The Wonderfonteinspruit (WFS) flows through the richest gold mining region in the world and has subsequently been exposed to the related pollution for more than a century. In order to determine the extent of mining related pollution in the WFS, sediment, water, soil, grass and cattle tissue samples were collected, analysed and compared from an experimental group and a control group. This study identified cobalt, nickel, zinc, selenium, cadmium, gold, lead and uranium as elements of interest by comparing sediment samples from the WFS and the Mooi River (MR) (which served as a control or background site). The cobalt concentration was found to be 16.37 times higher, the nickel concentration was 30.4 times higher, the copper concentration was 3.59 times higher, the zinc concentration was 103.49 times higher, the selenium concentration was 7.14 times higher, the cadmium concentration was 17.88 times higher, the gold concentration was 4.78 times higher, the lead concentration was 1.32 times higher and the uranium concentration was 375.78 times higher in the initial comparison with sediments from the MR. These results were all found to be significant. All these elements are by products of non-ferrous mining activities as was described in the literature review. The elevated concentrations of these elements, which were found in the streambed sediment of a site in the Lower-Wonderfonteinspruit, suggest that they could have resulted due to upstream gold mining activities. These gold mining activities were initiated more than a century ago and continue to this day. Analysis of the different particle size fractions (sand, silt and clay fractions) revealed that the highest elemental concentrations were found in the clay sized fractions. The clay sized fraction usually contains secondary soil minerals which have the ability to adsorb dissolved cations onto their surface areas. Further analysis revealed that the sand fraction of the WFS sediment contained a substantial concentration of cobalt, nickel, copper, zinc, lead and uranium which, upon initial inspection could not be explained. X-Ray Diffraction (XRD) analysis revealed that more than 90 % of the WFS sand, silt and clay fractions consisted of quartz, which was much higher than that of the MR. Due to the particle size of quartz, it generally dominates the sand and silt fractions, and finding it at levels above 90 % in the clay sized fraction is thought to be highly irregular. This could be explained by the extraction and processing of gold reefs from the goldfields in the catchment. The gold reefs consisted of quartz veins that were milled to a fine dust and pumped onto slime and sand dumps after the gold was extracted. The most abundant ore minerals found within these dumps were uraninite(UO2), brannerite (UO3Ti2O4), arsenopyrite (FeAsS), cobaltite (CoAsS), galena (PbS), pyrrhotite (FeS), gersdofite (NiAsS) and chromite (FeCr2O4), which contain some of the elements of interest. These dumps are either located in close proximity to the WFS or connected to the WFs via canals or pipelines. Erosion of these dumps would then introduce this finely milled quartz into the stream system. Therefore, the elements found in the sediment of the WFS were not only introduced to the system in the dissolved form, but also in the particulate form. The water samples that were collected from the experimental site (WFS) were found to exceed the cobalt, nickel, copper, zinc, selenium and cadmium concentrations ranges which are normally found in natural waters. In addition to this, the cadmium, lead and nickel concentration in the WFS water samples were found to occasionally exceed the target water quality ranges for livestock water as set by DWAF (1996). Water samples that were collected from the control group were found to exceed only the selenium concentration found in natural water sources as found by Crittenden et al., (2005). Cattle in the experimental group drink directly from the WFS and may stir up the sediment and thereby increasing the elemental concentrations within the water prior to ingestion. The target water quality ranges (TWQR) for livestock watering, as set by DWAF 1996, were exceeded by the average nickel and lead concentrations found in the disturbed WFS water samples. Although the elemental concentrations in the respective water samples were fairly low there was a definite practical significant difference between the WFS water and the MR water samples, as well as the disturbed WFS water and the MR water samples. The WFS water quality seemed to have a very large standard deviation which could serve as an indication that the elemental concentrations are highly variable over time. The elemental concentrations that were found in soil samples from the respective sites were compared to elemental concentrations found in normal agricultural soil as presented by Bergman (1992), which revealed the following