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groundwater reflects the average year round temperature, 25ºC is used, this is the average yearly temperature for Jacksonville. In addition as much of the aquifer is made up of sand and gravel all predictions are based on texture 5 (Sand). Where required an assumed bulk density of 1.25 gm cm -3 is used. The pH and organic carbon values are obtained from the soil survey database from the Natural Resources Conservation Service (NRCS), soil Survey Geographic (SSURGO) database. The information from the NRCS data is averaged for the area and additional sources such as literature data were then used to gather input information for the Study area in Jacksonville. The following information is used to calculate the denitrification rates for the Lake Shore region, Texture 5 (Sand), Temperature 20ºC, WFP (100%) as we are concerned with the saturated zone, pH 5.2 (average pH of soils in the region), bulk density 1.5 gm cm -3 , saturated thickness of 1000 cm, Organic carbon percentage of 2.1% and an average concentration of 10.3 μg N gm-1soil. All of the values were derived based on averages. 7.1. Linear Regression and Monte Carlo analysis The closest equation that can be used is the linear regression developed from 5-15-100-6 (Texture 5, Temperature 15, WFP 100, Nitrate Concentration 6). Based on Figure 2.60 the denitrification rate can be estimated using the following equation R dn = 1.984 ∗ organic carbon - 0.3768 ---- 165 Equation 7.1 Based on an organic carbon value of 2.1 percent, the denitrification rate is estimated to be 3.7896 Kg N ha -1 d −1 . As reaction rates are assumed to double for every 10 ºC the denitrification rate at 25 ºC is 7.5792 Kg N ha -1 d −1 . In addition to using the equation above the Monte Carlo simulation is run for an organic carbon value of 2.1 percent. The simulation results in a predicted mean of 8.4628 1.0025 Kg N ha -1 d −1 Kg N ha and a maximum value of 11.7355 Kg N ha -1 d −1 with a minimum value of -1 d −1 .
7.2. Multiple Regression Three equations are used to estimate a denitrification rate, the first uses Equation 4.1 and results in a negative estimated denitrification rate, the second uses Equation 4.8 and results in an estimated value of 4.588 Kg N ha 166 -1 d −1 the final uses Equation 4.9 and results in a negative estimated rate of denitrification. The results are spurious and as the coefficient of determination for the equations are low the results are viewed with caution. While the value of 4.588 Kg N ha -1 d −1 is within the range of values estimated by the Monte-Carlo method this method is perhaps the least likely to accurately estimate the rate of denitrification. 7.3. Neural Network For Lakeshore the following information is input into the ANN 5-7-20, temperature 25ºC, WFP (100%) as we are concerned with the saturated zone, pH 5.2 ( average pH of soils in the region), bulk density 1.5 gm cm -3 , saturated thickness of 1000 cm, Organic carbon −1 percentage of 2.1% and an average concentration of 216.6 μ g N gm soil . All of the values are derived based on averages. Based on the above data it is estimated that the denitrification rate in the Lake Shore region of the Jacksonville City in Duval county Florida is 8.3396 Kg N ha -1 d −1 . The ANN used for Julington Creek and Eggleston heights is the same as for lakeshore. The temperature, WFP and soil depth are the same as for the Lakeshore region and the rest of the input data is given in Table 7.1 Summary of Input data for the study area Study Area Input Data pH OC Nitrate Concentration Eggleston Heights (April 2010) 5.4 1.9 390 Eggleston Heights (June 2010) 5.4 1.9 1093 Eggleston Heights (Sept 2010) 5.4 1.9 875 Julington Creek (April 2010) 5.5 2.2 1757 Julington Creek (Dec 2010) 5.5 2.2 1233 Lakeshore 5.2 2.1 216.6
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THE FLORIDA STATE UNIVERSITY ARTS A
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To my Dad, Mum and Brother iii
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Dr. Stephen Kish for his words of e
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1.5. Evaluation of methods to estim
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2.5.8. Texture 8 (Silt Loam).......
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4.2.6. Texture 6 (Sandy Clay Loam).
