–110. World Health Organization, Geneva (1998)Google Scholar
  15. 15.
    Merel S., Walker D., Chicana R., Snyder S., Baurès E., Thomas O.: State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environ. Int. 59, 303–327 (2013)CrossRefGoogle Scholar
  16. 16.
    Ho L., Tanis-Plant P., Kayal N., Slyman N., Newcombe G.: Optimising water treatment practices for the removal of Anabaena circinalis and its associated metabolites, geosmin and saxitoxins. J. Water Health 7, 544–556 (2009)CrossRefGoogle Scholar
  17. 17.
    Falconer I.R.: Cyanobacterial Toxins of Drinking Water Supplies: Cylindrospermopsins and Microcystins. CRC, Boca Raton (2005)Google Scholar
  18. 18.
    Fawell J.K., Mitchell R.E., Everett D.J., Hill R.E.: The toxicity of cyanobacterial toxins in the mouse: I microcystin-LR. Hum. Exp. Toxicol. 18, 162–167 (1999)CrossRefGoogle Scholar
  19. 19.
    Van Apeldoorn M.E., Van Egmond H.P., Speijers G.J., Bakker G.J.: Toxins of cyanobacteria. Mol. Nutr. Food Res. 51, 7–60 (2007)CrossRefGoogle Scholar
  20. 20.
    Kao, C.Y.: Paralytic shellfish poisoning. In: Falconer, C.Y. (ed.) Algal Toxins in Seafood and Drinking Water, pp. 75–86. Academic Press, London (1993)Google Scholar
  21. 21.
    Silva de Assis, H.C.; da Silva, C.A.; Oba, E.T.; Pamplona, J.H.; Mela, M.; Doria, H.B.; Guiloski, I.C.; Ramsdorf, W.; Cestari, M.M.: Hematologic and hepatic responses of the freshwater fish Hoplias malabaricus after saxitoxin exposure. Toxicon 66, 25–30 (2011)CrossRefGoogle Scholar
  22. 22.
    Cruz, A.de la; Antoniou, M.; Hiskia, A.; Pelaez, M.; Song, W.; OShea, K.; He, X.; Dionysiou, D.: Can we effectively degrade microcystins? Implications on human health. Anti-Cancer Agents Med. Chem. 11, 19–37 (2011)Google Scholar
  23. 23.
    Sharma, V.K.; Triantis, T.M.; Antoniou, M.G.; He, X.; Pelaez, M.; Han, C.; Song, W.; O’Shea, K.E.; de la Cruz, A.; Kaloudis, T.; Hiskia, A.; Dionysiou, D.D.: Destruction of microcystins by conventional and advanced oxidation processes: a review. Sep. Purif. Technol. 91, 3–17 (2012)Google Scholar
  24. 24.
    Maghsoudi E., Fortin N., Greer C., Duy S.V., Fayad P., Sauvé S., Prévost M., Dorner S.: Biodegradation of multiple microcystins and cylindrospermopsin in clarifier sludge and a drinking water source: effects of particulate attached bacteria and phycocyanin. Ecotoxicol. Environ. Saf. 120, 409–417 (2015)CrossRefGoogle Scholar
  25. 25.
    Hatami-Marbini A., Tavana M., Moradi M., Kangi F.: A Fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Saf. Sci. 51, 414–426 (2013)CrossRefGoogle Scholar
  26. 26.
    Kaya T., Kahraman C.: An integrated Fuzzy AHP–ELECTRE methodology for environmental impact assessment. Expert Syst. Appl. 38, 8553–8562 (2011)CrossRefGoogle Scholar
  27. 27.
    Beccali M., Cellura M., Mistretta M.: Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renew. Energy 28, 2063–2087 (2003)CrossRefGoogle Scholar
  28. 28.
    Roy B.: The outranking approach and the foundations of ELECTRE methods. Theor. Decis. 31, 49–73 (1991)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Sevkli M.: An application of the Fuzzy ELECTRE method for supplier selection. Int. J. Prod. Res. 48, 3393–3405 (2010)CrossRefzbMATHGoogle Scholar
  30. 30.
    Rouyendegh B.D., Erkan T.E.: An application of the Fuzzy electre method for academic staff selection. Hum. Factors Ergon. Manuf. Serv. Ind. 23, 107–115 (2013)CrossRefGoogle Scholar
  31. 31.
