To protect the environment from contaminants, environmental risk assessment (ERA) evaluates the risk of adverse effects to populations, communities and ecosystems. Environmental management decisions rely on ERAs, which commonly are based on a few endpoints at the individual organism level. To bridge the gap between what is measured and what is intended for protection, individual-level effects can be integrated in population models, and translated to the population level. The general aim of this doctoral thesis was to extrapolate individual-level effects of harpacticoid copepods to the population level by developing and using population models. Matrix models and individual based models were developed and applied to life-history data of Nitocra spinipes and Amphiascus tenuiremis, and demographic equations were used to calculate population-level effects in low- and high-density populations. As a basis for the population models, individual-level processes were studied. Development was found to be more sensitive compared to reproduction in standard ecotoxicity tests measuring life-history data. Additional experimental animals would improve statistical power for reproductive endpoints, but at high labor and cost. Therefore, a new test-design was developed in this thesis. Exposing animals in groups included a higher number of animals without increased workload. The number of reproducing females was increased, and the statistical power of reproduction was improved. Individual-level effects were more or equally sensitive compared to population-level effects, and individual-level effects were translated to the population level to various degrees by population models of different complexities. More complex models showed stronger effects at the population level compared to the simpler models. Density dependence affected N. spinipes populations negatively so that toxicant effects were stronger at higher population densities. The tools presented here can be used to assess the toxicity of environmental contaminants at the individual and population level, improve ERA, and thereby the basis for environmental management. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Manuscript.</p>

Thesis (Ph.D.)--Tufts University, 2000. / Adviser: Stephen H. Levine. Submitted to the Dept. of Civil Engineering. Includes bibliographical references (leaves 259-277). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (M.A.)--University of Canterbury, 2007. / Typescript (photocopy). Includes bibliographical references (leaves 121-131) Also available via the World Wide Web.

Thesis (M. Sc.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 69-73).

Engineered nanomaterials (ENMs) have been increasingly used in various applications. Often, the ENMs are functionalised with a surface coating to enhance their properties. Decades of research has provided information on mostly pristine and unmodified ENMs, while ecotoxicity of coated ENMs and how their hazard changes with age in soils is still uncertain. The thesis aimed to determine the toxic effects and bioaccumulation potential of CuO ENMs and CdTe quantum dots (QDs) with different chemical coatings (carboxylate, COOH; polyethylene glycol, PEG; ammonium, NH4+) on the earthworm (Eisenia fetida), and compare the effects to their metal salt (CuSO4) or micron-sized counterpart. Then, to determine if any observed toxicity was altered after ageing the soils for up to one year. Incidental plant growth was studied in the exposure soils to maximise the scientific value of the earthworm tests. Toxic effects of CuO ENMs were also assessed in Caenorhabditis elegans exposed in liquid and soil media to understand effects of the media and method of dosing on ENM toxicity. CuO ENMs were equally toxic to earthworms, or less toxic to plants than the dissolved Cu; whereas CdTe QD ENMs were more toxic than the micron-sized CdTe QDs. There was a coating effect in both, CuO and CdTe QD ENM experiments, the -COOH coated ENMs were most toxic in the fresh soil study, while -NH4+ coated ENMs were most toxic in the aged soil study. Despite the similarities in the toxicity ranking, the biological effects exerted were different between CuO and CdTe QD ENMs. In C. elegans exposures, the ENMs were more hazardous than dissolved Cu, but ranking of ENMs depended on the media and method of dosing. The results suggest the coating effect is determined by the reactivity of the coating in a given media, and it also depends on the core of the ENMs. As such, coating and ageing effects should be considered in the risk assessment of ENMs.

Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2011. / Please refer to full text for abstracts

