Decision Making Using Genetic Algorithm to Select the Best Plant Genotype for Phytoremediation of Petroleum - Contaminated Soils

Document Type : Research Paper

Authors

1 PhD. Student, Professor and Associate Professor of Soil Sci., respectively, Department of Soil Sci., College of Agriculture, Isfahan University of Technology, Isfahan, Iran

2 Associate Professor of Elect., Department of Elect., and Computer Sci., Isfahan University of Technology, Isfahan, Iran

Abstract

Genetic algorithm is an optimization method for finding the best solution in large design spaces in a way similar to chromosomes and genes in biological systems. In this study, the potential use of classical statistical analysis is compared with genetic algorithm optimization methods for finding the best plant cultivation and the best mixing petroleum-contamination level (1:1 and 3:1, contaminated: uncontaminated soil) for phytoremediation of petroleum-contaminated soil around the Tehran Oil Refinery. Results from the germination and subsequent growth trials showed that, according to the classical statistical analysis method, agropyron, tall fescue, and puccinellia were the optimum plant species for both contamination levels (1:1 and 3:1). In contrast, selection of agropyron, tall fescue, sunflower, and safflower was the optimum solution using the genetic algorithm optimization method. In the phytoremediation experiments, agropyron and contamination level 1:1 were the optimum solutions for achieving successful phytoremediation of the investigated petroleum-contaminated soil using genetic algorithm optimization method. Furthermore, the agropyron-tall fescue combined species and the level 1:1 were the best combined two-plant cultivations and the best mixing petroleum-contamination level, respectively. Therefore, the agropyron-tall fescue combined cultivation and level 1:1 were recommended for phytoremediation of the investigated petroleum-contaminated soil.

