A method for the separation and detection of standard resin acids (RAs), commonly found in pulp mill effluent and known to bioaccumulate in fish bile, was optimized using cyclodextrin modified electrokinetic capillary electrophoresis (CD-EKC) with ultra violet (UV) and laser induced fluorescence (LIF) detection. Optimal separation conditions were found with RA standards using UV detection at 214 nm, with a 72 mM sodium borate buffer pH 9.25, containing 35 mM beta-cyclodextrin sulfobutyl ether (SPCD), 15 mM of methyl-beta-cyclodextrin (MECD) and a 37 cm capillary with an internal diameter of 50pm. This resulted in a 12-min separation and the identification of 9 peaks, with a LOD of 10 ppm. To enable increased sensitivity, RAs were derivatized using the fluorescent label 4-BrMMc. A method for extracting resin acids from spiked fish bile and pulp mill effluent was developed, the extracted samples were derivatized, separated and identified using CD-EKC with LIF detection. The method of extraction and derivatization using CD-EKC was applied to biological samples of contaminated effluent and fish bile. (Abstract shortened by UMI.)

Ligand I- along with nonionic surfactant, Triton X-100 or anionic surfactant, sodium dodecyl sulfate (SDS) were applied as soil washing agents to desorb Cd from both naturally and artificially contaminated soils. After seven consecutive washings, up to 90% of Cd was desorbed from both soils. Triton X-100 with I- showed a higher capacity to desorbing Cd than did SDS with I-. The increase of ligand concentration was a critical factor for increasing leaching capacity. Without the ligand, surfactant alone could not desorb Cd effectively from either soil. After seven consecutive washings, a sequential extraction experiment was performed for soil residuals to define the soil fraction of Cd removed by the washing agent. Among the washing agents, only Triton X-100/I- could remove Cd from the exchangeable fraction of both soils. / Various ligands including I-, SCN-, and I-/SCN- in combination with Triton X-100 were tested for their efficacy in desorbing heavy metals such as Cd, Zn, Cu, and Pb from a field contaminated soil. Cadmium was preferentially desorbed by Triton X-100/I- whereas Zn and Cu were preferentially desorbed by Triton X-100/SCN-. The mixture of I- and SCN- with Triton X-100 desorbed the most Cd and Cu, but not for Zn, as I- inhibited Zn desorption. Sequential extraction experiments after seven washings showed that metals held in the exchangeable fraction can be desorbed only by a combination of ligand and surfactant. / Nonionic surfactants having different alkyl chain lengths in combination with ligand I- were tested for the desorption of Cd and PCBs from soil. Cadmium desorption was increased at the lower surfactant concentration and higher ligand concentration. The increase in the hydrophilic alkyl chain length of the surfactant adversely affected Cd desorption. Up to 100% of PCBs were successfully removed by most of surfactant-ligand combinations and the desorption was less dependent, compared to heavy metals, on the concentration of washing agents and length of alkyl chain. The linear relationships between number of washings and Cd desorption, and between alkyl chain length and Cd desorption was defined. (Abstract shortened by UMI.)

Eutrophication resulting from phosphorus (P) accumulation in water systems has been a worldwide concern for three decades. Agricultural soils are known to be an important non-point source of P in waterways. The objectives of this research are to identify agricultural management practices that reduce the risk of P loss from soils, and to investigate the underlying mechanisms of P retention and loss from soils. In the short term (4 years), dissolved P loads were not affected by tillage and were similar in corn (in a continuous corn rotation) and soybean (in a soybean/corn rotation) production systems. Soils amended with composted cattle manure had a greater P load than chemically fertilized soils. On average, 30% of the total P leached was in organic P forms, indicating that organic P compounds could also be problematic to water systems. Although manure application improved soil aggregation and thus may increase P retention by avoiding soil erosion, P loss through subsurface flow by leaching may be substantial. A simple soil test, either Mehlich-3 P or P saturation ratio can predict the P leaching potential, but water ponding on the surface of agricultural land could significantly affect the accuracy of the prediction. / The P adsorption data was fit with the Langmuir 2-surface model, which predicted that up to 90% of the native adsorbed P was distributed on the high-energy surface. Native adsorbed P in manured soils was weakly retained, as the binding strength coefficient was 50 times less in manured than chemically fertilized soil. This findings was confirmed by measuring P desorption, which showed that P desorption rate was almost 3 times greater from manured soils than from chemically fertilized soils. Manuring alters soil particle surfaces by increasing negative charge. This is the direct reason for less P adsorption and greater P desorption by manured soils. / The Langmuir 2-surface model and the adapted non-ideal competitive adsorption (MICA) model were equally good at modeling P adsorption data. However, the NICA model is more robust and can predict phosphate adsorption with changing soil solution pH. The simultaneously modeling of P adsorption and hydroxyl adsorption with the NICA model makes it a promising tool for analyzing competitive adsorption among anions in soils.