results. The cobalt concentrations in the soil samples from the soil along WFS site, soil along MR site and irrigation MR site exceeded the agricultural threshold value. The nickel concentrations in the soil samples from the soil along WFS site, soil along MR site, wetland WFS and irrigation MR site exceeded the agricultural threshold value. The zinc concentrations in the soil samples from the soil along WFS site exceeded the agricultural threshold value. Copper, selenium, cadmium and lead concentrations did not exceed the agricultural threshold values in any of the respective sites. The agricultural threshold value for uranium concentrations was exceeded in the soil samples from the soil along the WFS site and the wetland WFS site. The comparison between the elemental concentrations that were found in the soil samples from the irrigated soil WFS site and the irrigated soil MR site revealed a practically significant difference for the copper, zinc and uranium concentrations. The comparison between the elemental concentrations found in soil samples from the soil along the WFS site and the soil along the MR site revealed a practically significant difference for all elements of interest. The analysis of the elemental concentration in the different particle size fractions of soil samples from all the sites (excluding the irrigated pastures) displayed highest elemental concentrations in the clay sized fraction. The elemental concentrations that were found in this fraction are generally considered to be available for plant uptake, as most of them are usually bound to the surface of secondary soil minerals. The sites with the highest concentration of plant available elements were found to be the soil along WFS site and the wetland WFS site. The elemental concentrations found in the grass samples from the respective sampling sites were compared to elemental concentrations that are normally found in grass pastures (Underwood & Suttle, 2001). The cobalt, nickel, copper and concentrations that were found in the grass samples from most of the sites in both the control and experimental groups were all found to exceed the concentration ranges found in natural pastures. The cadmium and zinc concentrations in the grass samples from the soil along WFS site were found to exceed the respective concentration ranges found in natural pastures. The normal uranium concentration found in irrigated or natural grasses could not be found in an extensive search. Dreesen et al. (1982) reported 0.16 mg/kg uranium in grasses and 1.8 mg/kg uranium in shrubs that grew on soil-covered tailings material. All the sites in the experimental group, including the control WFS site, drastically exceeded these concentrations, which may suggest that the grasses in the experimental sites have been exposed to elevated uranium concentrations. The grass samples with the highest average elemental concentrations were found in the soil along WFS site and irrigated soil WFS site. Lead was to be the only element of interest to have the highest concentration in grass samples from the irrigated soil WFS site. The irrigated soil WFS site portrayed significant transfer factors for nickel, copper, zinc, lead and uranium. This could serve as an indication that the grasses under irrigation in the WFS site absorb and accumulate the highest concentration of elements in respect to the soil concentrations found in the various sites. Therefore, the irrigation from the WFS has a profound effect on the nickel, copper, zinc, lead and uranium concentration in the grass samples under irrigation. The results obtained from the comparative analysis of the elemental concentration in grass samples from the irrigation WFS and irrigation MR sites revealed that all elemental concentrations except for that of zinc had a difference that was practically significant, with the uranium concentration having the largest effect size. The results obtained from the comparative analysis of the elemental concentration in grass samples from the soil along WFS and soil along MR sites revealed that all elemental concentrations had a difference that was practically significant uranium, nickel and zinc concentrations having the largest effect sizes. Considering that a large effect size is achieved at a value equal to or greater than 0.8, the uranium concentration therefore had a massive difference in both comparisons. The results obtained from the comparative analysis of the elemental concentration in grass samples from the wetland WFS and control WFS sites revealed that only the cobalt, nickel and uranium concentrations had differences that were practically significant, with the cobalt concentration having the largest effect size. The results obtained from the comparative analysis of the elemental concentration in the grass samples from the soil along WFS and control WFS sites revealed that all the elemental concentrations except