Chloramine is a widely used alternative disinfectant for chlorine in potable distribution water. This alternative was investigated and employed to show its effect for suppressing coliforms, trihalomethanes(THMs), disinfection by-products (DBPs), and corrosivity.Coliform analyses were performed with m-Endo(total coliform) and m-T7 agar(injured coliform) by using a standard Membrane Filtration method. Heterotrophic bacteria were monitored with HPC agar(PCA) and R2A agar (nutrient limited agar). EPA methods 502.2, 524.2, and 504 were used to determine levels of Trihalomethanes(THMs) and Disinfection by-products(DBPs).In our study, we observed no significant differences in coliform counts, that could be attributed to the switch in disinfectant. The most common coliform identified was Enterobacter cloacae. We also noted that m-T7 performed better than m-Endo in the detection of coliforms. We also observed a low level of corrosion (0.4-3.8 mils/year) in the distribution system (DS). Higher counts of heterotrophic bacteria were enumerated on R2A when compared to HPC. DBP values decreased two fold when compared with DBP values for the two previous years during which chlorine was used as the disinfectant. / Department of Biology

Tap water collected according to standard methods was examined for microbial presence. Epifluorescent diagnoses using redox probe 5-cyano-2,3ditolyl tetrazolium chloride (CTC), 4',6-diamidino-2-phenylindole (DAPI), and acridine orange (AO) were employed for direct evidence of microorganisms. Evidence of total (DAPI or AO), respiring (CTC) bacteria, and heterotrophic plate count (HPC) was determined on multiple occasions during the summer, fall, and winter 1996-1997. Pseudomonas aeruginosa, Acinetobacter sp., Bacillus licheniformis, and Methylobacterium rhodinum were isolated and identified by the API and Biolog system using GN and GP procedures. On the basis of comparisons presented in this study between the CTC method and the standard HPC procedure, it appeared that the number of CTC-reducing bacteria in the tap water samples was typically higher than that determined by HPC, indicating that many respiring bacteria detected by the CTC reduction technique fail to produce visible colonieson the agar media used. In the seasonal data obtained by the CTC method, no difference was shown among respiring bacterial counts obtained from June through January. In the examination of P. aeruginosa viability in presence of chlorine, the number of CTC-positive bacteria exceeded the number of CFU by more than 2 logs after exposure to chlorine, suggesting that reliance on HPC overestimate the efficacy of disinfection treatment. In inactivation assays using the Biolog MT plate, no sensitivity to chlorine or chloramine disinfectants was noted even at high concentration levels (5 mg/liter). Following initial drop, bacterial activities increased as contact time increased. Thus, it appears that the MT microplate provides too low a cell concentration, too great a contact time, and/or too low a concentration of tetrazolium dye within the well for successful analysis of disinfectant capability to selected bacterial strains isolated from distribution water. / Department of Biology

Submitted in fulfilment of the academic requirements for the Degree of M.Tech: Chemical Engineering, M. L. Sultan Technikon, 2001. / Historically inorganic coagulants were the coagulants of choice for OAF treatment of potable water. Water treatment practitioners using OAF technology preferred ferric chloride, an inorganic coagulant. Ferric chloride formed light, floatable floes at relatively low flocculation intensities and detention times. The inorganic coagulant was also more forgiving during incidents of overdosing and raw water and pH variability / M

M.Sc. / Drinking water treatment plants in South Africa rely almost entirely on surface water sources, which are often compromised due to high return flows and indirect reuse. The typical treatment plants focus on the removal of physical and microbial contaminants which include turbidity, colour, chemical compounds and micro-organisms. A relatively new alarm to this list is natural organic matter (NOM) which has become a major concern in potable water treatment due to its recent regulation. Conventionally, the drive to remove NOM from potable water would be the desire to remove colour from public water supplies. However, more problems in drinking water treatment associated with NOM have been recently identified. These include taste and odour, its tendency to foul membranes, interference with the removal of other contaminants and its potential to contribute to corrosion and slime growth in distribution systems. Moreover, it causes high demands for coagulants and disinfectants. The NOM is also the main precursor for disinfection by-products (DBPs) formed when it interacts with disinfectants such as chlorine during water disinfection. In this study, different raw water samples, of different NOM composition were collected from selected sources across the country and assessed for the removal of dissolved organic carbon (DOC) and UV absorbance at a wavelength of 254 nm (UV 254) using enhanced coagulation (EC). The efficacy of EC, which can be employed as a practical technology in the removal of both turbidity and NOM, was evaluated in these raw water sources. Jar tests were conducted with ferric chloride as the coagulant, and specific pH values were chosen as target values guiding the different coagulant dosages for the jar tests. The pH of the low-alkalinity (<60 mg/ℓ CaCO3) raw waters were adjusted and raised by the addition of sodium carbonate. Algorithms for finding the optimum coagulant dosage for both turbidity and UV 254 removal were developed and consistently applied to all the results in independent v batch tests, in which residual amounts of UV 254, DOC and turbidity were measured. The raw water parameters and results obtained from these tests were used to develop feed-forward multiplicative models predicting the performance of EC using ferric chloride. The results showed that the raw waters chosen were, indeed, representative of the different water types present in South Africa, and that the general water characteristics were affected by seasonal variations. The EC procedure developed was able to reduce turbidity to levels low enough for removal by subsequent treatment steps in the water treatment train. The residual UV 254 values were in all cases lower than 6 m-1, which theoretically corresponds to about 3.5 mg/ℓ DOC. This was confirmed by the residual DOC values which were found to be lower than 4 mg/ℓ. Generally, the waters of South Africa were found to be amenable to coagulation. In almost all cases, the costs for EC were comparable to those for conventional coagulation, hence EC could be employed as a NOM removal strategy in the South African context.