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LIST OF TABLES Table 1.1 Additional
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LIST OF FIGURES Figure 1.1 Oxidatio
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Figure 2.51 8-20-84; Denitrificatio
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Figure 3.6 Probability plot (Textur
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Three statistical methods were used
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• Microbial biomass/ plant uptake
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Figure 1.1 Oxidation of organic car
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possibility of denitrification (Smi
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50ºC (36ºF - 122ºF) (Brady & Wei
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1.2.6. Salinity Salinity is a known
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1.3.1. The Acetylene inhibition met
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Among the variety of successful met
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a first-order decay process. The ma
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look at the data shows that it fail
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Da is the denitrification rate (mg
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a simple linear regression of organ
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of the annual N2O emission and deni
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easons for the limitation of the mo
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Table 1.2 Continued dC / dt Organic
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also implies that the transferabili
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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
Page 135 and 136: R dn Results C = 9.389 - 3.210* M -
Page 137 and 138: The actual denitrification values f
Page 139 and 140: Eigenvalue 1.5 1.4 1.3 1.2 1.1 1.0
Page 141 and 142: R indicated by the significant code
Page 143 and 144: R Table 4.5 Comparison of actual an
Page 145 and 146: Table 4.8 Texture 2, Comparison of
Page 147 and 148: Table 4.11 Comparison of actual and
Page 149 and 150: The two equations are applied to th
Page 151 and 152: R R dn dn = - 0.05 ∗Temperature (
Page 153 and 154: Table 4.19 Texture 8, Linear multi-
Page 155 and 156: Table 4.20 Continued Actual Rdn Pre
Page 157 and 158: Once again the equations developed
Page 159 and 160: 4.2.11. Texture 11 (Silt) No data a
Page 161 and 162: CHAPTER FIVE 5. ANALYSIS USING NEUR
Page 163 and 164: Figure 5.2 McCulloch-Pitts (Meyer-B
Page 165 and 166: developed. In order to control over
Page 167 and 168: Eventually the final set of network
Page 169 and 170: 5.4.4. Texture 5 (Sand) Figure 5.5
Page 171 and 172: 5.4.6. Texture 8 (Silt Loam) Figure
Page 173 and 174: 5.4.8. Texture 10 (Silty Clay Loam)
Page 175 and 176: CHAPTER SIX 6. USE OF ISOTOPES TO E
Page 177 and 178: 18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
Page 179 and 180: Figure 6.1 Estimation of Nitrate Lo
Page 181 and 182: δ 18 O 12 10 8 6 4 2 Table 6.1 Egg
Page 183 and 184: δ 18 O 20.00 18.00 16.00 14.00 12.
Page 185: CHAPTER SEVEN 7. APPLICATION TO JAC
Page 189 and 190: Table 7.4 Multi-Regression and Neur
Page 191 and 192: 8.3. Main Results The linear equati
Page 193 and 194: isotopes can be particularly useful
Page 195 and 196: APPENDIX B CONVERSION SHEET FOR DEN
Page 197 and 198: iv. v. vi. Given in the dataset: 1.
Page 199 and 200: Conversion from −3 −1 g N m d t
Page 201 and 202: Convert From Multiply by Convert to
Page 203 and 204: Texture 4 Variable Count N* Mean St
Page 205 and 206: Texture 10 Variable Count N* Mean S
Page 207 and 208: Texture-Temperature-WFP-pH Code Equ
Page 209 and 210: 09-15-25-09 Rdn = 0.0106688*OC + 0.
Page 211 and 212: Code Rdn - OC Code Rdn-WFP Code Rdn
Page 213 and 214: - Code Rdn - OC Code Rdn - pH Code
Page 215 and 216: - Code Rdn - OC Code Rdn - NO3 Conc
Page 217 and 218: Code Rdn - OC Code Rdn - pH 08-25-9
Page 219 and 220: REFERENCES Almasri, M. N., & Kaluar
Page 221 and 222: Fellows, C., Hunter, H., Eccleston,
Page 223 and 224: King, D., & Nedwell, D. B. (1985).
Page 225 and 226: Ozden, T., & Muhammetoglu, H. (2008
Page 227 and 228: Smith, R., Bohlke, J., Garabedian,
Page 229 and 230: Zhu, J., Liu, G., Han, Y., Zhang, Y
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