Mining industry has been identified as the main sustenance of the South African economy, however the negative impacts of the industry on the ecological systems cannot be over emphasized due to the released waste which is mostly heavy metals into the environment. The study evaluated six heavy metal (A1, Cu, Fe, Ni, Pb and Cr) contents in a tailings dam from a specific mine site. Two sets of samples for the investigation were measured, that is, one in year 2012 and the other in year 2013. In the year 2012, the sample set was only taken at a distance profile of 500 meters from the foot of the dam, whereas the sample set taken in the year 2013 was for the 500 and 1500 meter distance profiles from the foot of the dam. The year 2012 and 2013 sample sets for the 500m distance profile were sampled very similarly to each other. A kilogram of each sample was taken as per grid format. The samples at varied depths were taken at 0-cm depth for the top layer, 20cm depth for the second layer, and 30cm depth for the third layer. The samples for the surface varied distance were taken at 1 m, 2m, 3m, and 4m away from each 500m and 1500 sampling points. The 2012 samples were analysed using characterization methods namely ICP MS and The 2013 samples were analysed using the ICP OES. The comparison of the field results for the six heavy metals studied (A1, Fe, Pb, Cu, Ni and Cr) was performed using statistical analytical methods, namely ANOVA. The statistical analysis results for heavy metals (A1, Fe, Pb, Cu, Ni and Cr) from sample and 2013 revealed that the group means are not significantly different from each other which means that there is no significant difference in (A1, Fe, Pb, Cu, Ni and Cr) concentrations with respect to both depth and distance. The observations from both 2012 and 2013 indicate the results of the samples are in agreement. In addition, the comparative average concentrations of the three results obtained reach the same conclusion that the tailing dam probably does not introduce considerable or significant amounts of these metals (A1, Fe, Pb, Cu, Ni and Cr) into the surrounding soils. / Chemical Engineering / M. Tech. (Chemical Engineering)

Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy. School of Animal, Plant and Environmental Science, University of the Witwatersrand, Johannesburg, South Africa. October 2016. / Mining has negative impacts on the environment in many different ways. One method developed to quantify some of these impacts is Landscape Function Analysis (LFA) and this has been accepted by some mining companies and regulators. In brief, LFA aims at quantifying the organization of vegetative and landscape components in a landscape into patches along a transect and quantifying, in a relative manner, three basic processes important to landscape functioning, namely: soil stability or susceptibility to erosion, infiltration or runoff, and nutrient cycling or organic matter decomposition. However, LFA is limited in large heterogeneous environments, such as those around mining operations, due to its localized nature, and the man hours required to collect a representative set of measurements for such large and complex environments. Remote sensing using satellite-acquired data can overcome these limitations by sampling the entire environment in a rapid and objective manner. What is required is a method of connecting these satellite-based measurements to LFA measurements and then being able to extrapolate these measurements across the entire mine surface. The aim of this research was to develop a method to use satellite-based hyperspectral imagery to predict landscape function analysis (LFA) using partial least squares regression (PLSR). This was broken down into three objectives: (1) Collection of the LFA data in the field and validation of the LFA indices against other environmental variables collected at the same time, (2) validation of PLSR models predicting LFA indices and various environmental variables from ground-based spectra, and (3) production of risk maps based on predicting LFA indices and above-ground biomass using PLSR models and Hyperion satellite-based hyperspectral imagery. Although the study was based in grasslands at two mining regions, West Wits and Vaal River, a suitable Hyperion image was only available for Vaal River. A minimum of 374 points were sampled for LFA indices, ground-based spectra, above-ground biomass and soil cores along 2880 m of LFA transect from both mine sites. Soil cores were weighed fresh before sieving with a 2 mm sieve to separate root and stone fractions. The sieved soil fraction was tested for pH, EC, SOM, and for the West Wits samples, organic nitrogen and total extractable inorganic nitrogen. There was one modification to the LFA method where grass patches were collapsed into homogenous units as it was deemed not feasible to sample 180 m transects at grass tuft scales of 10 – 30 cm, but other patch definitions followed the LFA manual (Tongway and Hindley, 2004). Evidence suggested that some of the different patch types, in particular the bare/biological soil crust – bare grass – sparse grass patch types, represented successional stages in a continuum although this was not conclusive. There also was evidence that the presence or absence of cattle play a role in some processes active in these grasslands and erosion is mainly through deflation, rain splash and sheet wash. Generally the environmental variables supported the LFA indices although the nutrient cycling index was representative of above-ground nutrient cycling but not below-ground nutrient cycling. Models derived with PLSR to predict the LFA indices from ground-based spectral measurements were strong at both mine sites (West Wits: LFA stability r2 = 0.63, P < 0.0001; LFA infiltration r2 = 0.75, P < 0.0001; LFA nutrient cycling r2 = 0.73, P < 0.0001; Vaal River: LFA stability r2 = 0.39, P < 0.0001, LFA infiltration r2 = 0.72, P < 0.0001, LFA nutrient cycling r2 = 0.54, P < 0.0001), as were PLSR models predicting above-ground biomass (West Wits above-ground biomass r2 = 0.55, P = 0.0003; Vaal River above-ground biomass r2 = 0.79, P < 0.0001) and soil moisture (West Wits soil moisture r2 = 0.45, P = 0.0017; Vaal River soil moisture r2 = 0.68, P < 0.0001). However, for soil organic matter (r2 = 0.50, P < 0.0001) and EC (r2 = 0.63, P < 0.0001), Vaal River had strong prediction models while West Wits had weak models for these variables (r2 = 0.31, P = 0.019 and r2 = 0.10 and P < 0.18, respectively). For EC, the wide range of soil values at Vaal River in association with gypsum crusts, and low values throughout West Wits explained these model results but for soil organic matter, no clear explanation for these site differences was identified. Patch-based models could accurately discriminate between spectrally well-defined patch types such S. plumosum patches but were less successful with patch types that were spectrally similar such as the bare/biological soil crust – bare grass – sparse grass patch continuum. Clustering similar patch types together before PLSR modelling did improve these patch-based spectral models. To test the method proposed to predict LFA indices from satellite-based hyperspectral imagery, a Hyperion image matching 6 transects at Vaal River was acquired by NASA’s EO-1 satellite and downloaded from the USGS Glovis website. LFA transects were partitioned to match and extract pixel spectra from the Hyperion data cube. Thirty-one spectra were separated into calibration (20) and validation (11) data. PLSR models were derived from the calibration data, tested with validation data to select the optimum model, and then applied to the entire Hyperion data cube to produce prediction maps for five LFA indices and above-ground biomass. The patch area index (PAI) produced particularly strong models (r2 = 0.79, P = 0.0003, n =11) with validation data, whereas the landscape organization index (LOI) produced weak models. It is argued that this difference between these two essentially similar indices is related to the fact that the PAI is a 2-dimensional index and the LOI is a 1-dimensional index. This difference in these two indices allowed the PAI to compensate for some burned pixels on the transects by “seeing” the density pattern of grass tufts and patches whereas the linear nature of the LOI was more susceptible to the changing dimensions of patch structure due to the effects of fire. Although validation models for the three LFA indices of soil stability, infiltration and nutrient cycling were strong (r2 = 0.72, P = 0.004; r2 = 0.66, P = 0.008; r2 = 0.70, P = 0.005, n = 9 respectively), prediction maps were confounded by the presence of fire on some transects. The poor quality of the Hyperion imagery also meant great care had to be taken in the selection of models to avoid poor quality prediction maps. The 31 bands from the VNIR (478 – 885 nm) portion of the Hyperion spectra were generally the best for PLSR modelling and prediction maps, presumably because of better signal-to-noise ratios due to higher energy in the shorter wavelengths. With two satellite-based hyperspectral sensors already operational, namely the US Hyperion and the Chinese HJ-1A HSI, and a number expected to be launched by various space agencies in the next few years, this research presents a method to use the strengths of LFA and hyperspectral imagery to model and predict LFA index values and thereby produce risk maps of large, heterogeneous landscapes such as mining environments. As this research documents a method of partitioning the landscape rather than the pixel spectra into pure endmembers, it makes a valuable contribution to the fields of landscape ecology and hyperspectral remote sensing. / LG2017

