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δ 18 O 12.00 10.00 8.00 6.00 4.00 2.00 y = 0.4358x + 0.3699 R 2 = 0.4536 δ 18 O Vs. δ 15 N 0.00 0.00 5.00 10.00 15.00 20.00 25.00 δ 15 N Figure 6.6 δ 18 O Vs. δ 15 N Julington Creek (December, 2010). Table 6.5 Julington Creek (December, 2010). Sub-division Julington Enrichment Factor (‰) -1.29 Location Ct/C0 % Loss (Rdn) CST1 0.09 18.52 CST1 0.09 18.52 CST11 0.54 9.94 CST11A 0.11 17.20 CST2 0.09 18.08 DH1 0.07 19.59 DH1 0.07 19.59 DH1A 0.08 18.67 DH2 0.03 25.01 DH2 0.03 25.01 LP3 0.67 9.09 LP4 0.21 14.25 LP6 1.00 - LP6 1.00 - LPP71 0.11 17.44 MM1 0.10 17.68 MM1A 0.08 18.83 MM4 0.16 15.33 163
CHAPTER SEVEN 7. APPLICATION TO JACKSONVILLE, FL Three study areas are considered; Eggleston Heights, Julington Creek and Lake Shore. The areas are subdivisions within the city of Jacksonville which is a part of Duval County and under the St Johns River management district. This region is composed of well drained soils. The primary aquifer for drinking water is the Floridian aquifer which is overlain by the surficial aquifer. In most of Duval County, the surficial aquifer system is divided into two water-bearing units, the upper water-table unit and the lower limestone unit. These two units are separated by sediments of low permeability and water cannot easily move from one unit to the other. The surficial aquifer system ranges in thickness from less than 10 ft (3.03 m) in the St. Johns River Valley to about 100 (30.3 m) ft in western Duval County (Fairchild, 1972) and is separated from the underlying Floridan aquifer system by the intermediate confining unit. The water-table unit is the upper part of the surficial aquifer system and consists of sediments ranging from 25 ft (7.5 m) to 50 ft (15.15 m) that were deposited during the formation of the marine terraces and beach ridges associated with glaciation.. Geologically the surficial aquifer system is composed of undifferentiated fine to medium- grained quartz sands, siliciclastics, fine to coarse grained sand but could contain thin beds of sandy clay (Miller, 1997; Davis, 1998; Belanger et al., 2009) The three statistical methods developed in this work are used to estimate a denitrification rate; the methods while mainly statistical have also included the use of stable isotopes. All the methods are applied to the three study areas and results are compared to understand the importance of denitrification in the area. As this work is primarily concerned with denitrification in saturated zone of the surficial aquifer the water filled porosity is considered to be 100 percent. Since temperature of the 164
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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
Page 133 and 134: 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: δ 18 O 20.00 18.00 16.00 14.00 12.
Page 187 and 188: 7.2. Multiple Regression Three equa
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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