for the lead concentration had a difference that was practically significant. The cobalt, nickel and zinc had the largest effect sizes. The elemental concentrations that were found in cattle liver, kidney and muscle tissue samples from both the experimental and control groups were compared to elemental concentrations normally found in cattle samples as found in Pulse (1994), ATSDR (2004), and ATSDR (2011). This comparison revealed the following results: The nickel, cadmium and lead concentration that were found in the cattle liver, kidney and muscle tissue samples from both the experimental and control groups were found to be within the ranges normally found in cattle. Cobalt concentrations found in the liver and muscle tissue samples of cattle from both the experimental and control groups exceeded the normal ranges, and the cobalt concentrations found in the kidney samples from the experimental group exceeded the normal range. The copper concentration found in the kidney samples from the cattle in the experimental group exceeded that of the normal concentration range. The zinc concentration found in the liver and kidney samples in the cattle from the experimental group, and the kidney samples from the cattle in the control group exceeded the normal range. The selenium concentration found in the liver, kidney and muscle tissue samples in the cattle from the experimental group, and the kidney samples from the cattle in the control group exceeded the normal range. The uranium concentration found in the liver, kidney and muscle tissue samples in the cattle from the experimental group exceeded the normal range. The comparison between cattle tissue samples from the experimental and control group revealed that nickel, zinc, selenium, lead and uranium concentrations all reveal a practically significant difference. Uranium, nickel and lead portrayed the largest differences between the two groups. The uranium concentration in the cattle samples from the experimental group was 126.75 times higher in the liver, 4350 times higher in the kidney, 47.75 times higher in the spleen, 31.6 times higher in the muscle tissue, 60 times higher in the bone and 129 times higher in the hair than that of the cattle samples from the control group. In addition to this, the uranium did not only accumulate in the predicted tissue samples (bone, liver and kidney), but also in the muscle tissue samples. The nickel concentrations in the cattle samples were all found to be higher in the experimental group, with liver 1.4 times higher, kidney 387.5 times higher, spleen 2.1 times higher, muscle tissue 2.8 times higher, bone 167.5 times higher and hair 76.5 times higher than that of the cattle samples from the control group. The lead concentrations found in the cattle samples from the experimental group were found to be 3.8 times higher in the liver, 17.3 times higher in the kidney, 3.3 times higher in the spleen, 3.2 times higher in the muscle tissue, 9 times higher in the bone and 12.2 times higher in the hair than the cattle samples from the control group. Furthermore, the study revealed that the major route of ingestion for all the elements of interest, excluding nickel and cobalt was via the ingestion of grass. The major route for nickel and cobalt ingestion was via soil ingestion. The elemental concentrations from water ingestion were found to be a less significant. It was shown that a predictive cattle consumption model was developed and calibrated from data gathered from a control and experimental group. Animal matter analysed for both groups were related to the cattle age of six years. Although good correlation between observed and simulated values was achieved, the exiting model fit is non-unique. To obtain a more precise model fit a similar dataset is required for both groups, but at a different age. The predictive model also showed that if only grass were to be used as input, there were no significant changes in the correlation between observed and simulated values. This has a huge advantage in terms of costs associated with laboratory analyses as the analysis of grass will be sufficient for using the model. A human health risk assessment was performed based on the results of the cattle consumption model. It was shown that no toxic risk exits for both the control and experimental groups if an intake rate of 0.13 kg of meat per day was assumed. Furthermore, Figure 6-11 clearly indicates that an intake rate of up to 0.38 kg of meat per day also has no toxic risk for both groups, which strongly suggests that there is no risk to the human food chain. The cattle grazing in the WFS appear to be in a good physical condition and according to the farmer; the reproduction rate is at desirable levels. Good farming practices would have also played a significant role to achieve this. / Thesis (MSc (Environmental Sciences))--North-West University, Potchefstroom Campus, 2012.

