Industrial effluents with high concentrations of heavy metals are widespread pollutants of great concerns as they are known to be persistent and non-degradable. Continuous monitoring and treatment of the effluents become pertinent because of their impacts on wastewater treatment plants. The aim of this study is to determine the correlation between heavy metal pollution in water and the location of industries in order to ascertain the effectiveness of the municipal waste water treatment plant. Heavy metal identification and physico-chemical analysis were done using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and multi-parameter probe respectively. Correlation coefficients of the measured values were done to investigate the effect of the industrial effluents on the treatment plants. Heavy metal resistant bacteria were identified and characterised by polymerase chain reaction and sequencing. Leeuwkuil wastewater treatment plants were effective in maintaining temperature, pH, and chemical oxygen demand within South Africa green drop and SAGG Standards whereas the purification plant was effective in maintaining the values of Cu, Zn, Al, temperature, BOD, COD, and TDS within the SANS and WHO standard for potable water. This findings indicated the need for the treatment plants to be reviewed.The industrial wastewater were identified as a point source of heavy metal pollution that influenced Leeuwkuil wastewater treatment plants and the purification plants in Vaal, Vereenining South Africa. Pseudomonas aeruginosa, Serratia marcescens, Bacillus sp. strain and Bacillus toyonensis that showed 100% similarity were found to be resistant to Al, Cu, Pb and Zn. These identified bacteria can be considered for further study in bioremediation. / Environmental Sciences / M. Sc. (Environmental Science)

This study is a component of a research project on the economic costs of eutrophication in the Vaal River system. Its objective is to investigate the relationship between raw water quality and the chemical costs of producing potable water at two water treatment plants: Zuikerbosch Station #2 (owned by Rand Water) in the Upper Vaal Water Management Area (UVWMA), and Balkfontein (owned by Sedibeng Water) in the Middle Vaal Water Management Area (MVWMA). Time series data on raw water quality and chemical dosages used to treat raw water were obtained for Zuikerbosch Station #2 (hereafter referred to as Zuikerbosch) for the period November 2004 – October 2006 and for Balkfontein for the period January 2004 to December 2006. Descriptive statistics reveal that raw water in the Vaal River is of a poorer quality at Balkfontein compared to that at Zuikerbosch. Furthermore, the actual real chemical water treatment costs (measured in 2006 ZAR) averaged R89.90 per megalitre at Zuikerbosch and R126.31 at Balkfontein, indicating that the chemical water treatment costs of producing potable water tend to increase as raw water quality declines. Collinearity among water quality (WQ) variables at both water treatment plants was analysed using Principal Component Analysis (PCA). The dimensions of water quality identified in the analysis are similar to those reported in Pieterse and van Vuuren’s (1997) study of the Vaal River. For both water treatment plants, Ordinary Least Squares (OLS) regression was used to identify the relationship between real chemical costs of water treatment and the dimensions of water quality identified through the respective Principal Components Analyses. The estimated regression models account for over 50.2% and 34.7% of variation in real chemical water treatment costs at Zuikerbosch and Balkfontein, respectively. The coefficient estimated for PC1 at Zuikerbosch is statistically significant at the 1% level of probability with high negative loadings of total alkalinity and turbidity. Increases in the levels of total alkalinity and turbidity in raw water treated at Zuikerbosch is negatively related to the chemical costs of water treatment. An increased total alkalinity level was found to reduce the chemical costs of treating potable water. PC2 is statistically the most important variable in the estimated explanatory model for Balkfontein. The estimated regression coefficient for PC2 is statistically significant at the 5% level of probability. The estimated relationship between chemical water treatment costs and PC2 shows that there is a positive relationship between the raw water temperature and chemical water treatment costs. However, increases in the levels of chlorophyll and pH in raw water treated at Balkfontein is negatively related to the chemical costs of water treatment. Total hardness, magnesium, calcium, sulphate, conductivity, and chloride, being the highest positive loadings in PC1, relate negatively to the chemical cost of treating water. For predictive rather than explanatory purposes, a partial adjustment regression model was estimated for each