Keywords


  1. بسالت پور، ع.ا.، م.ع. حاج عباسی، ا.م. خوشگفتار منش و م. افیونی. 1386. واکنش برخی از گیاهان به آلاینده­های نفتی موجود در اطراف پالایشگاه تهران. علوم و فنون کشاورزی و منابع طبیعی، 44 (الف): 13-24.
  2. باوی، ا. و م. صالحی. 1387. الگوریتم­های ژنتیک و بهینه­سازی­ سازه­های مرکب. چاپ اول. انتشارات عابد. ص 75.
  3. Adam, G. and H. Duncan. 2002. Influence of diesel fuel on seed germination. J. Pollut. 120: 363-370.
  4. Alef, K. 1995. Soil Respiration. p. 214-216. In: Alef, K. and Nannipieri, P. (Eds.), Methods in applied soil microbiology and biochemistry, chapter 5, Harcourt brace & company publishers.
  5. April, W. and R.C. Sims. 1990. Evaluation of prairie grasses for stimulating polycyclic aromatic hydrocarbon treatment in soil. Chemosphere. 20: 253-265.
  6. Back, T. 1996. Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, UK.
  7. Black, A., Evans D.D., White J.L., Ensminger L.E. and F.E. Clark. 1965. In: Page, A.L. (Ed.), Methods of soil analysis, Part 2. American Society of Agronomy, Madison, Wis.
  8. Bremner, J.M. and C.S. Mulvaney. 1982. Nitrogen-total. p. 595-624. In: Page, A.L. (Ed.), Methods of soil analysis, Part 2, American Society of Agronomy, Madison,
  9. Chaineau, C.H., Morel J.L. and J. Oudot. 1997. Phytotoxicity and plant uptake of fuel oil hydrocarbons. J. Eviron. Qual. 26: 1478-1483.
  10. Christopher, S., Hein P., Marsden J. and A.S. Shurleff. 1988. Evaluation of methods 3540 (soxhlet) and 3550 (Sonication) for evaluation of appendix IX analyses from solid samples. S-CUBED, Report for EPA contract 68-03-33-75, work assignment No.03, Document No. SSS-R-88-9436.
  11. Crowe, A.M., McClean C.J. and M.S. Cresser. 2006. An application of genetic algorithms to the robust estimation of soil organic and mineral fraction densities. Environ. Model. Software. 21: 1503-1507.
  12. De Jong, K.A. 1993. Genetic algorithms are NOT function optimizers. In: Whitley, L.D. (Ed.), Foundations of Genetic Algorithms 2. San Mateo, CA, USA.
  13. Goldberg, DE. 1989. p. 415. In: Addison-Wesley (Ed.), Genetic Algorithm in Search, Optimization and Machine Learning.
  14. Li, C.H., Ma B.L. and T.O. Zhang. 2002. Soil bulk density effects on soil microbial population and enzyme activities during the growth of maize (Zea Mays) planted in large pots under field exposure. Plant. Sci. 82: 147- 154.
  15. Lindsay, W.L. and W.A. Norvell. 1978. Development of a DTPA soil test for zinc, iron, manganese, and copper. Am. Soil Sci. Soc. J. 42: 421-428.
  16. Liu, Sh., Butler D., Brazier R., Heathwaite L. and S. Khu. 2007. Using genetic algorithm to calibrate a water quality model. Sci. Total Environ. 374: 260-272.
  17. Merkl, N., Karft R.S. and C. Infant. Assessment of tropical grasses and legumes for phytoremediation of petroleum contaminated soils. Water, Air, Soil Pollut. 165: 195-209.
  18. Michalewicz, Z. 1996. Genetic Algorithms‏ + Data Structures = Evolutionary Programs. (Third Eds.). Springer-Verlag, Berlin.
  19. Mitchell, M. 1996. An Introduction to Genetic Algorithms. MIT Press, Cambridge, Massachusetts, USA.
  20. Nedunuri, K.V., Govindaraju R.S., Banks M.K., Schwab A.P. and Z. Chen. 2000. Evaluation of phytoremediation for field scale degradation of total petroleum hydrocarbons. Environ. Engine. 126: 483-490.
  21. Olsen, S.R. and L.E. Sommers. 1982. Phosphorus. pp. 403-431. In: Page, A.L. (Ed.), Methods of soil analysis, Part 2, American Society of Agronomy, Madison, Wis.
  22. Pulford, I.D. and C. Watson. 2003. Phytoremediation of heavy metal contaminated land by tree- a review. J. Environ. Int. 29: 529-40.
  23. Qin, X.S., Huang, G.H. and A. Chakma. 2007. A Stepwise-Inference-Based Optimization System for Supporting Remediation of Petroleum-Contaminated Sites. Water, Air, Soil Pollut. 185: 349–368
  24. Schwab, A.P. and M.K. Banks. 1994. Biologically mediated dissipation of Polyaromatic hydrocarbons in the root zone. p.132-141. In: Anderson, T. and J. Coats (Eds.), Bioremediation through rhizosphere technology, American Chemistry Society, Washington, DC.
  25. Smith, M.J., Flowers T.H., DuncanJ. and J. Alder. 2005. Effects of PAHs on germination and subsequent growth of grasses and legumes in freshly contaminated soil and soil with aged PAH residues. J. Environ. Pollute. 101: 1-7.
  26. S. EPA. 1984. Interalaboratory Comparison Stunt: Methods for volatile and semi–volatile compounds, Environmental monitoring systems laboratory, office of research and development, Las Vegas, NV, EPA. 600/4- 84- 027.
  27. Wang, Q.J. 1997. Using genetic algorithms to optimize model parameters. Environ. Model. Software. 12: 27-34.
  28. Whigham, P.A. and F. Recknagel. 2001. Predicting chlorophyll-α in freshwater lakes by hybridizing process-based models and genetic algorithms. Ecol. Model. 146: 243-251.
  29. Xu, J.G. and R.L. Johnson. 1997. Nitrogen dynamics in soils with different hydrocarbon contents planted to barley and field pea. Canadian J. Soil Sci. 77: 453-458.
  30. Xu S.Y., Chen Y.X., Wu W.X., Wang K.X., Lin Q. and X.Q. Liang. 2005. Enhanced dissipation of phenantherene and pyrene in spiked soils by combined plants cultivation. Sci. Total Environ. 363: 206–215.