Titanium dioxide nanoparticles were prepared using the sol-gel process. The effect of temperature and precursor concentration on particle size was investigated. The optimum conditions were then used to prepare carbon and nitrogen doped titanium dioxide (TiO2) nanoparticles. Doping was done to reduce band gap of the nanoparticles in order to utilize visible light in the photocatalytic degradation of organic compounds. A significant shift of the absorption edge to a longer wavelength (lower energy) from 420 nm to 456 nm and 420 nm to 428 nm was observed for the carbon doped and nitrogen doped TiO2 respectively. In this study, the prepared TiO2 photocatalyst was immobilized on carbon nanofibres to allow isolation and reuse of catalyst. The photocatalytic activity of the catalyst was tested using methyl orange as a model pollutant and was based on the decolourization of the dye as it was degraded. The doped TiO2 exhibited higher photocatalytic activity than the undoped TiO2. The materials prepared were characterized by XRD, TEM, SEM, FT-IR, DSC and TGA while the doped TiO2 was characterized by XPS, ESR and Raman Spectroscopy.

M.Sc. / The occurrence of invertebrates in drinking water supplies is a common consumer complaint with studies showing that very few drinking water distribution networks are totally free of organisms. A detailed investigation of different types of metazoan animals in the drinking water supply networks of South Africa has not been undertaken. In limited worldwide studies, invertebrates (mainly Amphipoda, Chironomidae, Cladocera, Copepoda and Ostracoda) have been detected in produced drinking water. In countries that have started monitoring these organisms, the quality of the produced water has improved due to the improved methods of filtering. The occurrence of “worms” (Nematodes and Diptera) and Crustaceans decreases the aesthetic value of the drinking water, and pathogenic organisms, which may also be associated with them, can affect human health. Limited reference works have been compiled during the conducted studies on drinking water distribution networks throughout the world. All fresh water invertebrates have the potential to be passed into the drinking water supply network (Rising mains, pipelines, reservoirs, and consumer taps). There are few complete reference works available for fresh water invertebrates occurring in the fresh water sources and those of Southern Africa. Therefore, there are no reference works regarding fresh water invertebrates that can be used to monitor drinking water supply networks in South Africa. / Prof. J.H.J. van Vuren

Monodisperse titanium dioxide (TiO2) nanoparticles were synthesized by a novel freeze-drying process herein called lyophilization. The process of lyophilization is described in detail. The materials were characterized by scanning electron microscopy SEM) including energy dispersive x-ray spectroscopy (EDXS), high resolution transmission electron microscopy (HRTEM), x-ray diffraction (XRD), Raman spectroscopy and UV-Vis-IR spectrophotometry. The TiO2 nanoparticles have narrow size distribution, mono-disperse, strained with most of the characteristics showing presence of the four phases of TiO2 thus: anatase, brookite, rutile with each lyophilization process producing its own phase mostly controlled by pH and precursor concentration and anneal/calcining temperatures. With specific reference to HRTEM, Raman spectroscopy results and XRD, it was found that the Scherrer equation, the Williamson-Hall method and others of similar nature were not enough to explain the strain and the grain sizes of these particles. Therefore the Williamson-Hall method was revised to properly explain the new results. The obtained TiO2 nanoparticles were used in three applications: (1) gas sensing (2) degradation of organic water-borne pollutants using methylene blue as an indicator (3) anti-bacterial activity. / Physics / D. Phil. Physics)

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria. / PhD (Botany), North-West University, Potchefstroom Campus, 2015