When designing a building, the architect has typically relied on the input of outside experts to determine the performance of building systems. When done properly this collaboration can yield highly effective designs, but typically this reliance has left the architect outside of the loop on performance based decisions and impeded the development of innovative solutions. With the availability of powerful building simulation tools, designers can have direct access to building performance attributes and use them to qualify the environmental impact of design-decisions. With knowledge of fundamental principles in building performance and computer modeling, a designer can effectively harness the power of these tools from the beginning of the design process. While this does not eliminate the need for expert opinion, it allows the designer to further develop and have more control over the solution through collaboration. By working effectively in this digital design environment, the practice of architecture can meet its responsibility to reduce the impact of buildings on the physical environment. To test this statement, a brief overview of the integration of analysis tools in two projects that represent the current state of the art for digital performance simulation describes the need for multiple tools to achieve effective results. Based on this experience, a study was done to explore the capabilities of four representative simulation tools to support a design process that is entirely digital. The software evaluated was Energy-10, eQUEST, Sketch-Up with Demeter (a recently released plug-in for energy analysis) and ECOTECT. These tools were chosen because they have been targeted toward architects and claim to be easy to use. The results of this investigation were used to determine an appropriate tool set to develop a design for submission to the Leading Edge Competition, chosen because one of the requirements is that entrants perform energy analyses on their schemes to show how design decisions led to improved performance, making it a good vehicle to explore the process of designing in a simulation environment.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1995. / Includes bibliographical references (leaves 556-580). / Microfiche. / 2 v. (xv, 580 leaves, bound) ill., maps, col. photos. 29 cm

The use of pesticides poses a serious threat to the limited water resources of South Africa. The amounts which are not taken up by crop plants, are often washed away by runoff into surface waters, or leached through the soil, causing groundwater pollution. The problem of pesticide pollution is often intensified by inappropriate usage, disposal and monitoring in agriculture and predictive models have proven to be an effective tool for improving management practices. Research, however, has focused mainly on surface water contamination and groundwater impacts are largely unknown. Furthermore, pesticide registration in South Africa is largely determined by international standards and there is a need for impact assessments to be carried out under local conditions. The aims of this study included the determination of priority pesticides in South Africa based on usage and properties, the determination of pesticide sorption in two selected South African soils, and an assessment of pesticide fate by modelling.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