of the two water treatment plants. Using this model, real chemical water treatment costs were specified as a function of real chemical water treatment costs in the previous time period, and of raw water quality variables in the current period. The R2 statistics for the two regression models were 61.4% using the data for Zuikerbosch and 59.9% using the data for Balkfontein, suggesting that both models have reasonable levels of predictive power. The chemical cost of water treatment for Zuikerbosch and Balkfontein are predicted at R96.25 and R90.74 per megalitre per day respectively. If raw water nitrate in the UVWMA increases by 1% per megalitre a day while other factors remain constant, chemical water treatment costs at Zuikerbosch can be expected to increase by 0.297% per megalitre and the cost accompanied this change is (R0.285*1998ML*365days) R207,841.95 provided that Zuikerbosch treats an average of 1998 megalitres per day. Likewise, if Zuikerbosch maintains its daily average operating capacity and is able to maintain an optimal level of total alkalinity in UVWMA, the estimated saving on chemical water treatment cost will be R150.063.78 per annum. At Balkfontein, chemical water treatment cost is expected to increase on average by 0.346% per megalitre per day for a 1% per megalitre per day increase in the level of chlorophyll-a, and the cost accompanied this change is R41,128.20 per annum. The prediction also shows a 2.077% per megalitre per day increase chemical water treatment cost for a 1% increase in turbidity and this accompanied with a chemical water treatment cost of R 249,003 per annum, provided that Balkfontein operates at its full capacity (i.e., 360 megalitres per day). / Thesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.

MENVSC / Department of Ecology and Resource Management / Fluoride is one of the anionic contaminants which is found in excess in groundwater because of geochemical reaction or anthropogenic activities such as the disposal of industrial wastewaters. Among various methods used for defluoridation of water such as precipitation, ion-exchange processes, membrane processes, the adsorptions process is widely used. It offers satisfactory results and seems to be a more attractive method for the removal of fluoride in terms of cost, simplicity of design and operation. In this work, the preparation of clay polymer nanocomposites (CPNCs) used in defluoridation began by modifying the original natural Mukondeni clay to render the layered silicate miscible with the chosen polymer, microcrystalline cellulose. Clay polymer nanocomposites (CPNCs) were synthesized using the melt intercalation method. Mukondeni black clay with microcrystalline cellulose as polymers was melt mixed at 220 °C for 10 minutes in an extruder for exfoliation of the resulting composite. Physicochemical characteristics and mineralogical characteristics of the CPNC was determined using XRD, XRF, BET, FTIR and SEM. Batch adsorption experiments were conducted to determine the efficiency of CPNCs in defluoridation of groundwater. The pH, EC, TDS and fluoride concentration of field water was determined using the CRISON MM40 multimeter probe and the Orion versastar fluoride selective electrode for fluoride concentration. Elemental analysis revealed that CPNC 1:1 is mainly characterized of cellulose, Quartz and Albatite as the major minerals with traces of Montmorillonite, Ednite and Magnesium as minor minerals constituting CPNC 1:1. The structure of 1:4 CPNC was partially crystalline and partially amorphous showing increased cellulose quantity (1:4 clay to cellulose) as compared 1:1 CPNC, 1:2 CPNC and 1:3 CPNC. Maximum adsorption of fluoride was attained in 10 minutes using 0.5g of 1:4 CPNC removed 22.3% of fluoride. The initial fluoride concentration for the collected field groundwater was 5.4 mg/L, EC 436 μS/cm, and TDS 282 mg/L. The regeneration potential of CPNCs was evaluated through 3 successive adsorption desorption cycles. Fluoride removal decreased after the first cycle for all ratios of CPNCs, a continued decreased can be observed following the second cycle. CPNC 1:2 decreased from 9.32 % at the 1st cycle to 2.84 % and 0.56 % on the 2nd and 3rd cycle respectively. CPNC 1:4 decreased from 8.22 % at the 1st cycle to 4.80 % and 0.72 % on the 2nd and 3rd cycle respectively. The fluoride-rich Siloam groundwater had a slightly alkaline pH of 9.6. iv The low adsorptive characteristic displayed by all 4 CPNCs can be deduced from the BET analysis that revealed low surface area, pore volume, and pore size, it is evident from the BET analysis that less fluoride will be absorb as adsorption sites will be limited. Based on the findings of this study, recommendations are designing of correct preparation techniques to obtain nanocomposites with desirable properties, polymer melting points and evaporation point of the binder should be taken into consideration. / NRF