A research report submitted to the Faculty of Science in partial fulfillment of the requirements for the degree of Master of Science, 2016. / South African gold mines are associated with the generation of a lot of mine waste in the form of milled rock (tailings). Tailings contain the mineral pyrite which, when exposed to air and water, cause acid mine drainage (AMD). Due to the large environmental impact (footprint and scale) ofthe tailings storage facilities (TSFs) on soils and groundwater, there has been much research done in phytoremediation. Some plants, such as Eucalyptus, used in this method are able to control seepage by using their extensive roots but this may inadvertently extract some contaminants from the water and accumulate them in the above ground parts of the tree. Upon harvesting of these plants, there is the potential for them to be used as biofuel for the generation of bioenergy, and by industry or the public as timber/construction wood, firewood, charcoal, generation of electricity, etc. In this study, three species of Eucalyptus trees grown by the University of the Witwatersrand in three site-species trials on AMD were evaluated for their concentrations of elements in leaves, bark, branches/twigs and stem wood, in order to determine the safety of the biomass for fuel, and the potential for environmental pollution (dissemination of metals) that could be caused by combustion. The study focused on Eucalyptus camaldulensis, E. grandis x camaldulensis hybrid and E. dunnii trees grown for eight years in three different trial sites, with one trial ("Mispah") situated at AngloGold Ashanti's Vaal River Mining Operations (VR, near Orkney - Klerksdorp) and two trial("Madala", "Red Soil") situated at the West Wits Mining Operations (WW, near Carletonville). The sites were typical of soils on the mine properties (WW Madala: Clovelly, WW Red Soil: Hutton, VR Mispah: Hutton and Mispah), and impacted by seepage from adjacent TSFs. Three entire above-ground trees were harvested per species (three trees per site, nine in total), weighed fresh and after drying. Samples of leaves, bark, twig/small branches, and main stem wood were analysed for their elemental contents; alongside a Certified Reference Material (CRM) (Orchard Leaves no. 1571); using Leco CNS analyser, Inductively Coupled PlasmaOptical Emission Spectroscopy (ICP-OES) and Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) to determine the concentrations of major and trace elements such as Aluminium (AI), Barium (Ba), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Manganese (Mn), Sodium (Na), Nickel (Ni), Phosphorus (P), Sulphur (S), Strontium (Sr), Titanium (Ti), Zinc (Zn), Vanadium (V), Chromium (Cr), Cobalt (Co), Copper (Cu), Arsenic (As), Gold (Au), Mercury (Hg), Lead (Pb), Uranium (U), carbon (C) and ash content. The CRM was used to validate the two analytical methods. There was variation in the concentrations of nutrients measured. There were no significant differences noted in the metallloids concentrations between all the Eucalyptus 11\ species studied (p>O.05). Variation between sites could not be determined as there were no replicates available to perform the comparison. The World Health Organisation (WHO) maximum permissible level (MPL) in plants for arsenic (As) is 1 mg/kg. The MPL was exceeded in all tissues of all three Eucalyptus species studied. Arsenic concentrations of 5.09, 4.36 and 5.48 mg/kg were found in the wood of E. camaldulensis, E. grandis x camaldulensis and E. dunnii respectively. A risk assessment performed found that there was no evidence that there will be adverse effects caused by supplying fuelwood from these contaminated Eucalyptus trees. Even though high arsenic concentrations were recorded in this study, if the wood is used as fertilizer in a vegetable bed, the transfer of the arsenic to the common vegetables is below the daily oral reference dose. The general trend in the concentration of metals and metalloids in different plant tissues was in the order of leaves > bark > branches/twigs > wood. The results of the biomass exposure assessment showed that the exposure through use of the ash as fertiliser was lower than the oral reference dose for Mn, Fe, Ba and As. The biomass risk assessment showed that the best-performing tree, in terms of wood production on AMO, was the E. camaldulensis. The risk of other metallioids was not evaluated as there was no good agreement between the results recorded with those certified of the CRM. It is suspected that the CRM used was old. / AC2016