    Giannoulis C., Ishizaka A.: A web-based decision support system with ELECTRE III for a personalised ranking of British universities. Decis. Support Syst. 48, 488–497 (2010)CrossRefGoogle Scholar
  32. 32.
    Montazer G.A., Saremi H.Q., Ramezani M.: Design a new mixed expert decision aiding system using Fuzzy ELECTRE III method for vendor selection. Expert Syst. Appl. 36, 10837–10847 (2009)CrossRefGoogle Scholar
  33. 33.
    Raj P.A.: Multicriteria methods in river basin planning—a case study. Water Sci. Technol. 31, 261–272 (1995)Google Scholar
  34. 34.
    Buchanan J., Vanderpooten D.: Ranking projects for an electricity utility using ELECTRE III. Int. Trans. Oper. Res. 14, 309–323 (2007)CrossRefGoogle Scholar
  35. 35.
    Özcan T., Çelebi T., Çelebi N., Esnaf Ş.: Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Syst. Appl. 38, 9773– (2011)CrossRefGoogle Scholar
  36. 36.
    Hatami-Marbini, A.; Tavana, M.: An extension of the Electre I method for group decision-making under a Fuzzy environment. Omega 39, 373‒386 (2011)CrossRefzbMATHGoogle Scholar
  37. 37.
    Figueira J., Greco S., Ehrgott M.: Multiple Criteria Decision Analysis: State of the Art Surveys, vol. 78. Springer, New York (2005)CrossRefzbMATHGoogle Scholar
  38. 38.
    Chang P.C., Liu C.H.: A TSK type Fuzzy rule based system for stock price prediction. Expert Syst. Appl. 34, 135–144 (2008)CrossRefGoogle Scholar
  39. 39.
    Zadeh L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Bellman R.E., Zadeh L.A.: Decision-making in a Fuzzy environment. Manag. Sci. 17, B141–B164 (1970)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Dubois D., Prade H.: Operations on Fuzzy numbers. Int. J. Syst. Sci. 9, 613–626 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Kaufmann A., Gupta M.M.: Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold, New York (1991)zbMATHGoogle Scholar
  43. 43.
    Wang T.C., Chang T.H.: Application of TOPSIS in evaluating initial training aircraft under a Fuzzy environment. Expert Syst. Appl. 33, 870–880 (2007)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Vlad S., Anderson W.B., Peldszus S., Huck P.M.: Removal of the cyanotoxin anatoxin-a by drinking water treatment processes: a review. J. Water Health 12, 601–617 (2014)CrossRefGoogle Scholar
  45. 45.
    Ghernaout B., Ghernaout D., Saiba A.: Algae and cyanotoxins removal by coagulation/flocculation: a review. Desalin. Water Treat. 20, 133–143 (2010)CrossRefGoogle Scholar
  46. 46.
    Shi H., Ding J., Timmons T., Adams C.: pH effects on the adsorption of saxitoxin by powdered activated carbon. Harmful Algae 19, 61–67 (2012)CrossRefGoogle Scholar
  47. 47.
    Teixeira M.R., Rosa M.J.: Neurotoxic and hepatotoxic cyanotoxins removal by nanofiltration. Water Res. 40, 2837–2846 (2006)CrossRefGoogle Scholar
  48. 48.
    Oberholster P.J., Botha A.M., Grobbelaar J.U.: Microcystis aeruginosa: source of toxic microcystins in drinking water. Afr. J. Biotechnol. 3, 159–168 (2004)CrossRefGoogle Scholar
  49. 49.
    Onstad G.D., Strauch S., Meriluoto J., Codd G.A., von Gunten, U.: Selective oxidation of key functional groups in cyanotoxins during drinking water ozonation. Environ. Sci. Technol. 41, 4397–4404 (2007)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2016

Authors and Affiliations

  1. 1.Civil Engineering DepartmentNational Institute of Technology AgartalaJirania, BarjalaIndia
  2. 2.School of Hydro InformaticsNational Institute of Technology AgartalaJirania, BarjalaIndia

Personalised recommendations