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ACKNOWLEDGEMENTS I would like to thank and acknowledge the following Dr. Ming Ye of the Department of Scientific Computing, Florida State University for his guidance, feedback and contributions to this work. I would also like to thank him for his patience, understanding and willingness to teach. I am certainly pleased to have an advisor like him Dr. William Parker of the Department of Earth, Ocean and Atmospheric Sciences, Florida State University for his help on statistics. I would also like to thank him for a lot of support, encouragement and in general a huge amount of help. Dr. Francois Oehler, of the National Institute of Water & Atmospheric Research (New Zealand) for his assistance and generosity in sharing his dataset and codes with me. I also appreciate the time he took to talk to me about denitrification. His enthusiastic response to my request for data and codes and his willingness share his knowledge and data is immensely appreciated. Dr. Paul Z. Lee and Mr. Rick Hicks of the Florida Department of Environmental Protection for their input and valuable contributions during the entire development process. Dr. Liying Wang of the Department of Scientific Computing, Florida State University for her input regarding the denitrification model. Mr. Fernando Rios of the Department of Scientific Computing for his contributions toward the development of the GIS based denitrification model. Dr. Leroy Odom, for his generosity, support and in general being a really wonderful person that is always willing to help in any way that he can. iv
Dr. Stephen Kish for his words of encouragement, support and always taking an interest in my well being. Dr. Bill Hu and Dr.Yang Wang for their input to this project and for being wonderful teachers. Dr. Meyer-Baese, Dr. Peterson and Dr. Wise for generously allowing me to audit their classes. My family, Mum, Dad, Llewellyn, Bach and my friends at FSU, Josue, Jen, Ayca, Xichen and Burcu. A really special thanks to Sharon Wynn, Mary Gilmore and Necole Bowens-White. Funding for this project was provided by the Florida Department of Environmental Protection –DEP WM956 with additional support provided by the Institute for Energy Systems, Economics and Sustainability (IESES). v
Page 1 and 2: THE FLORIDA STATE UNIVERSITY ARTS A
Page 3: To my Dad, Mum and Brother iii
Page 7 and 8: 1.5. Evaluation of methods to estim
Page 9 and 10: 2.5.8. Texture 8 (Silt Loam).......
Page 11 and 12: 4.2.6. Texture 6 (Sandy Clay Loam).
Page 13 and 14: LIST OF TABLES Table 1.1 Additional
Page 15 and 16: LIST OF FIGURES Figure 1.1 Oxidatio
Page 17 and 18: Figure 2.51 8-20-84; Denitrificatio
Page 19 and 20: Figure 3.6 Probability plot (Textur
Page 21 and 22: Three statistical methods were used
Page 23 and 24: • Microbial biomass/ plant uptake
Page 25 and 26: Figure 1.1 Oxidation of organic car
Page 27 and 28: possibility of denitrification (Smi
Page 29 and 30: 50ºC (36ºF - 122ºF) (Brady & Wei
Page 31 and 32: 1.2.6. Salinity Salinity is a known
Page 33 and 34: 1.3.1. The Acetylene inhibition met
Page 35 and 36: Among the variety of successful met
Page 37 and 38: a first-order decay process. The ma
Page 39 and 40: look at the data shows that it fail
Page 41 and 42: Da is the denitrification rate (mg
Page 43 and 44: a simple linear regression of organ
Page 45 and 46: of the annual N2O emission and deni
Page 47 and 48: easons for the limitation of the mo
Page 49 and 50: Table 1.2 Continued dC / dt Organic
Page 51 and 52: also implies that the transferabili
Page 53 and 54: CHAPTER TWO 2. LINEAR REGRESSION An
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an improvement on the earlier attem
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2.3. Texture Table 2.1 Soil Textura
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2.3.5. Texture 5 (Sand) The surfici
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2.3.10. Texture 10 (Silty Clay Loam
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Textural Class Table 2.2 Coefficien
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R_d_n (kgN ha-1 d-1) R_d_n (kgN ha-
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2.4.3. Texture 3 (Loam) Texture 3 c
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R_d_n (kgN ha-1 d-1) Texture 3 Temp
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R_d_n (kgN ha-1 d-1) 6 5 4 3 2 1 0
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2.4.7. Texture 7 (Sandy Loam) The S
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Texture 7 Temperature 12 : Denitrif
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Rdn (kgN ha-1 d-1) Texture 9 Temper
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Rdn (kgN ha-1 d-1) 16 12 8 4 0 Text
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2.5. Break down by Texture, Tempera
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8-20-34 (n=3), 8-20-84 (n=4), 8-20-
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Subsets 9-7-100, 9-25-100 and 9-30-
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Texture 10 Temperature 25 WFP 100 :
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a significant linear relationship b
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Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