Thesis (MTech (Business Administration))--Cape Peninsula University of Technology, 2017. / This research study investigates how environmental tools such as Resource Efficiency and Cleaner Production (RECP) can contribute to sustaining and supporting economic growth in South Africa. Resource optimisation is crucial when considering the concept of sustainable development. It also contributes to addressing the challenges of global warming and climate change, which in turn threaten industrial growth and sustainability in the long term. The study places emphasis on the barriers that prevent industry from implementing RECP recommendations, and identifies opportunities that could potentially reposition businesses should they consider implementation. It also promotes other sustainability tools that are available through collaboration with international entities, and this could be of great benefit to the South African industry. Factors of unemployment and urbanisation restricts national growth to some degree, hence the study explores how RECP can contribute to job retention by introducing new resource optimisation methodologies for the manufacturing sector, Moreover, it examines the imbalance between the demand as well as limitations of these resources. Through the compilation of data collated from questionnaires completed by industry, government and civil society participants, this study looks at achieving a balance between environmental sustainability and growth. It also looks at aligning this balance with the integration of specific economic and environmental policies, which also includes social aspects. What comes through significantly in this research is the lack of awareness within industry in terms of RECP, as well as the importance of prioritising the uptake of environmental initiatives to ensure that industry is compliant with the stringent policies and legislation designed by government to drive the sustainability process. Consequently the study shows that communication between public and private sector, as well as the awareness raising and marketing of sustainability to consumers need to be improved. An analysis of the various government support mechanisms is conducted, in respect of how industry could potentially leverage growth and drive positive change within their businesses. In addition to RECP, emphasis is placed on other Sustainable Consumption and Production (SCP) initiatives and tools that could possibly entrench sustainable practices and help with their incorporation into their business strategies.

Although remotely sensed data have been employed to assess various environmental problems, relatively few previous studies have focused on the impacts of mining. In Indonesia, mining activities have increasingly become one of major drivers of land cover change. The majority of remote sensing research projects on mining environments have exploited optical data which are frequently complicated by tmospheric disturbance, especially in tropical territories. Active remote sensors such as Synthetic Aperture Radar (SAR) are invaluable in this case. Monitoring by Independent SAR data has been limited due to single polarisation. Dual-polarised data have been employed considerably, although for some forestry applications the data were found insufficient to retrieve basic information. This Masters thesis is devoted to assess fully polarimetric SAR data for environmental monitoring of the tailings deposition zone of the PT Freeport Indonesia Grasberg mine in Papua, Indonesia. The main data were two granules of the AIRSAR datasets acquired during the PACRIM-II campaign. To support the interpretation and analysis, a scene of Landsat ETM February 2001) was used, juxtaposed with classified aerial photographs and a series of SPOT VEGETATION images. Both backscattering information and complex coherence matrices, as common representations of polarimetric data, were studied. Primary applications of this research were on degraded forest and environmental rehabilitation. Most parts of Indonesian forests have experienced abrupt changes as an impact of clear-cut deforestation. Gradual changes such as those due to fire or flooded tailings, however, are least studied. It was shown that the Cloude-Pottier polarimetric decomposition provided a convenient way to interpret various stages of forest disturbance. The result suggested that the Entropy parameter of the Cloude-Pottier decomposition could be used as a disturbance indicator. Using the fully polarimetric dataset combined with Support Vector Machine learning, the outcomes were generally acceptable. It was possible to improve classification accuracy by incorporating decomposition parameters, although it seemed insignificant. Land rehabilitation on tailings deposits has been a central concern of the government and the mining operator. Indigenous plant pioneers such as reeds (Phragmites) can naturally grow on dry tailings where soil structure is fairly well developed. To assist such efforts, a part of this research involved identification of dry tailings. On the first assessment, interpretation of surface scatterers was aided by polarimetric signatures. Apparently, longer wavelengths such as L- and P-band were overpenetrated; hence, growing reeds on dry tailings were less detectable. In this case, the use of C-band data was found fairly robust. Employing Mahalanobis statistics, the combination of HH and VV performed well on classification, having similar accuracy with quad polarimetric data. Extension on previous results was made through the Freeman-Durden decomposition. Interpretation using a three-component image of odd, even bounce and volume scattering showed that dry and wet tailings could be well distinguished. The application was benefited from unique responses of dielectric materials in the tailings deposit on SAR signals; hence it is possible to discriminate tailings with different moisture levels. However, further assessment of tailings moisture was not possible due to security reasons and access limitations at the study site. Fully polarimetric data were also employed to support rehabilitation of stressed mangrove forest on the southern coast. In this case, the Cloude-Pottier decomposition was employed along with textural parameters. Inclusion of textural properties was found invaluable for the classification using various statistical trees, and more important than decomposition parameters. It was concluded that incorporating polarimetric decompositions and textural parameters into coherence matrix leads to profound accuracy.

