Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Environmental Fate Tracking of Manure-borne NH3-N in Paddy Field Based on a Fugacity Model

Fugacity 모델에 기초한 논토양에서의 액비살포에 따른 암모니아성 질소 거동추적

  • Kim, Mi-Sug (Dept. of Environmental Engineering, Mokpo National University) ;
  • Kwak, Dong-Heui (Center for Jeongeup Academy-Industry Cooperation, Dept. of Physically Active Material Science, Chonbuk National University)
  • 김미숙 (목포대학교 환경공학과) ;
  • 곽동희 (전북대학교 정읍산학연협력지원센터, 생리활성소재과학과)
  • Received : 2019.02.21
  • Accepted : 2019.05.02
  • Published : 2019.05.30
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Abstract

Nitrogen components in liquid manure can reduce safety and quality of environment harmfully. To minimize the environmental risks of manure, understanding fate of manure in environment is necessary. This study aimed at investigating applicability of a simplified Level III fugacity model for simulating $NH_3-N$ component to analyze environmental fate and transport of $NH_3-N$ in liquid manure and to provide basis for improving management of N in the liquid manure system and for minimizing the environmental impacts of N. The model simulation conducted for four environmental compartments (air, water, soil, and rice plants) during rice-cropping to trace $NH_3-N$ component and provided applicability of the Level III fugacity model in studying the environmental fate of $NH_3-N$ in manure. Most of $NH_3-N$ was found in water body and in rice plants depending upon the physicochemical properties and proper removal processes. For more precise model results, the model is needed to modify with the detailed removal processes in each compartment and to collect proper and accurate information for input parameters. Further study should be about simulations of various N-typed fertilizers to compare with the liquid manure based on a modified and relatively simplified Level III fugacity model.

액비(분뇨)에 포함된 질소성분은 환경의 질을 악화시키고 안정성을 감소시킬 수 있다. 액비로 인한 환경적 위해성을 최소화하기 위해서는 환경 매체 내에서의 액비의 거동을 이해할 필요가 있다. 액비에 포함된 암모니아성 질소($NH_3-N$)의 환경 내 거동과 이송을 분석하고, 액비시스템에서 질소(N)관리의 개선을 위한 기반을 제공하며 질소의 환경에 미치는 악영향을 최소화하기 위해서, 본 연구는 단순화된 Level III fugacity 모델의 적용 가능성을 조사하는 것을 목적으로 하였다. 벼 재배 기간 중 4개의 환경구획(공기, 물, 토양 및 벼)에서 암모니아성 질소($NH_3-N$) 성분을 축적하기 위해 정상상태의 fugacity 개념을 이용한 모델의 모의 실험을 실시하였으며 그 결과 Level III fugacity 모델의 적용 가능성을 검증하였다. 모델 결과, 대부분의 암모니아성 질소($NH_3-N$)는 논물(수체)과 벼(식물)에 분포하였으며 공기와 논물 그리고 토양에 대한 로그-로그 그래프선상에서 fugacity와 농도는 시간에 따라 선형적으로 감소한 반면에 벼(식물)에서의 변화는 비선형적으로 나타났다. 제거과정의 민감성을 살펴본 결과 제거과정(침적과 유출)이 고려된 경우 대부분의 암모니아성 질소는 논물에 분포하였으며 제거과정이 무시된 경우에는 벼(식물)가 암모니아성 질소를 흡수하는 것으로 나타났다. 또한 질소의 물질수지에 따라 각 구획별로 질소가 분포됨을 알 수 있었다. 본 연구는 실제 관측 자료에 의한 모델 보정을 수행하지 않고 토양층에 의한 잔류량 및 질소 형태의 변화를 기술하지 않았으며 모델 시뮬레이션은 간헐적인 실제 배출량 입력과 달리 연속 배출량으로 간주하였다. 그러므로 수질에 대한 비점 오염원으로서의 액비의 영향을 정량화하기 위해서는 보다 구체적이고 지속적인 모델링 및 모니터링 연구가 필요하다. 향후 연구의 Level III fugacity 모델에서는 더 정확한 제거 과정을 기술하고, 입력 변수의 적절한 값을 적용하여 다양한 N 형 비료에 대한 모델 시뮬레이션을 실시하고, 액비와 다양한 N 형 비료를 사용하여 얻은 N 성분의 관측 자료를 이용하여 모델을 평가할 것이다.

Keywords

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Fig. 1. Schematic representation of nitrogen balance model with reaction constants.

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Fig. 2. Schematic representation of the N-transformations in flooded rice field.

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Fig. 3. Content of fugacity capacity Zi in each compartment resulted from the model simulation. The values of Zi are 0.2% for air Z1, 48.3% for water Z2, 8.8% for soil Z3 and 42.7% for rice plant Z4.

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Fig. 4. Changes of Fugacity and concentration in each compartment for different detention times from 1hour to 20 days.

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Fig. 5. Changes of Fugacity and concentration in each compartment for different detention times from 1hour to 20 days when the removal process in the rice plants was ignored.

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Fig. 6. Graphical representation of mass balance (mol/hr) in and between compartments.