The application of substantial quantities of phosphorus (P) has been required to increase productivity on many Australian soils. Unfortunately, these applications have often resulted in increased concentrations of P in surface runoff that contributes to excessive algal growth in surface waters and consequently a decline in their quality. The concentrations of P in runoff from intensively managed pastures are often high (1-5 mg/L) and typically at least an order of magnitude higher than water quality targets. Although a substantial amount of research has been devoted to the problem of P accumulation and mobilisation in arable systems ( in which P is typically mobilised by the action of raindrop impact and subsequently transported in particulate form ), there has been substantially less research in intensively managed pasture systems. Consequently, there is a paucity of knowledge concerning the fundamental processes and factors responsible for P in runoff from these systems and a dearth of truly effective remedial strategies. In this thesis, the accumulation of P in soil under intensively managed pastures used for dairying and the processes responsible for its mobilisation in surface runoff were investigated. This research was undertaken at two research sites in South - east Australia, i.e. Camden in New South Wales and Flaxley in South Australia. A number of factors relating to scale and hydrology may influence the processes of P mobilisation and its concentration in runoff. A comparison was made of the forms and concentrations of P in runoff between a typical rainfall simulation methodology and large runoff plots. The effect of rainfall intensity on the forms and concentrations of P was also investigated. The concentrations of P in runoff from small - scale, high - intensity rainfall simulations were on average 33 % lower than those from large plots ( approximating hillslopes ) although the processes of mobilisation ( as evidenced by runoff P forms ) were similar. Increasing rainfall intensity resulted in decreasing P concentrations, but similar forms of P. It was hypothesised that changes in hydrological characteristics ( residence time and depth of runoff ) were responsible for the differences in the P concentrations. A model of P mobilisation ( incorporating hydrological and P - release characteristics ) was developed and shown to successfully predict runoff P concentrations under a range of rainfall intensities. These findings and the subsequent model were used in the successful modelling of landscape scale nutrient exports based on rainfall simulation data as part of a separate, but complementary project. There is anecdotal evidence to suggest that Australian soils are relatively ' leaky ' in terms of P in runoff compared to soils overseas. Consequently, comparisons of the labile soil P characteristics and soil P - runoff P relationships were made between Australian soils and soils of similar fertility from the USA, UK and New Zealand ( using both experimental data and data sourced from the literature ). It was concluded that Australian soils leak more P than soils of similar fertility in the USA, UK and New Zealand, although it was beyond the scope of the thesis to make more detailed comparisons between Australian and overseas soils. The accumulation and mobilisation of P in two soils used for intensive pasture production in Australia were investigated. In intensive pasture systems P accumulated in the shallowest zones of the soil and principally as inorganic P. The concentrations of labile P were 3 - 5 times higher in the top 0.01 m than in the top 0.1 m. Using a simple model, it was estimated that only the top several mm of soil influence runoff P concentrations. The dominant form of P in runoff was shown to be orthophosphate although in low to moderate fertility soils, dissolved organic P can constitute a substantial proportion of the P in runoff. These results confirm the need to reduce the pool of P available for mobilisation in the immediate topsoil in order to reduce runoff P concentrations. Because P is stratified, it was hypothesised that one method to reduce the pool of P available for mobilisation is to de - stratify the soil ( i.e. mix the topsoil ). The effect of this technique on runoff P concentrations was investigated in laboratory and rainfall simulation experiments. These experiments revealed that reductions in runoff P concentrations between 45 and 70 % can be achieved by de - stratification of soils under permanent pastures. It was hypothesised that the benefits of de - stratification could be maximised using a combination of information relating to catchment hydrology and the spatial distribution of soil P and that this would result in large reductions in P exports with a relatively small degree of inconvenience to land managers. Given the limited opportunities identified in previous research to reduce P exports in runoff, the strategic utilisation of de - stratification is a potentially important option in water quality management for the dairy industry and warrants further investigation. / Thesis (Ph.D.)--School of Earth and Environmental Sciences, 2006.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. Therefore, manufacturers are struggling to find ways to recover maximum value from returned products. This goal can best be achieved by promoting multiple reuse programs as reuse is one of the most effective ways to enhance a sustainable engineering economy. Since the essential goal of the reuse strategy is to reuse parts, the reliability of used parts becomes a core issue. Research indicates that reuse is technologically feasible, associated with a significant manufacturing cost saving, and it does not compromise product quality. However, it is not easy to be applied in reality. There are several uncertainties associated with reuse, the most common is the uncertainty of the product???s quality after use. A widespread implementation of the reuse strategy could be triggered, subject to the availability of reliable methods to assess the useful remaining life of parts. The evolution of such a methodology would play a pivotal role in making decisions on the supply chain process and the recovery value of returned products. Reliability assessment by life cycle data analysis is the basis of this research. The proposed methodology addresses the problem of reliability assessment of used parts by considering two important aspects. It performs statistical as well as condition monitoring data analysis for decision-making on reuse. The analysis is carried out in two stages. Firstly, a wellknown reliability assessment procedure, the Weibull analysis, is applied to analyse time-tofailure data to assess the overall reuse potential of components. In the second stage, the used capacity (actual life) of components is determined by analysing their operating history (condition monitoring data). The linear and nonlinear regression analysis, Kriging procedures and artificial neural networks (ANN) are employed in this stage. Finally, the Weibull analysis and ANNs are integrated to estimate the remaining useful life of components/assemblies of a product at the end of its first life cycle. The model was validated by using life cycle data from consumer products.