Bibliography: leaves 253-280.

This study is aimed at developing policies for energy efficiency by observing the past changes of energy use in Indonesia???s manufacturing sector over the period 1980???2000, and to investigate mitigation options for energy-related CO2 emissions in the sector. The first part of the study uses decomposition analysis to assess the effect of the changes in energy consumption and the level of CO2 emissions, while the second part investigates energy efficiency improvement strategies and the use of economic instruments to mitigate CO2 emissions in the manufacturing sector. Economic activity was the dominant factor in increasing energy consumption over the whole period of analysis, followed by the energy intensity effect and then the structural effect. The increase in aggregate energy intensity over the period 1980-2000 was mainly driven by the energy intensity effect. In turn, the technical effect was the dominant contributor to changes in energy intensity effect, with the fuel-mix effect being of lesser importance. Changes in CO2 emissions were dominated by economic activity and structural change. Sub-sectors that would benefit from fuel switching and energy efficiency improvements are the textile, paper, and non-metal sub-sectors. Three main options for reducing CO2 emissions from the manufacturing sector were considered: the imposition of a carbon tax, energy efficiency initiatives, and other mitigation measures. A carbon tax was found to reduce sectoral emissions from the direct use of oil, gas and coal, but increased the demand for electricity. At the practical level, energy efficiency improvements can be implemented by adopting energy efficient technologies that can reduce aggregate energy intensity up to 37.1 per cent from the base-year level, estimated after imposition of a carbon tax at $30 per tonne of carbon. A major priority for energy efficiency improvements was found to be in the textile and the paper and chemical sub-sectors. A mitigation measure such as the Clean Development Mechanisms could be encouraged in order to reduce projected emission levels. The preferred option would be the adoption of energy efficient technologies in the textile, chemical, paper and non-metal sub-sectors.

Bibliography: leaves 253-280. / viii, 280 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of History, 2002?

Extended producer responsibility (EPR) as a holistic approach to product stewardship has been increasingly incorporated into environmental policy agenda in the last two decades. One major expectation of EPR is to prevent environmental problems at source by providing appropriate incentives for product eco-design changes. However, available studies empirically examining the actual influence of EPR on eco-design are limited. This research therefore aims to fill the research gap by evaluating the effectiveness of existing EPR programmes in driving eco-design changes from the perspective of China’s electrical and electronic (EE) industry, and shed some light on the prospects of EPR development in China. Based on the analytical framework developed from reconstructed intervention theory, three intensive empirical studies have been carried out. The first study examines the perception and responses of 50 China’s EE manufacturers regarding the EU EPR programmes (i.e. the WEEE and RoHS Directives). The second study investigates the responses of 36 China’s EE manufacturers to the Chinese EPR programmes (i.e. China WEEE and China RoHS legislation) and identifies possible factors that exert causal impact on the occurrence of eco-design. In order to gain a deeper insight into the practical manifestations of effective EPR implementation in the private sector, three company case studies have been conducted in the third stage of the research, serving as an important supplement to the first two studies. The three studies show that EPR programmes in a stringent regulatory approach focusing on products’ environmental properties such as substance bans can exert strong influence on design changes. However, mandated recycling legislation seems to have little driving effect for companies to change product design. The research points to the weakness of collective producer responsibility that is currently adopted in many EU Member States for WEEE implementation in achieving continuous eco-design improvements. It highlights the significance of realising the principle of individual producer responsibility in EPR programmes for achieving this goal. Based on an attributability assessment, the research further shows that there are a number of factors that influence the adoption of eco-design in China, and the link between legislative EPR programmes and eco-design is complex and evolving. Not only may the EPR programmes have different existing, anticipating and potential effect on the incorporation of eco-design, but they may have very different driving influence on environmental laggards and leaders. In this regard, this research suggests that there is no one-stop solution for EPR in China. A mixed mechanism that integrates various policy instruments such as administrative, economic and informative instruments, in conjunction with cooperation and infrastructure facilities, would be the most effective means to implement EPR in the context of China, thereby achieving the goal of sustainable production and consumption. / published_or_final_version / Kadoorie Institute / Doctoral / Doctor of Philosophy

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