Table 1. Mass balance equations for each compartment in the model

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Table 2. Summary of Z-value and D-value used in the model

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Table 3. Important parameters for the model calculation

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Table 4. Physical and chemical property of NH3-N

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Table 5. Important input variables for the model simulation

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References

  1. Batiha, M. A., Kadhum, A. A. H., Mohamad, A. B., Takriff, M. S., Fisal, Z., Daud, W. R. W., and Batiha, M. M. (2008). MAM-an aquivalence-based dynamic mass balance model for the fate of non-volatile organic chemicals in the agricultural environment, American Journal of Engineering and Applied Sciences, 1(4), 252-259. https://doi.org/10.3844/ajeassp.2008.252.259
  2. Budavari, S., O'Neil, M. J., Smith, A., Heckelman, P. E., and Kinneary, J. F. (Eds). (1996). The Merck Index, 12th ed, Merck & Co., Inc, Whitehouse Station, NJ.
  3. Calderon, F. J., McCarty, G. W., Van Kessel, J. A. S., and Reeves, J. B. (2004). Carbon and nitrogen dynamics during incubation of manured soil, Soil Science Society of America Journal, 68, 1592-1599. https://doi.org/10.2136/sssaj2004.1592
  4. Chowdary, V. M., Rao, N. H., and Sarma, P. B. S. (2004). A Coupled soil water and nitrogen balance model for flooded rice fields in India, Agriculture, Ecosystems and Environment, 103, 425-441. https://doi.org/10.1016/j.agee.2003.12.001
  5. Contreras, W. A., Ginestar, D., Paraiba, L. C., and Bru, R. (2008). Modelling the pesticide concentration in a rice field by a level IV fugacity model coupled with a dispersion- advection equation, Computers and Mathematics with Applications, 56, 657-669. https://doi.org/10.1016/j.camwa.2008.01.009
  6. Cousins, I. T. and Mackay, D. (2000). Transport parameters and mass balance equations for vegetation in Level III fugacity models, Internal report published on the website of the Canadian Environmental Modeling Centre. http://www.trentu.ca/academic/aminss/envmodel/CEMC200001.pdf
  7. Cousins, I. T. and Mackay, D. (2001). Strategies for including vegetation compartments in multimedia models, Chemosphere, 44, 643-654. https://doi.org/10.1016/S0045-6535(00)00514-2
  8. Csiszar, S. A., Gandhi, N., Alexy, R., Benny, D. T., Struger, J., Marvin, C., and Diamond, M. L. (2011). Aquivalence revisited-New model formulation and application to assess environmental fate of ionic pharmaceuticals in Hamilton harbor, Lake Ontario, Environment International, 37, 821-828. https://doi.org/10.1016/j.envint.2011.02.001
  9. Daubert, T. E. and Danner, R. P. (1989). Physical and thermodynamic properties of pure chemicals: Data compilation, Taylor & Francis: Washington, DC.
  10. Dunn, S. M., Vinten, A. J. A., Lilly, A., DeGroote, J., Sutton, M. A., and McGechan, M. (2004). Nitrogen risk assessment model for Scotland: I. Nitrogen leaching, Hydrology and Earth System Sciences, 8, 191-204. https://doi.org/10.5194/hess-8-191-2004
  11. Gandhi, N., Bhavsar, S. P., Diamond, M. L., Kuwabara, J. S., Marvin-Dipasquale, M., and Krabbenhoft, D. P. (2007). Development of a mercury speciation, fate, and biotic uptake (BIOTRANSPEC) model: application to Lahontan reservoir (Nevada, USA), Environmental Toxicology and Chemistry, 26(11), 2260-2273. https://doi.org/10.1897/06-468R.1
  12. Hu, Y., Wang, D. Z., Zhang, C., Wang, Z. S., Chen, M. H., and Li, Y. (2013). An interval steady-state multimedia aquivalence (ISMA) model of the transport and fate of chloridion in a surface flow constructed wetland system treating oil field wastewater in China, Ecological Engineering, 51, 161-168. https://doi.org/10.1016/j.ecoleng.2012.12.029
  13. Hubbard, R., Sheridan, J. M., Lowrance, R., Bosch, D. D., and Vellidis, G. (2004). Fate of nitrogen from agriculture in the southeastern Coastal Plain, Journal of Soil and Water Conservation, 59, 72-86.
  14. Kim, J., Kim, B., Shin, M., Kim, J. K., Jung, S., Lee, Y., and Park, J. H. (2009). The distribution of nitrogen and the decomposition rate of organic nitrogen in the Youngsan River and the Sumjin River, Korea, Journal of Korean Society on Water Environment, 25, 135-143.
  15. Larcher, W. (1995). Physiological Plant Ecology, 3rd ed., Springer, Berlin, Germany.
  16. LeBlanc, J. R., Madhavan, S., and Porter, R. E. (1978). Ammonia. In Kirk-Othmerencyclopedia of chemical technology. 3rd, ed., Grayson, M., D. (Eds), John Wiley & Sons, Inc.: New York, pp. 470-516.
  17. MacKay, D. (1991). Multimedia environmental models: The fugacity approach, Lewis Publishers, Chelsea.
  18. Mackay, D., Sang, S., Vlahos, P., Gobas, F., Diamond, M., and Dolan, D. (1994). A rate constant model of chemical dynamics in a Lake ecosystem: PCBs in lake Ontario, Journal of Great Lakes Research, 20(4), 625-642. https://doi.org/10.1016/S0380-1330(94)71183-7
  19. U.S. Department of Health and Human Services (USDHHS). (2004). Toxicological profile for ammonia, Public Health Service Agency for Toxic Substances and Disease Registry, Washington DC. http://www.atsdr.cdc.gov/toxprofiles/tp126.pdf
  20. Voltolini, J., Althoff, D. A., and Back, A. J., (2002). Agua de irrigacao para a cultura do arrozirrigado no Sistemapre-germinado, In: Arrozirrigado: Sistema pre-germinado, EPAGRI, Florianopolis, 101-112 [in Portuguese].
  21. Weast, R. C., Astle, M. J., and Beyer, W. H. (Eds). (1988). CRC Handbook of chemistry and physics, CRC Press, Inc., Boca Raton, Florida.
  22. Windholz, M., Budavari, S., Blumetti, R. F., and Otterbein, E. S. (1983). The Merck index, 10thed., Merck & Co., Inc, Rahway, NJ.