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published_or_final_version / abstract / Mechanical Engineering / Master / Master of Philosophy

No description available.

Transition state theory was applied to the nitrification process in soil-water systems, and a computerized, theoretical rate model was developed to include NH₄⁺ and 0₂ concentrations, pH, temperature, moisture content, and local differences in nitrifying capacities of Nitrosomonas bacteria. The model was restricted to enriched calcareous soils thus simplifying the application of basic physicochemical principles. Experimental rate data from an agricultural and a native desert soil provided verification of a zero order reaction for nitrification with respect to NH₄⁺ concentrations above a certain saturation level, as previously reported. The saturation concentration in soils was found to be about 1.0 to 5.0 ppm. A theoretical linear relationship between activation energy and ionic strength was confirmed by application of the above data. However, each local population of nitrifiers tended to display different values for the slope and intercept of the linear relationship. The structure of the activated complex for NH₄⁺ oxidation to NO₂⁻ was determined to be more like NH2OH or NH₄⁺ than NO₂⁻. As a first approximation, the NH₂OH activated complex was included in the rate model. The equation form for the equilibrium between the reactants and the activated complex was found to differ from the stoichiometric reaction between NH₄⁺ and O₂ to form NH₂OH. The equilibrium expression was found to be more closely approximated by the relationship, 2 NH₄⁺ + O₂ ≶ (ACTIVATED COMPLEX) + + H⁺. A method was developed to compute soil pH values as a function of moisture content. Verification was obtained by using data obtained from the agricultural and native desert soils, including cases where samples were acidified. The calculated pH values were used in the nitrification rate model. Further verification of the model was obtained using data from the literature for two soils from the Northern Great Plains. Data pairing of observed and predicted rates for these soils yielded R values of 0.944 and 0.940. The rate model was programmed in FORTRAN IV computer language and designed to operate in conjunction with existing computer models. Thus, this relatively sophisticated model may be applied to field simulation studies with a minimum of adaptive procedures. The model should aid in obtaining reliable predictions of NO₃⁻ formation and movement under a wide range of field conditions.

published_or_final_version / abstract / Mechanical Engineering / Master / Master of Philosophy

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

Suppose C(x,t) is the concentration at position x in Rᵈ and time t > 0 of a solute which is diffusing in some medium. If on a local scale the dispersion of the solute is governed by a constant dispersion matrix, 1/2(δ²), and a random velocity field, V(x), then C satisfies a convection-diffusion equation with random coefficients, (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) (1). Usually V(x) is taken to be μ + εU(x) where μ ε Rᵈ, U(x) is a given stationary random field with mean zero, and ε > 0 is a dimensionless parameter which measures the variability of V(x). Hydrological experiments suggest that on a regional scale the diffusion is classically Fickian with effective diffusion matrix D(ε) and drift velocity α(ε). Thus for large scales (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) (2) is satisfied by the solute concentration. Here τ and χ are respectively time and space measured on large scales. It is natural to investigate the relation of the large scale coefficients D and α to the statistical properties of V(x). To relate (1) to (2)--and thus to approximate D(ε) and α(ε)--it is necessary to rescale t and x and average over the distribution of V. It can then be shown that the transition form (1) to (2) is equivalent to (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) (3) where A = (∇•δ²∇)/2 + √nμ• ∇ and B(U) = √nU(√nx) • ∇. By expanding each side of (3) estimates of D(ε) and α(ε) can be obtained. The estimates have the form (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI) (4). Both D₂ and α₂ depend on the power spectrum of U. Analysis shows that in at least the case of incompressible fluids D₂ is positive definite. In one dimensional transport α₂ < 0, hence α(k) < μ(k) through second order.

Wing-Yeong Lee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 73-75). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The observed data --- p.3 / Chapter 1.3 --- Outline of the thesis --- p.8 / Chapter Chapter 2 --- Modeling using Latent Variable --- p.9 / Chapter Chapter 3 --- Imputation Procedure --- p.16 / Chapter 3.1 --- Introduction --- p.16 / Chapter 3.2 --- Introduction to Metropolis-Hastings algorithm --- p.18 / Chapter 3.3 --- Introduction to Gibbs sampler --- p.19 / Chapter 3.4 --- Imputation step --- p.21 / Chapter 3.5 --- Initialization of the missing values by regression --- p.23 / Chapter 3.6 --- Initialization of the parameters and creating the latent variable and noises --- p.27 / Chapter 3.7 --- Simulation of Y's --- p.30 / Chapter 3.8 --- Simulation of the parameters --- p.34 / Chapter 3.9 --- Simulation of T by use of the Metropolis-Hastings algorithm --- p.41 / Chapter 3.10 --- Distribution of Vij's given all other values --- p.44 / Chapter 3.11 --- Simulation procedure of Vij's --- p.46 / Chapter Chapter 4 --- Data Analysis of the Pollutant Data --- p.48 / Chapter 4.1 --- Convergence of the process --- p.48 / Chapter 4.2 --- Data analysis --- p.53 / Chapter Chapter 5 --- Conclusion --- p.69 / REFERENCES --- p.73

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