Abandoned metal mines in the Tamar catchment, south west England, represent a significant threat to surface water quality via generation of acid mine waters. Currently the River Tamar fails environmental quality standards (EQS) established under the Water Framework Directive (2000/60/EC) for dissolved Cu (x ̅ = 0.19 ± 0.05 μmol L-1) and Zn (x ̅ = 0.19 ± 0.06 μmol L-1, both 1997-2007) downstream of historic mining area of Gunnislake. The aim of this study was to quantify the risk to surface water quality by diffuse drainage generated by mine waste tips. For the first time, a GIS model was compiled and used to generate a priority list of known areas of mine waste, based on physical and environmental factors. The methodology was consistent with European guidance documentation published to meet the requirements of the Mining Waste Directive (2001/21/EC) and has since been applied, in a modified form, to other catchments in south west England. Two study sites, with contrasting mineralogy and hydrology, scored highly in the model and were the subject of field investigations from 2007-2009. These were Devon Great Consols (DGC), an abandoned Cu-As mine near Gunnislake and Wheal Betsy (WB), an abandoned Pb-Ag mine, near Mary Tavy. At each site, surface waters and shallow groundwaters were sampled and analysed for dissolved metals (including Al, Cu, Zn, Mn, Pb, Ni, and Cd), metalloids (As, Sb), major ions and anions. Samples of four selected mine waste tips were also gathered and subjected to a range of laboratory leaching experiments including the novel application of a dynamic upflow percolation test, based on an existing European method (CEN TS 14405). Leachates generated by the waste tips in the field were highly variable and elevated with respect to EQS for Al (up to 1850 μmol L-1), Cu (570 μmol L-1), Zn (34 μmol L-1), Ni (3.8 μmol L-1), Cd (0.17 μmol L-1), Mn (216 μmol L-1), Fe(537 μmol L-1) , As (380 μmol L-1) and Sb (5.4 μmol L-1). Estimated annual fluxes of dissolved metals were predicted using average rainfall data and catchment areas calculated in ArcHydro9 to estimate the annual discharge of waters from the tip. These calculations showed annual contaminant flux from the tips to exceed, or be of the same order of magnitude to, major adit discharges in the catchment (e.g. Cu 50900-66900 mol y-1 at DGC and 470 mol y-1 Cd at WB) and represented a significant contributor to metal flux in the Tamar catchment. Primary sulphide minerals in the waste were generally highly altered and metals (Pb, Cu, Zn, and Mn) and As were found to be strongly associated with secondary iron minerals, precipitated under oxic conditions. In finer wastes, sorption to clay minerals was also found to be very important for the retention of dissolved metals, particularly Pb. Concentrations of contaminants in column field leachates were similar for most metals (Cu, Zn, Mn, Ni and Cd) and may provide a useful tool for prediction of leachate composition. However, sorption and release of metals and As to the secondary phases and clays were highly sensitive to pH change and where laboratory experiments did not replicate field pH, discrepancies between in situ and laboratory results were observed up to two orders of magnitude in scale (particularly for As and Pb).

Individuals are exposed to chemical mixtures while carrying out everyday tasks, with unknown risk associated with exposure. Given the number of resulting mixtures it is not economically feasible to identify or characterize all possible mixtures. When complete dose-response data are not available on a (candidate) mixture of concern, EPA guidelines define a similar mixture based on chemical composition, component proportions and expert biological judgment (EPA, 1986, 2000). Current work in this literature is by Feder et al. (2009), evaluating sufficient similarity in exposure to disinfection by-products of water purification using multivariate statistical techniques and traditional hypothesis testing. The work of Stork et al. (2008) introduced the idea of sufficient similarity in dose-response (making a connection between exposure and effect). They developed methods to evaluate sufficient similarity of a fully characterized reference mixture, with dose-response data available, and a candidate mixture with only mixing proportions available. A limitation of the approach is that the two mixtures must contain the same components. It is of interest to determine whether a fully characterized reference mixture (representative of the random process) is sufficiently similar in dose-response to a candidate mixture resulting from a random process. Four similarity measures based on Euclidean distance are developed to aid in the evaluation of sufficient similarity in dose-response, allowing for mixtures to be subsets of each other. If a reference and candidate mixture are concluded to be sufficiently similar in dose-response, inference about the candidate mixture can be based on the reference mixture. An example is presented demonstrating that the benchmark dose (BMD) of the reference mixture can be used as a surrogate measure of BMD for the candidate mixture when the two mixtures are determined to be sufficiently similar in dose-response. Guidelines are developed that enable the researcher to evaluate the performance of the proposed similarity measures.

