Cyanobacterial Incident Management Frameworks (CIMFs) for application by drinking water suppliers

Cyanobacteria are commonly found in freshwater systems that are the source waters for the production of drinking water. This is of special importance to the drinking water suppliers as several genera of cyanobacteria can produce cyanotoxins that can affect human health. The possibility that drinking water can be a cyanobacterial-exposure route has resulted in the development of Cyanobacterial Incident Frameworks (CIMFs) that will guide water treatment managers to deal pro-actively with cyanobacteria and their associated toxins in source water by using a step-by-step alert levels framework to ensure provision of safe drinking water. In this paper two CIMF models are described, namely a CIMF model using cyanobacteria identification and enumeration as a primary trigger; and a CIMF model using chlorophyll a as primary trigger. These frameworks are based on the same principle, but differ in minor actions taken, especially at the lower alert levels. It is envisaged that the developed CIMFs would be the platform on which to evaluate the capacity to manage a cyanobacterial incident. Based on the requirements stipulated in the CIMFs and their assessment, the drinking water treatment works (DWTW) would then develop and implement their customised CIMFs.Keywords: cyanobacteria, Cyanobacterial Incident Management Framework (CIMF), drinking water, drinking water treatment works (DWTW), alert level

Early warning system for the prediction of algal-related impacts on drinking water purification

PhD (Botany), North-West University, Potchefstroom Campus, 2015Algae 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.Doctora

A baseline study on the prevalence of microplastics in South African drinking water: from source to distribution

Due to the worldwide increasing prevalence of microplastics in the aquatic environment, this study aimed to perform a screening of the source and drinking water of South Africa’s largest bulk drinking water supplier to determine the extent to which microplastics occur in the water. Source water samples, samples immediately after treatment, and samples in the distribution network (Johannesburg, Mabopane, Garankua and Pelindaba) were analysed. Microplastics concentrations in the source water ranged from 0.24 to 1.47 particles/L, immediately after treatment from 0.56 to 0.9 particles/L, and in the distribution network from 0.26 to 0.88 particles/L. Most of the microplastics found in the water were classified as ‘fragments’ and a few as ‘fibres’. The control sample (indicating contamination during sample preparation and analysis) showed 0.34 particles/L, which was higher than some of the samples taken, indicating very low microplastics concentrations in these samples. Little evidence was found that the drinking water treatment processes reduced the number of microplastics from the source to the final treated water. No evidence could be found that the pipes in the distribution network contribute to microplastics in the tap water. The most frequently found polymer in the samples was rubber. Based on mass, however, as a function of particle size and polymer density, ethylene-vinyl-acetate (a polymer commonly used as foam in sporting equipment and flip-flops) comprised 54% of the microplastics and polyethylene (standard and chlorinated) 25%

Rol van Lesotho–Hooglandwater op Vaalrivieralge

Long-term changes in the algal composition of the Vaal River, South Africa – did the Lesotho Highlands Water Project play a role? The Vaal River has become so nutrient-enriched that algal blooms pose problems. A unique opportunity arose to determine if there were changes in the chemistry en algal composition of the Vaal River after oligomesotrophic Katse Dam (Lesotho) water was imported to augment supplies in the light of growing water demands in the Vaal River catchment area. Algal concentration and composition in the Vaal River during three periods (between 1992 and 1994, 1998 and 2000, as well as 2004 and 2006) were compared to those in the Katse Dam (1998–2006). Some algal species, initially absent from the Vaal River, appeared in the river during and after transfer. Mixed algal assemblages found in the Vaal River before transfer of Katse Dam water gradually changed after transfer to assemblages mainly composed of cyanobacteria. The total algal concentration in the Vaal River Barrage doubled from the period between 1992 and 1994 to that between 2004 and 2006, indicating that the transfer of clear, oligomesotrophic Katse Dam water did not dilute the eutrophic Vaal River water sufficiently in order to reduce algal concentrations. Results showed that continuous downstream pollution and eutrophication of the Vaal River system eliminated the diluting effect of Katse imports. This resulted in changes in algal composition and concentration in the Vaal, characteristic of those associated with increasing eutrophicationhttp://www.satnt.ac.za/index.php/satnthttp://www.satnt.ac.za/index.php/satnt/article/view/335/63