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C9 Rdn (kgN ha-1 d-1) 12 10 8 6 4 2
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Rdn (kgN ha-1 d-1) Texture 10 Tempe
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2.6.11. Texture 11 (Silt) No data a
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2.7.6. Texture 6 (Sandy Clay Loam)
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Rdn (kgN ha-1 d-1) Texture 8 Tepera
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Rdn (kgN ha-1 d-1) Texture 8 Tepera
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2.8. Summary The correlation coeffi
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where, M is the slope of the linear
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Hence for the first set of equation
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3.2.2. Texture-Temperature-WFP-pH T
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Organic Carbon (%) Actual Rdn Gener
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R dn Results C = 9.389 - 3.210* M -
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The actual denitrification values f
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Eigenvalue 1.5 1.4 1.3 1.2 1.1 1.0
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R indicated by the significant code
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R Table 4.5 Comparison of actual an
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Table 4.8 Texture 2, Comparison of
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Table 4.11 Comparison of actual and
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The two equations are applied to th
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R R dn dn = - 0.05 ∗Temperature (
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Table 4.19 Texture 8, Linear multi-
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Table 4.20 Continued Actual Rdn Pre
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Once again the equations developed
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4.2.11. Texture 11 (Silt) No data a
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CHAPTER FIVE 5. ANALYSIS USING NEUR
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Figure 5.2 McCulloch-Pitts (Meyer-B
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developed. In order to control over
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Eventually the final set of network
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5.4.4. Texture 5 (Sand) Figure 5.5
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5.4.6. Texture 8 (Silt Loam) Figure
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5.4.8. Texture 10 (Silty Clay Loam)
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CHAPTER SIX 6. USE OF ISOTOPES TO E
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18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
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Figure 6.1 Estimation of Nitrate Lo
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δ 18 O 12 10 8 6 4 2 Table 6.1 Egg
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δ 18 O 20.00 18.00 16.00 14.00 12.
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CHAPTER SEVEN 7. APPLICATION TO JAC
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7.2. Multiple Regression Three equa
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Table 7.4 Multi-Regression and Neur
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8.3. Main Results The linear equati
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isotopes can be particularly useful
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APPENDIX B CONVERSION SHEET FOR DEN
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iv. v. vi. Given in the dataset: 1.
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Conversion from −3 −1 g N m d t
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Convert From Multiply by Convert to
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Texture 4 Variable Count N* Mean St
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Texture 10 Variable Count N* Mean S
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Texture-Temperature-WFP-pH Code Equ
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09-15-25-09 Rdn = 0.0106688*OC + 0.
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Code Rdn - OC Code Rdn-WFP Code Rdn
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- Code Rdn - OC Code Rdn - pH Code
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- Code Rdn - OC Code Rdn - NO3 Conc
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Code Rdn - OC Code Rdn - pH 08-25-9
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REFERENCES Almasri, M. N., & Kaluar
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Fellows, C., Hunter, H., Eccleston,
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King, D., & Nedwell, D. B. (1985).
Page 225 and 226:
Ozden, T., & Muhammetoglu, H. (2008
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Smith, R., Bohlke, J., Garabedian,
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Zhu, J., Liu, G., Han, Y., Zhang, Y
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