Made available in DSpace on 2016-12-12T20:22:53Z (GMT). No. of bitstreams: 1 Mauro Hinz.pdf: 2658972 bytes, checksum: 50ee82c291499d5ddc390671e05329d4 (MD5) Previous issue date: 2015-09-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The outsourcing of computing services has been through constant evolutions in the past years, due to the increase of demand for computing resources. Accordingly, data centers are the main suppliers of computing service and cloud-based computing services provide a new paradigm for the offer and consumption of these computing resources. A substantial motivator for using cloud computing is its pricing model, which enables to charge the customer only for the resources he used, thus adopting a pay-as-you-use cost model. Among cloud-based computing services, the service type Infrastructure-as-a-Service (IaaS) is the one mostly used by companies that would like to outsource their computing infrastructure. The IaaS service, in most cases, is offered through virtual machines. This paper revisits the cost models used by data centers and analyses the costs of supply of virtual machines based on IaaS. This analysis identifies that electricity represents a considerable portion of this cost and that much of the consumption comes from the use of processors in virtual machines, and that this aspect is not considered in the identified cost models. This paper describes the Virtual Power Model, a cost model based on energy consumption of the processor in cloud-based, virtual machines in IaaS. The model is based on the assumptions of energy consumption vs. processing load, among others, which are proven through experiments in a test environment of a small data center. As a result, the Virtual Power Model proves itself as a fairer pricing model for the consumed resources than the identified models. Finally, a case study is performed to compare the costs charged to a client using the cost model of Amazon for the AWS EC2 service and the same service charged using the Virtual Power Model. / A terceirização dos serviços de computação tem passado por evoluções constantes nos últimos anos em função do contínuo aumento na demanda por recursos computacionais. Neste sentido, os data centers são os principais fornecedores de serviço de computação e os serviços de computação em nuvem proporcionam um novo paradigma na oferta e consumo desses recursos computacionais. Um considerável motivador do uso das nuvens computacionais é o seu modelo de tarifação que possibilita a cobrança do cliente somente dos recursos que ele utilizou, adotando um modelo de custo do tipo pay-as-you-use. Dentre os serviços de computação em nuvem, o serviço do tipo IaaS (Infrastructure-as-a-Service) é um dos mais utilizados por empresas que desejam terceirizar a sua infraestrutura computacional. O serviço de IaaS, na grande maioria dos casos, é ofertado através de instâncias de máquinas virtuais. O presente trabalho revisita os modelos de custos empregados em data centers analisando a formação dos custos no fornecimento de máquina virtuais em nuvens baseadas em IaaS. Com base nesta análise identificasse que a energia elétrica possui uma parcela considerável deste custo e que boa parte deste consumo é proveniente do uso de processadores pelas máquinas virtuais, e que esse aspecto não é considerado nos modelos de custos identificados. Este trabalho descreve o Modelo Virtual Power, um modelo de custo baseado no consumo de energia do processador por máquina virtual em nuvens IaaS. A constituição do modelo está baseada nas premissas de consumo de energia vs. carga de processamento, entre outros, que são comprovados através de experimentação em um ambiente de testes em um data center de pequeno porte. Como resultado o Modelo Virtual Power mostra-se mais justo na precificação dos recursos consumidos do que os modelos identificados. Por fim, é realizado um estudo de caso comparando os custos tarifado a um cliente empregando o modelo de custo da Amazon no serviço AWS EC2 e o mesmo serviço tarifado utilizando o Modelo Virtual Power.

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ) as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.

This thesis aims to contribute new knowledge to the debate on corporate urbanism. Generally, it seeks to identify the process and implications of globalization tendencies on the development of industrial and postindustrial cities while exploring the aspects that this specific production of space entails. Specifically it studies the case of the Baťa shoe company, whose model of an „ideal industrial town“, its production and export, has been one of the first examples of enforcement/implementation of corporate urbanism on a global scale. The 1930s model of the Indian enterprise city, Batanagar, is since 2006 being replaced by a new one: the model of a city-venture, in which the actual production of shoes is strictly separated from the new production of lifestyle. This model can today be found on the outskirts of large cities throughout the world, and especially in countries of the global south, where it clearly reflects the socio-economic transformation of the whole society, from a production driven, to a consumption driven society.

The thesis deals with the analysis of the properties of the external and internal environment of the buildings, the possibilities of heating and cooling. The emphasis is mainly on the energy intensity and the impact of weather conditions on the building temperature during the year. The model created by UPPAAL SMC describes the behavior of heating and cooling during the year and identifies the energy demand of the given building. The building model itself can be partially modified using the built-in user interface.