Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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Ecological modeling for toxic substances - I . Numerical simulation of transport and fate of Nonylphenol in Tokyo Bay-

유해화학물질의 생태계 모델링 - I. 동경만 Nonylphenol의 환경동태 해석 -

  • 김동명 (부경대학교 환경시스템공학부) ;
  • Published : 2005.09.01
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Abstract

A three-dimensional ecological model (EMT -3D) was applied to Nonylphenol in Tokyo Bay. EMT -3D was calibrated with data obtained in the study area. The simulated results of dissolved Nonylphenol were in good agreement with the observed values, with a correlation coefficient(R) of 0.7707 and a coefficient of determination (R2) of 0.5940. The results of sensitivity analysis showed that biodegradation rate and bioconcentration factor are most important factors for dissolved Nonylphenol and Nonylphenol in phytoplankton, respectively. In the case of Nonylphenol in particulate organic carbon, biodegradation rate and partition coefficient were important factors. Therefore, the parameters must be carefully considered in the modeling. The mass balance results showed that standing stocks of Nonylphenol in water, in particulate organic carbon and in phytoplankton are $8.60\times 10^5\;g,\;2.19\times 10^2\;g\;and\;3.78\times 10^0\;g$ respectively. With respect to the flux of dissolved Nonylphenol, biodegradation in the water column, effluent to the open sea and partition to particulate organic carbon were $6.02\times10^3\;g/day,\;6.02\times10^2\;g/day\;and\;1.02\times10^1\;g/day$, respectively.

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References

  1. Cahill, T. M., I. Cousins and D. Mackay, 2003, General Fugacity-Based Model to Predict the Environmental Fate of Multiple Chemical Species, Environmental Toxicology and Chemistry, 22, 483-493 https://doi.org/10.1897/1551-5028(2003)022<0483:GFBMTP>2.0.CO;2
  2. Environment Agency, Japan, 1993, Comparisons of Global Environmental Fate Models Applicability of Global Models to Japanese Environment, Prepared to OECD Phase 1 SIDS Initial Assessment Meeting
  3. Polder, M. D., E. M Hulzebos and D. T. Jager, 1998, Bioconcentration of gaseous organic chemicals in plant leaves: Comparison of experimental data with model predictions, Environmental Toxicology and Chemistry, 17, 962-968 https://doi.org/10.1897/1551-5028(1998)017<0962:BOGOCI>2.3.CO;2
  4. Linders, J. and R. Luttik, 1995, Uniform system for the evaluation of substances. 5. ESPE, Risk Assessment for pesticides, Chemosphere, 31, 3237-3248 https://doi.org/10.1016/0045-6535(95)00185-B
  5. Seligman, P. F., C. M. Adema and P. M. Stang, 1987, Monitoring and prediction of tributyltin in the Elizabeth River and Hampton Roads, Virginia, Oceans '87, 1357-1363
  6. Kim, D. M, N. Nakada, T. Horiguchi, H. Takada, H. Shiraishi and O. Nakasugi, 2004, Numerical simulation of organic chemicals in a marine environment using a coupled 3D hydrodynamic and ecotoxicological model, Marine Pollution Bulletin, 48(7-8), 671-678 https://doi.org/10.1016/j.marpolbul.2003.11.006
  7. 磯部友彦, 金東明, 芹澤滋子, 堀口敏宏, 柴田康行, 白石不二雄, 森田昌敏, 白石寛明, 2004, ノニ ルフェノール關連物質の東京湾への流入と湾内 での擧動, 日本環境化學會, 第13回環境化學討論會
  8. Jorgensen, L. A, S. E. Jorgensen and S. N. Nielsen, 2000, Ecological Modelling and Ecotoxicology, Elsevier
  9. 環境省總合環境政策局環境保健部環境安全課, 2003, 平成15年度内分泌攪亂化學物質における 詳細メカニズム調査について
  10. Horiguchi, F., J. Yamamoto and K. Nakata, 2001, A numerical simulation of the seasonal cycle of temperature, salinity and velocity fields in Tokyo Bay, Marine Pollution Bulletin, 43(7-12), 145-153 https://doi.org/10.1016/S0025-326X(01)00116-3
  11. Hashimoto, T., T. Yanag, H. Takeoka and H. Takada, 1998, Distribution and Sedimentation Model of PCB in Tokyo Bay, Bulletin on Coastal Oceanography, 36, 77-82