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the Degree of Master of Science in Engineering, 2016 / Previously undermined areas pose a significant challenge to mining by opencast due to the risk of sinkhole occurrence. In order to optimise reserve utilisation as well as safeguard personnel and equipment there was need to develop a “Sinkhole Prediction Model” to assist in the prediction of areas prone to sinkhole formation. The aim of this research therefore was to develop a “Sinkhole prediction tool” with a view to pre-identifying areas of potential sinkhole hazard to inform better controls to assist in mining these areas safely. This was done utilising the current Hill (1996) caving height method culminating in the development of a hazard index model dividing the mining zones into high and low hazard. These areas were colour coded Red (High hazard) and Green (Low Hazard). The “Sinkhole Prediction Model” evolved to include over hundred sinkhole incidences that were statistically analysed to firm up on the robustness of the Prediction Model capabilities. The Hill (1996) caving height formula was discounted after the statistical analysis indicated that a good prediction model lies in the interrogation of site specific data. The outcome of the work conducted in this research report indicated a 97% correlation between the refined “Sinkhole Prediction Model” and the actual sinkhole occurrence at the Anglo American case study area (Mine X). Various refinements inclusive of lithological assessments, blast and drilling reconciliations as well as the implementation of the roughening up quality audits led to the implementation of a robust sinkhole management process that has managed to consistently assist in safeguarding equipment and personnel thus allowing for coal extraction optimisation in areas that could have been written off due to the sinkhole hazard. This risk can only be eliminated by mining the areas with the sinkhole risk. Currently the method is being impacted by significant roughening up cost incurred in a drive to make the areas safe to allow for coal extraction. The roughening up process on average costs R3.5 million per sinkhole and is a function of the number of sinkholes found, which translates to an equivalent cost of R7 / sales tonne. The current sinkhole prediction model being employed in deficient in that it cannot pinpoint the actual location of the void in the area previously undermined by bord and pillar and this is a great limitation of this report. Various geophysical techniques were pursued to assist in the precise identification of the actual sinkhole spatially. This process was aimed to reduce the roughening up cost (entire block stabilisation) as opposed to targeted sinkhole excavation and stabilisation. This process proved futile as the void identification systems are highly incapable of identifying the voids / iv sinkholes spatially (x, y and z coordinates) to assist targeted sinkhole treatment as a result of the following:  System inability to penetrate areas comprised of highly conductive strata such as clays.  Inability to distinguish between the underground voids and geological anomalies such as dykes.  Not suitable for penetrating wet strata.  Impacted by noise interference from mining machinery. The major result of this research is the establishment of a site specific “Sinkhole Prediction Model” that can generate hazard plans in real time thus informing the management on areas associated with a potential sinkhole hazard. The hazard plans can be generated timely and decisions made to facilitate safe coal extraction in areas of high sinkhole hazard. This has culminated in a robust sinkhole management process within the group that has managed to eliminate the risk of personnel and equipment exposure at Mine X. The roughening up process is accepted as the primary sinkhole mitigation or rehabilitation process with the need to work towards reducing the roughening up costs through development of the tool capable of precisely identifying the voids routinely to facilitate targeted rehabilitation. Significant research is required in this area as the mining environment is comprised of strata that currently cannot support the use of real time void identification to facilitate targeted void identification and rehabilitation. There is also merit in the future to formulate the database capable of assisting in the prediction of sinkholes in the Witbank coalfield as well as assist in robust management of mining boundaries across the different mining houses. The system implemented at Mine X is currently being deployed to other operations in the group where modification will be made to match the site specific conditions. Future research into understanding the sinkhole occurrence dynamics is quite crucial if targeted rehabilitation is to be achieved for cost reduction and mining sustainability. A combination of the understanding of the sinkhole occurrence driving mechanisms in conjunction with use of modelling packages such as ELFEN (a hybrid Modelling) tool will go a long way in enhancing the development of precise sinkhole prediction point in space.

The environment is increasingly recognised as a key player in the emergence and mobilisation of antibiotic resistance, which negatively impacts human health, healthcare systems, and farming practices worldwide. Recent work has demonstrated concentrations of antibiotics in the natural environment may select for resistance in situ, but a scarcity of meaningful data has prevented rigorous environmental risk assessment of antibiotics. Without such data, mitigation strategies, such as improved antibiotic stewardship or environmental discharge limits, cannot be effectively designed or implemented. This thesis designed and developed two methods for determining effect concentrations of antibiotics in complex microbial communities, thereby generating a significant amount of data to address this knowledge gap. Minimal selective concentrations (MSCs) were determined in long term selection experiments for four classes of antibiotic at concentrations as low as 0.4 μg/L, which is below many measured environmental concentrations. Lowest observed effect concentrations were determined using a short term, growth based assay which were highly predictive of MSCs. A novel finding was significant selection for cefotaxime resistance occurred at a wide range of antibiotic concentrations, from 125 μg/L - 64 mg/L, which has important clinical implications. Determination of MSC in single species assays was also shown to be a poor predictor of MSC in a complex microbial community. Co-selection for antimicrobial resistance was demonstrated in selection experiments and through improved understanding of class 1 integron evolution, assessing selective effects on resistance gene acquisition using a novel PCR method and next-generation sequencing. In the final study, a novel resistance determinant (UDP-galactose 4-epimerase) conferring cross-resistance to biocides and antibiotics was discovered, providing a target for further study. These findings indicate selection and co-selection for antimicrobial resistance is likely to occur in the environment, and provides the means to rapidly generate further data to aid in the development of appropriate mitigation strategies.