Submitted in fulfilment of the requirements of the degree of Master of Technology: Environmental Health, Durban University of Technology, 2014. / The study entailed the determination of key water quality parameters significantly influencing treatment cost in uMngeni Basin. Chemical dosage was used as a substitute for treatment cost as the study indicated that cost, in its monetary value, is influenced by market forces, demand and supply, which are both not directly linked to water quality. Chemical dosage is however, determined by the quality of water and thus provides a clear illustration of the effect of pollution on treatment cost. Three specific objectives were set in an effort to determine key water quality parameters influencing treatment costs in uMngeni Basin. The fourth objective was to develop a model for predicting chemical dosages. The first approach was analysis of temporal and spatial variability of water quality in relation to chemical dosage during production of potable water. The trends were explained in relation to river health status. For this purpose, time-series, box-plot, and the Seasonal-Kendal test were employed. The results showed that the quality of water significantly deteriorated from upstream to downstream in relation to algae, turbidity and Escherichia coli (E. coli). High mean range of E. coli (126-1319 colony count/100mL) and turbidity (2.7-38.7 NTU) observed indicate that the quality of water along the basin is not fit for human consumption as these parameters exceeded the target range stipulated in South Africa’s guidelines for domestic use. For water intended for drinking purpose, turbidity should be below 5 NTU, while zero E. coli count is expect in 100 mL. Among the six sampling stations considered along the uMngeni Basin, three dam outflows (Midmar, Nagle and Inanda) showed an improved quality compared with their respective inflow stations. This was expected and could be attributed to the retention and dilution effects. These natural processes help by providing a self-purification process, which ultimately reduces the treatment cost. While considering the importance of disseminating water quality information to the general public and non-technical stakeholders, the second objective of the study was to develop two water quality indices. These were; (1) Treatability Water Quality Index and (2) River Health Water Quality Index. The Treatability Water Quality Index was developed based on the Canadian Council Minister of Environment Water Quality Index (CCME-WQI). The technique is used to determine fitness of water against a set of assigned water quality resource objectives (guidelines). The calculated Harmonised Water Quality Resource Objectives (HWQRO) were used to compare the qualities of the raw water being abstracted at Nagle and Inanda Dam for the purpose of treatment. The results showed that Nagle Dam, which supplies Durban Heights, is significantly affected by E. coli (42% non-compliance), turbidity (20% non-compliance) and nitrate (18% non-compliance) levels. Wiggins Water Treatment Plant which abstracts from Inanda Dam has a problem of high algae (mean 4499 cell/mL), conductivity (mean 26.21 mS/m) and alkalinity (mean 62.66 mg/L) levels. The River Health Water Quality Index (RHWQI) was developed using the Weighted Geometric Mean (WQM) method. Eight parameters, namely, E. coli, dissolved oxygen, nitrate, ammonia, turbidity, alkalinity, electrical conductivity and pH were selected for indexing. Rating curves were drawn based on the target ranges as stipulated in South Africa’s guidelines for freshwater ecosystems. Five classes were used to describe the overall river health status. The results showed that the water is still acceptable for survival of freshwater animals. A comparison of the RHWQI scores (out of 100) depicted that dam inflow station (MDI(61.6), NDI(74.6) and IDI(63.8)) showed a relatively deteriorated quality as compared with their outflows (MDO(77.8), NDO(74.4) and IDO(80)). The third objective was to employ statistical analysis to determine key water quality parameters influencing chemical dosage at Durban Heights and Wiggins Water Treatment Plants. For each of the two treatment plants, treated water quality data-sets were analysed together with their respective raw water data-set. The rationale was to determine parameters showing concentration change due to treatment. The t-test was used to determine the significance of concentration change on each of the 23 parameters considered. Thereafter, the correlations between water quality parameters and the three chemicals used during treatment (polymer, chlorine and lime) were analysed. The results showed that the concentrations of physical parameters namely, algae, turbidity and total organic carbon at both treatment showed a significant statistical (p<0.05) reduction in concentration (R/Ro<0.95). This results implies that such parameters were key drivers for chemical dosage. From the results of the first three objectives, it is recommended that implementing measures to control physical parameter pollution sources, specifically sewage discharges and rainfall run-off from agricultural lands along the uMngeni Basin should assist in reducing the chemical dosage and ultimately cost. The fourth objective was to develop chemical dosage models for prediction purposes. This was achieved by employing a polynomial non-linear regression function on the XLStat 2014 program. The resultant models showed prediction power (R2) ranging from 0.18 (18%) up to 0.75 (75%). However, the study recommends a comparative study of the developed models with other modelling techniques.